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Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms

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  • Shu, Xing
  • Shen, Jiangwei
  • Chen, Zheng
  • Zhang, Yuanjian
  • Liu, Yonggang
  • Lin, Yan

Abstract

Accurate capacity estimation of lithium-ion batteries is of great significance to guarantee their reliability and safety operation. In this paper, a fused capacity estimation method is devised via the co-operation of multi-machine learning algorithms. First, the peak value of incremental capacity curve is extracted as the health feature, and the support vector machine is engaged in data processing and mitigation of the noise-induced unfavorable interference. Then, the preliminary remaining capacity values are estimated based on the incorporation of support vector machine, long short-term memory network and Gaussian process regression with the support of the abstracted health feature. Finally, the random forest algorithm is employed to supply more accurate capacity estimation to fuse the preliminary remaining capacity values. The experimental validations showcase that the advanced algorithm enables to fuse the advantages of individual learners and improve the estimation accuracy. The results indicate that the proposed method can estimate the remaining capacity with the root mean square error of less than 2.4%. In addition, the robustness to noise corruption and the generality to different battery cells are also verified.

Suggested Citation

  • Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022004409
    DOI: 10.1016/j.ress.2022.108821
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    4. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
    5. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).

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