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A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient

Author

Listed:
  • Diju Gao

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China)

  • Yong Zhou

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China)

  • Yide Wang

    (Key Laboratory Marine Technology and Control Engineering, Ministry of Transport, Shanghai Maritime University, Shanghai 201306, China
    Institut d’Électronique et des Technologies du numéRique, UMR CNRS 6164, Universite de Nantes, F-44000 Nantes, France)

Abstract

With the wide application of lithium batteries, battery fault prediction and health management have become more and more important. This article proposes a method for predicting the remaining useful life (RUL) of lithium-ion batteries to avoid a series of safety problems caused by continuing to use the battery after reaching its service life threshold. Since the battery capacity is not easy to obtain online, we propose that some measurable parameters should be used in the battery discharge cycle to estimate battery capacity. Then, the estimated capacity is used to replace the measured value of the particle filter (PF) based on the Kendall rank correlation coefficient (KCCPF) to predict the RUL of the lithium batteries. Simulation results show that the proposed method has high prediction accuracy, stability, and practical value.

Suggested Citation

  • Diju Gao & Yong Zhou & Tianzhen Wang & Yide Wang, 2020. "A Method for Predicting the Remaining Useful Life of Lithium-Ion Batteries Based on Particle Filter Using Kendall Rank Correlation Coefficient," Energies, MDPI, vol. 13(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4183-:d:398434
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    Cited by:

    1. Fan Yang & Guangqiu Huang & Yanan Li, 2023. "A New Combination Model for Air Pollutant Concentration Prediction: A Case Study of Xi’an, China," Sustainability, MDPI, vol. 15(12), pages 1-27, June.

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