Powertrain Modeling and Model Predictive Longitudinal Dynamics Control for Hybrid Electric Vehicles

This paper discusses modeling of a power-split hybrid electric vehicle and the design of a longitudinal dynamics controller for the University of Waterloo’s self-driving vehicle project. The powertrain of Waterloo’s vehicle platform, a Lincoln MKZ Hybrid, is controlled only by accelerator pedal actuation. The vehicle’s power management strategy cannot be altered, so a novel approach to grey-box modeling of the OEM powertrain control architecture and dynamics was developed. The model uses a system of multiple neural networks to mimic the response of the vehicle’s torque control module and estimate the distribution of torque between the powertrain’s internal combustion engine and electric motors. The vehicle’s power-split drivetrain and longitudinal dynamics were modeled in MapleSim, a modeling and simulation software, using a physics-based analytical approach. All model parameters were identified using Controller Area Network (CAN) data and measurements of wheel torque data that were gathered during vehicle road testing. Using the grey-box powertrain model as a framework, a look-ahead linear time-varying (LTV) model predictive controller (MPC) for reference velocity tracking is proposed. Using some simplifying assumptions about the powertrain dynamics, a control-oriented model was formulated. The performance of the MPC was tested using multiple model in the loop (MIL) reference velocity tracking scenarios, and benchmarked against a tuned proportional-integral (PI) controller. Using the novel control-oriented model of the OEM powertrain, the MPC was found to track the desired velocity trajectory and reject measurable disturbance inputs, such as road slope, better than the PI controller.