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A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Author

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  • Haipeng Pan

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Chengte Chen

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

  • Minming Gu

    (School of Mechanical and Automatic, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.

Suggested Citation

  • Haipeng Pan & Chengte Chen & Minming Gu, 2021. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration," Energies, MDPI, vol. 14(16), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5000-:d:614682
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    References listed on IDEAS

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    1. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
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    1. Artun Sel & Bilgehan Sel & Umit Coskun & Cosku Kasnakoglu, 2021. "Comparative Study of an EKF-Based Parameter Estimation and a Nonlinear Optimization-Based Estimation on PMSM System Identification," Energies, MDPI, vol. 14(19), pages 1-14, September.

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