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Lithium-ion battery life prognostic health management system using particle filtering framework

Author

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  • M Dalal
  • J Ma
  • D He

Abstract

In this paper, a detailed implementation of a lithium-ion battery life prognostic system using a particle filtering framework is presented. A lumped parameter battery model is used to account for all the dynamic characteristics of the battery: a non-linear open-circuit voltage, current, temperature, cycle number, and time-dependent storage capacity. The internal processes of the battery are used to form the basis of this model. Statistical estimates of the noise in the system and the anticipated operational conditions are processed to provide estimates of the remaining useful life. The model is then subsequently used in the particle-filtering framework with a sequential importance resampling algorithm to predict the remaining useful life of the battery for individual discharge cycles as well as for the battery cycle life. The research presented in this paper provides the necessary steps towards a comprehensive battery health management solution for energy storage devices.

Suggested Citation

  • M Dalal & J Ma & D He, 2011. "Lithium-ion battery life prognostic health management system using particle filtering framework," Journal of Risk and Reliability, , vol. 225(1), pages 81-90, March.
    • Handle: RePEc:sae:risrel:v:225:y:2011:i:1:p:81-90
    DOI: 10.1177/1748006XJRR342
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    Cited by:

    1. Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
    2. Wang, Yiwei & Gogu, Christian & Kim, Nam H. & Haftka, Raphael T. & Binaud, Nicolas & Bes, Christian, 2019. "Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 86-100.
    3. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
    4. Lin Li & Alfredo Alan Flores Saldivar & Yun Bai & Yun Li, 2019. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering," Energies, MDPI, vol. 12(14), pages 1-18, July.

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