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A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system

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  • Guo, Yuanjun
  • Yang, Zhile
  • Liu, Kailong
  • Zhang, Yanhui
  • Feng, Wei

Abstract

Accurate estimations of battery state-of-charge (SOC) for energy storage systems are popular research topics in recent years. Numerous challenges remain in several aspects, especially in dealing with the conflict of high model accuracy and complex model structure with heavy computational cost. This paper proposes a compact and optimized SOC estimation model, integrating a fast input selection algorithm to choose important terms as input variables, followed by a simple and efficient JAYA optimization scheme to tune the key parameters of neural network functions. From the real-system experiment results, it can be seen that the estimation model errors are greatly reduced by applying optimization method, and the model performance is validated through statistical error values including root mean square error, mean absolute error, mean absolute percentage error and SOC error. The experimental results demonstrate that the SOC estimations can be greatly improved after optimization of neural network parameters under different charging and discharging process.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:219:y:2021:i:c:s0360544220326360
    DOI: 10.1016/j.energy.2020.119529
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    6. Yan Cheng & Xuesen Zhang & Xiaoqiang Wang & Jianhua Li, 2022. "Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform," Energies, MDPI, vol. 15(6), pages 1-16, March.
    7. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    8. He, Lin & Hu, Xingwen & Yin, Guangwei & Shao, Xingguo & Liu, Jichao & Shi, Qin, 2023. "A voltage dynamics model of lithium-ion battery for state-of-charge estimation by proportional-integral observer," Applied Energy, Elsevier, vol. 351(C).
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    13. Fan, Xinyuan & Zhang, Weige & Zhang, Caiping & Chen, Anci & An, Fulai, 2022. "SOC estimation of Li-ion battery using convolutional neural network with U-Net architecture," Energy, Elsevier, vol. 256(C).

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