Self-Learning Control Strategy for Two-Speed I-AMT Clutch Kiss-Point Identification of Electric Vehicles

Abstract:

Two gear AMT （automatic mechanical transmission） can optimize the working range of drive motor and thus improve the drivability and economy of the electric vehicles. However， in the working process， the accurate identification of clutch kiss-point has a great impact on the gear shift performance. Once the clutch friction plate is worn or the diaphragm spring is tired， the actual working position of the clutch may offset correspondingly. Therefore， it is necessary to identify the position of the clutch kiss-point accurately through self-learning so as to ensure the high-quality shift control performance of the transmission. This paper takes a new type of non-torque interruption two-speed transmission I-AMT （Inverse AMT） of electric vehicle as the research object， aiming at the problems such as the change of the position of the clutch kiss-point after the wear， a self-learning strategy for the position identification is proposed. When the clutch is slowly separated， the accurate position identification of the clutch half joint point is realized， by detecting the change trend of the speed encoder of the driving motor connected to the clutch driving plate. The test shows that under the scenario that the clutch is worn， the proposed strategy can accurately identify the position of the clutch kiss-point so as to adaptively adjust the working state of the transmission， thus ensuring the high-quality non-power interruption shift of I-AMT.