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.