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Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation

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  • Duong, Van-Huan
  • Bastawrous, Hany Ayad
  • See, Khay Wai

Abstract

This paper presents a simple approach to estimate the state of charge of the LiFePO4 batteries in electric vehicles under dynamic loads and variable temperature, which are inevitable during practical working conditions. This approach employs a highly adaptive estimation algorithm based on the recursive least-squares method and an original simple model of the open circuit voltage to the state of charge over a wide range of temperature. The modeling and the estimation in this approach are based on a new term for the state of charge, which is defined based on experimental findings to take into account the battery recovery capacity due to temperature variations. The proposed approach is validated through Urban Dynamometer Driving Schedule experiments including harsh temperature conditions, which have been mostly overlooked in previous research. The obtained results show that this approach maintains an accurate state of charge estimation, with an error of less than 5.2% under such conditions. The accuracy and the simplicity of the proposed algorithm are crucial for a feasible battery management system to be used in electric vehicles.

Suggested Citation

  • Duong, Van-Huan & Bastawrous, Hany Ayad & See, Khay Wai, 2017. "Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation," Applied Energy, Elsevier, vol. 204(C), pages 560-571.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:560-571
    DOI: 10.1016/j.apenergy.2017.07.056
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    References listed on IDEAS

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    4. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    5. 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.
    6. 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.
    7. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    8. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    9. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

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