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Developed by the U.S. Environmental Protection Agency (EPA), the Multi-scale mOtor Vehicle Emission Simulator (MOVES) is used to estimate inventories and projections through 2050 at the county or national level for energy consumption, nitrous oxide (N2O), and methane (CH4) from highway vehicles. To simulate a large number of vehicles and fleets on numerous driving cycles, EPA developed a binning technique characterizing the energy rate for varying Vehicle Specific Power (VSP) under predefined vehicle speed ranges. The methodology is based upon the assumption that the vehicle behaves the same way for a predefined vehicle speed and power demand. While this has been validated for conventional vehicles, it has not been for advanced vehicle powertrains, including hybrid electric vehicles (HEVs) where the engine can be ON or OFF depending upon the battery State-of-Charge (SOC).

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 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.

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.

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.