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A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles

Author

Listed:
  • Zongjun Yin

    (School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241100, China
    These authors contributed equally to this work.)

  • Xuegang Ma

    (School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241100, China
    These authors contributed equally to this work.)

  • Chunying Zhang

    (School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241100, China)

  • Rong Su

    (School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241100, China)

  • Qingqing Wang

    (School of Mechanical Engineering, Anhui Institute of Information Technology, Wuhu 241100, China)

Abstract

With increasing global attention to climate change and environmental sustainability, the sustainable development of the automotive industry has become an important issue. This study focuses on the regenerative braking issues in pure electric vehicles. Specifically, it intends to elucidate the influence of the braking force distribution of the front and rear axles on access to energy recovery efficiency. Combining the I curve of a pure electric vehicle and the boundary line of the Economic Commission of Europe (ECE) regulations, the braking force distribution relationship between the front and rear axles is formulated to satisfy braking stability. The maximum regenerative braking force of the motor is determined based on the motor torque characteristics and battery charging power, and the regenerative braking torque is optimized by combining the constraints of the braking strength, battery state of charge ( SOC ), and vehicle speed. Six road working conditions are built, including the New European Driving Cycle (NEDC), the World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), the China Light-Duty Vehicle Test Cycle—Passenger (CLTC-P), and the New York City Cycle (NYCC). The efficiency of the regenerative braking strategy is validated by using the Simulink/MATLAB simulation. The simulation results show that the proposed dynamic logic threshold control strategy can significantly improve the energy recovery effect of electric vehicles, and the energy recovery efficiency can be improved by at least 25% compared to the situation without regenerative braking. Specifically, under the aforementioned road working conditions, the braking energy recovery efficiency levels are 27.69%, 42.18%, 49.54%, 47.60%, 49.28%, and 51.06%, respectively. Moreover, the energy recovery efficiency obtained by the current dynamic logic threshold is also compared with other published results. The regenerative braking control method proposed in this article makes the braking control of electric vehicles more precise, effectively reducing energy consumption and improving the driving range of electric vehicles.

Suggested Citation

  • Zongjun Yin & Xuegang Ma & Chunying Zhang & Rong Su & Qingqing Wang, 2023. "A Logic Threshold Control Strategy to Improve the Regenerative Braking Energy Recovery of Electric Vehicles," Sustainability, MDPI, vol. 15(24), pages 1-33, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16850-:d:1300319
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    References listed on IDEAS

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    1. Long, Guimin & Ding, Fei & Zhang, Nong & Zhang, Jie & Qin, An, 2020. "Regenerative active suspension system with residual energy for in-wheel motor driven electric vehicle," Applied Energy, Elsevier, vol. 260(C).
    2. Hongqiang Guo & Hongwen He & Xuelian Xiao, 2014. "A Predictive Distribution Model for Cooperative Braking System of an Electric Vehicle," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, February.
    3. Pavlovic, Jelica & Marotta, Alessandro & Ciuffo, Biagio, 2016. "CO2 emissions and energy demands of vehicles tested under the NEDC and the new WLTP type approval test procedures," Applied Energy, Elsevier, vol. 177(C), pages 661-670.
    4. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    5. Gianfranco Rizzo & Francesco Antonio Tiano & Valerio Mariani & Matteo Marino, 2021. "Optimal Modulation of Regenerative Braking in Through-The-Road Hybridized Vehicles," Energies, MDPI, vol. 14(20), pages 1-15, October.
    6. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    7. Giulia Sandrini & Daniel Chindamo & Marco Gadola, 2022. "Regenerative Braking Logic That Maximizes Energy Recovery Ensuring the Vehicle Stability," Energies, MDPI, vol. 15(16), pages 1-43, August.
    8. Bolund, Björn & Bernhoff, Hans & Leijon, Mats, 2007. "Flywheel energy and power storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(2), pages 235-258, February.
    9. Xiaobin Ning & Jiazheng Wang & Yuming Yin & Jiarong Shangguan & Nanxin Bao & Ning Li, 2023. "Regenerative Braking Algorithm for Parallel Hydraulic Hybrid Vehicles Based on Fuzzy Q-Learning," Energies, MDPI, vol. 16(4), pages 1-18, February.
    10. Qinghai Zhao & Hongxin Zhang & Yafei Xin, 2021. "Research on Control Strategy of Hydraulic Regenerative Braking of Electrohydraulic Hybrid Electric Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, February.
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