FINDINGS AND RECOMMENDATIONS

Finding 2-1. Fuel consumption (fuel used per distance traveled; e.g., gallons per mile) has been shown to be the fundamental metric to properly judge fuel efficiency improvements from both engineering and regulatory viewpoints, including yearly fuel savings for different technology vehicles. The often-used reciprocal, miles per gallon, called fuel economy, was shown in studies to mislead light-duty vehicle consumers to undervalue small increases (1 to 4 mpg) in fuel economy in lower-fuel-economy vehicles, even though there are large decreases in fuel consumption for small increases in fuel economy. This is because the relationship between fuel economy and fuel consumption is nonlinear. Truck and bus buyers could also likely be misled by using fuel economy data since their fuel economy values are in the lower range (3 to 15 mpg).


Finding 2-2. The relationship between the percent improvement in fuel economy (FE) and the percent reduction in fuel consumption (FC) is nonlinear, and the relationship between change in FE and FC is as follows:

% Increase in Fuel Economy

% Decrease in Fuel Consumption

10

9.1

50

33.3

100

50

Finding 2-3. Medium- and heavy-duty vehicles are designed as load-carrying vehicles, and consequently their most meaningful metric of fuel efficiency will be in relation to work performed, such as fuel consumption per unit payload carried, which is load-specific fuel consumption (LSFC). Because the main social benefit of trucks and buses is the efficient and reliable movement of goods or passengers, establishing a metric that includes a factor for the work performed will most closely match regulatory with societal goals. Methods to increase payload may be combined with technology to reduce fuel consumption to improve LSFC. Future standards might require different values to accurately reflect the applications of the various vehicle classes (e.g., buses, utility, line haul, pickup, and delivery).


Finding 2-4. Yaw-induced drag can be accurately measured only in a wind tunnel. Standard practice in wind tunnel testing reports a wind average drag (coefficient) that can be 15 percent higher than the drag neglecting yaw.


Finding 2-5.* The large per-vehicle annual miles traveled and fuel use by many heavy-duty vehicles magnify the importance, especially to the user, of technologies or design alternatives that can reduce fuel consumption by as little as 1 percent. As a result, accurate test procedures are required to reliably determine the potential benefit of technologies that reduce fuel consumption. Unfortunately, it is very difficult to achieve, at the 90 or 95 percent confidence interval, a precision of less than ±2 percent for vehicle fuel consumption measurements with the current SAE test procedures. The recently convened SAE Truck and Bus Aerodynamic and Fuel Economy Committee effort is a good start toward developing high-quality industry standards.


Recommendation 2-1. Any regulation of medium- and heavy-duty-vehicle fuel consumption should use load-specific fuel consumption (LSFC) as the metric and be based on using an average (or typical) payload based on national data representative of the classes and duty cycle of the vehicle. Standards might require different values of LSFC due to the various functions of the vehicle classes, e.g., buses, utility, line haul, pickup, and delivery. Regulators need to use a common procedure to develop baseline LSFC data for various applications, to determine if separate standards are required for different vehicles that have a common function. Any data reporting or labeling should state an LSFC value at specified tons of payload.


Recommendation 2-2.* Uniform testing and analysis standards need to be created and validated to achieve a high degree of accuracy in determining the fuel consumption of medium- and heavy-duty vehicles. NHTSA should work with industry to develop robust test and analysis procedures and standards for fuel consumption measurement.

BIBLIOGRAPHY

ANL (Argonne National Laboratory). 2009. Evaluation of Fuel Consumption Potential of Medium and Heavy Duty Vehicles Through Modeling and Simulation

ATA (American Trucking Associations, Inc.). 2007a. Top 100 private carriers 2007. Transport Topics. Available at http://www.ttnews.com/tt100.archive.

ATA. 2007b. Top 100 for-hire fleets 2007. Transport Topics. Available at http://www.ttnews.com/tt100.archive.

ATA. 2009. Top 100 commercial fleets 2009. Light and Medium Truck. July. Available at http://www.lmtruck.com/lmt100/index.asp.

Bradley, M.J., and Associates LLC. 2009. Setting the Stage for Regulation of Heavy-Duty Vehicle Fuel Economy and GHG Emissions: Issues and Opportunities. Washington, D.C.: International Council on Clean Transportation. February.

Chapin, C.E. 1981. Road load measurement and dynamometer simulation using coastdown techniques. SAE Paper 810828. Warrendale, Pa.: SAE International.

Clark, N.N., M. Gautam, W.S. Wayne, R.D. Nine, G.J. Thompson, D.W. Lyons, H. Maldonado, M. Carlock, and A. Agrawal. 2003. Creation and evaluation of a medium heavy-duty truck test cycle. SAE Transactions: Journal of Fuels & Lubricants, Vol. 112, Part 4, pp. 2654-2667.

*

Note added in proof: Recommendation 2-2 in the prepublication version of this report implied that a 1 percent level of accuracy is achievable, which may not be possible. The committee thus corrected and refined Recommendation 2-2 to make it a more general and actionable statement and added Finding 2-5 to summarize the motivation for the recommendation.



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