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A new SOH estimation method for Lithium-ion batteries based on model-data-fusion

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Listed:
  • Chen, Liping
  • Xie, Siqiang
  • Lopes, António M.
  • Li, Huafeng
  • Bao, Xinyuan
  • Zhang, Chaolong
  • Li, Penghua

Abstract

A new method for the estimation of the state-of-health (SOH) of lithium-ion batteries (LIBs) is proposed. The approach combines a LIB equivalent circuit model (ECM) and a deep learning network. Firstly, correlation analysis is performed between the LIB data and SOH and suitable portions are selected as health features (HFs). Simultaneously, a fractional-order RC ECM of the LIB is derived and a hybrid fractional particle swarm optimization with crisscross learning (FPSO-CL) strategy is used to identify the model parameters. Secondly, correlation analysis between the model parameters and SOH is conducted and those that best represent the battery health are selected as additional HFs. Thirdly, an improved vision transformer network (VIT) is designed by including a dimension transformation layer, multilayer perceptron and a trainable regression token. Finally, the VIT is trained with all determined HFs, yielding a compete framework for predicting the SOH of LIBs. Experimental verification is carried out on real LIBs data and the results show that the proposed scheme can achieve higher prediction accuracy than other alternative methods.

Suggested Citation

  • Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029912
    DOI: 10.1016/j.energy.2023.129597
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    References listed on IDEAS

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    1. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    2. Wu, Ji & Fang, Leichao & Dong, Guangzhong & Lin, Mingqiang, 2023. "State of health estimation of lithium-ion battery with improved radial basis function neural network," Energy, Elsevier, vol. 262(PB).
    3. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    4. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    5. Landini, S. & O’Donovan, T.S., 2021. "Novel experimental approach for the characterisation of Lithium-Ion cells performance in isothermal conditions," Energy, Elsevier, vol. 214(C).
    6. Suri, Girish & Onori, Simona, 2016. "A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries," Energy, Elsevier, vol. 96(C), pages 644-653.
    7. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    8. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    9. Abedi Pahnehkolaei, Seyed Mehdi & Alfi, Alireza & Tenreiro Machado, J.A., 2022. "Analytical stability analysis of the fractional-order particle swarm optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    10. Wen, Jianping & Chen, Xing & Li, Xianghe & Li, Yikun, 2022. "SOH prediction of lithium battery based on IC curve feature and BP neural network," Energy, Elsevier, vol. 261(PA).
    11. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
    12. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    Full references (including those not matched with items on IDEAS)

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