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State of charge estimation for energy storage lithium-ion batteries based on gated recurrent unit neural network and adaptive Savitzky-Golay filter

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Abstract

The accurate estimation of lithium-ion battery state of charge (SOC) is the key to ensuring the safe operation of energy storage power plants, which can prevent overcharging or over-discharging of batteries, thus extending the overall service life of energy storage power plants. In this paper, we propose a robust and efficient combined SOC estimation method, GRU-ASG, which combines the gated recurrent unit (GRU) neural network and the adaptive Savitzky-Golay filter (ASG). Firstly, the “many-to-one” structure GRU is used to establish the mapping model between the battery-measured variables (voltage, current, temperature) and SOC, and achieve the SOC initial estimation. Then, the output SOC of the GRU network is smoothed online using the Spielman coefficient–based ASG filtering algorithm proposed in this paper to reduce the fluctuation of SOC. Finally, the accurate and stable estimated SOC is obtained. This paper uses six different operating condition datasets collected from an energy storage plant during the discharge process and uses four of them as training datasets and the remaining two as test datasets. The results show that the proposed method can select the optimal window length online adaptively to smooth the initial estimate of SOC. Moreover, the estimation accuracy of the proposed method is the highest compared to the single GRU network and the GRU network with a combination of other filtering algorithms. In particular, the mean square error (MSE) is less than 0.15% and the mean absolute error (MAE) is less than 3% for the two test sets.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

We would like to acknowledge the grant from National Natural Science Foundation of China, Grant number 12171073.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Southwest Petroleum University, Chengdu, 610500, China

    Jinbo Lu, Yafeng He & Zhidan Li

  2. Tsinghua Sichuan Energy Internet Res Inst, Chengdu, 610200, China

    Huishi Liang, Miangang Li, Zinan Shi, Kui Zhou, Xiaoxu Gong & Guoqiang Yuan

Authors
  1. Jinbo Lu
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  2. Yafeng He
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  3. Huishi Liang
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  4. Miangang Li
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Contributions

Jinbo Lu and Yafeng He, Huishi Liang wrote the main text of the manuscript, and Miangang Li, Zinan Shi, Kui Zhou, Zhidan Li, Xiaoxu Gong, and Guoqiang Yuan made suggestions and revisions to the full text. All authors reviewed the manuscript.

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Correspondence to Huishi Liang.

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Lu, J., He, Y., Liang, H. et al. State of charge estimation for energy storage lithium-ion batteries based on gated recurrent unit neural network and adaptive Savitzky-Golay filter. Ionics 30, 297–310 (2024). https://doi.org/10.1007/s11581-023-05252-z

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