Dual particle swarm optimization based data-driven state of health estimation method for lithium-ion battery

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Impact Factor:9.4

DOI number:10.1016/j.est.2022.105908

Journal:Journal of Energy Storage

Key Words:Lithium-ion battery, State-of-health estimation, Particle swarm optimization, Extreme gradient boosting algorithm

Abstract: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 %.

Note:中科院2区Top

Co-author:Xiaojian Liu,Leichao Fang,Muyao Wu

First Author:Xingtao Liu

Indexed by:Journal paper

Correspondence Author:Ji Wu

Document Code:105908

Discipline:Engineering

Document Type:J

Volume:56

ISSN No.:2352-152X

Translation or Not:no

Date of Publication:2022-11-04

Included Journals:SCI、EI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S2352152X22018965