An Online OCV Calibration-Based Adaptive SOC Estimation Approach for Lithium Battery

With the continuous development of electric vehicles and renewable energy, the rapid and accurate estimation of the State of Charge (SOC) of lithium batteries is crucial for energy storage applications. Traditional Open Circuit Voltage (OCV) measurements, which require prolonged battery rest, are not applicable during battery operation. Therefore, Ampere-Hour Integration is commonly adopted to estimate the battery SOC in real-time during charging and discharging processes. However, this method is prone to cumulative errors, making it challenging to accurately estimate SOC during prolonged battery operation. To address this issue, an adaptive SOC estimation method based on online OCV calibration is proposed in this paper. By establishing the equivalent circuit model (ECM) and combining the experimental test data, the functional relationship between the calibration voltage and the battery resting time is obtained to compensate for the terminal voltage measurement error caused by the battery polarization reaction. Then by using the calibrated OCV, combined with the OCV-SOC relationship obtained from the experimental test, the difference fitting method is used to realize the OCV-SOC online calibration. Based on the SOC estimation results after online OCV calibration, the K-value adaptive mathematical model is established to correct the accumulated error during Ampere-hour integration. Experimental validation and simulation analysis demonstrate the effectiveness of the proposed method in compensating for OCV measurement errors caused by battery polarization and reducing cumulative errors in Ampere-hour integration.