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The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection

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  • Song, Ziyou
  • Hou, Jun
  • Li, Xuefeng
  • Wu, Xiaogang
  • Hu, Xiaosong
  • Hofmann, Heath
  • Sun, Jing

Abstract

When the State of Charge, State of Health, and parameters of a Lithium-ion battery are estimated simultaneously, estimation accuracy is hard to be ensured due to uncertainties in the estimation process. A sequential algorithm, which uses frequency-scale separation and estimates parameters/states sequentially by injecting currents with different frequencies, is proposed in this paper to improve estimation performance. Specifically, by incorporating a high-pass filter, the parameters can be independently characterized by injecting high-frequency and medium-frequency currents, respectively. Using the estimated parameters, battery capacity and State of Charge can then be estimated concurrently. Experimental results show that the estimation accuracy of the proposed sequential algorithm is much better than the concurrent algorithm where all parameters/states are estimated simultaneously, and the computational cost can also be reduced. Finally, experiments are conducted at different temperatures to verify the effectiveness of the proposed algorithm for varying battery capacities.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219324272
    DOI: 10.1016/j.energy.2019.116732
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    Cited by:

    1. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    2. Guo, Yuanjun & Yang, Zhile & Liu, Kailong & Zhang, Yanhui & Feng, Wei, 2021. "A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system," Energy, Elsevier, vol. 219(C).
    3. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    4. Song, Ziyou & Yang, Niankai & Lin, Xinfan & Pinto Delgado, Fanny & Hofmann, Heath & Sun, Jing, 2022. "Progression of cell-to-cell variation within battery modules under different cooling structures," Applied Energy, Elsevier, vol. 312(C).
    5. 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).
    6. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).

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