Investigation & Development of Vehicle Dynamics Control Frameworks for an Electric All-Wheel-Drive Hybrid Electric Vehicle
By: Jose Velazquez Alcantar Advisors: Professor Francis Assadian
Hybrid Electric Vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and, at times, reduced total power. As a result, HEV generally lack the dynamic driving performance that customers enjoy. To address this issue, this research presents a HEV with electric AllWheel-Drive (eAWD) capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of a TVeRAD to a front wheel drive HEV improves the total power output and enhances the handling characteristics via wheel torque vectoring at the rear axle. The eAWD HEV presents a unique engineering problem due to the fact that the front and rear axles are mechanically decoupled. Thus, a sophisticated control system is required to allocate torque to the front and rear axles.
The seminar presents an investigation and the development of three unique vehicle dynamics control frameworks for the eAWD HEV. The control frameworks developed in the research take advantage of the multilayered integrated vehicle dynamics control (IVDC) control structure and tailors the problem for powertrainbased vehicle dynamics control. The first control framework utilizes a simple, yet effective, optimization strategy to allocate longitudinal tire forces to the front and rear axles. A clever tire penalization strategy allows the system to allocate control to the front and rear axles while preventing tire force saturation. The second control framework utilizes the same optimization strategy to optimize and allocate slip ratio to the front and rear axles. The tire force and slip ratio allocation control frameworks are designed with implementation in mind; thus, two estimation strategies are developed to obtain accurate and robust estimates of longitudinal tire force and vehicle velocity states. The third and final control framework is designed to be the best-case framework and serves as a benchmark for the two allocation frameworks. The benchmark framework is designed utilizing Model Predictive Control (MPC) due to the fact that a complex constrained optimization is solved at every time step. The results of each control framework show that the proposed control systems are able to improve the traction control capabilities of the system and improve the handling performance of the vehicle. It is shown that the two relatively simple allocation control frameworks can achieve nearly identical performance as the benchmark MPC framework. The resulting control systems offers a unified approach to longitudinal and yaw control of the vehicle.
Date(s) - 05/17/2017
9:30 am - 11:00 am
2130 Bainer - MAE Conference Room