A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range.

Hao, Xueyi; Wang, Shunli; Fan, Yongcun; Liu, Donglei; Liang, Yawen; Zhang, Mengyun; Fernandez, Carlos

Abstract

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm optimization (DWPSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) algorithm is proposed to construct a particle filter to estimate the SOE of a lithium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model, and use the LSSVM model to optimize the SOE estimates obtained by the PF algorithm twice to improve the accuracy of SOE estimation for lithium-ion batteries. The feasibility of the proposed algorithm is verified using two complex operating conditions and at three different temperatures. The validation results show that the maximum error of SOE estimation of the proposed algorithm is 0.0284 for a wide temperature range under Beijing Bus Dynamic Stress Test (BBDST) condition, and 0.0226 for a wide temperature range under Dynamic Stress Test (DST) condition. The proposed algorithm significantly improves the accuracy of SOE estimation and provides a reference for fundamental applications of lithium-ion batteries.

Citation

HAO, X., WANG, S., FAN, Y., LIU, D., LIANG, Y., ZHANG, M. and FERNANDEZ, C. 2024. A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range. Journal of energy storage [online], 89, article number 111820. Available from: https://doi.org/10.1016/j.est.2024.111820

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2024
• Online Publication Date: Apr 26, 2024
• Publication Date: Jun 1, 2024
• Deposit Date: May 3, 2024
• Publicly Available Date: Apr 27, 2025
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 89
• Article Number: 111820
• DOI: https://doi.org/10.1016/j.est.2024.111820
• Keywords: Least square support vector machine; Particle filter; State of energy; Dynamic weights particle swarm optimization; Lithium-ion battery
• Public URL: https://rgu-repository.worktribe.com/output/2328451