A window-based battery parameter identification method can directly apply the historical battery operating data to observe the battery parameters and running states. However, the design principles of a parameter identification window remain unclear. In this study, two factors that affect the performance of a battery parameter identification algorithm are considered: characteristics of the parameter identification window and action mechanism of the historical data. Our aim is to understand the effects of the historical data application methods on the algorithm performance, including three perspectives: (1) the effect of a slow dynamic fluctuation in the parameter identification window on the battery identification results, (2) the effect of the parameter identification window length on the identification results, and (3) the difference in demand for the fractional- and integer-order equivalent circuit model for the parameter identification window. The battery parameters and states are simultaneously identified based on the coevolutionary particle-swarm optimization algorithm. A comparison of the results shows that the combination of a fractional-order equivalent circuit model and a linear approximation method can achieve more stable and consistent identification results. In addition, a linear approximation method has an advantage in terms of ensuring stationarity of the identification results.