基于双卡尔曼滤波的锂电池SOC估算

Estimation of state of charge of lithium-ion battery based on dual extend Kalman filter

以锂电池的荷电状态估算为目的,对传统锂电池等效电路模型进行改进,提高了模型的准确性,使之能更好地反应锂电池内部状态。以标称容量为2 000 m Ah,额定电压为3.7 V的18650锂电池作为研究对象,采用最小二乘法分别对该锂电池模型进行充放电方向的参数辨识。运用双卡尔曼滤波算法估算锂电池的SOC,并设计了基于安时计量法的相关测试实验。研究结果表明,双卡尔曼滤波算法估算18650锂电池SOC的绝对误差值小于0.019,具有较高的估算精度,在锂电池SOC估算领域内具有很高的实用价值。

With the aim to estimate the state-of-charge（SOC）of lithium batteries, the original battery model was reformed to improve the model accuracy and facilitate the battery model to reflect the actual internal state of the battery. Taking a nominal capacity of 2 000 m Ah and the rated voltage of 3.7 V 18650 lithium batteries as research subjects, the least squares method was used to identify the battery model＇s charge-discharge orientation parameters.The dual extend Kalman filter algorithm was adopted to estimate the SOC of lithium batteries and corresponding battery test experiments. Experiment results demonstrate that the algorithm＇s maximum error is less than 0.019,which have higher estimation accuracy and practical value in the field of lithium battery SOC estimation.