点击次数：

影响因子：9.0

DOI码：10.1016/j.est.2025.11664

发表刊物：Journal of Energy Storage

关键字：lithium-ion batteriesState of healthHealth indicatorsMultilayer-convolutional neural networkKepler optimization algorithm

摘要：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.

备注：中科院2区

合写作者：Xi Zhang,Zhongchun Wang,Changpeng Tan,Yuqing Wang

第一作者：Muyao Wu

论文类型：期刊论文

通讯作者：Li Wang

论文编号：116644

学科门类：工学

文献类型：J

卷号：122

ISSN号：2352-152X

是否译文：否

发表时间：2025-04-08

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X2501357X