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A stability-guaranteed and energy-conserving torque distribution strategy for electric vehicles under extreme conditions

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  • Hu, Xiao
  • Wang, Ping
  • Hu, Yunfeng
  • Chen, Hong

Abstract

For electric vehicles with in-wheel motors, the torque distribution strategy is used to manipulate their dynamics to reduce energy consumption and ensure safety. Under critical conditions, it is difficult to meet the necessary requirements with a simple torque distribution due to the coupled nonlinear characteristics and corresponding safety constraints. To address these problems, a stability-guaranteed and energy-conserving torque distribution strategy is proposed for the vehicles in an innovative master-slave control framework. Considering the dynamic characteristics of tires on a low-friction-coefficient road, a nonlinear controller is designed to regulate the steering angle of the front wheel and an additional yaw moment in the active safety control layer. According to the driver’s dynamic demand and actuator constraints, a torque distribution controller based on model predictive control theory is designed in the energy-efficiency control layer. The motor efficiency map is used in the objective function to reduce energy consumption while improving and balancing motor efficiency. The proposed torque distribution strategy managed to show an increment of 4.50%, 0.80% with previous in energy saving under double lane change and straight acceleration maneuvers respectively, while the power loss does not exceed 0.08%.

Suggested Citation

  • Hu, Xiao & Wang, Ping & Hu, Yunfeng & Chen, Hong, 2020. "A stability-guaranteed and energy-conserving torque distribution strategy for electric vehicles under extreme conditions," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318495
    DOI: 10.1016/j.apenergy.2019.114162
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    References listed on IDEAS

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    Cited by:

    1. Deng, Huifan & Zhao, Youqun & Feng, Shilin & Wang, Qiuwei & Zhang, Chenxi & Lin, Fen, 2021. "Torque vectoring algorithm based on mechanical elastic electric wheels with consideration of the stability and economy," Energy, Elsevier, vol. 219(C).
    2. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    3. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
    4. Tian, Yang & Zhang, Yahui & Li, Hongmin & Gao, Jinwu & Swen, Austin & Wen, Guilin, 2023. "Optimal sizing and energy management of a novel dual-motor powertrain for electric vehicles," Energy, Elsevier, vol. 275(C).
    5. Wei, Hongqian & Ai, Qiang & Zhao, Wenqiang & Zhang, Youtong, 2022. "Modelling and experimental validation of an EV torque distribution strategy towards active safety and energy efficiency," Energy, Elsevier, vol. 239(PA).
    6. Zhe Zhang & Haitao Ding & Konghui Guo & Niaona Zhang, 2022. "A Hierarchical Control Strategy for FWID-EVs Based on Multi-Agent with Consideration of Safety and Economy," Energies, MDPI, vol. 15(23), pages 1-18, December.

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