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Many of today's advanced simulation tools are suitable for modeling specific systems, but they provide rather limited support for automated model building and management. The diverse tools available for modeling different components of a vehicle make it all the more challenging to comprehend their integration and interactions and analyze the complete system. In addition, the complexities and sizes of the models require a better use of computing resources, such as multicore or remote processing, to greatly reduce the simulation time. In this paper we describe how modern software techniques can support modeling and design activities, with the objective to create system models quickly by assembling them in a “plug-and-play” architecture. System models can be integrated, co-simulated, and reused regardless of the environment in which they are developed, and their simulation results can be consolidated for analysis into a single tool.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

To reduce development time and introduce technologies to the market more quickly, companies are increasingly turning to Model-Based Design. The development process - from requirements capture and design to testing and implementation - centers around a system model. Engineers are skipping over a generation of system design processes based on hand coding and instead are using graphical models to design, analyze, and implement the software that determines machine performance and behavior. This paper describes the process implemented in Autonomie, a plug-and-play software environment, to evaluate a component hardware in an emulated environment. We will discuss best practices and show the process through evaluation of an advanced high-energy battery pack within an emulated plug-in hybrid electric vehicle.

Many of today's automotive control system simulation tools are suitable 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 models of physical components of the system and allow users to share and easily reuse them. 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 translate into more possible drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations.

The U.S. Department of Energy (DOE) has invested considerable research and development (R&D) effort into Plug-in Hybrid Electric Vehicle (PHEV) technology because of the potential fuel displacement offered by the technology. DOE's PHEV R&D Plan [1], which is driven by the desire to reduce dependence on foreign oil by diversifying the fuel sources of automobiles, describes the various activities required to achieve the goals. The U.S. DOE will use Argonne's Powertrain Systems Analysis Toolkit (PSAT) to guide its analysis activities, stating, “Argonne's Powertrain Systems Analysis Toolkit (PSAT) will be used to design and evaluate a series of PHEVs with various ‘primary electric’ ranges, considering all-electric and charge-depleting strategies.” PSAT was used to simulate three possible charge-depleting (CD) PHEV control strategies for a power split hybrid. Trip distance was factored into the CD strategies before the cycle was started.

Because Plug-in Hybrid Electric Vehicles (PHEVs) substitute electrical power from the utility grid for fuel, they have the potential to reduce petroleum use significantly. However, adoption of PHEVs has been hindered by expensive, low-energy batteries. Recent improvements in Li-ion batteries and hybrid control have addressed battery-related issues and have brought PHEVs within reach. The FreedomCAR Office of Vehicle Technology has a program that studies the potential benefit of PHEVs. This program also attempts to clarify and refine the requirements for PHEV components. Because the battery appears to be the main technical barrier, both from a performance and cost perspective, the main efforts have been focused on that component. Working with FreedomCAR energy storage and vehicle experts, Argonne National Laboratory (Argonne) researchers have developed a process to define the requirements of energy storage systems for plug-in applications.

Hybrid Electric Vehicle (HEV) owners have reported significantly lower fuel economy than the published estimates. Under on-road driving conditions, vehicle acceleration, speed, and stop time differ from those on the normalized test procedures. To explain the sensitivity, several vehicles, both conventional and hybrid electric, were tested at Argonne National Laboratory. The tests demonstrated that the fuel economy of Prius MY04 was more sensitive to drive-cycle variations. However, because of the difficulty in instrumenting every component, an in-depth analysis and quantification of the reasons behind the higher sensitivity was not possible. In this paper, we will use validated models of the tested vehicles and reproduce the trends observed during testing. Using PSAT, the FreedomCAR vehicle simulation tool, we will quantify the impact of the main component parameters, including component efficiency and regenerative braking.

A fuel-cycle model-called the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model-has been developed at Argonne National Laboratory to evaluate well-to-wheels (WTW) energy and emission impacts of motor vehicle technologies fueled with various transportation fuels. The new GREET version has up-to-date information regarding energy use and emissions for fuel production activities and vehicle operations. In this study, a complete WTW evaluation targeting energy use, greenhouse gases (CO2, CH4, and N2O), and typical criteria air pollutants (VOC, NOX, and PM10) includes the following fuel options-gasoline, diesel, and hydrogen; and the following vehicle technologies-spark-ignition engines with or without hybrid configurations, compression-ignition engines with hybrid configurations, and hydrogen fuel cells with hybrid configurations.

In 2002, the U.S. Department of Energy (DOE) launched FreedomCAR, which is a partnership with automakers to advance high-technology research needed to produce practical, affordable advanced vehicles that have the potential to significantly improve fuel economy in the near-term. Advanced materials (including metals, polymers, composites, and intermetallic compounds) can play an important role in improving the efficiency of transportation vehicles. Weight reduction is one of the most practical ways of increasing vehicle fuel economy while reducing exhaust emissions. In this paper, we evaluate the impact of vehicle mass reduction for several vehicle platforms and advanced powertrain technologies, including Internal Combustion Engine (ICE) Hybrid Electric Vehicles (HEVs) and fuel cell HEVs, in comparison with conventional vehicles. We also explain the main factors influencing the fuel economy sensitivity.

Argonne National Laboratory (ANL), working with the FreedomCAR Partnership, maintains the hybrid vehicle simulation software, Powertrain System Analysis Toolkit (PSAT). The importance of component models and the complexity involved in setting up optimized control laws require validation of the models and control strategies. Using its Advanced Powertrain Research Facilities (APRF), ANL thoroughly tested the 2004 Toyota Prius to validate the PSAT drivetrain. In this paper, we will first describe the methodology used to quality check test data. Then, we will explain the validation process leading to the simulated vehicle control strategy tuning, which is based on the analysis of the differences between test and simulation. Finally, we will demonstrate the validation of PSAT Prius component models and control strategy, using APRF vehicle test data.

Because of their high efficiency and low emission potential, fuel cell vehicles are undergoing extensive research and development. However, several major barriers have to be overcome to enable a hydrogen economy. Because fuel cell vehicles remain expensive, very few fueling stations are being built. To try to accelerate the development of a hydrogen economy, the automotive manufacturers developed a hydrogen-fueled Internal Combustion Engine (ICE) as an intermediate step. Despite being cheaper, the hydrogen-fueled ICE offers a lower driving range because of its lower efficiency. The current study evaluates the impact of combining a hydrogen-fueled ICE with a fuel cell to maximize fuel economy while minimizing the cost and amount of onboard fuel needed to maintain an acceptable driving range.

Because of their high efficiency and low emissions, fuel-cell vehicles are undergoing extensive research and development. When considering the introduction of advanced vehicles, engineers must perform a well-to-wheel (WTW) evaluation to determine the potential impact of a technology on carbon dioxide and Greenhouse Gas (GHG) emissions and to establish a basis that can be used to compare other propulsion technology and fuel choices. Several modeling tools developed by Argonne National Laboratory (ANL) were used to evaluate the overall environmental and fuel-saving impacts associated with an advanced powertrain configuration. The Powertrain System Analysis Toolkit (PSAT) transient vehicle simulation software was used for pump-to-wheel (PTW) analysis, and GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation) was used for well-to-pump (WTP) analysis. This paper assesses the impact of FreedomCAR vehicle goals on a WTW energy basis.

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.