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A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

Hybrid electric vehicle (HEV) systems offer significant improvements in vehicle fuel economy and reductions in vehicle generated greenhouse gas emissions. The widely accepted power-split HEV system configuration couples together an internal combustion engine with two electric machines (a motor and a generator) through a planetary gear set. This paper describes a methodology for analysis and optimization of alternative HEV power-split configurations defined by alternative connections between power sources and transaxle. The alternative configurations are identified by a matrix of kinematic equations for connected power sources. Based on the universal kinematic matrix, a generic method for automatically formulating dynamic models is developed. Screening and optimization of alternative configurations involves verification of a set of design requirements which reflect: vehicle continuous operation, e.g. grade test; and vehicle dynamic operation such as acceleration and drivability.

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.

To better reflect real world driving conditions, the EPA 5-Cycle Fuel Economy method encompasses high vehicle speeds, aggressive vehicle accelerations, climate control system use and cold temperature conditions in addition to the previously used standard City and Highway drive cycles in the estimation of vehicle fuel economy. A standard Powersplit Hybrid Electric Vehicle (HEV) system simulation environment has long been established and widely used within Ford to project fuel economy for the standard EPA City and Highway cycles. Direct modeling and simulation of the complete 5-Cycle fuel economy test set for HEV's presents significant new challenges especially with respect to modeling vehicle thermal management system and interactions with HEV features and system controls. It also requires a structured, systematic approach to validate the key elements of the system models and complete vehicle system simulations.

Engine control unit (ECU) modeling using engine feature C code is an increasingly important part of new vehicle analysis and development tools. The application areas of feature based ECU models are numerous: a) cold vehicle fuel economy (FE) prediction required for recently introduced 5-cycle certification; b) vehicle thermal modeling; c) evaporative (purge) systems design; d) model-in-the-loop/software-in-the-loop (MIL/SIL) vehicle control development and calibration. The modeling method presented in the paper embeds production C-code directly into Simulink at a feature level using an S-Function wrapper. A collection of features critical to accurate engine behavior prediction are compiled individually and integrated according to the newly developed Engine Control Model Architecture (ECMA). Custom MATLAB script based tools enable efficient model construction.

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.

The automotive industry is rapidly expanding its Hybrid, Plug-in Hybrid and Battery Electric Vehicle product offerings in response to meet customer wants and regulatory requirements. One way for electrified vehicles to have an increasing impact on fleet-level CO2 emissions is for their sales volumes to go up. This means that electrified vehicles need to deliver a complete set of vehicle level attributes like performance, Fuel Economy and range that is attractive to a wide customer base at an affordable cost of ownership. As part of “democratizing” the Hybrid and plug-In Hybrid technology, automotive manufacturers aim to deliver these vehicle level attributes with a powertrain architecture at lowest cost and complexity, recognizing that customer wants may vary considerably between different classes of vehicles. For example, a medium duty truck application may have to support good trailer tow whereas a C-sized sedan customer may prefer superior city Fuel Economy.