For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated
by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting
intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of
recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network
to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting
intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting
intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively
reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.
