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Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons

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  • Xi, Zhimin
  • Wang, Rui
  • Fu, Yuhong
  • Mi, Chris

Abstract

Various neural network models have been adopted for lithium ion battery state of charge (SOC) estimation with good accuracy. However, problems for battery states estimation from neural networks were usually not reported, which is mainly due to the lack of effective solutions other than a trial and error training process. This paper firstly proposes time-delayed recurrent neural network for lithium ion battery modeling and SOC estimation. Both exceptional performances and unexpected overfitting or poor performances are reported with in-depth analysis of the root cause. With explicit formulation of the network, each hidden neuron’s output is examined. It is discovered that overexcited neurons could be the root cause for unexpected poor performances of the neural network. Without overexcited neurons, expectational SOC estimation accuracy is consistently obtained with estimation error being less than 1% for lithium ion magnesium phosphate (LiFeMgPO4) batteries considering a fair comparison in literature.

Suggested Citation

  • Xi, Zhimin & Wang, Rui & Fu, Yuhong & Mi, Chris, 2022. "Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012691
    DOI: 10.1016/j.apenergy.2021.117962
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    Citations

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    4. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    5. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    6. Guangheng Qi & Ning Ma & Kai Wang, 2024. "Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions," Energies, MDPI, vol. 17(11), pages 1-18, May.
    7. Zafar, Muhammad Hamza & Khan, Noman Mujeeb & Houran, Mohamad Abou & Mansoor, Majad & Akhtar, Naureen & Sanfilippo, Filippo, 2024. "A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature," Energy, Elsevier, vol. 292(C).
    8. 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.
    9. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    10. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    11. Zhang, Zhengjie & Cao, Rui & Zheng, Yifan & Zhang, Lisheng & Guang, Haoran & Liu, Xinhua & Gao, Xinlei & Yang, Shichun, 2024. "Online state of health estimation for lithium-ion batteries based on gene expression programming," Energy, Elsevier, vol. 294(C).
    12. 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).
    13. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    14. Saikia, Pranaynil & Bastida, Héctor & Ugalde-Loo, Carlos E., 2024. "An effective predictor of the dynamic operation of latent heat thermal energy storage units based on a non-linear autoregressive network with exogenous inputs," Applied Energy, Elsevier, vol. 360(C).

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