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Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter

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  • Pang, Hui
  • Chen, Kaiqiang
  • Geng, Yuanfei
  • Wu, Longxing
  • Wang, Fengbin
  • Liu, Jiahao

Abstract

Accurate prediction of capacity and remaining useful life (RUL) for lithium-ion batteries (LIBs) is crucial for ensuring safe and reliable operation of electric vehicles. However, the battery capacity degradation and external environmental disturbances make it still challenging to achieve this goal. In this article, an accurate capacity and RUL prediction method is proposed by combining improved particle swarm optimization (IPSO) with particle filter (PF) algorithms. First, the parameters of particle swarm optimization (PSO) algorithm are adjusted by adaptive weights to avoid the problem of local optimal solution. Subsequently, the optimal particle searched by IPSO is updated continuously by the PF algorithm to achieve a more accurate posterior estimation. Finally, the proposed IPSO-PF method is verified by two independent and public datasets of NASA and CALCE batteries. The results validate that the proposed method has high precision and generalizability in predicting the capacity and RUL of LIBs even at various charging rates and battery types.

Suggested Citation

  • Pang, Hui & Chen, Kaiqiang & Geng, Yuanfei & Wu, Longxing & Wang, Fengbin & Liu, Jiahao, 2024. "Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter," Energy, Elsevier, vol. 293(C).
    • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003268
    DOI: 10.1016/j.energy.2024.130555
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    Cited by:

    1. Kim, Jaewon & Sin, Seunghwa & Kim, Jonghoon, 2024. "Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system," Energy, Elsevier, vol. 297(C).

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