Research on Shift Strategy of 2DCT for Pure Electric Vehicle Based on Driving Condition Identification

In order to enhance the economic performance of pure electric vehicles (EVs) while maintaining better dynamic performance, a real-time shifting strategy based on driving cycle recognition is proposed for the self-developed two-speed dry dual clutch transmission (2DCT) for EVs. A radial basis neural network is adopted to predict the vehicle speed and the optimal shifting points are extracted by dynamic programming for seven types of driving cycle. Then, a driving cycle recognition model based on similarity comparison is constructed to recognize vehicle-driving conditions so as to achieve real-time shifting. The simulation based on MATLAB/Simulink and the 2DCT bench experiments are completed. The results demonstrate that the proposed real-time shifting strategy based on condition recognition can simultaneously meet the requirements of economic performance and shift frequency. © 2024 SAE-China. All rights reserved.