Two Layer Model Reference Adaptive Control based on State Feedback for Engine Slipping Start of Parallel Hybrid Electric Vehicles

Abstract

During engine slipping start (ESS) of parallel hybrid electric vehicles (HEV) with a dual-clutch transmission, the motor needs to propel the vehicle and crank the engine via the slipping clutch simultaneously. However, a lack of capability to respond to changes in driver demand can disturb normal driving habits. Meanwhile, parametric uncertainty of the clutch friction coefficient makes it hard to compensate for the clutch slipping torque. To guarantee response speed to changes in driver demand torque, a two-layer model reference adaptive controller (MRAC) is proposed for ESS utilizing state varialbes as the feedback. Under this framework, appropriate reference profiles can be generated according to the realtime driver demand torque rather than assuming that the accelerator pedal is unchanged. An auxiliary reference model is introduced in addition to the major reference model to modify the convergence rate of the tracking error without changing the well-designed reference profiles, and adaptive control laws are accordingly developed to enhance the transient performance against matched parameter uncertainty of clutch and unmatched measurement noise.