State of charge estimation of lithium-ion battery based on improved forgetting factor recursive least squares-extended Kalman filter joint algorithm

In order to solve the problem that forgetting factor recursive least squares (FFRLS) is prone to abnormal jitter and even divergence under complex working conditions, improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed. A joint algori...

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Vydáno v:Journal of energy storage Ročník 55; s. 105474
Hlavní autoři: Ge, Caian, Zheng, Yanping, Yu, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.11.2022
Témata:
Joint algorithm
lithium-ion battery
On-line identification
SOC estimation
On-line identification
Joint algorithm
SOC estimation
lithium-ion battery
ISSN:2352-152X, 2352-1538
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Shrnutí:In order to solve the problem that forgetting factor recursive least squares (FFRLS) is prone to abnormal jitter and even divergence under complex working conditions, improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed. A joint algorithm of improved forgetting factor recursive least squares and extended Kalman filter (EKF) is used to estimate the state of charge (SOC) of lithium-ion battery. Firstly, parameters of Thevenin equivalent circuit model are identified on-line by the improved FFRLS considering dynamic constraint and parameter backtracking, and then the SOC of lithium-ion battery is estimated by extended Kalman filter. The results show that the improved forgetting factor recursive least squares has high accuracy of battery model parameters identification and the joint algorithm also has high accuracy and robustness of SOC estimation under dynamic stress test (DST) condition, the maximum absolute SOC estimation error is 2.49 % and the average absolute SOC estimation error is 1.39 %. •Thevenin equivalent circuit model of lithium-ion battery is established.•Improved forgetting factor recursive least squares based on dynamic constraint and parameter backtracking is proposed.•Improved forgetting factor recursive least squares extended Kalman filter joint algorithm is used to estimate SOC.•The joint algorithm has high accuracy and robustness of SOC estimation under dynamic stress test condition.
ISSN:2352-152X
2352-1538
DOI:10.1016/j.est.2022.105474