An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery.

Zhou, Yifei; Wang, Shunli; Xie, Yanxin; Zhu, Tao; Fernandez, Carlos

Abstract

For real-time monitoring and safe control of electrical vehicles, it is important to accurately estimate the state of charge of lithium-ion batteries. A combined data-driven modeling approach based on Least squares support vector machine based on particle swarm optimization and unscented Kalman filter is proposed to obtain a better state of charge estimation accuracy. In this article, least squares support vector machine is used to establish the nonlinear connection between current, voltage, and SOC, and the parameters of least squares support vector machine are optimized by particle swarm optimization to improve the accuracy of voltage estimation, and the state and measurement equations are established by Least squares support vector machine in unscented Kalman filter for SOC estimation. The experimental results show that the maximum voltage error for the voltage prediction made with the PSO optimized model is 0.5 V. The maximum SOC error under various working situations is similarly kept to 0.5%, which is a significant improvement compared to the traditional algorithm. The above data show that the PSO considerably increases the precision of the Least squares support vector machine, as well as the estimation accuracy of the voltage and SOC, demonstrating the effectiveness of the model.

Citation

ZHOU, Y., WANG, S., XIE, Y., ZHU, T. and FERNANDEZ, C. 2023. An improved particle swarm optimization-least squares support vector machine-unscented Kalman filtering algorithm on SOC estimation of lithium-ion battery. International journal of green energy [online], Latest Articles. Available from: https://doi.org/10.1080/15435075.2023.2196328

• Journal Article Type: Article
• Acceptance Date: Feb 9, 2023
• Online Publication Date: Mar 30, 2023
• Deposit Date: May 23, 2023
• Publicly Available Date: Mar 31, 2024
• Journal: International journal of green energy
• Print ISSN: 1543-5075
• Electronic ISSN: 1543-5083
• Publisher: Taylor and Francis
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/15435075.2023.2196328
• Keywords: Lithium-ion batteries; State of charge; Unscented Kalman filter; Particle swarm optimization; Least squares support vector machine; Electrical vehicles
• Public URL: https://rgu-repository.worktribe.com/output/1947319