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Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network

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  • Fan, Guodong
  • Zhang, Xi

Abstract

Accurate and reliable battery capacity estimation has been a key challenge to technology advancement of safety–critical systems such as electric vehicles and stationary energy storage systems. In this work, we propose a new battery capacity estimation approach using relaxation voltage data collected for only 10 s. A strong correlation is first identified between the relaxation voltage and battery capacity over the entire lifetime. Based on this key enabling correlation, a convolutional neural network model is then developed to estimate capacity for batteries with different degradation paths. The generalizability of the model is assessed by 28 batteries and the average percentage test error on 8 validation cells is only 1.8%. The high predictive power of the relaxation voltage is rationalized by demonstrating the impacts of degradation mechanisms on the ionic and electronic transport properties as the battery ages, ultimately manifesting in the relaxation voltage curves. We also show that the proposed method has the potential to be extended to batteries with different chemistries.

Suggested Citation

  • Fan, Guodong & Zhang, Xi, 2023. "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network," Applied Energy, Elsevier, vol. 330(PA).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015653
    DOI: 10.1016/j.apenergy.2022.120308
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    Cited by:

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    3. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).
    4. Chen, Si-Zhe & Liang, Zikang & Yuan, Haoliang & Yang, Ling & Xu, Fangyuan & Fan, Yuanliang, 2023. "A novel state of health estimation method for lithium-ion batteries based on constant-voltage charging partial data and convolutional neural network," Energy, Elsevier, vol. 283(C).
    5. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
    6. Li, Qingbo & Zhong, Jun & Du, Jinqiao & Yi, Yong & Tian, Jie & Li, Yan & Lai, Chunyan & Lu, Taolin & Xie, Jingying, 2024. "Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits," Energy, Elsevier, vol. 294(C).
    7. Ko, Chi-Jyun & Chen, Kuo-Ching & Su, Ting-Wei, 2024. "Differential current in constant-voltage charging mode: A novel tool for state-of-health and state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 288(C).
    8. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage," Applied Energy, Elsevier, vol. 356(C).
    9. Singh, S. & Budarapu, P.R., 2024. "Deep machine learning approaches for battery health monitoring," Energy, Elsevier, vol. 300(C).
    10. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).

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