In this article, a model predictive control strategy is presented for an all-speed governor of heavy-duty vehicles that satisfies the requirements of fast tracking and fuel economy. The control-oriented torque-based engine model is used for the design of a model predictive control-based speed tracking control algorithm. Two methods for improving the speed tracking and fuel economy synthesis are presented, which include engine load estimation and variable weighting factor. The engine speed and fuel mass are used to estimate the real-time engine torque. The variable weighting factor based on the driver's intention is used to adjust the control algorithm in MATLAB/Simulink. The simulation results show that the tracking performance and fuel economy of the model predictive controller are better than that of a proportional-integral-derivative controller.

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Drivability is the key factor for the automated manual transmission. It includes fast response to the driver’s demand and the driving comfort. This paper deals with a control methodology applied to an automated manual transmission vehicle for drivability enhancement during vehicle start-up phase. Based on a piecewise model of powertrain, a multiple-model predictive controller (mMPC) is designed with the engine speed, clutch disc speed, and wheel speed as the measurable input variables and the engine torque reference and clutch friction torque reference as the controller’s output variables. The model not only includes the clutch dynamic, the flexible shaft dynamic, but also includes the actuators’ delay character. Considering the driver’s intention, a slipping speed trajectory is generated based on the acceleration pedal dynamically. The designed control strategy is verified on a complete powertrain and longitudinal vehicle dynamic model with different driver’s torque demands.