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State of health estimation of lithium-ion battery with improved radial basis function neural network

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  • Wu, Ji
  • Fang, Leichao
  • Dong, Guangzhong
  • Lin, Mingqiang

Abstract

Accurate state of health (SOH) estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. However, traditional neural network algorithms to estimate SOH often focus on fitting nonlinear fluctuation and is weak in the overall tracking trend. This paper thus proposes an improved radial basis function neural network (IRBFNN) to estimate the SOH with the simultaneous fitting of general trends and local fluctuations. A polynomial is provided to describe the overall trend of SOH. Meanwhile, the hidden layer of the IRBFNN converts the features nonlinearly to simulate the local battery capacity regeneration. Moreover, the initial parameters of the IRBFNN are obtained after training and then optimized by the improved gray wolf optimization algorithm. Two different datasets are utilized to verify the effectiveness of the presented method by comparing it with several other algorithms. Experimental results show that the IRBFNN-based method can accurately estimate the SOH, and the maximum estimation errors are within ±4%. Therefore, the results imply that the proposed method can effectively alleviate the problem of the poor estimation performance of traditional neural network-based algorithms in the later stage of battery aging.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222022629
    DOI: 10.1016/j.energy.2022.125380
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    Cited by:

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    3. Liu, Xingtao & Tang, Qinbin & Feng, Yitian & Lin, Mingqiang & Meng, Jinhao & Wu, Ji, 2023. "Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment," Applied Energy, Elsevier, vol. 351(C).
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    5. Peng, Simin & Sun, Yunxiang & Liu, Dandan & Yu, Quanqing & Kan, Jiarong & Pecht, Michael, 2023. "State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network," Energy, Elsevier, vol. 282(C).
    6. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).
    7. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).

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