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Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles

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  • Wu, Yue
  • Huang, Zhiwu
  • Liao, Hongtao
  • Chen, Bin
  • Zhang, Xiaoyong
  • Zhou, Yanhui
  • Liu, Yongjie
  • Li, Heng
  • Peng, Jun

Abstract

This paper proposes an adaptive power allocation strategy using artificial potential field with a compensator for hybrid energy storage systems in electric vehicles. In the power allocation level, a potential field is constructed to guarantee the state-of-charge limitations of supercapacitors. Virtual forces of this field are mapped as the allocation ratio of load power. The cutoff frequency is obtained by cutting the real-time load spectrum with the allocation ratio. In the control level, a feed-forward compensator is designed to compensate for load variations in advance which can counteract dc-link fluctuations. Experimental tests under different supercapacitor initial state-of-charges and different driving cycles evaluate the superiority of proposed methods. The artificial potential field strategy provides lower battery capacity loss with supercapacitors state-of-charge limitations guaranteed compared with existing real-time power allocation strategies, e.g., a more than 15% reduction of battery capacity loss in the urban driving cycle. The feed-forward compensator allows the hybrid energy output to meet the load requirements better.

Suggested Citation

  • Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:appene:v:257:y:2020:i:c:s0306261919316708
    DOI: 10.1016/j.apenergy.2019.113983
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Wu, Yue & Huang, Zhiwu & Hofmann, Heath & Liu, Yongjie & Huang, Jiahao & Hu, Xiaosong & Peng, Jun & Song, Ziyou, 2022. "Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios," Energy, Elsevier, vol. 251(C).
    3. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    4. Mihai Machedon-Pisu & Paul Nicolae Borza, 2019. "Are Personal Electric Vehicles Sustainable? A Hybrid E-Bike Case Study," Sustainability, MDPI, vol. 12(1), pages 1-24, December.
    5. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    6. Li, Heng & Liu, Zheng & Yang, Yingze & Yang, Huihui & Shu, Boyu & Liu, Weirong, 2024. "A proactive energy management strategy for battery-powered autonomous systems," Applied Energy, Elsevier, vol. 363(C).
    7. Li, Bei & Miao, Hongzhi & Li, Jiangchen, 2021. "Multiple hydrogen-based hybrid storage systems operation for microgrids: A combined TOPSIS and model predictive control methodology," Applied Energy, Elsevier, vol. 283(C).
    8. Hongtao Liao & Fu Jiang & Cheng Jin & Yue Wu & Heng Li & Yongjie Liu & Zhiwu Huang & Jun Peng, 2020. "Lithium-Ion Battery SoC Equilibrium: An Artificial Potential Field-Based Method," Energies, MDPI, vol. 13(21), pages 1-15, October.

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