An improved compression factor particle swarm optimization-unscented particle filter algorithm for accurate lithium-ion battery state of energy estimation.

Hao, Xueyi; Wang, Shunli; Fan, Yongcun; Liang, Yawen; Wang, Yangtao; Fernandez, Carlos

Abstract

Accurate prediction of the remaining range remains a challenge for electric vehicles. The state of energy (SOE) is a state parameter representing the remaining mileage and remaining charge of a lithium-ion battery, which is related to the prediction of the remaining range of electric vehicles. To obtain the mathematical description and SOE parameters of lithium-ion batteries with high accuracy, a parameter identification method using an improved particle swarm optimization algorithm with compression factor is proposed. For the estimation of energy state, a particle filter (PF) is constructed in this paper, and the unscented particle filtering (UPF) algorithm with particle swarm optimization (PSO) is used to achieve the estimation of energy state, which can solve the problems of particle degradation and insufficient particle diversity of particle filtering. The experimental results show that the SOE estimation error is within 0.97% at 25 degrees for all three operating conditions and within 1.29% at 5 degrees for all three operating conditions. Therefore, the proposed algorithm has high accuracy and strong robustness at different temperatures and different working conditions, and the estimation results prove the validity of energy state estimation.

Citation

HAO, X., WANG, S., FAN, Y., LIANG, Y., WANG, Y. and FERNANDEZ, C. 2023. An improved compression factor particle swarm optimization-unscented particle filter algorithm for accurate lithium-ion battery state of energy estimation. Journal of The Electrochemical Society [online], 170(7), article 070507. Available from: https://doi.org/10.1149/1945-7111/acdf8a

• Journal Article Type: Article
• Acceptance Date: Jul 7, 2023
• Online Publication Date: Jul 7, 2023
• Publication Date: Jul 31, 2023
• Deposit Date: Aug 3, 2023
• Publicly Available Date: Jul 8, 2024
• Journal: Journal of the Electrochemical Society
• Print ISSN: 0013-4651
• Electronic ISSN: 1945-7111
• Publisher: Electrochemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 170
• Issue: 7
• Article Number: 070507
• DOI: https://doi.org/10.1149/1945-7111/acdf8a
• Keywords: Electric vehicles; Range; State of energy (SOC); Ions; Monte Carlo methods; Particle swarm optimization (PSO); State estimation
• Public URL: https://rgu-repository.worktribe.com/output/2015660