期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 72, 期 7, 页码 8613-8622出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2023.3244623
关键词
Electric vehicles; state of charge estimation; adaptive systems; nonlinear filters; stochastic systems
This paper presents a novel technique for constructing the SoC-OCV relation in batteries and converting it into a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and related states in batteries using the proposed parameter estimation and SoC-OCV construction technique. Numerical simulations demonstrate accurate parameter estimation and SoC estimation error below 2%. Experimental results validate the algorithm with an SoC estimation error remaining within 2.5%.
All state of charge (SoC) estimation algorithms based on equivalent circuit models (ECMs) estimate the open circuit voltage (OCV) and convert it to the SoC using the SoC-OCV nonlinear relation. These algorithms require the identification of ECM parameters and the nonlinear SoC-OCV relation. In literature, various techniques are proposed to simultaneously identify the ECM parameters. However, the simultaneous identification of the SoC-OCV relation remains challenging. This paper presents a novel technique to construct the SoC-OCV relation, which is eventually converted to a single parameter estimation problem. The Kalman filter is implemented to estimate the SoC and the related states in batteries using the proposed parameter estimation and the SoC-OCV construction technique. In the numerical simulations, the algorithm demonstrates that it accurately estimates the battery model parameters, and the SoC estimation error remains below 2%. We also validate the proposed algorithm with a battery experiment. The experimental results show that the error in SoC estimation remains within 2.5%.
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