Adaptive model predictive control of dual clutch transmission shift based on dynamic friction coefficient estimation

The dynamic friction coefficient of a wet clutch affects the vehicle shift quality and changes with factors such as the friction plate abrasion and oil and friction plate temperatures. Herein, an adaptive control strategy for dual clutch transmission (DCT) shifting is proposed. Based on the experimentally obtained dynamic friction coefficient of a wet clutch with a definite service mileage, the dynamic friction coefficient of a wet clutch is estimated online, considering the current service mileage and the oil and friction plate temperatures, using the recursive least squares method. By establishing a linear time-varying model of the DCT powertrain and using linear time-varying model predictive control to design the upper controller, the optimal shift reference curves of the clutch pressure and engine torque are obtained. Thereafter, the clutches and engine track the optimal reference curves through lower-layer proportional-integral-derivative controllers. The co-simulation results obtained using Simulink and AMESim show that the proposed control strategy can achieve good shift quality. It can also adapt to changes in the dynamic friction coefficient caused by clutch abrasion and oil and friction plate temperatures and ensure the shift quality of DCT vehicle.

Automated summary

This study proposes an adaptive control strategy for shifting in dual clutch transmission (DCT), which estimates the dynamic friction coefficient of a wet clutch and designs optimal shift reference curves. The results show that this control strategy can achieve good shift quality and adapt to changes in the dynamic friction coefficient caused by clutch abrasion and temperature variations.