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Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm

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  • Zhou, Yifei
  • Wang, Shunli
  • Xie, Yanxing
  • Shen, Xianfeng
  • Fernandez, Carlos

Abstract

The prediction of SOH for Lithium-ion battery systems determines the safety of Electric vehicles and stationary energy storage devices powered by LIBs. State of health diagnosis and remaining useful life prediction also rely significantly on excellent algorithms and effective indicators extraction. Since the data obtained from the aging experiment of Lithium-ion batteries is rich in electrochemical and dynamic information, useful health indicators can be extracted for SOH and RUL prediction of machine learning. This paper presents a method for predicting SOH and RUL based on a data-driven model of deep extreme learning machine based on improved Grey Wolf optimization algorithm. Firstly, GWO algorithm is improved by piecewise chaotic distribution and sine-cosine algorithm, and then multi-layer superposition is performed on an extreme learning machine to form DELM. Additionally, the experimental data of the Center for Advanced Life Cycle Engineering data set was extracted and analyzed, the aging state of batteries was analyzed and verified from multiple scales, and the strong correlation of aging characteristics was extracted and verified. After that, the model was driven by the extracted health indicators, and the accuracy and robustness of the results were checked.

Suggested Citation

  • Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Shen, Xianfeng & Fernandez, Carlos, 2023. "Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223021552
    DOI: 10.1016/j.energy.2023.128761
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    7. He, Zhongze & Wang, Shaoping & Shi, Jian & Liu, Di & Duan, Xiaochuan & Shang, Yaoxing, 2025. "Physics-informed neural network supported wiener process for degradation modeling and reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
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    9. Yao, Lei & Dai, Huilin & Xiao, Yanqiu & Zhao, Changsheng & Fei, Zhigen & Cui, Guangzhen & Zhang, Longhai, 2024. "An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making," Energy, Elsevier, vol. 306(C).
    10. Zhu, Tao & Wang, Shunli & Fan, Yongcun & Hai, Nan & Huang, Qi & Fernandez, Carlos, 2024. "An improved dung beetle optimizer- hybrid kernel least square support vector regression algorithm for state of health estimation of lithium-ion batteries based on variational model decomposition," Energy, Elsevier, vol. 306(C).
    11. Wang, Yonggang & Yu, Yadong & Ma, Yuanchu & Shi, Jie, 2025. "Lithium-ion battery health state estimation based on improved snow ablation optimization algorithm-deep hybrid kernel extreme learning machine," Energy, Elsevier, vol. 323(C).
    12. Li, Yanming & Qin, Xiaojuan & Chai, Min & Wu, Haoran & Zhang, Fujing & Jiang, Fenghe & Wen, Changbao, 2025. "SOH evaluation and RUL estimation of lithium-ion batteries based on MC-CNN-TimesNet model," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    13. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
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    15. Zuo, Ziyue & Xiao, Xinping & Gao, Mingyun & Rao, Congjun, 2025. "Mixed-frequency fusion grey panel model for spatiotemporal prediction of photovoltaic power generation," Renewable Energy, Elsevier, vol. 248(C).

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