Dynamics and Model-Predictive Anti-Jerk Control of Connected Electric Vehicles

Electric Vehicles (EVs) develop high torque at low speeds, resulting in a high rate of acceleration. However, the rapid rise in torque of an electric motor creates undesired torsional oscillations, with vehicle jerk arising as a result of wheel slip or flexibility in the half-shaft. These torsional oscillations in the halfshaft lead to longitudinal oscillations in the wheels, thus reducing comfort and drivability. In this research, we have designed an anti-jerk longitudinal dynamics controller that damps out driveline oscillations and improves the drivability of EVs with central-drivetrain architecture. The anti-jerk longitudinal dynamics controller has been implemented for both traction and cruise control applications. We have used a model predictive control (MPC) approach to design the controller since it allows us to deal with multiple objectives in an optimal sense. The major scope of this research involves modeling, parameter identification, design and validation of the longitudinal dynamics controller. The real-time implementation has been demonstrated using hardware-in-the-loop experiments utilizing fast MPC solvers. The MapleSim software, which utilizes symbolic computation and optimized-code generation techniques to create models that are capable of real-time simulation, has been used to develop the longitudinal dynamics plant model. Road tests have been conducted on our test vehicle, a Toyota Rav4 electric vehicle (Rav4EV), to identify the parameters for the longitudinal dynamics model. Experimental data measured using a vehicle measurement system (VMS), global-positioning system (GPS), and inertial measurement unit (IMU) was used for parameter identification. Optimization algorithms have been used to identify the model parameters. A control-oriented model of the EV, which includes a flexible halfshaft and effect of wheel-slip transients, has been developed with the aim of controlling driveline oscillations. The MPC-based anti-jerk traction controller regulates the motor torque corresponding to the accelerator pedal position, to serve the dual objectives of traction and anti-jerk control. The performance of this controllers has been compared to that of other controllers in the literature. Since most traction controllers are on-off controllers and are only activated when wheel slip exceeds a desired limit, they are not effective in anti-jerk control. The MPC-based anti-jerk controller is able to serve multiple objectives related to anti-jerk as well as traction, and is therefore superior to other controllers. A unified design combining the upper and lower level MPC-based cruise controller has also been formulated to meet the anti-jerk objective during cruise control. The cruise controller has been designed such that it is adaptive to changes in road friction conditions. The efficacy of both traction and cruise controllers has been demonstrated through model-in-the-loop simulation, and the real-time capability has been demonstrated through hardware-in-the-loop experiments

Advanced electric vehicle components for long-distance daily trips

This paper introduces a holistic engineering approach for the design of an electric sport utility vehicle focused on the reliable capability of long-distance daily trips. This approach is targeting integration of advanced powertrain and chassis components to achieve energy-efficient driving dynamics through manifold contribution of their improved functions. The powertrain layout of the electric vehicle under discussion is designed for an e-traction axle system including in-wheel motors and the dual inverter. The main elements of the chassis layout are the electro-magnetic suspension and the hybrid brake-by-wire system with electro-hydraulic actuators on the front axle and the electro-mechanical actuators on the rear axle. All the listed powertrain and chassis components are united under an integrated vehicle dynamics and energy management control strategy that is also outlined in the paper. The study is illustrated with the experimental results confirming the achieved high performance on the electric vehicle systems level

Predictive energy-efficient motion trajectory optimization of electric vehicles

This work uses a combination of existing and novel methods to optimize the motion trajectory of an electric vehicle in order to improve the energy efficiency and other criteria for a predefined route. The optimization uses a single combined cost function incorporating energy efficiency, travel safety, physical feasibility, and other criteria. Another focus is the optimal behavior beyond the regular optimization horizon

Modelling and Model Predictive Control of Power-Split Hybrid Powertrains for Self-Driving Vehicles

Designing an autonomous vehicle system architecture requires extensive vehicle simulation prior to its implementation on a vehicle. Simulation provides a controlled environment to test the robustness of an autonomous architecture in a variety of driving scenarios. In any autonomous vehicle project, high-fidelity modelling of the vehicle platform is important for accurate simulations. For power-split hybrid electric vehicles, modelling the powertrain for autonomous applications is particularly difficult. The mapping from accelerator and brake pedal positions to torque at the wheels can be a function of many states. Due to this complex powertrain behavior, it is challenging to develop vehicle dynamics control algorithms for autonomous power-split hybrid vehicles. The 2015 Lincoln MKZ Hybrid is the selected vehicle platform of Autonomoose, the University of Waterloo’s autonomous vehicle project. Autonomoose required high-fidelity models of the vehicle’s power-split powertrain and braking systems, and a new longitudinal dynamics vehicle controller. In this thesis, a grey-box approach to modelling the Lincoln MKZ’s powertrain and braking systems is proposed. The modelling approach utilizes a combination of shallow neural networks and analytical methods to generate a mapping from accelerator and brake pedal positions to the torque at each wheel. Extensive road testing of the vehicle was performed to identify parameters of the powertrain and braking models. Experimental data was measured using a vehicle measurement system and CAN bus diagnostic signals. Model parameters were identified using optimization algorithms. The powertrain and braking models were combined with a vehicle dynamics model to form a complete high-fidelity model of the vehicle that was validated by open-loop simulation. The high-fidelity models of the powertrain and braking were simplified and combined with a longitudinal vehicle dynamics model to create a control-oriented model of the vehicle. The control-oriented model was used to design an instantaneously linearizing model predictive controller (MPC). The advantages of the MPC over a classical proportional-integral (PI) controller were proven in simulation, and a framework for implementing the MPC on the vehicle was developed. The MPC was implemented on the vehicle for track testing. Early track testing results of the MPC show superior performance to the existing PI that could improve with additional controller parameter tuning

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

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Control Oriented Modeling of an Automotive Drivetrain for Anti-Jerk Control

Drivability is an important metric during the development of an automobile. Calibration engineers spend a significant amount of time trying to improve the drivability of vehicles for various driving conditions. With an increase in the available computational power in an automobile, novel model-based methods are being implemented for further improving the drivability, while reducing calibration time and effort. Phenomenon known as clunk and shuffle, which are caused due to backlash and compliance in the driveline, are a major cause of issues related to drivability and noise, vibration and harshness (NVH) during tip-in and tip-out scenarios. This thesis focuses on developing a high-fidelity, control-oriented vehicle driveline model, which can be used for developing systems, to improve the drivability of a vehicle, during tip-in and tip-out events. A first principle physics-based model is developed, which includes the engine as a torque generator, backlash elements as discontinuities, and driveshafts as compliant elements. Experimental validation results showed that the accuracy of the developed model, in representing shuffle oscillation frequency, during the tip-in scenarios, with locked torque converter clutch, is approximately 99 %. A parametric analysis is performed to characterize the behavior of the model during different input conditions, and to study the effect of backlash size, and driveshaft compliance on the response of the driveline. Based on the observations from the parametric analysis, the high-fidelity model is later condensed into a reduced-order model, and comparative analysis is carried out between two reduced-order model (ROM) designs. The comparative results between the full-order model and ROM show that the ROM with separate tire parameters is better in predicting the frequency and amplitude of shuffle oscillations during tip-in events