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Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

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  • Qian, Cheng
  • Xu, Binghui
  • Chang, Liang
  • Sun, Bo
  • Feng, Qiang
  • Yang, Dezhen
  • Ren, Yi
  • Wang, Zili

Abstract

Capacity estimation is an essential task for battery manage systems to ensure the safety and reliability of lithium-ion batteries. Considering the uncertainty of charging and discharging behavior in practical usage, this paper presents a one-dimensional convolution neural network (1D CNN)-based method that takes random segments of charging curves as inputs to perform capacity estimation for lithium-ion batteries. To improve the robustness and accuracy of the proposed 1D CNN network, a linear decreasing weighted particle swarm optimization algorithm is utilized to optimize the partial hyperparameters of neural network. Experimental data from two sets of batteries with different nominal capacities are employed for verification purpose. It is proved that the proposed method is feasible to provide accurate estimations on capacity degradation for both kinds of batteries. Furthermore, effects of length and relative position of segments on the capacity estimation are also investigated. The analysis results show that a more precise estimation of the battery capacity is prone to be obtained from the segment with a longer length or lower initial SOC.

Suggested Citation

  • Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s036054422100582x
    DOI: 10.1016/j.energy.2021.120333
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    7. Meng, Huixing & Geng, Mengyao & Han, Te, 2023. "Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    9. Guarino, Antonio & Trinchero, Riccardo & Canavero, Flavio & Spagnuolo, Giovanni, 2022. "A fast fuel cell parametric identification approach based on machine learning inverse models," Energy, Elsevier, vol. 239(PC).
    10. Han, Dongho & Kwon, Sanguk & Lee, Miyoung & Kim, Jonghoon & Yoo, Kisoo, 2023. "Electrochemical impedance spectroscopy image transformation-based convolutional neural network for diagnosis of external environment classification affecting abnormal aging of Li-ion batteries," Applied Energy, Elsevier, vol. 345(C).
    11. 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).
    12. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    13. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
    14. Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. 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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