Flexible calculation approaches to support the European CO2 emissions regulatory scheme for road vehicles

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Mitigating the Torque Ripple in Electric Traction using Proportional Integral Resonant Controller

Permanent magnet (PM) machines offer high efficiencies which are attractive to be used in vehicle propulsion systems, however, their design creates an inherent torque ripple. This is particularly problematic for electric vehicles (EV) due to low damping of torsional vibration which can result in reduced vehicle comfort. This can prohibit the take up of PM machines, missing opportunities for improving vehicle energy efficiency. This paper presents the application of resonant control (RC) to suppress the impact of the PM torque ripple this enabling take up of this technology and for the first time aims to demonstrate a reduction in vibration at a vehicle level. A prototype PM machine and driveline have been fitted to a light-duty off-road vehicle. Firstly an analysis of the vehicle vibration when it is driven in a speed-control mode with a conventional proportional-integral (PI) control. The main source of the vibration is identified as the 24th harmonic torque ripple of the PM machine, which originates from the cogging torque and air-gap flux harmonics. The vibration is more severe when the torque ripple frequency is close to the natural frequency of the drivetrain. The application of Resonance Control has demonstrated over 80% reduction in speed ripple even when the torque ripple frequency is close to the natural frequency of the vehicle

A Powertrain LQR Torque Compensator with Backlash Handling

This paper derives an LQR anti-jerk controller for an automotive driveline. The time derivative of the drive shaft torque, which is closely related to the vehicle jerk, is used as a virtual system output and regulated to zero. Thereby, the controller does not need a reference model for generation of reference trajectories for the control law evaluation. The controller acts as a torque compensator for the driver’s torque demand which the controller output asymptotically follows. The properties of the controller are discussed and the behavior is illustrated by simulation examples and verified with experiments on a heavy duty truck

Model-based control for automotive applications

The number of distributed control systems in modern vehicles has increased exponentially over the past decades. Today’s performance improvements and innovations in the automotive industry are often resolved using embedded control systems. As a result, a modern vehicle can be regarded as a complex mechatronic system. However, control design for such systems, in practice, often comes down to time-consuming online tuning and calibration techniques, rather than a more systematic, model-based control design approach. The main goal of this thesis is to contribute to a corresponding paradigm shift, targeting the use of systematic, model-based control design approaches in practice. This implies the use of control-oriented modeling and the specification of corresponding performance requirements as a basis for the actual controller synthesis. Adopting a systematic, model-based control design approach, as opposed to pragmatic, online tuning and calibration techniques, is a prerequisite for the application of state-of-the-art controller synthesis methods. These methods enable to achieve guarantees regarding robustness, performance, stability, and optimality of the synthesized controller. Furthermore, from a practical point-of-view, it forms a basis for the reduction of tuning and calibration effort via automated controller synthesis, and fulfilling increasingly stringent performance demands. To demonstrate these opportunities, case studies are defined and executed. In all cases, actual implementation is pursued using test vehicles and a hardware-in-the-loop setup. • Case I: Judder-induced oscillations in the driveline are resolved using a robustly stable drive-off controller. The controller prevents the need for re-tuning if the dynamics of the system change due to wear. A hardware-in-the-loop setup, including actual sensor and actuator dynamics, is used for experimental validation. • Case II: A solution for variations in the closed-loop behavior of cruise control functionality is proposed, explicitly taking into account large variations in both the gear ratio and the vehicle loading of heavy duty vehicles. Experimental validation is done on a heavy duty vehicle, a DAF XF105 with and without a fully loaded trailer. • Case III: A systematic approach for the design of an adaptive cruise control is proposed. The resulting parameterized design enables intuitive tuning directly related to comfort and safety of the driving behavior and significantly reduces tuning effort. The design is validated on an Audi S8, performing on-the-road experiments. • Case IV: The design of a cooperative adaptive cruise control is presented, focusing on the feasibility of implementation. Correspondingly, a necessary and sufficient condition for string stability is derived. The design is experimentally tested using two Citroën C4’s, improving traffic throughput with respect to standard adaptive cruise control functionality, while guaranteeing string stability of the traffic flow. The case studies consider representative automotive control problems, in the sense that typical challenges are addressed, being variable operating conditions and global performance qualifiers. Based on the case studies, a generic classification of automotive control problems is derived, distinguishing problems at i) a full-vehicle level, ii) an in-vehicle level, and iii) a component level. The classification facilitates a characterization of automotive control problems on the basis of the required modeling and the specification of corresponding performance requirements. Full-vehicle level functionality focuses on the specification of desired vehicle behavior for the vehicle as a whole. Typically, the required modeling is limited, whereas the translation of global performance qualifiers into control-oriented performance requirements can be difficult. In-vehicle level functionality focuses on actual control of the (complex) vehicle dynamics. The modeling and the specification of performance requirements are typically influenced by a wide variety of operating conditions. Furthermore, the case studies represent practical application examples that are specifically suitable to apply a specific set of state-of-the-art controller synthesis methods, being robust control, model predictive control, and gain scheduling or linear parameter varying control. The case studies show the applicability of these methods in practice. Nevertheless, the theoretical complexity of the methods typically translates into a high computational burden, while insight in the resulting controller decreases, complicating, for example, (online) fine-tuning of the controller. Accordingly, more efficient algorithms and dedicated tools are required to improve practical implementation of controller synthesis methods

Dynamic Modeling and Parameter Identification of a Plug-in Hybrid Electric Vehicle

In recent times, mechanical systems in an automobile are largely controlled by embedded systems, called micro-controllers. These automobiles, installed with micro-controllers, run complex embedded code to improve the efficiency and performance of the targeted mechanical systems. Developing and testing these control algorithms using the concept of model based design (MBD) is a cost-efficient and time-saving approach. MBD employs vehicle system models throughout the design process and offers superior understanding of the system behaviour than a traditional hardware prototype based testing. Consequently, accurate system identification constitutes an important aspect in MBD. The main focus of this thesis is to develop a validated vehicle dynamics model of a Toyota Prius Plug-in hybrid vehicle. This model plays a crucial role in achieving better fuel economy by assisting in the development process of various controller designs such as energy management system, co-operative adaptive cruise control system, and trip planning module. In this work, initially a longitudinal vehicle dynamics model was developed in MapleSim that utilizes acausal modeling techniques and symbolic code generation to create models that are capable of real-time simulation. Here, the motion in longitudinal direction was given importance as it is the crucial degree of freedom (DOF) for determining the fuel consumption. Besides, the generic and full-fledged vehicle dynamics model in Simulink-based Automotive Simulation Models (ASM) software was also modified to create a validated model of the Prius. This software specifically facilitates the implementation of the model for virtual data collection using a driving simulator. Both vehicle models were verified by studying their simulation results at every stage of the development process. Once the vehicle models were fully functional, the accurate and reliable parameters that control the vehicle motion were estimated. For this purpose, experimental data was acquired from the on-road and rolling dynamometer testing of the Prius. During these tests, the vehicle was instrumented with a vehicle measurement system (VMS), global-positioning system (GPS), and inertial measurement unit (IMU) to collect synchronized vehicle dynamics data. Parameters were identified by choosing a local optimization algorithm that minimizes the difference between simulated and experimental results. Homotopy, a global optimization technique was also investigated to check the influence of optimization algorithms on the suspension parameters. This method of parameter estimation from on-road data is highly flexible and economical. Comparison with the parameters obtained from 4-Post testing, a standardized test method, shows that the proposed methods can estimate parameters with an accuracy of 90%. Moreover, the longitudinal and lateral dynamics exhibited by the developed vehicle models are in accordance with the experimental data from on-road testing. The full vehicle simulations suggest that these validated models can be successfully used to evaluate the performance of controllers in real time

EXPERIMENTAL EVALUATION OF AN RWD VEHICLE WITH PARAMETER EXTRACTION FOR ANALYTICAL MODELING AND EVALUATION

This study was conducted to perform experimental vibration testing on a light duty rear wheel drive vehicle. The vehicle is known to have excessive longitudinal acceleration response perceived after step changes in the driver torque command. The excessive response includes shuffle and clunk transients. Experimental testing was performed to understand the coupling between driver torque commands and peak shuffle oscillations. Data was also targeted to understand the coupling between driveline torsional oscillations and longitudinal vehicle vibrations. This data was also used to establish vehicle parameters for use in an analytical CAE model of the driveline and coupling. Driver applied tip-in and tip-out transients were captured with road testing on a rear wheel drive dynamometer test rig. Transducer signatures were captured during testing to estimate backlash size, shuffle frequency, and the influence of vehicle speed or gear. The data successfully extracted the shuffle frequency in 3rd-6th gear. Vehicle parameters extracted were used to assemble a CAE model with correlatio

Hybrid Electric Power System Validation through Parameter Optimization

Battery models can be developed from first principles or from empirical methods. The work presented in this thesis is semi-empirical, the model was validated using test data through parameter optimization. Simulink Parameter Estimation toolbox was used to identify the battery parameters and validate the battery model with test data. Experimental data was obtained by discharging the battery of a modified 2013 Chevrolet Malibu hybrid electric vehicle. The resulting battery model provided accurate simulation results over the validation data. For the constant current discharge, the mean squared error between measured and simulated data was 0.26 volts for the terminal voltage and 6.07e-4 (%) for state of charge. For the extended variable current discharge, the mean squared error between measured and simulated data was 0.21 volts for terminal voltage and 9.25e-4 (%) for state of charge. The validated battery model was implemented in the hybrid electric vehicle model and an optimization routine was conducted in Simulink to validate a launch control strategy. The vehicle model was subject to two maximum acceleration tests from 0-60mph. Test 1 corresponded to a maximum acceleration in EV-only mode and test 2 corresponded to a maximum acceleration in HEV mode or launch control mode. In both tests, the simulated data matched the experimental data with a root mean square error below 0.45 mph for vehicle speed and 3.5 volts for bus voltage