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• W2968171852 abstract "Abstract It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge (SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model (ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium–ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter (EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization (HPPC) and the standard US06 driving cycles (US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions." @default.
• W2968171852 created "2019-08-22" @default.
• W2968171852 creator A5036009221 @default.
• W2968171852 creator A5036651046 @default.
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• W2968171852 date "2019-09-01" @default.
• W2968171852 modified "2023-10-17" @default.
• W2968171852 title "Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures*" @default.
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• W2968171852 doi "https://doi.org/10.1088/1674-1056/ab3af5" @default.
• W2968171852 hasPublicationYear "2019" @default.
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