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A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

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  • Zhengyu Liu

    (School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
    Key Laboratory of Industrial Safety and Emergency Technology of Anhui Province, Hefei 230009, China)

  • Jingjie Zhao

    (School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

  • Hao Wang

    (School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

  • Chao Yang

    (School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract

An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values.

Suggested Citation

  • Zhengyu Liu & Jingjie Zhao & Hao Wang & Chao Yang, 2020. "A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs," Energies, MDPI, vol. 13(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:830-:d:320622
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    References listed on IDEAS

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    Cited by:

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    4. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).

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