A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains

© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment

Model predictive torque vectoring control with active trail-braking for electric vehicles

In this work we present the development of a torque vectoring controller for electric vehicles. The proposed controller distributes drive/brake torque between the four wheels to achieve the desired handling response and, in addition, intervenes in the longitudinal dynamics in cases where the turning radius demand is infeasible at the speed at which the vehicle is traveling. The proposed controller is designed in both the Linear and Nonlinear Model Predictive Control framework, which have shown great promise for real time implementation the last decades. Hence, we compare both controllers and observe their ability to behave under critical nonlinearities of the vehicle dynamics in limit handling conditions and constraints from the actuators and tyre-road interaction. We implement the controllers in a realistic, high fidelity simulation environment to demonstrate their performance using CarMaker and Simulink

Optimal Torque-Vectoring Control Strategy for Energy Efficiency and Vehicle Dynamic Improvement of Battery Electric Vehicles with Multiple Motors

Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller. The torque is allocated on each wheel by solving a quadratic programming problem. The latter is solved in real-time to guarantee the desired yaw moment and the requested driver power demand while minimizing the system losses. The objective function of the quadratic problem accounts for the efficiency map of the electric machine as well as the dissipations due to tire slip phenomena. The torque-vectoring is evaluated in a co-simulation environment. Matlab/Simulink is used for the control strategy and VI-CarRealTime for the vehicle model and driver. The vehicle model represents a high performance pure electric SUV with four e-motors. The performance of the proposed controller is assessed using open loop maneuvers and in closed loop track lap scenarios. The results demonstrate that the proposed controller enhances the vehicle’s performance in terms of handling. Additionally, a significant improvement in energy saving in a wide range of lateral acceleration conditions is: presented. Moreover, the control strategy is validated using rapid control prototyping, thus guaranteeing a deterministic real-time implementation

Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

Torque Vectoring Predictive Control of a Four In-Wheel Motor Drive Electric Vehicle

The recent integration of vehicles with electrified powertrains in the automotive sector provides higher energy efficiency, lower pollution levels and increased controllability. These features have led to an increasing interest in the development of Advanced Driver- Assistance Systems (ADAS) that enhance not only the vehicle dynamic behaviour, but also its efficiency and energy consumption. This master’s thesis presents some contributions to the vehicle modeling, parameter estimation, model predictive control and reference generation applied to electric vehicles, paying particular attention to both model and controller validation, leveraging offline simulations and a real-time driving simulator. The objective of this project is focused on the Nonlinear Model Predictive Controller (NMPC) technique developing torque distribution strategies, specifically Torque Vectoring (TV) for a four-in wheel motor drive electric vehicle. A real-time TV-NMPC algorithm will be implemented, which maximizes the wheels torque usage and distribution to enhance vehicle stability and improve handling capabilities. In order to develop this control system, throughout this thesis the whole process carried out including the implementation requirements and considerations are described in detail. As the NMPC is a model-based approach, a nonlinear vehicle model is proposed. The vehicle model, the estimated parameters and the controller will be validated through the design of open and closed loop driving maneuvers for offline simulations performed in a simulation plant (VI-CarRealTime) and by means of a real-time driving simulator (VI-Grade Compact Simulator) to test the vehicle performance through various dynamic driving conditions

Optimal torque vectoring control strategies for stabilisation of electric vehicles at the limits of handling

The study of chassis control has been a major research area in the automotive industry and academia for more than fifty years now. Among the popular methods used to actively control the dynamics of a vehicle, torque vectoring, the method of controlling both the direction and the magnitude of the torque on the wheels, is of particular interest. Such a method can alter the vehicle’s behaviour in a positive way under both sub-limit and limit handling conditions and has become even more relevant in the case of an electric vehicle equipped with multiple electric motors. Torque vectoring has been so far employed mainly in lateral vehicle dynamics control applications, with the longitudinal dynamics of the vehicle remaining under the full authority of the driver. Nevertheless, it has been also recognised that active control of the longitudinal dynamics of the vehicle can improve vehicle stability in limit handling situations. A characteristic example of this is the case where the driver misjudges the entry speed into a corner and the vehicle starts to deviate from its path, a situation commonly referred to as a ‘terminal understeer’ condition. Use of combined longitudinal and lateral control in such scenarios have been already proposed in the literature, but these solutions are mainly based on heuristic approaches that also neglect the strong coupling of longitudinal and lateral dynamics in limit handling situations. The main aim of this project is to develop a real-time implementable multivariable control strategy to stabilise the vehicle at the limits of handling in an optimal way using torque vectoring via the two independently controlled electric motors on the rear axle of an electric vehicle. To this end, after reviewing the most important contributions in the control of lateral and/or longitudinal vehicle dynamics with a particular focus on the limit handling solutions, a realistic vehicle reference behaviour near the limit of lateral acceleration is derived. An unconstrained optimal control strategy is then developed for terminal understeer mitigation. The importance of constraining both the vehicle state and the control inputs when the vehicle operates at the limits of handling is shown by developing a constrained linear optimal control framework, while the effect of using a constrained nonlinear optimal control framework instead is subsequently examined next. Finally an optimal estimation strategy for providing the necessary vehicle state information to the proposed optimal control strategies is constructed, assuming that only common vehicle sensors are available. All the developed optimal control strategies are assessed not only in terms of performance but also execution time, so to make sure they are implementable in real time on a typical Electronic Control Unit