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A fuzzy robust two-stage unscented Kalman filter method for uncertainty and state of charge estimation of lithium-ion batteries by Omid Rezaei; Ali Rahdan; Sohrab Sardari; Masoud Dahmardeh; Zhanle Wang is a Engineering article available to read on EtoBox.

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## A C T For the practical state of charge (SoC) estimation of lithium-ion batteries, the unknown battery model uncertainties should be considered. Conventionally, the robust SoC estimation method uses a bounded matrix to model battery uncertainties. However, since the upper bound of the uncertainty is unknown, the robust estimation would be conservative and inaccurate. To accurately estimate the SoC of lithium-ion batteries, this paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method to model the battery uncertainty with unknown statistical characteristics. The proposed estimator is able to estimate the model uncertainties and does not require the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, and the destructive effect of the uncertainties on the estimation accuracy is eliminated correctly. To make the estimation more accurate and practical during experiments, the covariance matrix of the measurement noise is recursively adjusted using a fuzzy system. In addition, as the battery aging increases the model uncertainty, the proposed observer is able to adjust the SoC estimation at

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Author

Omid Rezaei; Ali Rahdan; Sohrab Sardari; Masoud Dahmardeh; Zhanle Wang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)