Real-Time State of Charge-Open Circuit Voltage Curve Construction for Battery State of Charge Estimation

卷:72

Article

关键词: LITHIUM-ION BATTERY;    EXTENDED KALMAN FILTER;    ONLINE PARAMETER-IDENTIFICATION;    OF-CHARGE;    SOC ESTIMATION;    HEALTH ESTIMATION;    MODEL;    HYSTERESIS;    DISCHARGE;    CAPACITY;   

DOI  :  10.1109/TVT.2023.3244623

来源: SCIE

【 摘 要 】

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%.

【 授权许可】

Free