assessment (within +/−5 percent or less) of the impacts on fuel consumption of implementing one or more technologies. The validity of FSS modeling depends on the accuracy of representations of system components (e.g., engine maps). Expert judgment is also required at many points (e.g., determining engine warm-up rates or engine control strategies) and is critical to obtaining accurate results.

Finding 8.2: The partial discrete approximation (PDA) method relies on other sources of data for estimates of the impacts of fuel economy technologies. Unlike FSS, the PDA method cannot be used to generate estimates of the impacts of individual technologies on vehicle fuel consumption. Thus, the PDA method by itself, unlike FSS, is not suitable for estimating the impacts on fuel consumption of technologies that have not already been tested in actual vehicles or whose fuel consumption benefits have not been estimated by means of FSS. Likewise, the effects of technology interactions must be determined from external estimates or approximated by a method such as lumped parameter modeling. Even FSS, however, depends directly on externally generated information on the performance of individual technology components.

Finding 8.3: Comparisons of FSS modeling and PDA estimation (within the range of cases where the PDA method is applicable) supported by lumped parameter modeling to eliminate double counting of energy efficiency improvements have shown that the two methods produce similar results when similar assumptions are used. In some instances, comparing the estimates made by the two methods has enhanced the overall validity of estimated fuel consumption impacts by uncovering inadvertent errors in one or the other method. In the committee’s judgment both methods are valuable, especially when used together, one providing a check on the other. However, more work needs to be done to establish the accuracy of both methods relative to actual motor vehicles. In particular, the accuracy of applying class-specific estimates of fuel consumption impacts to individual vehicle configurations needs to be investigated. The magnitude of the errors produced when such estimates are aggregated to calculate the potential of individual automobile manufacturers to reduce fuel consumption should also be analyzed.

Finding 8.4: The U.S. Department of Transportation’s Volpe National Transportation Systems Center has developed a model for the NHTSA to estimate how manufacturers can comply with fuel economy regulations by applying additional fuel savings technologies to the vehicles they plan to produce. The model employs a PDA algorithm that includes estimates of the effects of technology synergies. The validity of the Volpe model, and probably also the OMEGA model, could be improved by making use of main effects and interaction effects produced by the FSS methodology described in this chapter. In particular, research done for the committee has demonstrated a practical method for using data generated by FSS models to accurately assess the fuel consumption potentials of combinations of dozens of technologies on thousands of vehicle configurations. A design-of-experiments statistical analysis of FSS model runs demonstrated that main effects and first-order interaction effects alone could predict FSS model outputs with an R2 of better than 0.99. Using such an approach could appropriately combine the strengths of both the FSS and the PDA modeling methods. However, in Chapter 9 the committee recommends an alternate approach that would use FSS to better assess the contributory effects of technologies applied for the reduction of vehicle energy losses and to better couple the modeling of fuel economy technologies to the testing of such technologies on production vehicles.


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