A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries.

Liu, Donglei; Wang, Shunli; Fan, Yongcun; Fernandez, Carlos; Blaabjerg, Frede

Abstract

In view of the unmeasurable state parameters of electric-vehicle lithium-ion batteries, this paper investigates a novel multi-factor fuzzy membership function - adaptive extended Kalman filter (MFMF-AEKF) algorithm for the online joint estimation of the state of charge and energy. Strong nonlinear characteristics of model parameters are characterized by considering multiple processing factors of electrochemical and diffusion effects for lithium-ion batteries and constructing an optimized multifactor coupling model. In the proposed MFMF-AEKF method, multi-space-scale factors are introduced to realize the numerical analysis of the multi-factor coupled model parameters and state estimation under dynamic working conditions of electric-vehicle lithium-ion batteries. The proposed MFMF-AEKF algorithm estimates the state of charge (SOC) with the overall best mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and maximum error (ME) values of 1.822%, 4.322%, 1.947%, and 2.954%, respectively, under challenging working conditions. And The MAE, MAPE, RMSE, and ME values for the state of energy (SOE) are 0.617%, 1.711%, 0.695%, and 1.011%, respectively. Both state estimation results are better than the traditional method. The proposed MFMF-AEKF algorithm has higher estimation accuracy which provides a feasible estimation algorithm for the joint SOC and SOE of lithium-ion batteries.

Citation

LIU, D., WANG, S., FAN, Y., FERNANDEZ, C. and BLAABJERG, F. 2024. A novel multi-factor fuzzy membership function - adaptive extended Kalman filter algorithm for the state of charge and energy joint estimation of electric-vehicle lithium-ion batteries. Journal of energy storage [online], 86(part A), article number 111222. Available from: https://doi.org/10.1016/j.est.2024.111222

• Journal Article Type: Article
• Acceptance Date: Mar 5, 2024
• Online Publication Date: Mar 14, 2024
• Publication Date: May 1, 2024
• Deposit Date: Apr 1, 2024
• Publicly Available Date: Mar 15, 2025
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 86
• Issue: part A
• Article Number: 111222
• DOI: https://doi.org/10.1016/j.est.2024.111222
• Keywords: Multi-factor coupling model; Fuzzy membership function; State of charge; State of energy
• Public URL: https://rgu-repository.worktribe.com/output/2279617