Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis’ operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.