Novel feedback-Bayesian BP neural network combined with extended Kalman filtering for the battery state-of-charge estimation.

Zhang, Yixing; Wang, Shunli; Xu, Wenhua; Fernandez, Carlos; Fan, Yongcun

Abstract

The state of charge estimation of lithium-ion batteries plays an important role in real-time monitoring and safety. To solve the problem that high non-linearity during real-time estimation of lithium-ion batteries who cause that it is difficult to estimate accurately. Taking lithium-ion battery as the research object, the working characteristics of lithium-ion ion battery are studied under various working conditions. To reduce the error caused by the nonlinearity of the lithium battery system, the BP neural network with the high approximation of nonlinearity is combined with the extended Kalman filtering. At the same time, to eliminate the over fitting of training, Bayesian regularization is used to optimize the neural network. Taking into account the real-time requirements of lithium-ion batteries, a feedback network is adopted to carry out real-time algorithm integration on lithium-ion batteries. A simulation model is established, and the results are analyzed in combination with various working conditions. Experimental results show that the algorithm has the characteristics of fast convergence and good tracking effect, and the estimation error is within 1.10%. It is verified that the Feedback-Bayesian BP neural network combined with the extended Kalman filtering algorithm can improve the accuracy of lithium-ion battery state-of-charge estimation.

Citation

ZHANG, Y., WANG, S., XU, W., FERNANDEZ, C. and FAN, Y. 2021. Novel feedback-Bayesian BP neural network combined with extended Kalman filtering for the battery state-of-charge estimation. International journal of electrochemical science [online], 16(6), article ID 210624. Available from: https://doi.org/10.20964/2021.06.40

• Journal Article Type: Article
• Acceptance Date: Apr 4, 2021
• Online Publication Date: Apr 30, 2021
• Publication Date: Jun 30, 2021
• Deposit Date: Jul 1, 2021
• Publicly Available Date: Jul 1, 2021
• Journal: International journal of electrochemical science
• Electronic ISSN: 1452-3981
• Publisher: Electrochemical Science Group
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 6
• Article Number: 210624
• DOI: https://doi.org/10.20964/2021.06.40
• Keywords: Feedback-Bayesian; Non-linearity; BP neural network; Extended Kalman filtering; State of charge
• Public URL: https://rgu-repository.worktribe.com/output/1375631