Bayesian information criterion based data-driven state of charge estimation for lithium-ion battery

Accurate state of charge (SOC) estimation is essential for the safe and reliable operation of Li-ion batteries. To solve the problem of poor generalisation caused by over-fitting, this paper presents a combination algorithm based on feature selection to estimate battery SOC. Firstly, a portion of th...

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Vydáno v:	Journal of energy storage Ročník 55; s. 105669
Hlavní autoři:	Liu, Xingtao, Yang, Jiacheng, Wang, Li, Wu, Ji
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 30.11.2022
Témata:	Bayesian information criterion Data-driven Lithium-ion battery State of charge estimation Support vector regression algorithm Lithium-ion battery State of charge estimation Data-driven Bayesian information criterion Support vector regression algorithm
ISSN:	2352-152X, 2352-1538
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Accurate state of charge (SOC) estimation is essential for the safe and reliable operation of Li-ion batteries. To solve the problem of poor generalisation caused by over-fitting, this paper presents a combination algorithm based on feature selection to estimate battery SOC. Firstly, a portion of the features is extracted from the extended Kalman filtering (EKF) results. It forms the set of features to be selected with four other measured features. Secondly, the optimal feature subset is adopted by designing a wrapped feature screening framework based on the Bayesian information criterion (BIC). Finally, the selected combination of features is adopted to train the support vector regression (SVR) model, which is applied to the battery SOC estimation. The experimental results reveal that the combination strategy of EKF and SVR improves the accuracy of SOC estimation. The optimal SVR model based on the feature selection criterion shows better generalisation. Better estimation results in four driving conditions are achieved, and the root-mean-square error of the battery SOC estimation is decreased by at least 64.1 % and 56.5 % compared to the EKF algorithm and SVR algorithm driven by full feature, respectively. •Three features are extracted from SOC estimation results of the EKF.•The average current and average terminal voltage are used as features.•Bayesian information criterion is used to determine the optimal features subset.•The EKF algorithm is combined with the SVR algorithm to estimate SOC.
ISSN:	2352-152X 2352-1538
DOI:	10.1016/j.est.2022.105669