基于BCRLS-AEKF的锂离子电池荷电状态估计及硬件在环验证

研究有色噪声下的锂离子电池参数辨识与荷电状态（SOC）估计，并进行硬件在环实验验证.在动力电池模型的参数辨识过程中，利用带遗忘因子的偏差补偿递推最小二乘法进行偏差补偿，提高了有色噪声数据的参数辨识精度.在此基础上，利用自适应扩展卡尔曼算法进行SOC估计，使得滤波算法中的估计结果可以随着噪声统计特性的变化而自适应更新，实现了模型参数和电池状态的联合估计.最后，借助BMS测试系统模拟电池电压电流信息输出，完成了硬件在环实验以验证所提出的方法.实验结果表明，利用所提出算法估计得到的电池端电压和SOC误差分别小于10 mV和0.5%. 

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.