A Moment-of-Inertia-Driven Engine Start-Up Method Based on Adaptive Model Predictive Control for Hybrid Electric Vehicles With Drivability Optimization

During the deceleration phase of a hybrid electric vehicle (HEV), a moment-of-inertia-driven (MoI-driven) engine start-up process can provide a potential economic benefit because it reduces the energy consumption of the starting device. During this start-up process, it is important to maintain drivability by enabling a quick start-up, low driveline vibration, and fast response to torque demand. The wheel rolling distance control should also be considered. This paper proposes electrical motor (EM) participation in an MoI-driven engine start-up process and studies an adaptive model-based predictive optimization method for the drivability control of P2 parallel hybrid vehicles. Based on a new triple mass-spring-system model, an adaptive model predictive controller (MPC) is designed with EM torque set points, clutch friction torque set points, and engine torque set points as manipulated inputs and engine speed, torsion speed, and wheel rolling distance as the measured outputs. A predicted torque demand is introduced to enhance the torque response performance. By considering the constraints of power source components, an optimization algorithm is developed. A simulation is conducted to verify the control strategy on the HEV powertrain and on vehicle dynamics models. The results show that under the same level of start-up, the torsion speed can be reduced by up to 50% with an improved wheel rolling distance and low torque demand error during a certain deceleration.