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For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

Plug-in Hybrid Electric Vehicles (PHEVs) offer the ability to significantly reduce petroleum consumptions. Argonne National Laboratory (ANL), working with the FreedomCAR and Fuels Partnership, participated in the definition of the battery requirements for PHEVs. Previous studies have demonstrated the impact of vehicle characteristics such as vehicle class, mass or electrical accessories. However, outstanding questions remain regarding the impact of drive cycles on the requirements. In this paper, we will first evaluate the consequences of sizing the electrical machine and the battery powers to follow the Urban Dynamometer Driving Schedule (UDDS) to satisfy CARB requirements, including how many other driving cycles can be followed in Electric Vehicle (EV) mode. Then, we will study the impact of sizing the electrical components on other driving cycles.

Because of their high efficiency and low emissions, fuel-cell vehicles are undergoing extensive research and development. When considering the introduction of advanced vehicles, a complete well-to-wheel evaluation must be performed to determine the potential impact of a technology on carbon dioxide and Green House Gases (GHGs) emissions. Several modeling tools developed by Argonne National Laboratory (ANL) were used to evaluate the impact of advanced powertrain configurations. The Powertrain System Analysis Toolkit (PSAT) transient vehicle simulation software was used with a variety of fuel cell system models derived from the General Computational Toolkit (GCtool) for pump-to-wheel (PTW) analysis, and GREET (Green house gases, Regulated Emissions and Energy use in Transportation) was used for well-to-pump (WTP) analysis. This paper compares advanced propulsion technologies on a well-to-wheel energy basis by using current technology for conventional, hybrid and fuel cell technologies.

Because of their high efficiency and low emissions, fuel-cell vehicles are undergoing extensive research and development. As the entire powertrain system needs to be optimized, the requirements of each component to achieve FreedomCAR goals need to be determined. With the collaboration of FreedomCAR fuel cell, energy storage, and vehicle Technical Teams, Argonne National Laboratory (ANL) used several modeling tools to define the energy storage requirements for fuel cell vehicles. For example, the Powertrain System Analysis Toolkit (PSAT), which is a transient vehicle simulation software, was used with a transient fuel cell model derived from the General Computational Toolkit (GCtool). This paper describes the impact of degree of hybridization, control strategy, and energy storage technology on energy storage requirements for a fuel cell SUV vehicle platform.

Many of today's vehicle modeling tools are good for simulation, but they provide rather limited support for model building and management. Setting up a simulation model requires more than writing down state equations and running them on a computer. The role of a model library is to manage the physics of the system and allow users to share and reuse component models. In this paper, we describe how modern software techniques can be used to support modeling and design activities; the objective is to provide better system models in less time by assembling these system models in a “plug and play” architecture. With the introduction of hybrid electric vehicles, the number of components that can populate a model has increased considerably, and more components translates into more drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations.

Fuel cell vehicles are currently undergoing extensive research and development because of their potential for high efficiency and low emissions. A complete well-to-wheels evaluation is helpful when considering the introduction of advanced vehicles that could use a new fuel, such as hydrogen. Several modeling tools developed by Argonne National Laboratory were used to evaluate the impact of several new vehicle configurations. A transient vehicle simulation software code, PSAT (Powertrain System Analysis Toolkit), was used with a transient fuel cell model derived from GCTool (General Computational Toolkit); and GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation) was employed in estimating well-to-tank performances. This paper compares the well-to-wheels impacts of several advanced SUVs, including conventional, parallel and series hybrid-electric and fuel cell vehicles.