粒子群优化
锂(药物)
粒子(生态学)
颗粒过滤器
离子
滤波器(信号处理)
材料科学
核工程
计算机科学
工程类
物理
算法
电气工程
生物
内分泌学
量子力学
生态学
作者
Hui Pang,Kaiqiang Chen,Yuanfei Geng,Longxing Wu,Fengbin Wang,Jiahao Liu
出处
期刊:Energy
[Elsevier]
日期:2024-04-01
卷期号:293: 130555-130555
被引量:11
标识
DOI:10.1016/j.energy.2024.130555
摘要
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.
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