Optimization of a Shift Control Strategy for Pure Electric Commercial Vehicles Based on Driving Intention

Abstract

In order to improve the shifting quality of pure electric commercial vehicles, a torque control strategy based on the driving intention during the shifting process is presented in this paper. Firstly, dynamic analysis is conducted on the lifting and twisting stage in the two-speed Automated Mechanical Transmission (AMT) shift process without a synchronizer. Secondly, fuzzy identification is performed on the driver’s expected acceleration, incorporating the driver’s acceleration intention into the lifting and twisting process, and, further, the output time correction factor k is deblurred. Finally, the control time of the lifting and reducing torque is corrected to achieve dynamic adjustment of the control parameters during the shift process. The actual vehicle test results indicate that the proposed control strategy can enhance the shifting quality and adapt the performance of a vehicle to the driver’s expectations and requirements.

Analysis of Scalable Resonant DC–DC Converter Using GaN Switches for xEV Charging Stations

“…Conduction between the resonant inductor Lr1 and the resonant capacitor Cr1 commences simultaneously with the secondary current of iNS1 charging the filter capacitor C0. Equation (5) represents the voltage VNS1 at this juncture, while Equation ( 6) depicts the state space equation [24][25][26]. Stage 1: All secondary-side diodes are deactivated prior to time zero, while switches S 2 and S 3 are in the active state.…”