To improve the recognition accuracy and robustness of the adjustable capability of electric vehicles

a Bidirectional Gated Recurrent Unit(BiGRU) deep learning model that integrates Adaptive Boosting(AdaBoost) mechanism is proposed. Firstly

based on the characterization of charging behavior from three dimensions: user

region

and time

an input system covering 13 key variables such as charging period

electricity consumption

and external environment is constructed to cluster the electric vehicle population and lay the foundation for regulating charging load in the power grid. Secondly

the BiGR model is utilized to mine temporal features

and the AdaBoos ensemble learning mechanism is used to enhance the model's generalization ability

improve the accuracy and robustness of identifying the adjustable capabilities of electric vehicles

and further improve the efficiency of electric vehicle scheduling. Finally

experimental validation is conducted on a real historical dataset to evaluate the identification effect of the model in different scenarios. The results show that the proposed method can effectively improve the accuracy of identifying the adjustable capability of electric vehicles

enhance their schedulability

and has a good effect on identifying the adjustable capability of electric vehicles in different dates and scenarios.