A Novel Fading Memory Recursive Least Square Method (FMLS) for Accurate State of Charge Estimation of Lithium-ion Batteries Combined with Improved Second Order PNGV Modeling

• Published in: International journal of electrochemical science Vol. 16; no. 9; p. 21097
• Main Authors: Liu, Donglei, Fan, Yongcun, Wang, Shunli, Xia, Lili, Qiu, Jingsong, Bobobee, Etse Dablu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2021
• Subjects: electrical equivalent circuit model; Extended Kalman filter; FMRLS; lithium-ion batteries; state of charge estimation; electrical equivalent circuit model; lithium-ion batteries; FMRLS; Extended Kalman filter; state of charge estimation
• ISSN: 1452-3981; 1452-3981
• DOI: 10.20964/2021.09.34

As the main energy storage element and power source of electric vehicles, the accurate estimation of the state of charge (SOC) of lithium-ion batteries is very important for the battery management system of electric vehicles, as well as for the driving range and safety. To accurately describe the characteristics of the lithium-ion battery, an improved PNGV model was formed by connecting an RC circuit based on the traditional PNGV lithium battery equivalent model. Considering the aging characteristics of lithium batteries in use, the recursive least square method (FMRLS) with fading memory was adopted to identify parameters to meet the requirements of estimation accuracy, and the Extended Kalman Filter (EKF) algorithm was used to estimate SOC. The accuracy of MAE, MAPE and RMSE was improved by 9.21%, 4.85% and 9.38% respectively under DST condition. The improvement was 34.35%, 36.59% and 31.97%, respectively, Under BBDST condition.