A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter

Cong Jiang, Shunli Wang, Bin Wu, Carlos Fernandez, Xin Xiong and James Coffie-Ken

Energy, 2021, vol. 219, issue C

Abstract: The control strategy of electric vehicles mainly depends on the power battery state-of-charge estimation. One of the most important issues is the power lithium-ion battery state-of-charge (SOC) estimation. Compare with the extended Kalman filter algorithm, this paper proposed a novel adaptive square root extended Kalman filter together with the Thevenin equivalent circuit model which can solve the problem of filtering divergence caused by computer rounding errors. It uses Sage-Husa adaptive filter to update the noise variable, and performs square root decomposition on the covariance matrix to ensure its non-negative definiteness. Moreover, a multi-scale dual Kalman filter algorithm is used for joint estimation of SOC and capacity; the forgetting factor recursive least-square method is used for parameter identification. To verify the feasibility of the algorithm under complicated operating conditions, different types of dynamic working conditions are performed on the ternary lithium-ion battery. The proposed algorithm has robust and accurate SOC estimation results and can eliminate computer rounding errors to improve adaptability compared to the conventional extended Kalman filter algorithm.

Keywords: State-of-charge; Extended Kalman filter; Adaptive; Square root; Power lithium-ion battery (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:219:y:2021:i:c:s0360544220327109

DOI: 10.1016/j.energy.2020.119603

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