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Remaining Useful Life Prediction for Lithium-Ion Batteries Based on the Partial Voltage and Temperature

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  • Yanru Yang

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
    Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Jianyu Liang

    (School of Data Science and Technology, North University of China, Taiyuan 030051, China)

  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Yukai Tian

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Jiang Wang

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

Abstract

Remaining useful life (RUL) prediction is vital to provide accurate decision support for a safe power system. In order to solve capacity measurement difficulties and provide a precise and credible RUL prediction for lithium-ion batteries, two health indicators (HIs), the discharging voltage difference of an equal time interval (DVDETI) and the discharging temperature difference of an equal time interval (DTDETI), are extracted from the partial discharging voltage and temperature. Box-Cox transformation, which is data processing, is used to improve the relation grade of HIs. In addition, the Pearson correlation is employed to evaluate the relationship degree between HIs and capacity. On this basis, a local Gaussian function and a global sigmoid function are utilized to improve the multi-kernel relevance vector machine (MKRVM), whose weights are optimized by applying a whale optimization algorithm (WOA). The availability of the extracted HIs as well as the accuracy of the RUL prediction are verified with the battery data from NASA.

Suggested Citation

  • Yanru Yang & Jie Wen & Jianyu Liang & Yuanhao Shi & Yukai Tian & Jiang Wang, 2023. "Remaining Useful Life Prediction for Lithium-Ion Batteries Based on the Partial Voltage and Temperature," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1602-:d:1035276
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    References listed on IDEAS

    as
    1. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    2. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    3. Xiaoyu Li & Xing Shu & Jiangwei Shen & Renxin Xiao & Wensheng Yan & Zheng Chen, 2017. "An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-15, May.
    4. Jianfang Jia & Jianyu Liang & Yuanhao Shi & Jie Wen & Xiaoqiong Pang & Jianchao Zeng, 2020. "SOH and RUL Prediction of Lithium-Ion Batteries Based on Gaussian Process Regression with Indirect Health Indicators," Energies, MDPI, vol. 13(2), pages 1-20, January.
    5. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    6. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    7. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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