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A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach

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  • Wang, Ju
  • Xiong, Rui
  • Li, Linlin
  • Fang, Yu

Abstract

The state of charge (SOC) estimation is extremely important for the wide commercialization and safe operation of electric vehicle (EV), especially under cold conditions, which is also a critical technology for battery system in EVs used in the 2022 Beijing winter Olympics. Three efforts have been made in this paper: (1) A general joint estimation framework with dual estimators is set up. Based on this frame, a joint algorithm using the recursive least square (RLS) and the adaptive H infinity filter (AHIF) is realized. (2) Four filter-based algorithms have been systematically compared and analyzed at the wide temperature range. The results show that RLS-AHIF algorithm has better performance for SOC estimation even at low temperatures, such as −10 °C, and the SOC error is within 3.5%. (3) A hardware-in-loop validation platform including the battery management system (BMS) and battery test instruments has been built to verify the proposed method. The results from the platform show that the maximum error of SOC is less than 2% at 0 °C and 25 °C. Consequently, the proposed algorithm can achieve the application over a wide temperature range in an actual BMS.

Suggested Citation

  • Wang, Ju & Xiong, Rui & Li, Linlin & Fang, Yu, 2018. "A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach," Applied Energy, Elsevier, vol. 229(C), pages 648-659.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:648-659
    DOI: 10.1016/j.apenergy.2018.08.022
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    2. Chun Wang & Chaocheng Fang & Aihua Tang & Bo Huang & Zhigang Zhang, 2022. "A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty," Energies, MDPI, vol. 15(12), pages 1-16, June.
    3. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    4. Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    5. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    6. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).

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