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

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

Energy, 2024, vol. 293, issue C

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.

Keywords: Lithium-ion batteries; Capacity prediction; Remaining useful life; Particle filter; Improved particle swarm optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003268

DOI: 10.1016/j.energy.2024.130555

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