Enhancing Lithium-Ion Battery State-of-Health Estimation via an IPSO-SVR Model: Advancing Accuracy, Robustness, and Sustainable Battery Management

Siyuan Shang, Yonghong Xu (), Hongguang Zhang (), Hao Zheng, Fubin Yang, Yujie Zhang, Shuo Wang, Yinlian Yan and Jiabao Cheng
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Siyuan Shang: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Yonghong Xu: Mechanical Electrical Engineering School, Beijing Information Science and Technology University, Beijing 100192, China
Hongguang Zhang: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Hao Zheng: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Fubin Yang: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Yujie Zhang: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Shuo Wang: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Yinlian Yan: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China
Jiabao Cheng: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100124, China

Sustainability, 2025, vol. 17, issue 13, 1-18

Abstract: Precise forecasting of lithium-ion battery health status is crucial for safe, efficient, and sustainable operation throughout the battery life cycle, especially in applications like electric vehicles (EVs) and renewable energy storage systems. In this study, an improved particle swarm optimization–support vector regression (IPSO-SVR) model is proposed for dynamic hyper-parameter tuning, integrating multiple intelligent optimization algorithms (including PSO, genetic algorithm, whale optimization, and simulated annealing) to enhance the accuracy and generalization of battery state-of-health (SOH) estimation. The model dynamically adjusts SVR hyperparameters to better capture the nonlinear aging characteristics of batteries. We validate the approach using a publicly available NASA lithium-ion battery degradation dataset (cells B0005, B0006, B0007). Key health features are extracted from voltage–capacity curves (via incremental capacity analysis), and correlation analysis confirms their strong relationship with battery capacity. Experimental results show that the proposed IPSO-SVR model outperforms a conventional PSO-SVR benchmark across all three datasets, achieving higher prediction accuracy: a mean MAE of 0.611%, a mean RMSE of 0.794%, a mean MSE of 0.007%, and robustness a mean R 2 of 0.933. These improvements in SOH prediction not only ensure more reliable battery management but also support sustainable energy practices by enabling longer battery life spans and more efficient resource utilization.

Keywords: lithium-ion battery; SOH; IPSO-SVR model; dynamic hyper-parameter; sustainable energy management; battery life extension; resource optimization (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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