Optimal Torque Allocation for Energy Efficiency Enhancement in In-Wheel Motor Drive Electric Vehicles
Subject Areas : Analytical and Numerical Methods in Mechanical Design
1 - Qazvin Islamic Azad University
Keywords: control allocation, EV, sliding mode control, torque distribution, stability enhancement,
Abstract :
This paper proposes a multi-level hierarchical control method to enhance the stability of electric vehicles (EVs) equipped with four independent in-wheel motors. At the high level, a sliding mode controller determines the total desired force and yaw moment. At the low level, an optimal energy-efficient control allocation scheme distributes torques among the four in-wheel motors. This research investigates both handling performance and energy efficiency, evaluated through a co-simulation approach using MATLAB/Simulink and CarSim. A torque distribution algorithm, based on energy efficiency optimization, is applied to control the EV during lane change maneuvers, both with and without the proposed controller. Simulation results demonstrate that the proposed torque control system and distribution algorithm effectively maintain vehicle stability, reduce energy consumption, and accurately track the desired yaw rate and longitudinal velocity during these maneuvers.
[1] D. Wang et al., Review of Energy-Saving Technologies for Electric Vehicles, from the Perspective of Driving Energy Management, Sustainability, 15 (2023) 7617.
[2] A. Khajepour, M. S. Fallah, and A. Goodarzi, Electric and Hybrid Vehicles: Technologies, Modeling and Control-A Mechatronic Approach. John Wiley & Sons, 2014.
[3] B. Mashadi and M. Majidi, Integrated AFS/DYC sliding mode controller for a hybrid electric vehicle, International Journal of Vehicle Design, 56 (2011) 246-269.
[4] J. Park, H. Jeong, I. G. Jang, and S.-H. Hwang, Torque distribution algorithm for an independently driven electric vehicle using a fuzzy control method, Energies, 8 (2015) 8537-8561.
[5] A. M. Dizqah, B. Lenzo, A. Sorniotti, P. Gruber, S. Fallah, and J. De Smet, A fast and parametric torque distribution strategy for four-wheel-drive energy-efficient electric vehicles, IEEE Transactions on Industrial Electronics, 63 (2016) 4367-4376.
[6] S. Koehler, A. Viehl, O. Bringmann, and W. Rosenstiel, Energy-efficiency optimization of torque vectoring control for battery electric vehicles, IEEE Intelligent Transportation Systems Magazine, 9 (2017) 59-74.
[7] G. De Filippis, B. Lenzo, A. Sorniotti, P. Gruber, and W. De Nijs, Energy-efficient torque-vectoring control of electric vehicles with multiple drivetrains, IEEE Transactions on Vehicular Technology, 67 (2018) 4702-4715.
[8] W. Sun, J. Wang, Q. Wang, F. Assadian, and B. Fu, Simulation investigation of tractive energy conservation for a cornering rear-wheel-independent-drive electric vehicle through torque vectoring, Science China Technological Sciences, 61 (2018) 257-272.
[9] W. Xu, H. Chen, H. Zhao, and B. Ren, Torque optimization control for electric vehicles with four in-wheel motors equipped with regenerative braking system, Mechatronics, 57 (2019) 95-108.
[10] X. Hu, P. Wang, Y. Hu, and H. Chen, A stability-guaranteed and energy-conserving torque distribution strategy for electric vehicles under extreme conditions, Applied Energy, 259 (2020) 114162.
[11] X. Hu, H. Chen, Z. Li, and P. Wang, An energy-saving torque vectoring control strategy for electric vehicles considering handling stability under extreme conditions, IEEE Transactions on Vehicular Technology, 69 (2020) 10787-10796.
[12] S. H. Kim and K.-K. K. Kim, Model Predictive Control for Energy-efficient Yaw-stabilizing Torque Vectoring in Electric Vehicles with Four In-wheel Motors, IEEE Access, (2023).
[13] H. Deng, Y. Zhao, F. Lin, and Q. Wang, Deep Reinforcement Learning-Based Torque Vectoring Control Considering Economy and Safety, Machines, 11 (2023) 459.
[14] J. Wang, S. Gao, K. Wang, Y. Wang, and Q. Wang, Wheel torque distribution optimization of four-wheel independent-drive electric vehicle for energy efficient driving, Control Engineering Practice, 110 (2021) 104779.