State of Charge Estimation and Hardware-in-Loop Verification of Lithium-ion Battery Based on BCRLS-AEKF

The parameter identification and state of charge (SOC) estimation of lithium-ion battery under colored noise were studied and verified by hardware-in-the-loop experiments. In the parameter identification process of the power battery model, the bias compensation recursive least squares with forgetting factor (BCRLS) was used to compensate the deviation, improving the parameter identification accuracy of the colored noise data. On this basis, an adaptive extended Kalman algorithm (AEKF) was used to estimate the SOC, making the estimation result in the filtering algorithm adaptively updated with the change of the statistical characteristics of the noise, and the joint estimation of the model parameters and the battery state be realized. Finally, the battery voltage and current information output was simulated by the BMS test system, and the hardware-in-the-loop experiment was completed to verify the proposed method. The experimental results show that the battery terminal voltage and SOC error estimated by the proposed algorithm are less than 10 mV and 0.5%, respectively.