An intelligent digital twin model for the battery management systems of electric vehicles
Heng Li, Muaaz Bin Kaleem, I-Ju Chiu, Dianzhu Gao, Jun Peng, Zhiwu Huang
Abstract
Battery management systems (BMS) are crucial for electric vehicles because effective battery management is essential to their safe and reliable operation. However, BMS experience some problems like high cost, reduced space, low efficiency, and high failure rates. This paper proposes an intelligent digital twin model for the BMS which utilized the historical battery data obtained from real driving scenarios to measure, estimate, predict, and diagnose the battery pack states. The digital twin model consists of two parts, one is the state estimation based on the regression model and the second one is the fault diagnosis. The regression model between the variables of BMS is developed using a back propagation neural network (BPNN). Moreover, a whale optimization algorithm (WOA) is utilized to further optimize the parameters of the regression model. A threshold-based fault detection method is applied to diagnose the faults in the BMS. The dataset gathered from an electric vehicle with one year duration is utilized to evaluate the performance of the proposed method. Experiment results verify that the proposed model achieved over 95% prediction accuracy and can effectively diagnose the faults in the BMS.