A rolling resistance model (RRM) has been created and parametrised with the purpose of modelling tyre rolling resistance within complete vehicle dynamics simulations. The RRM is based on a combination of the Masing and Zener models to simulate the Payne effect and the viscoelastic properties of rubber. The parametrised model is able to recreate the relationship between the rolling resistance and the tyre deformation well and it has a low computational power requirement. Today the model is limited to simulation of free-rolling tyres on a flat surface, but it can be extended to also include the effects of changes in operating conditions such as wheel angles or road surface.

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine- battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine-battery coupled thermal management system (engine-battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine-battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.

Stable and precise temperature control can effectively improve the engines fuel consumption and emissions. An advanced engine thermal management system (AETMS) composed of electric actuators has great potential in increasing the temperature tracking control accuracy and decreasing the system energy consumption. Time -varying disturbance and pure time delay in the system make it challenging to harmoniously control multiple actuators to ensure efficient temperature tracking performance. In addition, a reasonable power distribution of the actuators is beneficial to reduce the parasitic energy consumption of the system and further improve the system efficiency. In this article, an adaptive optimization control strategy consisting of a state predictor, a disturbance estimator, an energy consumption optimization block and a state tracking controller is proposed for the temperature tracking and energy consumption optimization of AETMS. Experimental verification was performed on a range extender bench of an extended-range electric vehicle, and the results show that the system achieves high-precision temperature control and energy consumption near the theoretical optimal value under changing working conditions. The temperature steady-state error is within 0.3 oC, and the temperature adjustment time is within 100 s after the first overshoot.

An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.

In markets with strict emission legislations Selective Catalytic Reduction (SCR) has become the industry standard for NOx abatement in heavy-duty vehicles, and therefore modeling and control of these systems are vital. Many SCR catalyst models are available in the literature and in this paper different models are discussed and classified into groups. Two models, based on the two most popular classes for control-oriented models, are implemented and compared with each other, one based on the continuously stirred-tank reactor approximation, and the other on a quasi-static behavior of the gas phase. The results show that assuming a quasi-static behavior of the gas phase in the catalyst gives better results in terms of accuracy and simulation time, especially when it comes to predictions of ammonia slip.

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.

Feature selection is an important task in data-driven control applications to identify relevant features and remove non-informative ones, for example residual selection for fault diagnosis. For multi-class data, the objective is to find a minimal set of features that can distinguish data from all different classes. A distributed feature selection algorithm is derived using convex optimization and the Alternating Direction Method of Multipliers. The distributed algorithm scales well with increasing number of classes by utilizing parallel computations. Two case studies are used to evaluate the developed feature selection algorithm: fault classification of an internal combustion engine and the MNIST data set to illustrate a larger multi-class classification problem.

Fault diagnosis of a certain class of hybrid systems referred to as structurally reconfigurable (SR) systems is complicated. This is because SR systems tend to switch their configuration, which may or may not be faulty. It is important to identify the mode of the SR system along with the corresponding fault if any, in order to facilitate a fault tolerant action. This paper combines discrete fault diagnosis with mode identification for SR systems to achieve two main objectives: Sensor selection for fault detection, isolation and mode identification, and residual selection for mode identification. The framework is built using a structural analysis-based approach to meet these objectives. This framework is demonstrated for a 10-speed Automatic Transmission, which is an illustrative example of SR systems.

A combustion engine-driven vehicle can be made more fuel efficient over some drive cycles by, for example, introducing electric machines and solutions for electrical energy storage within the vehicles driveline architecture. The possible benefits of different hybridization concepts depend on the architecture, i.e., the type of energy storage, and the placement and sizing of the different driveline components. This paper examines a diesel electric plug-in hybrid truck, where the powertrain includes a diesel engine supported with two electric motors, one supporting the crank shaft and one the turbocharger. Numerical optimal control was used to find energy-optimal control strategies during two different accelerations; the trade-off between using electrical energy and diesel fuel was evaluated using a simulation platform. Fixed-gear acceleration was performed to evaluate the contribution from the two electric motors in co-operation, and individual operation. A second acceleration test case from 8 to 80 km/h was performed to evaluate the resulting optimal control behavior when taking gear changes into account. A cost factor was used to relate the cost of diesel fuel to electrical energy. The selection of the cost factor relates to the allowed usage of electrical energy: a high cost factor results in a high amplification from electrical energy input to total system energy savings, whereas a low cost factor results in an increased usage of electrical energy for propulsion. The difference between fixed-gear and full acceleration is mainly the utilization of the electric crank shaft motor. For the mid-range of the cost factors examined, the crank shaft electric motor is used at the end of the fixed-gear acceleration, but the control sequence is not repeated for each gear during the full acceleration. The electric motor supporting the turbocharger is used for higher cost factors than the crank shaft motor, and the amplification from electrical energy input to total energy savings is also the highest.

Efficient trajectory planning of autonomous vehiclesin complex traffic scenarios is of interest both academically andin automotive industry. Time efficiency and safety are of keyimportance and here a two-step procedure is proposed. First, aconvex optimization problem is solved, formulated as a supportvector machine (SVM), in order to represent the surroundingenvironment of the ego vehicle and classify the search spaceas obstacles or obstacle free. This gives a reduced complexitysearch space and an A* algorithm is used in a state space latticein 4 dimensions including position, heading angle and velocityfor simultaneous path and velocity planning. Further, a heuristicderived from the SVM formulation is used in the A* search anda pruning technique is introduced to significantly improve searchefficiency. Solutions from the proposed planner is compared tooptimal solutions computed using optimal control techniques.Three traffic scenarios, a roundabout scenario and two complextakeover maneuvers, with multiple moving obstacles, are used toillustrate the general applicability of the proposed method.

This paper presents a multi-objective energy management strategy for hybrid electric vehicles. It aims at reducing fuel consumption and minimizing battery wear simultaneously while fulfilling system’s constraints. A control-oriented differential model is considered to account for battery aging effects, and an algorithm is developed to identify its parameters. The energy management is formulated as an optimal control problem and is solved by Pontryagin’s minimum principle. The controller is then implemented for a hybrid electric wheel loader to demonstrate its effectiveness. In short-term simulations for four drive cycles, behavior of the vehicle is compared to the case where the energy management policy does not encompass battery wear minimization. Long-term simulations suggest that there is a huge potential in extending battery life while the price to pay is a negligible increase in fuel consumption. It is observed that the proposed methodology works best for nonaggressive drive cycles.

A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.

Handling of critical situations is an important part in the architecture of an autonomous vehicle. A controller for autonomous collision avoidance is developed based on a wary strategy that assumes the least tireroad friction for which the maneuver is still feasible. Should the friction be greater, the controller makes use of this and performs better. The controller uses an acceleration-vector reference obtained from optimal control of a friction-limited particle, whose applicability is verified by using numerical optimization on a full vehicle model. By employing an analytical tire model of the tireroad friction limit, to determine slip references for steering and body-slip control, the result is a controller where the computation of its output is explicit and independent of the actual tire-road friction. When evaluated in real-time on a high-fidelity simulation model, the developed controller performs close to that achieved by offline numerical optimization.

Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple-readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.

Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on sensor outputs resulting in fault classification ambiguities, i.e., multiple fault hypotheses can explain the data. One solution is to identify and rank all plausible fault classes that give useful information, for example, at a workshop when performing troubleshooting. A probabilistic fault classification algorithm is proposed for residual data classification combining the Weibull-calibrated one-class support vector machines for fault class modeling and Bayesian filtering for time-series analysis. The fault classifier ranks different fault classes and can identify sequences from unknown fault realizations, i.e., faults not represented in training data. Real residual data computed from sensor data and model analysis of an internal combustion engine are used as a case study illustrating the usefulness of the proposed method.

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of WiFi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm. The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances. The system is demonstrated on experimental data collected in a Swedish underground mine.

With new developments in sensor technology, a new generation of vehicle dynamics controllers is developing, where the braking and steering strategies use more information, e.g. knowledge of road borders. The basis for vehicle-safety systems is how the forces from tyre–road interaction is vectored to achieve optimal total force and moment on the vehicle. To study this, the concept of attainable forces previously proposed in literature is adopted, and here a new visualisation technique is devised. It combines the novel concept of attainable force volumes with an interpretation of how the optimal solution develops within this volume. A specific finding is that for lane-keeping it is important to maximise the force in a certain direction, rather than to control the direction of the force vector, even though these two strategies are equivalent for the friction-limited particle model previously used in some literature for lane-keeping control design. More specifically, it is shown that the optimal behaviour develops on the boundary surface of the attainable force volume. Applied to lane-keeping control, this observation indicates a set of control principles similar to those analytically obtained for friction-limited particle models in earlier research, but result in vehicle behaviour close to the globally optimal solution also for more complex models and scenarios.

We hope this special issue will provide new inspiring to the community of control theory, especially for the youngerresearchers and students. We wish to thank Yiguang Hong, Editor of the journal of Control Theory and Technology,for his timely organization of this special issue.

The increasing complexity and size of cyber-physical systems (e.g., aircraft, manufacturing processes, and power generation plants) is making it hard to develop centralized diagnosers that are reliable and efficient. In addition, advances in networking technology, along with the availability of inexpensive sensors and processors, are causing a shift in focus from centralized to more distributed diagnosers. This paper develops two structural approaches for distributed fault detection and isolation. The first method uses redundant equation sets for residual generation, referred to as minimal structurally-over-determined sets, and the second is based on the original model equations. We compare the diagnosis performance of the two algorithms and clarify the pros and cons of each method. A case study is used to demonstrate the two methods, and the results are discussed together with directions for future work.

Driving cycles are nowadays, to an increasing extent, used as input to model-based vehicle design and as training data for development of vehicle models and functions with machine learning algorithms. Recorded real driving data may underrepresent or even lack important characteristics, and therefore there is a need to complement driving cycles obtained from real driving data with synthetic data that exhibit various desired characteristics. In this paper, an efficient method for generation of mission-based driving cycles is developed for this purpose. It is based on available effective methods for traffic simulation and available maps to define driving missions. By comparing the traffic simulation results with real driving data, insufficiencies in the existing behavioral model in the utilized traffic simulation tool are identified. Based on these findings, four extensions to the behavioral model are suggested, staying within the same class of computational complexity so that it can still be used in a large scale. The evaluation results show significant improvements in the match between the data measured on the road and the outputs of the traffic simulation with the suggested extensions of the behavioral model. The achieved improvements can be observed with both visual inspection and objective measures. For instance, the 40% difference in the relative positive acceleration of the originally simulated driving cycle compared to real driving data was eliminated using the suggested model.

Stability control of a vehicle in autonomous safety-critical at-the-limit manoeuvres is analysed from the perspective of lane keeping or lane changing, rather than that of yaw control as in traditional ESC systems. An optimal control formulation is developed, where the optimisation criterion is a linear combination of the initial and final velocity of the manoeuvre. Varying the interpolation parameter in this formulation turns out to result in an interesting family of optimal braking and steering patterns in stabilising manoeuvres. The two different strategies of optimal lane-keeping control and optimal yaw control are shown to be embedded in the formulation and result from the boundary values of the parameter. The results provide new insights and have the potential to be used for future safety systems that adapt the level of braking to the situation at hand, which is demonstrated through examples of how to exploit theresults.

Large marine two-stroke diesel engines are widely used as propulsion systems for shipping worldwide and are facing stricter NOx emission limits. Exhaust gas recirculation is introduced to these engines to reduce the produced combustion NOx to the allowed levels. Since the current number of engines built with exhaust gas recirculation is low and engine testing is very expensive, a powerful alternative for developing exhaust gas recirculation controllers for such engines is to use control-oriented simulation models. Unfortunately, the same reasons that motivate the use of simulation models also hinder the capacity to obtain sufficient measurement data at different operating points for developing the models. A mean value engine model of a large two-stroke diesel with exhaust gas recirculation that can be simulated faster than real time is presented and validated. An analytic model for the cylinder pressure that captures the effects of changes in the fuel control inputs is also developed and validated with cylinder pressure measurements. A parameterization procedure that deals with the low number of measurement data available is proposed. After the parameterization, the model is shown to capture the stationary operation of the real engine well. The transient prediction capability of the model is also considered satisfactory which is important if the model is to be used for exhaust gas recirculation controller development during transients. Furthermore, the experience gathered while developing the model about essential signals to be measured is summarized, which can be very helpful for future applications of the model. Finally, models for the ship propeller and resistance are also investigated, showing good agreement with the measured ship sailing signals during maneuvers. These models give a complete vessel model and make it possible to simulate various maneuvering scenarios, giving different loading profiles that can be used to investigate the performance of exhaust gas recirculation and other controllers during transients.

The pursuit of lower fuel consumption and stricter emission legislation has made a simulation- and optimization-based development methodology important to the automotive industry. The keystone in the methodology, is the system model. But for the results obtained using a model to be credible, the model has to be validated. The paper validates an open-source, meanvalue engine model of a 13 liter CI inline 6 cylinder heavyduty engine, and releases it as open-source.

In model-based diagnosis there are often more candidate residual generators than what is needed and residual selection is therefore an important step in the design of model-based diagnosis systems. The availability of computer-aided tools for automatic generation of residual generators have made it easier to generate a large set of candidate residual generators for fault detection and isolation. Fault detection performance varies significantly between different candidates due to the impact of model uncertainties and measurement noise. Thus, to achieve satisfactory fault detection and isolation performance, these factors must be taken into consideration when formulating the residual selection problem. Here, a convex optimization problem is formulated as a residual selection approach, utilizing both structural information about the different residuals and training data from different fault scenarios. The optimal solution corresponds to a minimal set of residual generators with guaranteed performance. Measurement data and residual generators from an internal combustion engine test-bed is used as a case study to illustrate the usefulness of the proposed method.

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Taking offspring in a problem of ship emission reduction by exhaust gas recirculation control for large diesel engines, an underlying generic estimation challenge is formulated as a problem of joint state and parameter estimation for a class of multiple-input single-output Hammerstein systems with first-order dynamics, sensor delay, and a bounded time-varying parameter in the nonlinear part. This brief suggests a novel scheme for this estimation problem that guarantees exponential convergence to an interval that depends on the sensitivity of the system. The system is allowed to be nonlinear, parameterized, and time dependent, which are characteristics of the industrial problem we study. The approach requires the input nonlinearity to be a sector nonlinearity in the time-varying parameter. Salient features of the approach include simplicity of design and implementation. The efficacy of the adaptive observer is shown on simulated cases, on tests with a large diesel engine on test bed, and on tests with a container vessel.

As a promising solution to the reduction of fuel consumption and CO2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrains early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light-and heavy-duty application. Based on the power ratio between powertrains power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light- and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light-and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagins Minimum Principle) approaches.

An algorithm for high-performance path tracking for robot manipulators in the presence of model uncertainties and actuator constraints is presented. The path to be tracked is assumed given, and the nominal trajectories are computed using, for example, well-known algorithms for time-optimal path tracking. For online path tracking, the nominal, feedforward trajectories are combined with feedback in a control architecture with a secondary controller, such that robustness to uncertainties in model or environment is achieved. The control law is based on existing path-velocity control (PVC), or so called online time scaling, but in addition to speed adaptation along the tangent of the path, the algorithm also comprises an explicit formulation and approach, with several attractive properties, for handling the deviations along the transversal directions of the path. For achieving fast convergence along the normal and binormal directions of the path in 3D motion, the strategy proposed has inherent exponential convergence properties. The result is a complete architecture for path-tracking velocity control (PTVC). The method is evaluated in extensive simulations with manipulators of different complexity, and PTVC exhibits superior performance compared to PVC. (C) 2017 Elsevier Ltd. All rights reserved.

The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

Downsizing and turbocharging with single or multiple stages has been one of the main solutions to decrease fuel consumption and harmful exhaust emissions, while keeping a sufficient power output. An accurate and reliable control-oriented compressor model can be very helpful during the development phase, as well as for engine calibration, control design, diagnostic purposes or observer design. A complete compressor model consisting of mass flow and efficiency models is developed and motivated. The proposed model is not only able to represent accurately the normal region measured in a compressor map but also it is capable to extrapolate to low compressor speeds. Moreover, the efficiency extrapolation is studied by analyzing the known problem with heat transfer from the hot turbine side, which introduces errors in the measurements done in standard gas stands. Since the parameterization of the model is an important and necessary step in the modeling, a tailored parameterization approach is presented based on Total Least Squares. A standard compressor map is the only data required to parameterize the model. The parameterization is tested with a database of more than 230 compressor maps showing that it can deal well with different compressor sizes and characteristics. Also, general initialization values for the model parameters are provided using the complete database parameterization results. The results show that the model accuracy is good and in general achieves relative errors below one percent. A comparison of the model accuracy for compressor maps with and without heat transfer influence is carried out, showing a similar model accuracy for both cases but better when no heat transfer is present. Furthermore, it is shown that the model is capable to predict the efficiency characteristics at low speed of two compressor maps, measured with near adiabatic conditions.

Exhaust gas recirculation (EGR) systems have been introduced to large marine engines in order to reduce NOx formation. Adequate modeling for control design is one of the bottlenecks to design EGR control that also meets emission requirements during transient loading conditions. This paper therefore focuses on deriving and validating a mean-value model of a large two-stroke crosshead diesel engine with EGR. The model introduces a number of amendments and extensions to previous, complex models and shows in theory and practice that a simplified nonlinear model captures all essential dynamics that is needed for EGR control. Our approach is to isolate and reduce the gas composition part of the more complex models using nonlinear model reduction techniques. The result is a control-oriented model (COM) of the oxygen fraction in the scavenge manifold with three molar flows being inputs to the COM, and it is shown how these flows are estimated from signals that are commonly available. The COM is validated by first comparing the output to a simulation of the full model, then by comparing with measurement series from two engines. The control-oriented nonlinear model is shown to be able to replicate the behavior of the scavenge oxygen fraction well over the entire envelope of load and blower speed range that are relevant for EGR. The simplicity of the new model makes it suitable for observer and control design, which are essential steps to meet the emission requirements for marine diesel engines that take effect from 2016.

Measurements and optimal control are used to study whether the fuel economy of a diesel engine can be improved through periodic control of the wastegate, illustrating how modern optimal control tools can be used to identify non-trivial solutions that can improve performance. The measurements show that the pumping torque of the engine is changed when the wastegate is controlled in a periodic manner versus stationary even if the mean position is the same. If this decreases the fuel consumption or not is seen to be frequency and operating point dependent. The measurements indicate that the phenomenon occurs in the time scales capturable by mean value engine models (MVEM). The operating points are further analyzed using a MVEM and optimal control. It is shown that whether the optimal solution exhibits periodic oscillations or not is operating point dependent, but is not due to the instantaneous nature of the controls. Even if an actuator model is added the oscillations persist for reasonable time constants, the frequency of the oscillations is however affected. Further it is shown that the periodic control can be predicted by optimal periodic control theory and that the frequency of the control affects the resulting efficiency.

The work extends a methodology, for searching for optimal heat release profiles, by adding complex constraints on states. To find the optimum heat release profile a methodology, that uses available theory and methods, was developed that enables the use of state of the art optimal control software to find the optimum combustion trace for a model. The methodology is here extended to include constraints and the method is then applied to study how sensitive the solution is to different effects such as heat transfer, crevice flow, maximum rate of pressure rise, maximum pressure, knock and NO generation. The Gatowski single zone model is extended to a pseudo two zone model, to get an unburned zone that is used to describe the knocking and a burned zone for NO generation. A modification of the extended Zeldovich mechanism that makes it continuously differentiable, is used for NO generation. Previous results showed that the crevice effect had a significant influence on the shape for the unconstrained case where a two mode combustion was seen, one initial pressure rise and one constant pressure phase. Here it is shown that it still has a significant influence on the appearance until the maximum pressure limit is reached and becomes the dominating constraint. In the unconstrained case no conditions had combustion before TDC all started after, but when limitations are considered and come into play the combustion can now start before TDC to avoid excessive losses during the expansion. When introducing constraints on the NO formation through the extended Zeldovich mechanism the combustion takes the shape of a three mode combustion, one initial rapid burning, one later rapid burning and a constant pressure phase. In summary it is shown that the methodology is able to cope with the introduced constraints.

In this brief, we propose a novel approach to implement multiplierless unity-gain single-delay feedback fast Fourier transforms (FFTs). Previous methods achieve unity-gain FFTs by using either complex multipliers or nonunity-gain rotators with additional scaling compensation. Conversely, this brief proposes unity-gain FFTs without compensation circuits, even when using nonunity-gain rotators. This is achieved by a joint design of rotators, so that the entire FFT is scaled by a power of two, which is then shifted to unity. This reduces the amount of hardware resources of the FFT architecture, while having high accuracy in the calculations. The proposed approach can be applied to any FFT size, and various designs for different FFT sizes are presented.

A common situation in industry is to store measurements for different operating points in the lookup tables, often called maps. They are used in many tasks, e.g., in control and estimation, and therefore considerable investments in engineering time are spent in measuring them which usually make them accurate descriptions of the fault-free system. They are thus well suited for fault detection, but, however, such a model cannot give fault isolation since only the fault free behavior is modeled. One way to handle this situation would be also to map all fault cases but that would require measurements for all faulty cases, which would be costly if at all possible. Instead, the main contribution here is a method to combine the lookup model with analytical fault models. This makes good use of all modeling efforts of the lookup model for the fault-free case, and combines it with fault models with reasonable modeling and calibration efforts, thus decreasing the engineering effort in the diagnosis design. The approach is exemplified by designing a diagnosis system monitoring the power electronics and the electric machine in a hybrid electric vehicle. An extensive simulation study clearly shows that the approach achieves both good fault detectability and isolability performance. A main point is that this is achieved without the need for neither measurements of a faulty system nor detailed physical modeling, thus saving considerable amounts of development time.

Due to the increasing complexity of vehicle design, understanding driver behavior and driving patterns is becoming increasingly more important. Therefore, a large amount of test driving is performed, which together with recordings of normal driving, results in large databases of recorded drives. A fundamental question is how to make best use of these data to devise driving cycles suitable in the development process of vehicles. One way is to generate driving cycles that are representative for the data or for a suitable subset of the data, e.g., regarding geographical location, driving distance, speed range, or many other possible selection variables. Further, to make a fair comparison on two such driving cycles possible, another fundamental requirement is that they should have similar excitation of the vehicle. A key contribution here is an algorithm that combines the two given objectives. A formulation with Markov processes is used to obtain a condensed and effective characterization of the database and to generate candidate driving cycles (CDCs). In addition to that is a method transforming a candidate to an equivalent driving cycle (EqDC) with desired excitation. The method is a general approach but is here based on the components of the mean tractive force (MTF), and this is motivated by a hardware-in-the-loop experiment showing the strong relevance of these MTF components regarding fuel consumption. The result is a new method that combines the generation of driving cycles using real-world driving cycles with the concept of EqDCs.

Increasingly stringent emissions legislation combined with consumer performance demands, have driven the development of downsized engines with complex turbocharger arrangements. To handle the complexity model-based methods have become a standard tool, and these methods need models that are capable of describing all operating modes of the systems. The models should also be easily parametrized and enable extrapolation. Both single and multiple stage turbo systems can operate with a pressure drop over their compressors, both stationary and transient. The focus here is to develop models that can describe centrifugal compressors that operate both in normal region and restriction region from standstill to maximum speed. The modeling results rely on an analysis of 305 automotive compressor maps, whereof five contain measured restriction operation, and two contain measured standstill characteristic. A standstill compressor is shown to choke at a pressure ratio of approximately 0.5, and the corresponding choking corrected mass flow being approximately 50% of the compressor maximum flow capacity. Both choking pressure ratio and flow are then shown to increase with corrected speed, and the choking pressure ratio is shown to occur at pressure ratios larger than unity for higher speeds. Simple empirical models are proposed and shown to be able to describe high flow and pressure ratios down to choking conditions well. A novel compressor flow model is proposed and validated to capture the high flow asymptote well, for speeds from standstill up to maximum.

Geometric imperfections on brake rotor surface are well-known for causing periodic variations in brake torque during braking. This leads to brake judder, where vibrations are felt in the brake pedal, vehicle floor and/or steering wheel. Existing solutions to address judder often involve multiple phases of component design, extensive testing and improvement of manufacturing procedures, leading to the increase in development cost. To address this issue, active brake torque variation (BTV) compensation has been proposed for an electromechanical brake (EMB). The proposed compensator takes advantage of the EMB’s powerful actuator, reasonably rigid transmission unit and high bandwidth tracking performance in achieving judder reduction. In a similar vein, recent advancements in hydraulic system design and control have improved the performance of hydraulic brakes on a par with the EMB, therefore invoking the possibility of incorporating the BTV compensation feature of the EMB within hydraulic brake hardware. In this paper, the typical characteristics of electromechanical and electro-hydraulic brake systems are presented. Based on the experimental results, the feasibility of active BTV compensation on the electro-hydraulic brake (EHB) systems is discussed. Furthermore, a BTV compensation algorithm designed for the EMB is presented and is shown to be applicable to the EHB. Using an experimentally validated model of BTV, the compensation was performed on a hardware in-the-loop EHB test rig. The preliminary results demonstrate the potential of using an EHB to compensate for brake judder.

Coiled coils with defined assembly properties and dissociation constants are highly attractive components in synthetic biology and for fabrication of peptide-based hybrid nanomaterials and nanostructures. Complex assemblies based on multiple different peptides typically require orthogonal peptides obtained by negative design. Negative design does not necessarily exclude formation of undesired species and may eventually compromise the stability of the desired coiled coils. This work describe a set of four promiscuous 28-residue de novo designed peptides that heterodimerize and fold into parallel coiled coils. The peptides are non-orthogonal and can form four different heterodimers albeit with large differences in affinities. The peptides display dissociation constants for dimerization spanning from the micromolar to the picomolar range. The significant differences in affinities for dimerization make the peptides prone to thermodynamic social self-sorting as shown by thermal unfolding and fluorescence experiments, and confirmed by simulations. The peptides self-sort with high fidelity to form the two coiled coils with the highest and lowest affinities for heterodimerization. The possibility to exploit self-sorting of mutually complementary peptides could hence be a viable approach to guide the assembly of higher order architectures and a powerful strategy for fabrication of dynamic and tuneable nanostructured materials.

A nonlinear four state-three input mean value engine model (MVEM), incorporating the important turbocharger dynamics, is used to study optimal control of a diesel-electric powertrain during transients. The optimization is conducted for the two criteria, minimum time and fuel, where both engine speed and engine power are considered free variables in the optimization. First, steps from idle to a target power are studied and for steps to higher powers the controls for both criteria follow a similar structure, dictated by the maximum torque line and the smoke-limiter. The end operating point, and how it is approached is, however, different. Then, the power transients are extended to driving missions, defined as, that a certain power has to be met as well as a certain energy has to be produced. This is done both with fixed output profiles and with the output power being a free variable. The time optimal control follows the fixed output profile even when the output power is free. These solutions are found to be almost fuel optimal despite being substantially faster than the minimum fuel solution with variable output power. The discussed control strategies are also seen to hold for sequences of power and energy steps.

A model-based misfire detection algorithm is proposed. The algorithm is able to detect misfires and identify the failing cylinder during different conditions, such as cylinder-to-cylinder variations, cold starts, and different engine behavior in different operating points. Also, a method is proposed for automatic tuning of the algorithm based on training data. The misfire detection algorithm is evaluated using data from several vehicles on the road and the results show that a low misclassification rate is achieved even during difficult conditions. (C) 2014 Elsevier Ltd. All rights reserved.

This paper presents two conceptual methods, based on dynamic programming, for one-step look-ahead control of a Continuously Variable Transmission (CVT) in a wheel loader. The first method developed, designated Stochastic Dynamic Programming (SDP), uses a statistical load prediction and stochastic dynamic programming for minimizing fuel use. The second method developed, designated Free-Time Dynamic Programming (FTDP), has vehicle speed as a state and introduces a fixed 0.1 s delay in the bucket controls in a combined minimization of fuel and time. The methods are evaluated using a set of 34 measured loading cycles, used in a ‘leave one out’ manner.

The evaluation shows that the SDP method requires about 1/10th of the computational effort of FTDP and has a more transparent impact of differences in the cycle prediction. The FTDP method, on the other hand, shows a 10% lower fuel consumption, which is close to the actual optimum, at the same cycle times, and is able to complete a much larger part of the evaluation cycles.

A transmission with both high comfort and high efficiency is the Automated Manual Transmission (AMT). To be able to control and fully utilize this type of transmission it is of great importance to have knowledge about the torque transmissibility curve of the clutch. The transmitted torque in a slipping dry clutch is therefore studied in experiments with a heavy duty truck (HDT). It is shown that the torque characteristic has little or no dependence on slip speed, but that there are two dynamic effects that make the torque vary up to 900 Nm for the same clutch actuator position. Material expansion with temperature can explain both phenomena and a dynamic clutch temperature model that can describe the dynamic torque variations is developed. The dynamic model is validated in experiments, and it is shown to reduce the error in transmitted torque from 7 % to 3 % of the maximum engine torque compared to a static model. Clutch wear is also a dynamic phenomenon that is of interest to track and compensate for, and therefore the model is augmented with an extra state describing wear. An observability analysis is performed showing that the augmented model is fully or partially observable depending on the mode of operation. In particular, by measuring the actuator position the temperature states are observable, both during slipping of the clutch and when it is fully closed. An Extended Kalman Filter (EKF), which observes the temperature states, was developed since it is straight forward to incorporate different modes of operation. The EKF was evaluated on measurement data and the estimated states converged from poor initial values, enabling prediction of the translation of the torque transmissibility curve. The computional complexity of the EKF is low and thus it is suitable for real-time applications. Modeling, parameter estimation, observer design and validation are all carried out using production sensors only and therefore it is straight forward to implement the observer in a production HDT following the presented methodology.

The importance of including turbocharger dynamics in diesel engine models are studied, especially when optimization techniques are to be used to derive the optimal controls. This is done for two applications of diesel engines where in the first application, a diesel engine in wheel loader powertrain interacts with other subsystems to perform a loading operation and engine speed is dictated by the wheel speed, while in the second application, the engine operates in a diesel-electric powertrain as a separate system and the engine speed remains a free variable. In both applications, mean value engine models of different complexities are used while the rest of system components are modeled with the aim of control study. Optimal control problems are formulated, solved, and results are analyzed for various engine loading scenarios in the two applications with and without turbocharger dynamics. It is shown that depending on the engine loading transients, fuel consumption and operation time can widely vary when the turbocharger dynamics are considered in the diesel engine model. Including these, have minor effects on fuel consumption and operation time at minimum fuel operations of the first application (~0.1 %) while the changes are considerable in the second application (up to 60%). In case of minimum time operations however, fuel consumption and operation time are highly affected in both applications implying that not considering turbocharger dynamics in the diesel engine models may lead to overestimation of the engine performance especially when the results are going to be used for control purposes.

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting-charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagins minimum principle-derived optimization tool HOT.

In parallel turbocharged V-engines, with two separate air paths connected before the throttle, an oscillation in the flow can occur.If the compressor operates close to the surge line, typically during low speed and high load, and a disturbance alters the massflow balance, the compressors can begin to alternately go into surge. This phenomenon is called co-surge and is unwanted due tohigh noise and risk for turbocharger destruction. Co-surge is measured on a test vehicle in a chassis dynamometer and the systemanalyzed and modeled using a mean value engine model. The investigation shows that the alternating compressor speeds have animportant role in the prolonged oscillation. A reconstruction of the negative flow from measurements is made and compared tosimulation results, showing similar amplitudes, and supports the model validation. A new co-surge detection algorithm is presented,suitable for a pair of sensors measuring either mass flow, boost pressure or turbo speed in the two air paths. Furthermore, a newcontroller is proposed that uses a model based feedforward for the throttle, together with wastegate actuation to force the compressorspeeds together and improve balance at the recovery point. This has shown to be sufficient with moderate to high pressure ratiosover the throttle, only for zero or very low pressure drop the use of bypass valves are necessary. The advantage of not opening thebypass valves is a smaller drop in boost pressure which also reduces the torque disturbance. The performance of the controller is evaluated both in simulation and in the test vehicle.

Information about wheel loader usage can be used in several ways to optimize customer adaption. First, optimizing the configuration and component sizing of a wheel loader to customer needs can lead to a significant improvement in e.g. fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, on-line usage identification opens up for the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective here is to develop an on-line algorithm that automatically, using production sensors only, can extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to berobust when evaluated on measured data of wheel loaders loading gravel and shot rock.

Abstract Optimal control of a wheel loader operating in the short loading cycle is studied in order to investigate the potentials for fuel consumption reduction while maintaining acceptable production rates. The wheel loader is modeled as a system with five states and three control inputs including torque converter nonlinearities. The torque converter is modeled with no lockup enabling power transmission in both directions. The geometry of the wheel loader boom and the demanded force in the lift cylinders during lifting are used to ensure that the in-cylinder pressure remains below component’s limits. The lift-transport section of the short loading cycle is divided into four phases due to discontinuities in the gearbox ratios and fuel consumption is calculated in each phase. Time optimal and fuel optimal transients of the system and the power consumption in each and every component is presented showing the dominance of the torque converter losses compared to the other components especially in the time optimal solutions. It is shown that introducing path constraints on the maximum lifting speed of the bucket due to limitations in hydraulic pumping speed moves the diesel engine operation towards higher speeds in order to maintain the lifting speed. Trade-off between fuel optimal and time optimal transients is calculated which is found to be in agreement with the results of experimental studies.

No-x estimation in diesel engines is an up-to-date problem but still some issues need to be solved. Raw sensor signals are not fast enough for real-time use while control-oriented models suffer from drift and aging. A control-oriented gray box model based on engine maps and calibrated off-line is used as benchmark model for No-x estimation. Calibration effort is important and engine data-dependent. This motivates the use of adaptive look-up tables. In addition to, look-up tables are often used in automotive control systems and there is a need for systematic methods that can estimate or update them on-line. For that purpose, Kalman filter (KF) based methods are explored as having the interesting property of tracking estimation error in a covariance matrix. Nevertheless, when coping with large systems, the computational burden is high, in terms of time and memory, compromising its implementation in commercial electronic control units. However look-up table estimation has a structure, that is here exploited to develop a memory and computationally efficient approximation to the KF, named Simplified Kalman filter (SKF). Convergence and robustness is evaluated in simulation and compared to both a full KF and a minimal steady-state version, that neglects the variance information. SKF is used for the online calibration of an adaptive model for No-x estimation in dynamic engine cycles. Prediction results are compared with the ones of the benchmark model and of the other methods. Furthermore, actual online estimation of No-x is solved by means of the proposed adaptive structure. Results on dynamic tests with a diesel engine and the computational study demonstrate the feasibility and capabilities of the method for an implementation in engine control units.

Control of exhaust gas recirculation (EGR) and variable geometry turbine in diesel engines is a challenging problem and model predictive control (MPC) seems to be a promising method. In MPC the choice of output variables, and thereby the criterion, has a direct impact on the optimization problem to solve and the resulting control performance. Different selections of outputs are investigated and discussed, proposing that it is beneficial to include EGR-fraction and pumping losses in the criterion while having the oxygen/fuel ratio as a constraint. The rational for this constraint is that, in diesel engines, it is allowed to have the oxygen/fuel ratio larger than a set-point. The proposed design also includes integral action of the EGR-fraction to handle model errors and prediction of engine load and speed. A comparison is made between the proposed MPC, a proportional-integral-derivative (PID) controller, and an MPC with intake manifold pressure and compressor flow as outputs, which is the common choice in the literature. Comparisons are performed in simulation on the European transient cycle showing the following two points. First, the proposed design gives 9% lower oxygen/fuel ratio error, 80% lower EGR-error, and 12% lower pumping losses compared to an MPC design with intake manifold pressure and compressor flow as outputs. Second, the proposed design gives 9% lower EGR-error and 6% lower pumping losses compared to a control structure with PID controllers with oxygen/fuel ratio and EGR-fraction as the main outputs.

In recent years the need for testing, calibration and certification of automotive components and powertrains have increased, partly due to the development of new hybrid concepts. At the same time, the development within electrical drives enables more versatile chassis dynamometer setups with better accuracy at a reduced cost. We are developing a new chassis dynamometer laboratory for vehicle research, aiming at extending a recently commercially available dynamometer, building a new laboratory around it, and applying the resulting facility to some new challenging vehicle research problems. The projects are enabled on one hand by collaboration with the dynamometer manufacturer, and on the other hand on collaboration with automotive industry allowing access to relevant internal information and equipment. The test modes of the chassis dynamometer are under development in a joint collaboration with the manufacturer. The laboratory has been operational since September 2011 and has already been used for NVH-analysis for a tire pressure indication application, chassis dynamometer road force co-simulation with a moving base simulator, co-surge modeling and control for a 6-cylinder bi-turbo engine, and traditional engine mapping. We are also looking at projects with focus on look-ahead control, as well as clutch and transmission modeling and control, and driving cycle related research.

Analyzing fault diagnosability performance for a given model, before developing a diagnosis algorithm, can be used to answer questions like “How difficult is it to detect a fault f_i?” or “How difficult is it to isolate a fault f_i from a fault f_j?”. The main contributions are the derivation of a measure, distinguishability, and a method for analyzing fault diagnosability performance of discrete-time descriptor models. The method, based on the Kullback–Leibler divergence, utilizes a stochastic characterization of the different fault modes to quantify diagnosability performance. Another contribution is the relation between distinguishability and the fault to noise ratio of residual generators. It is also shown how to design residual generators with maximum fault to noise ratio if the noise is assumed to be i.i.d. Gaussian signals. Finally, the method is applied to a heavy duty diesel engine model to exemplify how to analyze diagnosability performance of non-linear dynamic models.

Fault detection and isolation (FDI) in automotive diesel engines is important in order to achieve and guarantee low exhaust emissions, high vehicle uptime, and efficient repair and maintenance. This paper illustrates how a set of general methods for model-based sequential residual generation and data-driven statistical residual evaluation can be combined into an automated design methodology. The automated design methodology is then utilized to create a complete FDI-system for an automotive diesel engine. The performance of the obtained FDI-system is evaluated using measurements from road drives and engine test-bed experiments. The overall performance of the FDI-system is good in relation to the required design effort. In particular no specific tuning of the FDI-system, or any adaption of the design methodology, was needed. It is illustrated how estimations of the statistical powers of the fault detection tests in the FDI-system can be used to further increase the performance, specifically in terms of fault isolability.

Recent development has renewed the interest in drivetrain concepts which give a higher degree of freedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for active control, which especially during transients is not trivial but of which the quality is crucial for the success of the drivetrain concept. In this work the fuel optimal solution for a turbocharged diesel engine connected to a load which does not restrict the engine speed is derived, analysed and utilized for finding a suboptimal operating point trajectory. We use a Willan s efficiency model for the engine, expanded with a first order delay dependent torque reduction representing the turbocharger pressure, and study different output power transients. The analysis is made with dynamic programming, Pontryagin’s maximum principle and a suboptimal strategy based on the static optimal operating points. We present a method for using Pontryagin’s maximum principle for deriving the optimal operating point trajectory. The time needed for computation was reduced a factor >100 compared to dynamic programming, but this method is only applicable to load cases with steps between different high output powers. We also present a suboptimal method which shows a <1% increase in fuel consumption compared to the optimal, while reducing the time needed for computation a factor >1000 compared to dynamic programming.

Computer assisted troubleshooting with external interventions is considered. The work is motivated by the task of repairing an automotive vehicle at lowest possible expected cost. The main contribution is a decision theoretic troubleshooting system that is developed to handle external interventions. In particular, practical issues in modeling for troubleshooting are discussed, the troubleshooting system is described, and a method for the efficient probability computations is developed. The troubleshooting systems consists of two parts; a planner that relies on AO* search and a diagnoser that utilizes Bayesian networks (BN). The work is based on a case study of an auxiliary braking system of a modern truck. Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes in the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies. To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.

This paper focuses on residual generation for model-based fault diagnosis. Specifically, a methodology to derive residual generators when nonlinear equations are present in the model is developed. A main result is the characterization of computation sequences that are particularly easy to implement as residual generators and that take causal information into account. An efficient algorithm, based on the model structure only, which finds all such computation sequences, is derived. Furthermore, fault detectability and isolability performances depend on the sensor configuration. Therefore, another contribution is an algorithm, also based on the model structure, that places sensors with respect to the class of residual generators that take causal information into account. The algorithms are evaluated on a complex highly nonlinear model of a fuel cell stack system. A number of residual generators that are, by construction, easy to implement are computed and provide full diagnosability performance predicted by the model.

Turbo performance is represented using maps, measured for one set of inlet conditions. Corrections are then applied to scale the performance to other inlet conditions. A turbo compressor for automotive applications experiences large variations in inlet conditions, and the use of two stage charging increases these variations. The variations are the motivation for analyzing the correction quantities and their validity. The corrections reveals a novel surge avoidance strategy, where the result is that a reduction in inlet pressure increases the surge margin for eight maps studied. The method to investigate the applicability of the strategy is general.

An experimental analysis of the applicability of the commonly used correction factors, used when estimating compressor performance for varying inlet conditions, is presented. The experimental campaign uses measurements from an engine test cell and from a gas stand, and shows a small, but clearly measurable trend, with decreasing compressor  pressure ratio for decreasing compressor inlet pressure. A method is  developed, enabling measurements to be analyzed with modified corrections.

An adjusted shaft speed correction quantity is proposed, incorporating also the inlet pressure in the shaft speed correction. The resulting decrease in high altitude engine performance, due to compressor limitations, are quantified and shows a reduction in altitude of 200 – 600 m, for when engine torque has to be reduced to due limited compressor operation.

Turbochargers stand for the dominating dynamics in engines and in the design, analysis, and optimization of new engines, it becomes more and more important to analyze system interactions and dynamics. In the development process, modeling, simulation, and optimization have evolved from being used in research to being stan- dard tools for engineers and play an important role in the engine development. To be successful in the process, one needs to both have component models, and methods and tools where system models can be built, analyzed, and optimized. The component models should also have capabilities to extrapolate behavior outside the nominal re- gion since design explorations can go to extreme points while searching for optimal solutions.

The first part of the chapter summarizes the compressor and turbine maps and how they can be used in simulation models. A generic model structure for compressors and turbines that fit into an engine modeling and simulation framework is described. Then the Ellipse compressor flow model and the Enthalpy based efficiency model will be described, they have been developed so that they can be integrated in a simulation environment and also used in optimization. Their main features are that they are ca- pable of extrapolating compressor behavior outside the normal range of the map in a physically sane way. In addition to this, a tuning method has been developed that takes a normal manufacturer map and returns all model parameters for compressor flow and efficiency models. Thereafter, compact turbine flow and efficiency models will be described.

Then the attention is turned to simulation and optimization applications where compressor models are used. First an engine experiment where compressor surge oc- curs is modelled and used to illustrate the extrapolation capabilities using the models presented. Then the scope is turned to control and optimization of turbocharger opera- tion on an engine, where the focus will be on a VGT controlled diesel engine equipped with EGR. First the steady state mapping of the engine is demonstrated, then optimal control of the turbo operation is investigated using modern computer tools for dynamic optimization. 

The focus of this paper is to use Naturalistic Driving Data to understand how the drivers manoeuvre an A-double combination in the roundabouts and evaluate performance in the roundabouts using measures like Low-Speed Swept Path (LSSP) and Tail Swing (TS). The analyses of the steering patterns and speed variations depict that the standard deviations of the responses of the drivers for a given travel direction in a roundabout are within 35o (17 % of the baseline) for the steering wheel angle and 8 km/h (40 % of the baseline) for the speed. It is also found that the cognitive workload of the drivers due to the steering pattern is higher in right turns compared to straight crossings through the roundabout. The performance analyses show a dependency of LSSP on the instantaneous radius obtained from the vehicle's path, and the vehicle's travel direction in the roundabout. LSSP ranges from 7.7 m for a left turn in a roundabout with an inner radius of 12 m to 3.1 m for a straight crossing in a roundabout with a 30 m inner radius. TS is observed in only one roundabout and its magnitude goes up to 0.4 m in a roundabout of 30 m inner radius.

Data-driven modeling and machine learning have received a lot of attention in fault diagnosis and system monitoring research. Since faults are rare events, conventional multi-class classification is complicated by incomplete training data and unknown faults. One solution is anomaly classification which can be used to detect abnormal behavior when only training data from the nominal operation is available. However, data-driven fault isolation is still a non-trivial task when training data is not representative of nominal and faulty behavior. In this work, the importance of redundancy for a set of known variables that are fed to a data-driven anomaly classification is discussed. It is shown that residual-based anomaly detection can be used to reject the nominal class which is not possible with one-class classifiers, such as one-class support vector machines. Based on these results, it is also discussed how data-driven residuals can be integrated with model-based fault isolation logic.

This paper is focused on fault detection and isolation of component-based multi-mode systems, i.e., systems that can be operated in different continuous modes. As the system mode changes, the structure of the system also changes which impacts diagnosability analysis and synthesis. To meet this challenge, diagnosis based on a structural approach is modified to also detect and isolate faults when modes change. Here, definitions for some important diagnosis concepts are extended to cover also multi-mode systems. Then, a method for hierarchical diagnosis of component-based systems is proposed. The method is exemplified on a Li-ion battery pack to show its effectiveness. Copyright (c) 2023 The Authors.

Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heuns can greatly improve predictions.

Battery systems are used in a wide range of safety-relevant applications, such as electric vehicles, unmanned aerial vehiclesand home storage systems. Safety, reliability and availability of the battery system therefore play a key role. In addition, theuseful service lifetime of the batteries determines the environmental impact and economic efficiency of the overall system. Onepossible solution is to give batteries a second life in applications with lower requirements in terms of dynamic behavior or capacity.Heterogeneous battery systems consist of batteries with differences in cell technology, age, capacity, and optimal operating range.To meet the safety, reliability, and availability requirements a scalable, Decentralized Battery Management System (DBMS) basedon a distributed control system is proposed. Batteries, generators, and loads have Local Control Units (LCUs) consisting of amicrocontroller, a measurement unit, and a DC/DC converter with adjustable voltage and current limits. These LCUs are the basis forthe communication-based, cooperative system control and enhance the reliability and scalability of the battery system compared toconventional centralized structures. They record and manage the operating parameters and provide the basis for predictive energymanagement and battery residual value estimation. As a fallback strategy, a droop-based control of the DC/DC converters is used inaddition to the communication-based one. Transition conditions between the control modes are defined and the control methods arecompared and differentiated. The performance and the resulting benefits of batteries are determined by the control strategies. In thispaper, the requirements for the control strategies for different operating modes, including startup, severe fluctuations of the DC powerline voltage, and safe shutdown, are analyzed.

Real-time avoidance maneuvers have been developed using a force-centric perspective, where the founding principles are obtained from studies of optimal maneuvers. The developed optimization framework, the different criteria used, and the obtained solutions give insight into how to control the forces on the vehicle. A highlight in this presentation is the first algorithm not needing a tire-road friction estimate.

Data-driven fault diagnosis of dynamic systems is complicated by incomplete training data, unknown faults, and overlapping classes. Many existing machine learning models and data-driven classifiers are not expected to perform well if training data is not representative of all relevant fault realizations. In this work, a data-driven model, called a flexi-pipe model, is proposed to capture the variability of data in residual space from a few realizations of each fault class. A diagnosis system is developed as an open set classification algorithm that can handle both incomplete training data and overlapping fault classes. Data from different fault scenarios in an engine test bench is used to evaluate the performance of the proposed methods. Results show that the proposed fault class models generalize to new fault realizations when training data only contains a few realizations of each fault class.

This paper investigates an integrated traffic environment modeling and model predictive control (MPC) system to realize interaction-aware dynamic motion planning of an autonomous vehicle with multiple surrounding vehicles. The interaction-aware interacting multiple model Kalman filter (IAIMM-KF) from the literature is used to hierarchically predict maneuvers and trajectories of surrounding vehicles and to compute safe targets for the ego vehicle. The targets are terminal speed and reference lane, which are moving targets as they are updated at each time step. Then, an MPC controller is designed for the ego vehicle to generate an optimal trajectory by following the moving targets and including the prediction results to formulate collision-free constraints. The proposed interaction-aware planning method has a proactive planning ability and can avoid collisions by non-local replanning. The strengths and effectiveness of the approach are verified in challenging highway lane-change simulation scenarios.

Data-driven fault diagnosis requires training data that is representative of the different operating conditions of the system to capture its behavior. If training data is limited, one solution is to incorporate physical insights into machine learning models to improve their effectiveness. However, while previous works show the usefulness of hybrid approaches for isolation of faults, the impact of training data must be taken into consideration when drawing conclusions from data-driven residuals in a consistency-based diagnosis framework. By giving an understanding of the physical interaction between the signals, a hybrid fault diagnosis approach, can enforce model properties of residual generators to isolate faults that are not represented in training data. The objective of this work is to analyze the impact of limited training data when training neural network-based residual generators. It is also investigated how the use of structural information when selecting the network structure is a solution to limited training data and how to ameliorate the performance of hybrid approaches in face of this challenge.

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The study shows that different model parameters are more or less critical depending on the investigated scenario.- Using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warning.

Accurate simulation of the numerical optimal control in software environments where call to simulation routines is explicit, for instance Matlab and SciPy. A discussion on the simulation aspects of numerical optimal control, how it may fail, and how such erroneous results can be detected using accurate simulation. The key contribution is how to accurately include a piecewise constant control input in the simulations, which is discussed in detail, including code examples. The technique is demonstrated on an example problem which show how simulation can be used to analyze optimal control problems with uncertainty, but also demonstrates how erroneous simulation may lead to erroneous conclusions.

The automotive industry has grown considerably over the last century consequently increasing green-house gas emissions and thus contributing towards increase in the average global temperature. It is thus of paramount importance to increase the use of alternative energy sources. Electric vehicles have gained popularity over the last decade. However, a major concern with electric vehicles is their range. The range of an electric vehicle is limited by the battery pack, in particular, the weakest cell of the pack. One method of increasing the available energy from the battery pack is by introducing more electronics. Modular multilevel converters, with their modular concept, could be a viable solution. The concept of battery-integrated modular multilevel converters (BI-MMC) for automotive applications is explored. In particular, the impact of the number of cascaded cells per submodule is investigated, considering battery losses, DC-link capacitor losses, and the converter losses. Furthermore, an optimization of the DC-link capacitors and the selection of MOSFET switching frequency is presented in order to minimize the total losses.

Simultaneous optimization of fuel consumption and battery lifetime is addressed in this work. A differential capacity degradation model is used to predict capacity loss, and linear time-varying and nonlinear MPC techniques are used to solve the energy management problem. It is shown that penalizing battery power in the MPC cost function can prolong battery lifetime by about 50 percent while achieving small gains in fuel economy compared to when the cost function only aims to minimize fuel consumption. An analysis of robustness against uncertainties in drive-cycle information shows that the controller is well-behaved and has good performance under uncertainty.

Knocking is an unwanted behavior that is affected by the intake manifold temperature. This paper demonstrates through simulation how nonlinear Model Predictive Control design could be used as a reference governor for the control of the throttle position, with a soft constraint on intake manifold temperature. The implementation is able to suppress the peak temperature during an acceleration by slowing down the pressure build-up. Because of the usually slow dynamics of the temperature sensors, the paper proposes an Extended Kalman Filter implementation that uses a transient detection to decide whether to rely on the sensor feedback or the model. Copyright (C) 2021 The Authors.

The assembly of threaded fasteners may seem straightforward. However, there are many factors to consider to achieve quality tightened joints, including the joint material, threaded fastener, and coatings. Additionally, there are many assembly tool types and torque application strategies to choose from. This investigation studies the tightening speed dynamics when using torque as a control method. The clamp force obtained in the joint changes when tightening at high speed or when the speed varies greatly during tightening. This type of tightening is called highly dynamic. Highly dynamic torque control tightening strategies are studied, such as impact, pulse, and inertia-controlled methods, and compared with the continuous drive strategy, which is a standard dynamic torque tightening method. The clamp force and its scatter caused by the torque accuracy in the assembly tool type are investigated for the abovementioned torque application strategies. The study also focuses on the different results obtained from the International Organization for Standardization’s (ISO) 16047:2005 (Fasteners-Torque/clamp force testing) standard compared to a production-like setup. 

When optimising the vehicle trajectory and powertrain energy management of hybrid electric vehicles, it is important to include look-ahead information such as road conditions and other traffic. One method for doing so is dynamic programming, but the execution time of such an algorithm on a general purpose CPU is too slow for it to be useable in real time. Significant improvements in execution time can be achieved by utilising parallel computations, for example, using a Field-Programmable Gate Array (FPGA). A tool for automatically converting a vehicle model written in C++ into code that can executed on an FPGA which can be used for dynamic programming-based control is presented in this paper. A vehicle model with a mild-hybrid powertrain is used as a case study to evaluate the developed tool and the output quality and execution time of the resulting hardware. Copyright (C) 2020 The Authors.

This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from an initial observer-based residuals setup. The best candidates from all potential additional residuals are selected using the concept of sequential residual generation to ensure best fault isolation performance for the least number of additional residuals required. A simulation testbed is used to generate realistic engine data for the design of the additional residuals and the fault isolation performance is verified using structural analysis method.

Segmenting a motion-planning problem into smaller subproblems could be beneficial in terms of computational complexity. This observation is used as a basis for a new sub-maneuver decomposition approach investigated in this paper in the context of optimal evasive maneuvers for autonomous ground vehicles. The recently published alternating augmented Lagrangianmethod is adopted and leveraged on, which turns out to fit the problem formulation with several attractive properties of the solution procedure. The decomposition is based on moving the coupling constraints between the sub-maneuvers into a separate coordination problem, which is possible to solve analytically. The remaining constraints and the objective function are decomposed into subproblems, one for each segment, which means that parallel computation is possible and benecial. The method is implemented and evaluated in a safety-critical double lane-change scenario. By using the solution of a low-complexity initialization problem and applying warm-start techniques in the optimization, a solution is possible to obtain after just a few alternating iterations using the developed approach. The resulting computational time is lower than solving one optimization problem for the full maneuver.

Green zones are challenging problems for the thermal management systems of hybrid vehicles. This is because within the green zone the engine is turned off, and the only way to keep the aftertreatment system warm is lost. This means that there is a risk of leaving the green zone with a cold and ineffective aftertreatment system, resulting in high emissions.A thermal management strategy that heats the aftertreatment system prior to turning off the engine, in an optimal way, to reduce the NOx emissions when the engine is restarted, is developed. The strategy is also used to evaluate under what conditions pre-heating is a suitable strategy, by evaluating the performance in simulations using a model of a heavy-duty diesel powertrain and scenario designed for this purpose.The results show that, for the studied vehicle, pre-heating of the aftertreatment system is an effective strategy to reduce NOx for engine-off events shorter than two hours, and is most effective for engine off events of around 1.5 hours. The results also show that for engine-off events longer than two hours, pre-heating quickly becomes an inefficient strategy. At this point, ammonia storage when the engine is turned off is more important, and pre-heating can even make the results worse, since an increased SCR temperature results in lower ammonia storage before turning off the engine, which is detrimental for NOx conversion during the restart.

This paper presents a simulation framework for modeling off-road transport operations at construction worksites using electric construction vehicles. A dynamic model is developed to describe the longitudinal behavior of the electric vehicles, and the outputs from the vehicle dynamic model are fed into a fleet model to evaluate the transport efficiency performance. Discrete event simulation technique is used in the fleet model to represent the logistics of the transport operations and capture the interactions among the vehicles and resources. The simulation framework is applied in a real world quarry operation to study the transport efficiency performance using different number of vehicles. The case study shows that the proposed mechanism can effectively allocate the optimal number of vehicles for the operation and hence serve as an efficient tool in decision-making for construction management.

Long haulage trucks consume large amounts of fuel, and fuel savings are desired both from economical and environmental aspects. When the upcoming road topology is known, the speed and gear shifts can be optimized in order to minimize the fuel consumption by e.g. minimizing the braking of the truck. Three different optimal control approaches are evaluated and compared for the speed and gear shift optimization problem. The results are based on simulations, but two of the three evaluated solvers are also implemented on-board a truck using rapid prototyping to investigate the feasibility of such systems. The results indicate that optimal control of the speed reduces the fuel consumption more than finding the optimal gear shift trajectory. The overall optimization problem contains one discrete and one continuous state, which makes the selection of optimization method complex. A sequential optimization scheme where the optimal speed profile is found using linear programming and the optimal gear profile is found using dynamic programming shows similar results as using dynamic programming for the overall problem simultaneously. One drawback with this solution is robustness and several tuning parameters. The driveability of the solutions are found good at the performed on-board tests. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Deterministic and real time calculation of safe and comfortable speed profiles is the main topic of this paper. Using vehicle properties and road characteristics, such as friction and road banking, safety limits for rollover and skidding are calculated and applied in the trajectory planning. To satisfy comfort criteria and obtain smooth speed profiles, jerk and acceleration of the vehicle are limited in the speed planning algorithm. For speed planner, an A* based search method is used to calculate a speed profile corresponding to shortest traveling time. In order to avoid stationary and moving obstacles, decoupled prioritized planning is used. A physical model is used to define the behavior of the vehicle in the speed planner, where jerk is main parameter for speed planner. The physical model enables the algorithm to take into account the safety and comfort limitations. The results attained from the search method are compared with optimal solutions in different test scenarios and the comparisons show the properties of the algorithm. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Cold start emissions are the most significant contributor to the accumulated emissions of a vehicle and poses a critical design limit for the design of clean and efficient vehicles. The core reasons for the emissions are the initial low temperature and the thermal inertia of the exhaust aftertreatment systems. Moreover, it also costs fuel to perform the heating of the catalyst. It is therefore of high interest to develop efficient control schemes that can reduce the time to light off. To facilitate this a model structure and a method, based on the explicit solution to the catalyst differential equations are developed, that can be used to analyze both time and fuel optimal heating control strategies. The method is developed to be applicable to both gasoline and diesel aftertreatment systems. A case study is performed on a Diesel engine and the results show that the solutions exhibit a structured and simple two-phase pattern. There is a first heating phase, where the catalyst is fed with a high temperature gas, building up a high inlet temperature. Then in a second phase the flow is kept high and the temperature is pushed through the catalyst. The strategy is easy to understand and realize in a real time control system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Turbocharger testing is a time consuming process, and as rapid-prototyping technology advances, so must other areas in the development chain. As an example, in one study a compressor map took over 34 hours to measure. In this paper, an effort to combat the main bottleneck of turbocharger testing, namely the thermal inertia, is made. When changing operating point during the measurement process, several minutes can be required before the turbocharger components reach temperature steady state. In an earlier paper, a method based on non-linear trajectory optimization was developed that significantly reduced the testing time required to produce compressor performance maps. The time was reduced by a factor of over 60, compared to waiting for the system to reach steady state with constant inputs. However, the method required a model of the turbocharger. This paper extends the method with system identification and model predictive control (MPC). This is an important step in order to use the optimal control method when only geometric information of the turbocharger is known, such as new prototypes. To demonstrate the effectiveness of the combination of system identification, non-linear trajectory optimization and MPC, the control strategy is applied to a virtual gas-stand implemented as a Simulink model, based on data from a Mitsubishi TD04 turbocharger. The data that was used to create the model was originally collected at Saab Automobile in Trollhättan, 2011. The results show that system identification captures the turbocharger behavior. Trajectory optimization finds a set of time optimal input trajectories. MPC successfully tracks the generated references. Real time implementation of the Matlab/Simulink based algorithm is planned for experimental testing.

Utilising optimal control presents an opportunity to increase the fuel efficiency in an off-road transport mission conducted by an articulated hauler. A human machine interface (HMI) instructing the hauler operator to follow the fuel optimal vehicle speed trajectory has been developed and tested in real working conditions. The HMI implementation includes a Dynamic Programming based method to calculate the optimal vehicle speed and gear shift trajectories. Input to the optimisation algorithm is road related data such as distance, road inclination and rolling resistance. The road related data is estimated in a map module utilising an Extended Kalman Filter (EKF), a Rauch-Tung-Striebel smoother and a data fusion algorithm. Two test modes were compared: (1) The hauler operator tried to follow the optimal vehicle speed trajectory as presented in the HMI and (2) the operator was given a constant target speed to follow. The objective of the second test mode is to achieve an approximately equal cycle time as for the optimally controlled transport mission, hence, with similar productivity. A small fuel efficiency improvement was found when the human machine interface was used.

This paper describes a decoupled sampling-based motion-planning method, based on the rapidly-exploring random tree (RRT) approach, that is applicable to autonomous vehicles, in order to perform different traffic maneuvers. This is a two-step motion-planning method including path-planning and motion timing steps, where both steps are sampling-based. In the path-planning part, an improved RRT method is defined that increases the smoothness of the path and decreases the computational time of the RRT method; it is called smooth RRT, SRRT. While some other RRT-based methods such as RRT can perform better in winding roads, in the problem of interest in this paper (which is performing some regular traffic maneuvers in usual urban roads and highways where the passage is not too winding), SRRT is more efficient since the computational time is less than for the other considered methods. In the motion timing or velocity-tuning step (VTS), a sampling-based method is introduced that guarantees collision avoidance between different vehicles. The proposed motion-timing algorithm can be very useful for collision avoidance and can be used with any other path-planning method. Simulation results show that because of the probabilistic property of the SRRT and VTS algorithms, together with the decoupling feature of the method, the algorithm works well for different traffic maneuvers.

Throttles and wastegates are devices used in modern engines for accurate control of the gas flows. It is beneficial, for the control implementation, to have compact and accurate models that describe the flow behavior. The compressible isentropic restriction is a frequently used model, it is simple and reasonable accurate but it has some issues. One special issue is that it predicts that the choking occurs at too high pressure ratios, for example the isentropic model predicts choking at a pressure ratio of 0.52, while experimental data can have choking at 0.4 or even lower. In this work, experimental data is acquired from throttles tested both in a flow bench and mounted as main throttle on a turbocharged gasoline engine. To analyze the flow behavior several flow characterizations are performed at different throttle openings. For the engine installation a special test procedure is adopted and the results show that the engine and the flow bench give the same characteristic behavior of the throttle. In particular, both installations show choking pressure ratios that are significantly lower than what the compressible isentropic restriction predicts. To remedy this and capture the behavior, different modifications of the isentropic model are investigated. Some promising model modifications are analyzed; one that uses the conservation of momentum, energy, and mass to derive a compact expression for the mass flow, and another that uses an ellipse model. All modifications analyzed give lower pressure ratios at choking.

With stricter emission legislations and demands on low fuel consumption, new engine technologies are continuously investigated. At the same time the accuracy in the over all engine control and diagnosis and hence also the required estimation accuracy is tightened. Central for the internal combustion control is the trapped cylinder charge and composition Traditionally cylinder charge is estimated using mean intake manifold pressure and engine speed in a two dimensional lookup table. With the introduction of variable valve timing, two additional degrees of freedom are introduced that makes this approach very time consuming and therefore  expensive. Especially if the cam phasers are given large  enough authority to offer powerful thermal management possibilities. The paper presents a physical model for estimating in-cylinder trapped mass and residual gas fraction utilizing cylinder pressure measurements, and intake and exhaust valve lift profiles. The cylinder pressure at intake and exhaust valve opening and closing together with manifold pressures and temperatures are combined with thermodynamic and heat transfer models to calculate the trapped cylinder mass. The estimator is validated on test data from a prototype engine with dual independent cam phasers under a wide range of operating conditions, including large variations in valve timing ranging from scavenging to early exhaust cam timing for thermal management. The main contribution is the developed model, with the ability to accurately estimate the trapped cylinder charge during large independent variations in both intake and exhaust valve timing.

Chargeable vehicles with focus on plug-in hybrid vehicles have become common. The impact PHEVs have on the energy consumption significantly varies with driving behaviour, charging possibilities, and the driving mission. This study investigates how PHEVs function during real driving. Questionnaires, interviews, and measurement vehicle data are evaluated. Key findings is that the fuel consumption decreases significantly at low speeds compared to a combustion engine vehicle, and that the drivers believe that they adopt the driving to the characteristics of the PHEV, but this is not found in the measurement data. The vehicle is behavious in the way the driver wants without any adaptation required.

Representative driving cycles are of key importance for design and dimensioning of powertrains. One approach for generation of representatives driving cycles is to define relevant driving missions which include different street types, obstacles and traffic conditions, and simulate them in a traffic simulation tool. Such a simulation approach will also require representative driver models to generate the speed profiles for the defined driving missions. Feasibility of this approach is investigated in this paper.

To leverage on model based engineering for fault diagnosis, it is useful to be able to do direct analysis of general purpose modelling languages for engineering systems. In this work, it is demonstrated how non-trivial Modelica models, for example utilizing the Modelica standard library, can be automatically transformed into a format where existing fault diagnosis analysis techniques are applicable. The procedure is demonstrated on a model of an air cooling system in the Gripen fighter aircraft developed by Saab, Sweden. It is discussed why the Modelica language is well suited for diagnosability analysis, and a number of non-trivial diagnosability analysis shows the efficacy of the approach. The methods extract the model structure, which gives additional insight into the system, e.g., highlighting model connections and possible model decompositions. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

To decrease the complexity of motion-planning optimizations, a segmentation and merging strategy for maneuvers is proposed. Maneuvers that are at-the-limit of friction are of special interest since they appear in many critical situations. The segmentation pointsare used to set constraints for several smaller optimizations for parts of the full maneuver, which later are merged and compared withoptimizations of the full maneuver. The technique is illustrated for a double lane-change maneuver.

To coordinate and optimise an off-road transport mission, on which a wheel loader and two articulated haulers cooperate, a fuel-optimal control algorithm is developed. The control algorithm utilises Pareto fronts of fuel consumption versus cycle time to e

The diesel engine remains one of the key components in the global economy, transporting most of the worlds goods. To cope with stricter regulations and the continuous demand for lower fuel consumption, optimization is a key method. To enable mathematical optimization of the diesel engine, appropriate models need to be developed. These are preferably continuously differentiable, in order to be used with a gradient-based optimization solver. Demonstration of the optimization-based methodology is also necessary in order for the industry to adapt it. The paper presents a complete mean value engine model structure, tailored for optimization and simulation purposes. The model is validated using measurements on a heavyduty diesel engine. The validated model is used to study the transient performance during a time-optimal tip-in, the results validate that the model is suitable for simulation and optimization studies.

The two perspectives of autonomous driving and new active safety in vehicles are complementary, and both hold promise to reduce the number of accidents and associated severe or fatal injuries. They both coincide in the recent interest in finding alternatives to traditional yaw-control systems that can utilize the full potential of the vehicle. By considering the control problem as that of lane-keeping, also at high speed and at-the-limit of tire friction, rather than that of yaw control, leads to the possibility of optimization-based active-braking systems with better performance than those existing today. Here, we investigate the optimal braking patterns in completely autonomous lane-keeping maneuvers resulting from a formulation where the optimization criterion used is an interpolation between the initial and final velocities of the maneuver. Varying the interpolation parameter, i.e., the relative weight between the initial and final velocity, results in different vehicle behavior. The analysis of these behaviors provides several new insights into stabilizing braking patterns for vehicles in at-the-limit maneuvers. Specifically, it is to be noted that the benefits of a lane-keeping strategy are immediate, both in terms of the maximum possible initial velocity and the velocity reduction. The formulation embeds the traditional yaw control and optimal lane-keeping as the end-point values of the interpolation parameter, and adds a continuous family of behaviors in between. This gives a new perspective for investigating the relation between traditional yaw control and optimal lane-keeping for autonomous vehicles.

To facilitate the use of advanced fault diagnosis analysis and design techniques to industrial sized systems, there is a need for computer support. This paper describes a Matlab toolbox and evaluates the software on a challenging industrial problem, air-path diagnosis in an automotive engine. The toolbox includes tools for analysis and design of model based diagnosis systems for large-scale differential algebraic models. The software package supports a complete tool-chain from modeling a system to generating C-code for residual generators. Major design steps supported by the tool are modeling, fault diagnosability analysis, sensor selection, residual generator analysis, test selection, and code generation. Structural methods based on efficient graph theoretical algorithms are used in several steps. In the automotive diagnosis example, a diagnosis system is generated and evaluated using measurement data, both in fault-free operation and with faults injected in the control-loop. The results clearly show the benefit of the toolbox in a model-based design of a diagnosis system. Latest version of the toolbox can be downloaded at faultdiagnosistoolbox.github.io. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The charge sustaining mode of a hybrid electric vehicle maintains the state of charge of the battery within a predetermined narrow band. Due to the poor system observability in this range, the state of charge estimation is tricky, and inadequate prior knowledge of the system uncertainties could lead to deterioration and divergence of estimates. In this paper, a comparative study of three estimators tuned based on the noise covariance matching technique is established in order to analyze their robustness in the state of charge estimation. Simulation results show a significant enhancement of filter accuracy using this adaptation. The adaptive particle filter has the best estimation results but it is vulnerable to model parameter uncertainties, further it is time consuming. On the other hand, the adaptive Unscented Kalman filter and the adaptive Extended Kalman filter show enough estimation accuracy, robustness for model uncertainty, and simplicity of implementation. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Fuel optimal lock-up transients for a heavy duty series hybrid electric vehicle are studied. A mean value engine model is used together with numerical optimal control to investigate the interplay between electric machine, gearbox and engine with its turbocharger dynamics in particular how they influence the manner and rate at which the engine should be controlled in order to reach a synchronized speed with the gear-box, enabling lock-up. This is studied both for prescribed gear-box speeds, simulating a mechanical transmission, and with gear-box speed an optimization variable, simulating a continuously variable transmission. The optimal engine transients and their duration are seen to be dictated by the stationary efficiency of the different drivetrain modes, showing that the ratio between the efficiencies of the electric and mechanical path dominates the dynamics and have a greater effect than the engine and turbocharger dynamics. In particular the transition between the modes is as fast as possible when the conventional powertrain is the most efficient and as slow as possible when the engine-generator set is more efficient. This points out that the stationary efficiency maps can be used in a central way for the control design of lock-up transients. (c) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Hybridization is a promising and obvious way of reducing fuel consumption in automotive applications, however, its ability to reduce emissions in long haulage trucks is not so obvious. The complexity of the powertrain is also increased which makes well designed control systems needed to fully utilize the potential benefits of the hybridization. In this paper, a control strategy that takes advantage of the complex structure of the powertrain in a hybrid electric long haulage truck is developed and evaluated. The control system is based on equivalent consumption minimization strategy where an equivalence factor is used to compare fuel and battery power so that an optimal distribution of power between the components in the powertrain can be calculated. The proposed control system is evaluated in a driving scenario using a model of a complete hybrid electric truck, including an aftertreatment system, and the results are compared with a conventional, non-hybrid, vehicle. The hybridization leads to 31 % lower NOx emissions, primarily due to better thermal conditions in the exhaust system during braking, and at the same time, the fuel consumption was reduced by 3.8 % compared to the non-hybrid vehicle. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Todays vehicle industry is converging more and more to electrification of vehicles, introducing electrical architectures to cooperate side by side with the combustion engine. This paper investigates the potential of using an electric turbocharger in a long haulage application during highway driving. A charge sustainable control strategy is developed, implemented, tuned, and evaluated on a heavy duty truck model. The benefits of using an electrical turbocharger on a heavy duty diesel truck, from a long haulage perspective, are evaluated. By calibrating the implemented controller, long haulage driving routes can be charge sustainable and consume less fuel than a conventional truck with fix turbine geometry, the fuel savings for the simulated case is 0.9%. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

A model predictive controller (MPC) including velocity and path planner is designed for real time calculation of a safe and comfortable velocity and steer angle in a heavy duty vehicle. The calculation time is reduced by finding, based on measurement data, simple roll and motion model. The roll dynamics of the truck is constructed using identification of proposed roll model and it is validated by measurements logged by a heavy duty truck and the suggested model shows good agreement with the measurement data. The safety issues such as rollover prevention and moving obstacle avoidance are taken into account. To increase comfort, acceleration, jerk, steer angle and steer angle rate are limited. The simulation and control algorithm is tested in different scenarios, where the test results show the properties of the algorithm.

Structural approaches have shown to be useful for analyzing and designing diagnosis systems for industrial systems. In simulation and estimation literature, related theories about differential index have been developed and, also there, structural methods have been successfully applied for simulating large-scale differential algebraic models. A main contribution of this paper is to connect those theories and thus making the tools from simulation and estimation literature available for model based diagnosis design. A key step in the unification is an extension of the notion of differential index of exactly determined systems of equations to overdetermined systems of equations. A second main contribution is how differential-index can be used in diagnosability analysis and also in the design stage where an exponentially sized search space is significantly reduced. This allows focusing on residual generators where basic design techniques, such as standard state-observation techniques and sequential residual generation are directly applicable. The developed theory has a direct industrial relevance, which is illustrated with discussions on an automotive engine example. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Turbocharging plays an important role in the downsizing of engines. Model-based approaches for boost control are going to increasing the necessity for controlling the wastegate flow more accurately. In today’s cars, the wastegate is usually only controlled with a duty cycle and without position feedback. Due to nonlinearities and varying disturbances a duty cycle does not correspond to a certain position. Currently the most frequently used feedback controller strategy is to use the boost pressure as the controller reference. This means that there is a large time constant from actuation command to effect in boost pressure, which can impair dynamic performance. In this paper, the performance of an electrically controlled vacuum-actuated waste-gate, subsequently referred to as vacuum wastegate, is compared to an electrical servo-controlled wastegate, also referred to as electric wastegate. Their performance is investigated with the two actuators installed on a turbocharged inline four gasoline engine in an engine test bench. Furthermore, different control synthesis designs for these different actuators are investigated. A state-feedback controller with standard models for the electric wastegate is described and implemented, which gives a position-controlled wastegate. One main difference between vacuum and electric wastegate is that the latter has a position sensor. To make an extended comparison between the solutions, the vacuum wastegate is also equipped with a position sensor and controller using standard controller design methods. The controllers are implemented and compared both in a simulation environment and evaluated in an engine test bench. In addition, for the electric wastegate, both soft-landing and tightening features are also implemented and investigated. Their aim is to improve the lifetime and behavior at or near the closed position.

Detecting changes in residuals is important for fault detection and is commonly performed by thresholding the residual using, for example, a CUSUM test. However, detecting variations in the residual distribution, not causing a change of bias or increased variance, is difficult using these methods. A plug-and-play residual change detection approach is proposed based on sequential quantile estimation to detect changes in the residual cumulative density function. An advantage of the proposed algorithm is that it is non-parametric and has low computational cost and memory usage which makes it suitable for on-line implementations where computational power is limited.

While measuring the compressor behavior at different load points in for example a gas stand, the inlet and outlet pressures are not always measured directly before and after the compressor. The friction inside the pipes and the physical piping configuration affect the measured compressor efficiency, due to the induced change of fluid enthalpy. If the measured pressures at the end of the inlet and outlet pipes are not the same as the actual pressure before and after the compressor, the acquired compressor map does not give the right description of it as an isolated component. The main contribution of this paper is the analysis of the impact of gas stand energy losses due to pipe friction on the compressor map. As a result the paper suggests a way to take the pressure losses in the inlet and outlet pipes into account. The suggested model takes pipe friction, diffuser, nozzle and pipe bends into account. The potential measurement error in compressor efficiency due to energy losses in the pipes in this experiment is 2.7% (percentage points) at maximum mass flow of air through the compressor.

Modelbased systems engineering is becoming an important tool when meeting the challenges of developing the complex future vehicles that fulfill the customers and legislators ever increasing demands for reduced pollutants and fuel consumption. To be able to work systematically and efficiently it is desirable to have a library of components that can be adjusted and adapted to each new situation. Turbocharged engines are complex and the compressor model serves as an in-depth example of how a library can be designed, incorporating the basic physics and allowing fine tuning as more information becomes available. A major part of the paper is the summary and compilation of a set of rules of thumb for compressor map extrapolation. The considerations discussed are extrapolation to surge, extrapolation to restriction region, and extrapolation out to choking. Furthermore the compressor diameter is coupled to the maximum performance of the compressor such as maximum speed, mass flow, and pressure ratio. All this is a result of an analysis of a database of more than 300 compressors. The paper uses the compressor modeling to discuss how wishes for extendability and reuse of component performance influences the library design. A Modelica library named Vehicle Propulsion Library VehProLib has been developed to meet these goals by including basic components that give a starting point for modeling and at the same time allows reuse and extendablility.

A hybrid diagnosis system design is proposed that combines model-based and data-driven diagnosis methods for fault isolation. A set of residuals are used to detect if there is a fault in the system and a consistency-based fault isolation algorithm is used to compute all diagnosis candidates that can explain the triggered residuals. To improve fault isolation, diagnosis candidates are ranked by evaluating the residuals using a set of one-class support vector machines trained using data from different faults. The proposed diagnosis system design is evaluated using simulations of a model describing the air-flow in an internal combustion engine.

The paper presents results of analytical and experimental investigations on advanced control functions of decoupled electro-hydraulic brake system. These functions address continuous wheel slip control, variation of the brake pedal feel, and brake judder compensation. The performed study demonstrates that the electro-hydraulic brake system has improved performance by relevant criteria of safety and driving comfort both for conventional and electric vehicles.

This paper investigates how model predictive control can be used to control the acceleration of a,an over actuated vehicle equipped with a combustion engine and friction. brakes. The control problem of keeping appropriate comfort and low energy consumption and simultaneously follow an acceleration reference is described. Vehicle and actuator models are developed and the model predictive controller is tested for an adaptive cruise control cut in scenario in simulation. To be aide to quantify the benefit, of the proposed model predictive controller, the performance is analyzed and compared with a state of the art PID controller. (C) 2016, IFAC (International Federation of Automatic: Control) Hosting by Elsevier Ltd. All rights reserved.

Predicting lead-acid battery failure is important for heavy-duty trucks to avoid unplanned stops by the road. There are large amount of data from trucks in operation, however, data is not closely related to battery health which makes battery prognostic challenging. A new method for identifying important variables for battery failure prognosis using random survival forests is proposed. Important variables are identified and the results of the proposed method are compared to existing variable selection methods. This approach is applied to generate a prognosis model for lead-acid battery failure in trucks and the results are analyzed. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Prognostics and health management is a useful tool for more flexible maintenance planning and increased system reliability. The application in this study is lead-acid battery failure prognosis for heavy-duty trucks which is important to avoid unplanned stops by the road. There are large amounts of data available, logged from trucks in operation. However, datais not closely related to battery health which makes battery prognostic challenging. When developing a data-driven prognostics model and the number of available variables is large,variable selection is an important task, since including non-informative variables in the model have a negative impact on prognosis performance. Two features of the dataset has been identified, 1) few informative variables, and 2) highly correlated variables in the dataset. The main contribution is a novel method for identifying important variables, taking these two properties into account, using Random Survival Forests to estimate prognostics models. The result of the proposed method is compared to existing variable selection methods,and applied to a real-world automotive dataset. Prognostic models with all and reduced set of variables are generated and differences between the model predictions are discussed, and favorable properties of the proposed approach are highlighted.

Gearshift optimal control of a hybrid powertrain with a lumped/decoupled transmission model and backlash dynamics in the driveline is studied. A model is used for a heavy duty powertrain including a validated mean value diesel engine model with electric generator, transmission dynamics representing the dynamics of the automated manual transmission system and driveshaft flexibilities. Backlash dynamics are also included in the driveline model by introducing a switching function. By applying numerical optimal control methods and dividing the gearshift process into separate phases, optimization problems are solved to investigate the minimum time and low Jerk gearshift transients. The controls are also calculated with fuel penalties added to the minimum Jerk optimization and the transients are analyzed.

This paper presents a framework for distributed fault detection and isolation in dynamic systems. Our approach uses the dynamic model of each subsystem to derive a set of independent, local diagnosers. If needed, the subsystem model is extended to include measurements and model equations from its immediate neighbors to compute its diagnosis. Our approach is designed to ensure that each subsystem diagnoser provides the correct results, therefore, a local diagnosis result is equivalent to the results that would be produced by a global system diagnoser. We discuss the distribute diagnosis algorithm, and illustrate its application using a multi-tank system.

Drive cycle following is important for concept comparisons when evaluating vehicle concepts, but it can be time consuming to develop good driver models that can achieve accurate following of a specific velocity profile. Here, a new approach is proposed where a simple driver model based on a PID controller is extended with an Iterative Learning Control (ILC) algorithm. Simulation results using a nonlinear vehicle and control system model show that it is possible to achieve very good cycle following in a few iterations with little tuning effort. It is also possible to utilize the repetitive behavior in the drive cycle to accelerate the convergence of the ILC algorithm even further.

Problems with starter batteries in heavy-duty trucks can cause costly unplanned stops along the road. Frequent battery changes can increase availability but is expensive and sometimes not necessary since battery degradation is highly dependent on the particular vehicle usage and ambient conditions. The main contribution of this work is case study where prognostic information on remaining useful life of lead-acid batteries in individual Scania heavy-duty trucks is computed. A data-driven approach using random survival forests is used where the prognostic algorithm has access to fleet operational data including 291 variables from $33 603$ vehicles from 5 different European markets. A main implementation aspect that is discussed is the treatment of accumulative variables such as vehicle age in the approach. Battery lifetime predictions are computed and evaluated on recorded data from Scania's fleet-management system and the effect of how accumulative variables are handled is analyzed.

A nonlinear mean value engine model (MVEM) of a two-stroke turbocharged marine diesel engine is developed, parameterized and validated against measurement data. The goal is to have a computationally fast and accurate engine model that captures the main dynamics and can be used in the development of control systems for the newly introduced EGR system. The tuning procedure used is explained, and the result is a six-state MVEM with seven control inputs that capture the main system dynamics.

This paper explores the control of an automobile mounted with a BlackWidow 1.0 microcontroller from a remote location using a smartphone or a computer as a data mining and analysis device as well as for the purposes of first response search and rescue during a disaster relief operation. Custom application programs; ReCon-A for the Android platform and ReCon-V for Microsoft Windows are designed specifically for this project to control the ReCon-AVe vehicle. It is found that the remote controllers are able to control the automobile from distance of more than 20m away in indoor conditions using direct connections. In outdoor conditions, communications using the computer as the remote controller can reach up to 35m while using the smartphone can achieve up to 25m away. Other than that, tests show that the response time of ReCon-AVe is less than 60ms in indoor conditions and 85ms in outdoor conditions. In addition, it can provide visual feedback to the user at a minimum 16fps that also comes with a panning capability. Apart from that, ReCon-AVe proves its capabilities to acquire two different types of sensory data obtained from sensors and then have them transferred back to the user wirelessly for data mining and analysis.

Abstract Development of efficient control algorithms for the control of automatic transmission systems is crucial to maintain passenger comfort and operational life of the transmission components. An optimization framework is developed by state space modeling of a powertrain including a nine speed automatic transmission, diesel engine, torque converter and a model for longitudinal vehicle dynamics considering drive shaft as the only flexibility of the driveline. Emphasis is set on the kinematics of the automatic transmission with the aim of modeling for gearshift optimal control during the inertia phase. Considering the interacting forces between planetary gearsets, clutches and brakes in the transmission, kinematic equations of motion are derived for rotating transmission components enabling to calculate both transmission dynamics and internal forces. The model is then used in optimal control problem formulations for the analysis of optimal control transients in two up-shift cases.

A mean value engine model is used to study optimal control of a diesel-electric powertrain. The resulting optimal controls are shown to be highly oscillating for certain operating points, raising the question whether this is an artifact of discretization, modeling choices or a phenomenon available in real engines. Several model extensions are investigated and their corresponding optimal control trajectories are studied. It is shown that the oscillating controls cannot be explained by the implemented extensions to the previously published model, nor by the discretization, showing that for certain operating points the optimal solution is periodic.

An approach to attenuate the brake judder actively is proposed. The proposed judder compensation algorithm generates a clamp force command that attenuates the judder inducing brake torque variation. An electromechanical brake with high-bandwidth closed-loop clamp force tracking performance is utilised to follow the generated command, where the judder is compensated at its source. Experimental results are presented and the compensator is validated over fixed and varying wheel speeds with significant judder attenuation demonstrated.

Three Stochastic Dynamic Programming (SDP) implementations are developed for control of a diesel-electric wheel loader transmission. The implementations each use a stochastic description of the load, with the probabilities either independent of the states, dependent on previous power or on distance driven. Both the cycles used for the controller development and for the evaluation are derived from a measured sequence of cycles.

The evaluation shows that SDP can be used for control of the engine speed and that the resulting trajectories from the three implementations are very similar. The most surprising part is that the method which has constant load probability is able to adjust to the actual load. The combination of the calculation efforts and the outcomes leads to the conclusion that the constant load probability implementation is superior to the other versions.

Model-based approaches to fault detection and isolation (FDI) rely on accurate models of the plant and a sufficient number of reliable measurements for residual generation and analysis. However, in realistic situations, there can be uncertainties in the plant models and measurements, which have a negative impact on the diagnosability performance that depends on the system state. In other words, the impact of the uncertainties can be larger in some operating regions as compared to others. To achieve acceptable performance in practice, it is necessary to find a set of residuals that are sufficiently sensitive to faults but robust to uncertainties across all operating conditions. In this paper, a quantitative measure, called detectability ratio, is used to evaluate and quantify detectability performance of different residuals in different operating regions. This measure is used to find a minimal residual set that fulfills a set of desired diagnosability performance requirements. The proposed method is demonstrated and validated through a case study.

An approach to loop-shaping feedback control design in the frequency domain via extremum seeking is proposed. Both plants and controllers are linear time-invariant systems of possibly infinite dimension. The controller is assumed to be dependent on a finite number of parameters. Discrete-time global extremum seeking algorithms are employed to minimise the difference between the desired loop shape and the estimate of the present loop shape by fine-tuning the controller parameters within a sampled-data framework. The sampling period plays an important role in guaranteeing global practical convergence to the optimum. A case study on PID control tuning is presented to demonstrate the applicability of the proposed method.

Abstract A nonlinear wheel loader model with nine states and four control inputs is utilized to study the fuel and time efficient optimal control of wheel loader operation in the short loading cycle. The wheel loader model consists of lifting, steering and powertrain subsystems where the nonlinearity originates from the torque converter in the drivetrain. The short loading cycle, from loading point to a load receiver and back to the loading point, for a fork lifting application is described in terms of boundary conditions of the optimization problem while the operation is divided into several phases with constant gearbox gear ratios in order to avoid discontinuities due to discrete gear ratios. The effect of load receiver standing orientation on the wheel loader trajectory, fuel consumption and cycle time is studied showing that a small deviation from the optimal orientation (≈ 20 [deg]) results in up to 18 % higher fuel consumption in the minimum time cycles. Also, an alternative lifting strategy where for operation safety load is lifted only when wheel loaders moves forward is studied showing that this increases the fuel consumption of a typical 25 [sec] cycle only less than 2 %. The wheel loader path between loading point and load receiver is also calculated by optimization and analyzed for different cases. It is shown that when the load receiver orientation is not optimized and is set manually, the time or fuel optimal paths will differ from the shortest distance path, however when the load receiver orientation is calculated by optimization the fuel, time and shortest distance paths become identical.

Optimal control of a heavy duty diesel engine with EGR and VGT during transients is investigated. Minimum time and fuel optimal control problems are defined for transients from low to high output torque. A validated diesel engine model is used with minor changes in order to be suitable for the selected solver. The problem is solved for several feasible minimum EGR fractions and smoke-limiter values in order to provide comparisons. The optimization results show that the smoke-limiter has great effect on the transient duration while the required EGR fraction influences the control signals' shape. The fuel optimal control keeps the control actuators more closed than the time optimal, however both time and fuel optimal results become very similar when high EGR fractions and smoke-limiter values are required.

An optimal control ready model of a diesel-electric powertrain is developed,validated and provided to the research community. The aim ofthe model is to facilitate studies of the transient control of diesel-electricpowertrains and also to provide a model for developers of optimizationtools. The resulting model is a four state three control mean valueengine model that captures the significant nonlinearity of the diesel engine, while still being continuously differentiable.

Structural methods in model-based fault diagnosis applications are simple and efficient tools for finding candidates for residual generation. However, the structural methods do not take model uncertainties and information about fault behavior into consideration. This may result in selecting residual generators with bad performance to be included in the diagnosis system. By using the Kullback-Leibler divergence, the performance of different residual generators can be compared to find the best one. With the ability to quantify diagnostic performance, the design of residual generators can be optimized by, for example, combining several residual generators such that the diagnostic performance is maximized. The proposed method for residual generation selection is applied to a water tank system to show that the achieved residual performance is improved compared to only use a structural method.

A vehicular powertrain is a lightly damped dynamic system that transfers the engine torque to the driving wheels through a number of inertias and elastic elements. Therefore, it is prone to vibrate and emit noise when disturbances are applied. Providing a methodology, for powertrain vibration modeling and simulation, is one of the key steps in various research topics in the field of automobile engineering. Verification of the engine crankshaft torsion and vibration model, as a subsystem of the powertrain, is proposed in this paper. This is achieved by constructing a rotational multi-body system in MATLAB and utilizing nonlinear least squares method for estimation of the model parameters. The simulated engine angular velocity is compared to the measured data, from a car, which shows a good agreement.

A method is developed for the minimization of time and fuel required for performing a short loading cycle with a CVT wheel loader. A factor β is used for weighing time to fuel in the optimization. Dynamic programming is used as optimization algorithm, and the developed method is based on the change of independent variable, from time to distance driven. It is shown that a change of states from speeds to kinetic energies in the internal simulations is essential.

A driving cycle, derived from measurements, representing a short loading cycle is introduced. Optimization is performed against this cycle according to the method presented, using two different values on the time to fuel weighing parameter. It is shown that this parameter can be used to find optimal solutions directed toward short time or low fuel consumption.

A flywheel angular velocity model for misfire and disturbance simulation is presented. Applications of the model are, for example, initial parameter calibration and robustness analysis of misfire detection algorithms. An analytical cylinder pressure model is used to model cylinder torque and a multi-body model with torsional flexibilities is used to model crankshaft and driveline oscillations. Misfires, cylinder variations, changes in auxiliary load, and flywheel manufacturing errors can be injected in the model and the resulting speed variations can be simulated. A qualitative validation of the model shows that simulated angular velocity captures the amplitude and oscillatory behavior of measurement data and the effects of different phenomena, such as misfire and flywheel manufacturing errors.

A method that finds fuel optimal speed profiles for traveling a predefined distance is presented. The vehicle is modeled using a quasistatic formulation and an optimal control problem is defined. In addition, the solving method is based on a multi-phase optimization algorithm based on dynamic programming. This approach results in lower computational time than solving the problem directly with dynamic programming, it also makes the computational time independent of the travel distance. In addition, the simulation generated data can be used to get the solution to several optimal control problems in parallel that have additional constraints. Further a finite time gear shift model is presented to include the gear selection in the optimization problem. The problem also considers speed losses and fuel consumption during the maneuver. The results presented show the optimal speed and gear profiles to cover a distance, making special emphasis at the acceleration phase, where it is optimal to perform a fast acceleration to engage the highest gear as soon as possible. Finally a proposed application is to use the simulation data to provide eco-driving tips to the driver.

Requirement verification is an important part of the development process, and the increasing system complexity has exacerbated the need for integrating this step into a formalized model driven development process, providing a dedicated methodology as well as tool support. In this paper the authors propose an extension for Modelica, an equation-based language for system modeling, that will allow to represent system requirements in the same formalism as the design model, thus reducing the need for transformations between different specialized formalisms, lowering maintenance and modification costs, and benefitting from the expression and simulation capabilities, as well as extensive tool support of Modelica. The object-oriented nature of the approach provides the advantages of modular design and hierarchical structuring of the requirement model. This paper also illustrates, with the help of an example, how requirement verification can be used alongside the simulation process to trace the components responsible for requirement violations. To this end, we introduce a formalism for expressing relationships between components and requirements, as well as a tracing algorithm.

The optimal control of wheel loader operation is used in order to investigate the potentials for fuel cost and cycle time minimization during the short loading cycle. The wheel loader is modeled as a nonlinear system with three control inputs and four state variables where a diesel engine generates the power utilized for lifting and traction. The lifting system is modeled considering the limitations in the hydraulics and also the structural constraints. A torque converter is included in the driveline model which introduces nonlinearities into the system and operates in different modes affecting the fuel consumption. The gear shifts during the loading cycle impose a discrete variable into the system and this is taken care of by representing the loading cycle as a multi-phase optimal control problem with constant gearbox gear ratio in each phase. Minimum fuel and minimum time system transients are calculated and analyzed for two alternative cases one where the torque converter is used to stop the vehicle before reaching the reversing point and another where the service brakes are utilized. The optimal control problem is iteratively solved in order to obtain the trade-off between fuel consumption and cycle time for both braking alternatives. It is shown that although the engine operates at lower speeds when the torque converter is used for braking, the fuel consumption increases as higher torques are demanded from the engine during braking. The increase in fuel consumption is higher in faster cycle operations as the vehicle travels at higher speeds and larger torques are required to stop the vehicle. Wheel loader operators tend to use torque converter braking alternative as it is more convenient; however, it accompanies higher fuel consumption which highlights the importance of developing intelligent and easy to use braking systems.

A new likelihood-based stochastic knock controller, that achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response is presented. Up until now it has only been evaluated using simulations and the main contribution here is the implementation and validation of the knock controller on a five cylinder engine with variable compression ratio. Furthermore, an extension of the fast response strategy and a re-tuning of the controller is shown to improve performance. The controller is validated with respect to its robustness to changes in engine operating condition as well as compression ratio. The likelihood-based controller is demonstrated in engine tests and compared to a conventional controller and it is shown that it is able to operate closer to the knock limit with less variations in control action without increasing the risk of engine damage.