NOVEMBER 2001 — Vehicle Systems
PSAT Software Offers "Real World" Hybrid Electric Vehicle Modeling
Argonne
has developed and used models in support of the U.S. Department of Energy's (DOE's)
advanced automotive research and development for several decades, addressing all
aspects of a vehicle's life cycle, from design and manufacturing through recycling.
Advanced batteries, fuel cells, engines, control systems and vehicles have been
modeled, developed, and tested at Argonne.
This combination of analytical, development, and testing experience has been applied to the latest version of the PNGV Systems Analysis Toolkit (PSAT V5.0) to be released in December. The "forward-looking" model simulates vehicle fuel economy, emissions, and performance in a realistic manner, taking into account transient behavior and control system characteristics from the driver to the wheels. The realistic behavior supports an ambitious software development goal for PSAT: to be transportable from the virtual world of component modeling and simulation to the emulated environment of component control in hardware-in-the-loop (HIL) testing and the physical environment of full powertrain control in a vehicle. This capability enhances DOE's ability to assess the potential of advanced automotive technologies and streamline the development process for promising technologies.
For PSAT V5.0, the Toyota Prius and Honda Insight were instrumented to extract key component and vehicle data, and then the data was compared to PSAT simulation results. The process demonstrated that PSAT is capable of predicting fuel economy within a few percent for all driving cycles and accurately reflecting transient component behavior on a sub-second scale (e.g., fuel rate and engine torque).
Fuzzy logic control was added to support more sophisticated engine control for emissions reduction. Preliminary analyses indicated that engine-out NOx can be reduced by up to 50% with no loss in fuel economy for a compression-ignition engine in a hybrid powertrain by using fuzzy control to minimize emissions. (The previous PSAT control strategy targeted minimum fuel consumption.)
Other enhancements include additional component models, refined controls and a new graphic user interface (GUI) with more advanced post-processing capabilities. Models were added/refined for the clutch and torque converter — critical for predicting vehicle transient behavior and controlling the engine-transmission interface in HIL testing. Neural network engine models were added to predict transient emissions for complex hybrid systems (such as the Prius) as well as conventional vehicles where engines operate a substantial period of time in transient regimes. Argonne also laid the groundwork for fuel cell modeling for vehicle applications in FY02 by internally funding development of a simplified (prototype) version of GCtool that is compatible with PSAT.
Sponsor
U.S. Department of Energy, Office of Transportation Technologies, Office of Advanced Automotive Technologies
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