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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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    1. Jun Peng & Rui Wang & Hongtao Liao & Yanhui Zhou & Heng Li & Yue Wu & Zhiwu Huang, 2019. "A Real-Time Layer-Adaptive Wavelet Transform Energy Distribution Strategy in a Hybrid Energy Storage System of EVs," Energies, MDPI, vol. 12(3), pages 1-17, January.
    2. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    3. Castaings, Ali & Lhomme, Walter & Trigui, Rochdi & Bouscayrol, Alain, 2016. "Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints," Applied Energy, Elsevier, vol. 163(C), pages 190-200.
    4. Zhang, Shuo & Xiong, Rui & Cao, Jiayi, 2016. "Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system," Applied Energy, Elsevier, vol. 179(C), pages 316-328.
    5. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
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    7. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    8. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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.
    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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