A Sustainable SOH Prediction Model for Lithium-Ion Batteries Based on CPO-ELM-ABKDE with Uncertainty Quantification

Meng-Xiang Yan, Zhi-Hui Deng, Lianfeng Lai (), Yong-Hong Xu (), Liang Tong, Hong-Guang Zhang, Yi-Yang Li, Ming-Hui Gong and Guo-Ju Liu
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Meng-Xiang Yan: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Zhi-Hui Deng: College of Mechanical and Electrical Engineering, Ningde Normal University, Ningde 352000, China
Lianfeng Lai: College of Mechanical and Electrical Engineering, Ningde Normal University, Ningde 352000, China
Yong-Hong Xu: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Liang Tong: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Hong-Guang Zhang: Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
Yi-Yang Li: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Ming-Hui Gong: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Guo-Ju Liu: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China

Sustainability, 2025, vol. 17, issue 11, 1-28

Abstract: The battery management system (BMS) is crucial for the efficient operation of batteries, with state of health (SOH) prediction being one of its core functions. Accurate SOH prediction can optimize battery management, enhance utilization and range, and extend battery lifespan. This study proposes an SOH estimation model for lithium-ion batteries that integrates the Crested Porcupine Optimizer (CPO) for parameter optimization, Extreme Learning Machine (ELM) for prediction, and Adaptive Bandwidth Kernel Function Density Estimation (ABKDE) for uncertainty quantification, aiming to enhance the long-term reliability and sustainability of energy storage systems. Health factors (HFs) are extracted by analyzing the charging voltage curves and capacity increment curves of lithium-ion batteries, and their correlation with battery capacity is validated using Pearson and Spearman correlation coefficients. The ELM model is optimized using the CPO algorithm to fine-tune input weights (IWs) and biases (Bs), thereby enhancing prediction performance. Additionally, ABKDE-based probability density estimation is introduced to construct confidence intervals for uncertainty quantification, further improving prediction accuracy and stability. Experiments using the NASA battery aging dataset validate the proposed model. Comparative analysis with different models demonstrates that the CPO-ELM-ABKDE model achieves SOH estimation with a mean absolute error (MAE) and root-mean-square error (RMSE) within 0.65% and 1.08%, respectively, significantly outperforming other approaches.

Keywords: state of health; health factor; crested porcupine algorithm; extreme learning machine; probability density estimation; confidence interval; battery life extension; long-term reliability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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