FIGURE H-15 Mean particulate matter results with two standard deviation error bars. SOURCE: Argonne National Laboratory.

FIGURE H-15 Mean particulate matter results with two standard deviation error bars. SOURCE: Argonne National Laboratory.

the sensitivity of the engine on/off related to the pedal position.

  • Delay impact. For several processes that include a mix of hardware and software, including CIL, SIL, or HIL, delays introduced by some hardware on the command and feedback can significantly affect the results. Dynamometer slew rate and command methodology (e.g., analog, digital, CAN) are some of the examples to be addressed. Such delays over an entire drive cycle could lead to several percentage point differences in energy, which would impact the results for both fuel efficiency and emissions. As a result, levels of acceptable delays should be defined for each process and potentially each technology to provide a low level of uncertainties.

  • Appropriate selection of level of modeling. To simulate specific phenomena properly, an appropriate level of modeling must be selected. As such, engineers do not use the same models at the beginning of a project to compare different power train configurations as toward the end of a project when the focus is on drive quality and emissions. For regulatory purposes, the approach might be similar. The committee recommends that a study should be conducted to assess the uncertainty between different levels of modeling for specific components.

  • Data collection for model instantiation. For the models to represent technologies properly, it is necessary to populate them with accurate sets of parameters. The conditions at which these parameters are measured along with the instrumentation will influence the uncertainty of the final simulations. As such, characterization of the parameters for several systems should be clearly defined. This would include rigorous evaluation protocols for the evaluation of coefficients for tire rolling resistance and aerodynamic drag to ensure consistency and minimize uncertainty.

In addition, even if using more detailed models (e.g., zero-dimensional engine model rather than steady state) can lead to better representation of the transients, such models do require significantly more testing and data collection to properly represent the system. As such, a trade-off analysis should be performed to evaluate the additional testing required to populate the detailed models compared to the added accuracy they provide. This accuracy evaluation may be dependent on the evolutionary stage of the technology and cannot be considered static.

In-between Processes

When selecting a process, it is important to understand the uncertainties introduced by the methodology employed. For example, using an engine on the dynamometer (CIL) versus testing the entire vehicle will lead to differences in results as they each have different uncertainty sources. While the driver will have the largest impact on the results during chassis dynamometer testing, the driver is not a factor anymore during



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