A novel back propagation neural network-square root cubature Kalman filtering strategy based on fusion dual factor parameter identification for state-of-charge estimation of lithium-ion batteries.

Xu, Xuntao; Wang, Shunli; Wang, Chao; Fernandez, Carlos; Blaabjerg, Frede

Abstract

Accurate real-time estimation of the state-of-charge (SOC) of the battery is of great significance for promoting the development of electric vehicles. In this research, a novel back propagation neural network-square root cubature Kalman filtering (BPNN-SRCKF) strategy based on fusion dual factor parameter identification for SOC estimation of lithium-ion batteries is proposed. First of all, with the organic integration of the forgetting factor and memory length, a dual factor parameter identification algorithm is designed. Secondly, the square root filtering is incorporated into the CKF algorithm to speed up the operation and avoid filter divergence. Finally, a BPNN is introduced to improve the fault tolerance of the model. The results show that the mean absolute error and root mean square error range from 1.13%~1.28% under complex working conditions, which proves that the proposed strategy has high precision and good robustness.

Citation

XU, X., WANG, S., WANG, C., FERNANDEZ, C. and BLAABJERG, F. 2024. A novel back propagation neural network-square root cubature Kalman filtering strategy based on fusion dual factor parameter identification for state-of-charge estimation of lithium-ion batteries. In Proceedings of the 4th IEEE (Institute of Electrical and Electronics Engineers) 4th New energy and energy storage system control summit forum 2024 (NEESSC 2024), 29-31 August 2024, Hohhot, China. Piscataway: IEEE [online], pages 120-132. Available from: https://doi.org/10.1109/neessc62857.2024.10733526

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 4th IEEE (Institute of Electrical and Electronics Engineers) 4th New energy and energy storage system control summit forum 2024 (NEESSC 2024)
• Start Date: Aug 29, 2024
• End Date: Aug 31, 2024
• Acceptance Date: Aug 25, 2024
• Online Publication Date: Aug 29, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Nov 1, 2024
• Publicly Available Date: Nov 1, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 120-126
• DOI: https://doi.org/10.1109/neessc62857.2024.10733526
• Keywords: Lithium-ion battery; State-of-charge; Dual-factor parameter identification; Square root cubature Kalman filtering; Back propagation neural network
• Public URL: https://rgu-repository.worktribe.com/output/2565297