State of health estimation of lithium-ion batteries based on the Kepler optimization algorithm-multilayer-convolutional neural network

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Impact Factor:9.0

DOI number:10.1016/j.est.2025.11664

Journal:Journal of Energy Storage

Key Words:lithium-ion batteriesState of healthHealth indicatorsMultilayer-convolutional neural networkKepler optimization algorithm

Abstract:Lithium-ion batteries are widely used in electric vehicles (EVs), and accurate SOH estimation is essential for ensuring EV safety. This paper proposes a novel SOH estimation method based on the Kepler optimization algorithm-multilayer-convolutional neural network. Firstly, the extracted health indicators (HIs) are filtered, and highly correlated, continuous HIs are identified using Pearson correlation coefficient and scatter plots. Subsequently, the ReliefF algorithm is employed for further dimensionality reduction. Subsequently, a multilayer-convolutional neural network is constructed for SOH estimation, with the Kepler optimization algorithm (KOA) for hyperparameter optimization, a novel application according to the authors' knowledge. The SOH estimation results demonstrate that, a deeper CNN does not necessarily yield better results and the KOA-2-layer-CNN performs the best. Additionally, compared with the 2-layer-CNN without hyperparameters optimization, the mean absolute error (MAE), the root mean square error (RMSE), maximum absolute error (Max-AE) of the KOA-2-layer-CNN are decreased by 58.97 %, 53.33 %, 39.05 %, respectively. Moreover, compared with commonly used SOH estimation methods based on feature engineering, the KOA-2-layer-CNN also achieves accurate SOH estimation results, with significantly smaller MAE, RMSE, and Max-AE.

Note:中科院2区

Co-author:Xi Zhang,Zhongchun Wang,Changpeng Tan,Yuqing Wang

First Author:Muyao Wu

Indexed by:Journal paper

Correspondence Author:Li Wang

Document Code:116644

Discipline:Engineering

Document Type:J

Volume:122

ISSN No.:2352-152X

Translation or Not:no

Date of Publication:2025-04-08

Included Journals:SCI、EI

Links to published journals:https://www.sciencedirect.com/science/article/pii/S2352152X2501357X