Evaluation and prediction of lithium-ion battery pack inconsistency in electric vehicles based on actual operating data

Battery inconsistency problems will inevitably occur in the process of battery operation after forming a pack, and the consistency of the battery pack is of great significance to the management, equalization and maintenance of the battery system. In this paper, a method for evaluating and predicting the inconsistency of lithium-ion battery pack in electric vehicles based on actual operating data is proposed. First, the recursive least squares with forgetting factor algorithm is used for the second-order equivalent circuit model parameter identification, and the ohmic internal resistance, electrochemical polarization internal resistance, and concentration difference polarization internal resistance are identified as the characteristic parameters from the electric vehicle operation data. Second, a comprehensive battery pack inconsistency evaluation model and a time series prediction model are proposed, which can accurately evaluate and predict the battery pack inconsistency. Finally, the algorithm is validated by nine months of actual electric vehicle operation dataset, and the results show that the proposed inconsistency evaluation and prediction method has high accuracy.