A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm.

Wang, Shunli; Fernandez, Carlos; Yu, Chunmei; Fan, Yongcun; Cao, Wen; Stroe, Daniel-Ioan

Abstract

As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as Rp = 16.66 mΩ and capacitance is identified as Cp = 13.71 kF. The ohm internal resistance is calculated as Ro = 68.71 mΩ and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs.

Citation

WANG, S., FERNANDEZ, C., YU, C., FAN, Y., CAO, W. and STROE, D.-I. 2020. A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm. Journal of power sources [online], 471, article ID 228450. Available from: https://doi.org/10.1016/j.jpowsour.2020.228450

• Journal Article Type: Article
• Acceptance Date: May 29, 2020
• Online Publication Date: Jun 20, 2020
• Publication Date: Sep 30, 2020
• Deposit Date: Jul 15, 2020
• Publicly Available Date: Jun 21, 2021
• Journal: Journal of Power Sources
• Print ISSN: 0378-7753
• Electronic ISSN: 1873-2755
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 471
• Article Number: 228450
• DOI: https://doi.org/10.1016/j.jpowsour.2020.228450
• Keywords: Charged state prediction; Lithium ion battery pack; Composite equivalent modeling; Splice Kalman filter; Model adaptive; Noise correction
• Public URL: https://rgu-repository.worktribe.com/output/938035