Dual particle swarm optimization based data-driven state of health estimation method for lithium-ion battery
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影响因子：8.907
DOI码：10.1016/j.est.2022.105908
发表刊物：Journal of Energy Storage
关键字：Lithium-ion battery State-of-health estimation Particle swarm optimization Extreme gradient boosting algorithm
摘要：Accurate estimation of Li-ion battery state of health (SOH) is essential to ensure battery safety and vehicle operation. Here, this paper proposes a dual particle swarm optimization algorithm-extreme gradient boosting algorithm (DP-X) with the battery's charging voltage and incremental capacity (IC) data. First, the features are extracted from the voltage curve and the IC curve of each charging cycle through curve compression and interpolation. Then, this paper utilizes the PSO-XGBoost (P-X) algorithm to optimize the selected features and reduce the dimensionality of the features. Finally, the P-X algorithm was applied to combine with the optimized features to adjust the model's hyperparameters and estimate the SOH. Experimental results show that the maximum SOH estimation error of the dual P-X algorithm is less than 2 %.
论文类型：期刊论文
卷号：56
页面范围：105908
是否译文：否
发表时间：2022-12-10
收录刊物：SCI、EI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X22018965