Optimal Tire Force Control & Allocation for Longitudinal and Yaw Moment Control of HEV with eAWD Capabilities

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 performance that customers enjoy. To address this issue, the paper presents a HEV with eAWD capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in co-simulation with CarSim. The paper proposes a control system which utilizes tire force optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal tire force targets to at each tire such that the targets avoid tire saturation. The Youla parameterization technique is used to develop robust tracking controllers for each axle. The proposed control system is ultimately tested on the drivetrain model with a high fidelity CarSim vehicle model for validation. Simulation results show that the control system is able to maximize vehicle longitudinal performance while avoiding tire saturation on a low mu surface. More importantly, the control system is able to track the desired yaw moment request on a high speed double lane change maneuver through the use of the TVeRAD to improve the handling characteristic of the vehicle.