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The desire for improved vehicle fuel economy, driven by high gas prices and concerns over energy independence, have sparked interest and demand for hybrid electric vehicles. Hybrid electric vehicle propulsion systems exhibit complex interactions which need to be understood in order to maximize fuel economy over the range of operating modes. Model-based development processes which use vehicle system models capable of representing the functional behaviors with embedded controls are needed for fast, efficient design of vehicle control systems which manage overall energy usage. Model-based vehicle system development processes have been employed for a Dual Drive HEV system. The process for creating these vehicle system models is described along with an approach for using these models to develop HEV systems. Details of key subsystem models and the process for integration of full vehicle implementation level controls are discussed.

In this paper, the tradeoff relationship between the Air Conditioning (A/C) system performance and vehicle fuel economy for a hybrid electric vehicle during the SC03 drive cycle is presented. First, an A/C system model was integrated into Ford’s HEV simulation environment. Then, a system-level sensitivity study was performed on a stand-alone A/C system simulator, by formulating a static optimization problem which minimizes the total energy use of actuators, and maintains an identical cooling capacity. Afterwards, a vehicle-level sensitivity study was conducted with all controllers incorporated in sensitivity analysis software, under three types of formulations of cooling capacity constraints. Finally, the common observation from both studies, that the compressor speed dominates the cooling capacity and the EDF fan has a marginal influence, is explained using the thermodynamics of a vapor compression cycle.

The powersplit transaxle is a key subsystem of Ford Motor Company's hybrid electric vehicle line up. The powersplit transaxle consists of a planetary gear, four reduction gears and various types of bearings. During vehicle operation, the transaxle is continuously lubricated by a lube oil pump. All these components consume power to operate and they contribute to the total transaxle losses which ultimately influences energy usage and fuel economy. In order to enable further model-based development and optimization of the transaxle design relative to vehicle energy usage, it is essential to establish a physics-based transaxle model with losses distributed across components, including gears, bearings etc. In this work, such a model has been developed. The model accounts for individual bearing losses (speed, torque and temperature dependency), gear mesh losses, lube pump loss and oil churning loss.