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

Abstract

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 greybox 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, the controloriented model was reformulated in a pseudo-Hammerstein form. Inversion of the nonlinearities allows linear MPC algorithms to be applied directly to the linear portion of the system. 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 controloriented 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.

Multibody dynamics and control using machine learning

“…Forward dynamics Byravan and Fox [48] Approximate rigid body motion due to SE (3) [56] Model and control of a powertrain of a hybrid vehicle FFNN Oishi and Yagawa [57] Extract rules inherent in a computational mechanics application FFNN Ye et al [58] General MSD simulation without contact with railway applications…”

“…When available and applicable, specialist knowledge can enhance the model's properties and allow for easier ML model creation. Expert knowledge can be included as a regularization term (like in the work of Angeli et al [55]), as a part of the model (as in Hosking and McPhee [56]), or can be exploited to design an appropriate structure for the ML model (as was done by Ye et al [58] and Oishi and Yagawa [57]). This section presents a variety of applications and modeling designs.…”

“…Hosking and McPhee [56] proposed a mixed approach to model the powertrain of a hybrid passenger car (Lincoln MKZ Hybrid). The car uses a complex power-split powertrain, which is challenging to model robustly using analytic modeling or experimental identification.…”