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Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort

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  • Zhang, Junjiang
  • Yang, Yang
  • Hu, Minghui
  • Yang, Zhong
  • Fu, Chunyun

Abstract

The energy recovery efficiency and ride comfort of electric vehicles are important performance indicators. Currently, few joint studies have been conducted on the energy recovery and ride comfort of electric vehicles. For enhanced energy recovery and ride comfort, a comprehensive control method that contains neuro-fuzzy control and model predictive control is proposed herein. First, a longitudinal–vertical interaction model under braking conditions is established that includes longitudinal–vertical variables interaction. Second, model predictive control is adopted to adjust the active suspension for improving ride comfort with the braking intensity as the disturbance. Subsequently, to improve the energy recovery efficiency of the vehicle, a neuro-fuzzy optimization framework is proposed for optimizing the neuro-fuzzy membership function to realize neuro-fuzzy control, the framework considers the constraints of the vehicle vertical motion on the braking torque. Furthermore, the neuro-fuzzy control is adopted to control the vehicle powertrain. Finally, a dual-loop multi-stage control is selected for comparison. The simulation results under combined braking conditions indicate that the proposed comprehensive control method simultaneously improves the energy recovery efficiency and ride comfort of the vehicle.

Suggested Citation

  • Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221016650
    DOI: 10.1016/j.energy.2021.121417
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    References listed on IDEAS

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    1. Yang Yang & Yundong He & Zhong Yang & Chunyun Fu & Zhipeng Cong, 2020. "Torque Coordination Control of an Electro-Hydraulic Composite Brake System During Mode Switching Based on Braking Intention," Energies, MDPI, vol. 13(8), pages 1-19, April.
    2. He, Hongwen & Wang, Chen & Jia, Hui & Cui, Xing, 2020. "An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    3. Shi, Dehua & Pisu, Pierluigi & Chen, Long & Wang, Shaohua & Wang, Renguang, 2016. "Control design and fuel economy investigation of power split HEV with energy regeneration of suspension," Applied Energy, Elsevier, vol. 182(C), pages 576-589.
    4. Yang Yang & Qiang He & Yongzheng Chen & Chunyun Fu, 2020. "Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor," Energies, MDPI, vol. 13(3), pages 1-21, February.
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    Citations

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    Cited by:

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    2. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    3. Li, Xianzhe & Liu, Mengnan & Hu, Chenming & Yan, Xianghai & Zhao, Sixia & Zhang, Mingzhu & Xu, Liyou, 2024. "Parameters collaborative optimization design and innovation verification approach for fuel cell distributed drive electric tractor," Energy, Elsevier, vol. 292(C).
    4. Xiaoping Li & Junming Zhou & Wei Guan & Feng Jiang & Guangming Xie & Chunfeng Wang & Weiguang Zheng & Zhijie Fang, 2023. "Optimization of Brake Feedback Efficiency for Small Pure Electric Vehicles Based on Multiple Constraints," Energies, MDPI, vol. 16(18), pages 1-20, September.
    5. Chen, Guanpeng & Jiang, Yue & Tang, Yuanjiang & Xu, Xiaojun, 2023. "Pitch stability control of variable wheelbase 6WID unmanned ground vehicle considering tire slip energy loss and energy-saving suspension control," Energy, Elsevier, vol. 264(C).
    6. Lipeng, Zhang & Xin, Liu & Shuaishuai, Liu & Haoran, Guo & Kaixin, Shi, 2024. "Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps," Energy, Elsevier, vol. 289(C).
    7. Ze Zhao & Lei Zhang & Jianyang Wu & Liang Gu & Shaohua Li, 2023. "Vertical-Longitudinal Coupling Effect Investigation and System Optimization for a Suspension-In-Wheel-Motor System in Electric Vehicle Applications," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    8. Aminu Babangida & Chiedozie Maduakolam Light Odazie & Péter Tamás Szemes, 2023. "Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle," Mathematics, MDPI, vol. 11(15), pages 1-19, August.

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