A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Shunli; Takyi-Aninakwa, Paul; Fan, Yongcun; Yu, Chunmei; Jin, Siyu; Fernandez, Carlos; Stroe, Daniel-Ioan
• Is Part Of: International journal of electrical power & energy systems. Volume 139(2022)
• Publication Date: 2022-07
• Subjects: State of charge -- Closed-circuit voltage -- Second-order equivalent circuit model -- Feedback correction-adaptive Kalman filter -- Whole-life-cycle variation -- Fast initial convergence; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108020
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online SOC and CCV co-prediction. An improved second-order equivalent circuit model (SO-ECM) is constructed by introducing two resistor–capacitor circuits. State initialization, iterative update, and error covariance matrix correction are investigated with a uncertainty matrix. The constructed SO-ECM and FC-AKF model promote the accurate battery state co-prediction effect, safety and longevity. Abstract: Accurate state of charge (SOC) and closed-circuit voltage (CCV) prediction is essential for lithium-ion batteries and their model performance. In this study, a novel feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online battery state co-prediction, which is adaptive to the whole-life-cycle of the lithium-ion battery based on the improved second-order equivalent circuit model (SO-ECM). For the feedback correction strategy, the optimized iterative state initialization is conducted using the uncertainty covariance matrix of the prior three-time points with the convergence of the updating process. The experimental results show that the SOC prediction error of the proposed FC-AKF method is 0.0099% and 0.975% compared with the ampere-hour integral method under the dynamic stress test (DST) and the Beijing bus dynamic stress test (BBDST) working conditions, respectively. Also, the CCV traction by the SO-ECM is 0.80 V and has fast initial convergence and quick prediction error reduction characteristics. The constructed iterative calculation model promotes the accurate SOC and CCV co-prediction effect, improving the safety and longevity of lithium-ion batteries with high precision and fast convergence advantages.