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State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network

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  • Zhang, Hao
  • Gao, Jingyi
  • Kang, Le
  • Zhang, Yi
  • Wang, Licheng
  • Wang, Kai

Abstract

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper proposes a data-driven estimation approach, where the TCN is combined with the modified flower pollination algorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

Suggested Citation

  • Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223021369
    DOI: 10.1016/j.energy.2023.128742
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    8. Cao, Zhi & Gao, Wei & Fu, Yuhong & Kurdkandi, Naser Vosoughi & Mi, Chris, 2025. "A general framework for lithium-ion battery state of health estimation: From laboratory tests to machine learning with transferability across domains," Applied Energy, Elsevier, vol. 381(C).
    9. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
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    12. Miao, Jianguo & Xie, Jilong & Deng, Congying & Piao, Changhao & Huang, Miao & He, Mingge, 2026. "State of health estimation of lithium-ion batteries using variational-diffusion generative model and attention-enhanced hybrid temporal network," Energy, Elsevier, vol. 344(C).
    13. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
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