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Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model

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  • Ni, Yulong
  • Xu, Jianing
  • Zhu, Chunbo
  • Pei, Lei

Abstract

Accurate residual capacity estimation of retired LiFePO4 batteries is critically important for second-use applications but is challenging with multiple aging pathways and nonlinear degradation mechanisms. In this study, a fast and accurate residual capacity estimation method based on the mechanism and data-driven model is developed with two main contributions. First, as the basis of the residual capacity estimation model, three new health indicators directly related to the capacity loss mechanism are derived from the prognostic and mechanism model using the Levenberg-Marquardt method and Spearman correlation. Second, residual capacity tests were conducted on 1000 retired batteries to establish a data-driven model for residual capacity estimation based on the proposed health indicators, guaranteeing better universality and estimation accuracy for different types of retired LiFePO4 batteries. To establish a data-driven model for the residual capacity estimation, an improved moth–flame optimization and support vector regression method is used; the adaptive weight and Levy flight are introduced in the moth–flame optimization algorithm to prevent the local optimal value. The residual capacity estimation results are compared with the results from three other typical methods and input health indicators. The results show that the root mean square error of the proposed method is within 2.18% using only the first 10% of the data, a smaller error than with the other methods. A fast and accurate residual capacity estimation method for retired batteries can reduce the cost and improve the development for second-use applications.

Suggested Citation

  • Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012332
    DOI: 10.1016/j.apenergy.2021.117922
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    8. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
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