Online parameter identification and state of charge estimation of lithium-ion batteries based on improved artificial fish swarms forgetting factor least squares and differential evolution extended Kalman filter.

Xiao, Weijia; Wang, Shunli; Yu, Chunmei; Yang, Xiao; Qiu, Jingsong; Fernandez, Carlos

Abstract

State of Charge (SOC) estimation is the focus of battery management systems, and it is critical to accurately estimate battery SOC in complex operating environments. To weaken the impact of unreasonable forgetting factor values on parameter estimation accuracy, an artificial fish swarm (AFS) strategy is introduced to optimize the forgetting factor of forgetting factor least squares (FFRLS) and to model the lithium-ion battery using a first-order RC model. A new method AFS-FFRLS is proposed for online parameter identification of the first-order RC model. In SOC estimation, it is not reasonable to fix the process noise covariance, and the differential evolution (DE) algorithm is combined with the extended Kalman filter (EKF) algorithm to achieve dynamic adjustment of the process noise covariance. A joint algorithm named AFS-FFRLS-DEEKF is proposed to estimate the SOC. to verify the reasonableness of the proposed algorithm, experiments are conducted under HPPC, BBDST and DST conditions, and the average errors of the joint algorithm under the three conditions are 1.9%, 2.7% and 2.4%, respectively. The validation results show that the joint algorithm improves the accuracy of SOC estimation.

Citation

XIAO, W., WANG, S., YU, C., YANG, X., QIU, J. and FERNANDEZ, C. 2022. Online parameter identification and state of charge estimation of lithium-ion batteries based on improved artificial fish swarms forgetting factor least squares and differential evolution extended Kalman filter. Journal of The Electrochemical Society [online], 169(12), 120534. Available from: https://doi.org/10.1149/1945-7111/acaa5b

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2022
• Online Publication Date: Dec 30, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Feb 2, 2023
• Publicly Available Date: Dec 31, 2023
• Journal: Journal of the Electrochemical Society
• Print ISSN: 0013-4651
• Electronic ISSN: 1945-7111
• Publisher: Electrochemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 169
• Issue: 12
• Article Number: 120534
• DOI: https://doi.org/10.1149/1945-7111/acaa5b
• Keywords: State of charge (SOC); Battery management systems; Lithium-ion battery
• Public URL: https://rgu-repository.worktribe.com/output/1862147