A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries.

Wang, Shunli; Fan, Yongcun; Yu, Chunmei; Jin, Siyu; Takyi?Aninakwa, Paul; Fernandez, Carlos; Stroe, Daniel?Ioan

Abstract

Accurate state of charge (SOC) estimation is essential for the whole-life-cycle safety guarantee and protection of lithium-ion batteries, which is quite difficult to realize. In this study, a novel weighting factor-adaptive Kalman filtering (WF-AKF) method is proposed for the accurate estimation of SOC with a collaborative model for parameter identification. An improved bipartite electrical equivalent circuit (BEEC) model is constructed to describe the dynamic characteristics combined with the mathematical correction of the time-varying factors. The model parameters are identified online, corresponding to various SOC levels and temperature conditions. Considering the internal resistances, ambient temperature, and complex current rate variations, an adaptive multi-time scale iterative calculation model is constructed and combined with the real-time estimation and correction strategies. The maximum closed-circuit voltage (CCV) traction error is 0.36% and 0.24% for the main pulse-current charging and discharging processes, respectively. The proposed WF-AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under the complex working condition. The SOC estimation is accurate and robust to the time-varying characteristics and working conditions even when the initial error is large, providing a safety protection theory for lithium-ion batteries.

Citation

WANG, S., FAN, Y., YU, C., JIN, S., TAKYI-ANINAKWA, P., FERNANDEZ, C. and STROE, D.-I. 2022. A novel collaborative multiscale weighting factor-adaptive Kalman filtering method for the time-varying whole-life-cycle state of charge estimation of lithium-ion batteries. International journal of energy research [online], 46(6), pages 7704-7721. Available from: https://doi.org/10.1002/er.7672

• Journal Article Type: Article
• Acceptance Date: Jan 5, 2022
• Online Publication Date: Jan 23, 2022
• Publication Date: May 31, 2022
• Deposit Date: Feb 7, 2022
• Publicly Available Date: Jan 24, 2023
• Journal: International Journal of Energy Research
• Print ISSN: 0363-907X
• Electronic ISSN: 1099-114X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Issue: 6
• Pages: 7704-7721
• DOI: https://doi.org/10.1002/er.7672
• Keywords: Collaborative bipartite electrical equivalent circuit model; Lithium-ion battery; State ofcharge estimation; Time-varying characteristics; Weighting factor-adaptive Kalman filter; Whole-life-cycle
• Public URL: https://rgu-repository.worktribe.com/output/1585201