An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation.

Hao, Xueyi; Wang, Shunli; Fan, Yongcun; Xie, Yanxin; Fernandez, Carlos

Abstract

As an indispensable part of the battery management system, accurately predicting the estimation of the state of charge (SOC) has attracted more attention, which can improve the efficiency of battery use and ensure its safety performance. Taking the ternary lithium battery as the research object, we present an improved forgetting factor recursive least square (IFFRLS) method for parameter identification and a joint unscented particle filter algorithm for SOC estimation. First, take advantage of the particle swarm optimization (PSO) algorithm to select the optimal parameter initial value and forgetting factor value to improve the precision of the FFRLS method. At the same time, make use of the unscented Kalman algorithm (UKF) as the density function of the particle filter algorithm (PF) to form the unscented particle filtering (UPF) algorithm. Then, the IFFRLS method and UPF algorithm are proposed in this paper. The different working conditions results show that the proposed algorithm estimates the SOC with good convergence and high system robustness. The final estimation error of the algorithm is stable at 1.6 %, which is lower than the errors of the currently used EKF algorithm, UKF algorithm and PF algorithm, which provides a reference for future research on lithium-ion batteries.

Citation

HAO, X., WANG, S., FAN, Y., XIE, Y. and FERNANDEZ, C. 2023. An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation. Journal of energy storage [online], 59, article 106478. Available from: https://doi.org/10.1016/j.est.2022.106478

• Journal Article Type: Article
• Acceptance Date: Dec 17, 2022
• Online Publication Date: Dec 28, 2022
• Publication Date: Mar 31, 2023
• Deposit Date: Jan 31, 2023
• Publicly Available Date: Dec 29, 2023
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Article Number: 106478
• DOI: https://doi.org/10.1016/j.est.2022.106478
• Keywords: Lithium-ion batteries; State of charge estimation; Particle swarm optimization; Forgetting factor least squares; Unscented particle filter
• Public URL: https://rgu-repository.worktribe.com/output/1853709