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This paper describes a proton-exchange-membrane Fuel Cells (FC) system dynamic model oriented to automotive applications. The dynamic model allows analysis of FC system transient response and can be used for: a) performance assessment; b) humidification analysis; c) analysis of special modes of operation, e.g., extended idle or freeze start; d) model based FC control design and validation. The model implements a modular structure with first principle based components representation. Emphasis is placed on development of a 1-D membrane water transport model used to simulate gas to gas humidification and stack membrane water diffusion. The Simulink implementation of the model is discussed and results showing FC system transient behavior are presented.

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.

This paper presents a purge system model developed for hybrid electric vehicle (HEV) applications. Assessment of purge capability is critical to HEV vehicles due to frequent engine off operation which limits carbon canister purging. The purge model is comprised of subsystems representing purge control strategy, carbon canister and engine plant. The paper is focused on modeling of the engine purge control feature. The purge model validation and purge capability predictions for an example HEV vehicle are presented and discussed.

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.

A robust Vehicle Model Architecture (VMA) has been developed to support model-based Vehicle System Control (VSC) design work and, in general, model-based vehicle system engineering activities. It is based on a logical breakdown of the vehicle into key subsystems with supporting bus infrastructure for distribution of signals between subsystems. Primary physical interfaces between the top level subsystems have been defined. Subsystem models that comply with these interfaces can be easily plugged into the architecture for complete simulation of vehicle systems. The VMA encourages model re-use and sharing between project teams and, furthermore, removes key obstacles to sharing of models with suppliers.

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.

As the complexity of automotive chassis control systems increases with the introduction of technologies such as yaw and roll stability systems, processes for model-based development of chassis control systems becomes an essential part of ensuring overall vehicle safety, quality, and reliability. To facilitate such a model-based development process, a vehicle modeling framework intended for chassis control development has been created. This paper presents a design methodology centered on this modeling framework which has been applied to real world driving events and has demonstrated its capability to capture vehicle dynamic behavior for chassis control development applications.

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.