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With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance. This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object.

This paper defines the shifting quality evaluation index in detail for DCT (Dual Clutch Transmission) from the perspective of control. The vehicle model for DCT is built using MATLAB / Simulink tools, including the models of driver, load, controller, engine, clutch, transmission (synchronizer), actuators, and vehicle dynamics model. And then a control quality evaluation system is designed. The AHP (Analytic Hierarchy Process) is used to determine weights of the control quality evaluation index and the shifting quality control objectives through the co-simulation of vehicle system model and evaluation system, namely expected control range of each evaluation index, which provides reference and guidance for shifting control strategy and control algorithm of DCT.

This investigation presents a methodology to develop and optimize shift process control strategy to improve shift quality as perceived by drivers during power-on upshift events for Dual Cultch Transmission (DCT) vehicles. As part of the first study, the main factors affecting shift quality during shifting process under typical working conditions are analyzed. And taking the power-on upshift as example, dynamic model of DCT shifting process is build. An Integrated control strategy is proposed for power-on upshift, which during torque phase slipping revolving speed controller is adapted to harmonically control two clutches power switching process, and during Inertial phase engine torque is regulated to synchronize with the value of target gear while holding the oncoming clutch pressure. Oncoming clutch oil pressure gradient in torque phase and engine torque reducing target decrement in inertial phase are chosen as controlled quantity.

In the electro-pneumatic automated mechanical transmission (AMT) system, the manufacturing, assembly, wear, replacement and other issues often lead to gear position change and some differences in various gears of transmission, which reduces the success rate and even results in abnormally working. To solve these problems, based on the intelligent control theories of fuzzy PI control, this research developed an intelligent self-learning system for the AMT gear position. This system contains the position initialization module after assembling (offline module) and the position correction module in use (online module). The system can automatically recognize gear position deviation and actively correct it, which improves the robustness of shift actuator. The precise control of shift actuator is the key of this self-learning system.