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Ford Motor Company has developed a full hybrid electric vehicle with a dual-drive hybrid powertrain configuration that has great potential to overcome some attribute deficiencies in existing hybrid powertrain architectures. This paper first provides an overview of the dual-drive hybrid electric vehicle architecture and its primary operating modes. The paper then presents the energy management control system that selects operating modes and desired powertrain operating points to improve fuel efficiency. Finally, experimental results from a dual-drive hybrid prototype vehicle are used to demonstrate the fuel efficiency improvement.

Ford Motor Company has investigated a series hybrid electric vehicle (SHEV) configuration to move further toward powertrain electrification. This paper first provides a brief overview of the Vehicle System Controls (VSC) architecture and its development process. The paper then presents the energy management strategies that select operating modes and desired powertrain operating points to improve fuel efficiency. The focus will be on the controls design and optimization in a Model-in-the-Loop environment and in the vehicle. Various methods to improve powertrain operation efficiency will also be presented, followed by simulation results and vehicle test data. Finally, opportunities for further improvements are summarized.

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.