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Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online

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  • Ning, Bo
  • Cao, Binggang
  • Wang, Bin
  • Zou, Zhongyue

Abstract

Simplicity and accuracy are both important factors in real-time battery states estimation applications. However, a battery model initialized with static parameters which are identified in ideal laboratory conditions will not be able to get an accurate estimation in various actual applications. Besides, it is time-consuming and complex in implement. To solve the above problem, a new battery states estimation method is proposed. Firstly, an adaptive battery model is proposed according to a new online parameter estimation algorithm. Based on it, the parameter adaptive sliding mode observer for state of charge is proposed. Thus, the state of charge systematic error led from various work environments could be effectively reduced. The parameter adaptive sliding mode observer for state of health is proposed by tracing the derivative of open circuit voltage estimated online. As the reference open circuit voltage is estimated based on measurable inputs and outputs, rather than conventional observer with an assumed constant capacity. The estimated battery capacity could converge to the actual value while the error of battery open circuit voltage converges to zero. The proposed method is verified through the urban dynamometer driving schedule driving cycle. The results indicate that:1) parameters estimated online are accurate, 2) the absolute error of state of charge is less than 2%, 3) the estimated lithium-ion battery capacity could converge to the actual value with small capacity error.

Suggested Citation

  • Ning, Bo & Cao, Binggang & Wang, Bin & Zou, Zhongyue, 2018. "Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online," Energy, Elsevier, vol. 153(C), pages 732-742.
    • Handle: RePEc:eee:energy:v:153:y:2018:i:c:p:732-742
    DOI: 10.1016/j.energy.2018.04.026
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    10. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
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    13. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    14. Song, Ziyou & Hou, Jun & Li, Xuefeng & Wu, Xiaogang & Hu, Xiaosong & Hofmann, Heath & Sun, Jing, 2020. "The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection," Energy, Elsevier, vol. 193(C).
    15. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    16. 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.
    17. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    18. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    19. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    20. Gao, Yizhao & Zhu, Chong & Zhang, Xi & Guo, Bangjun, 2021. "Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system," Energy, Elsevier, vol. 221(C).
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    22. 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).
    23. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.

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