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

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 ch...

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Vydáno v:International journal of electrochemical science Ročník 16; číslo 9; s. 21097
Hlavní autoři: Liu, Donglei, Fan, Yongcun, Wang, Shunli, Xia, Lili, Qiu, Jingsong, Bobobee, Etse Dablu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.09.2021
Témata:
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
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Shrnutí: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.
ISSN:1452-3981
1452-3981
DOI:10.20964/2021.09.34