Year: 2026
Type: article
Abstract: Accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is critical to ensuring the safety and reliability of battery management system (BMS). To achieve precise estimation, this study proposes a hybrid framework that integrates variational mode decomposition (VMD), kernel principal component analysis (KPCA), polar lights optimizer (PLO), and physics-informed neural network (PINN) for SOH estimation. First, multidimensional health features are extracted and decomposed by VMD into intrinsic mode functions (IMFs), which are then compressed into a one-dimensional principal component via KPCA, retaining over 95% of the original information. Subsequently, the PLO algorithm is used to adaptively optimize three key hyperparameters of the PINN-based model: the learning rate, the number of collocation points, and the regularization loss weight. Finally, the optimized PINN is deployed to predict the SOH of the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. Experimental results demonstrate that the proposed VMD-KPCA-PLO-PINN exhibits high prediction accuracy under both 7:3 and 5:5 training-to-testing data partitions. For example, under the 5:5 partition, the proposed model achieves an average R2 of 0.983 and an average RMSE of 0.0085 on the tested CALCE cells.
Source: World Electric Vehicle Journal
DOI: 10.3390/wevj17070368
Authors: Zhiwei Yang, Qianli Dong, Rui Dong, Guangjun Liu
Institution: Hubei University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
Award: G901237252
OpenAlex ID: W7169143658