This paper presents an experimental study on the parameter identification of a battery pack, which determines the relationship between identification accuracy and measurement data. Parameter identification of the lithium-ion battery is poor when the input-output data, i.e., the input current and output voltage, is not appropriate. In addition, selection/optimization of an appropriate data set for estimation needs to adapt to different applications. A first-order equivalent circuit model is adopted to model a battery pack, and the identification accuracy is analyzed for both the single-parameter and multi-parameter identification scenarios. It is found that the accuracy of different identification scenarios is influenced by the voltage noise, the current amplitude, and the current frequency. Three experiments using sine waves with different frequencies are then performed to characterize a lithium-ion battery pack. Experimental results show that the current profile with the optimal frequency content achieves the best identification performance. Therefore, it is validated that the identification accuracy can be improved when the current excitation satisfies certain criteria.

本文对电池组的参数识别进行了实验研究，该参数确定了识别精度与测量数据之间的关系。当输入输出数据（即输入电流和输出电压）不合适时，锂离子电池的参数识别能力很差。另外，选择/优化用于估计的适当数据集需要适应不同的应用。采用一阶等效电路模型对电池组进行建模，并分析了单参数和多参数识别场景的识别精度。发现不同识别方案的准确性受电压噪声，电流幅度和电流频率的影响。然后使用不同频率的正弦波进行了三个实验，以表征锂离子电池组。实验结果表明，具有最佳频率含量的电流曲线可以实现最佳的识别性能。因此，验证了当电流激励满足某些标准时可以提高识别精度。