"I want access to as much credible information as can be obtained, in order to determine the fuel savings potential for any technology that we may introduce into our fleet."

  Scott Perry, VP, Supply Management & Global Fuel Products
Ryder Fleet Management Solutions

Overview and Test Methods

There are many fuel efficiency technologies available to fleets but no truck could or should have all of them. Fleets must analyze the range of options and compare them against their own operations to determine which offer the fastest payback. To do this, fleets need a solid understanding of their own operation and test data on the performance of various vehicle technologies. However, it is not possible for fleets to test every device so they must rely on data from other sources.  

There are a variety of test methods and each has its appropriate application. Understanding what each test method involves, its benefits and challenges, and how it deviates from real world performance will help fleets have a better understanding of how to interpret the data.

OEMs should also conduct tests on the fuel efficiency of various components to help determine which technologies to offer on their vehicles. In addition, providers of technology should invest in testing in order to better develop and validate their designs.

Basic Test Methods

There are many published test methods, but all fall into one of five basic categories:

  • Computer Modeling—Computational Fluid Dynamics Analysis
    This method uses computer software such as facsimile digital geothermal models to calculate a vehicle configuration’s performance. .
  • Wind Tunnel Testing
    Tractor/trailer wind tunnel testing uses a physical scale model of a vehicle (or in some cases a full-sized vehicle) in a wind tunnel where environmental conditions can be controlled. The size of the wind tunnel dictates the size of the model needed.
  • Track Testing
    There are a variety of test protocols for use on a test track. Each attempts to quantify known components of the total performance and infer that what remains is attributed to the device being tested. Assumptions and simplifications can impact test results.
  • On-Road Testing
    This involves evaluating a tractor-trailer on an actual highway over a statistically significant number of miles with an acceptable level of repeatability. It measures the performance of the entire vehicle and all its factors as a net total.
  • Fleet Composite Evaluation
    All fleets perform fleet testing if they record miles driven, freight carried, and fuel purchased. This can validate technology choices when the data is collected and analyzed.

 

Watch The Video
Case Studies
  • Aerodynamics
    The EPA documented one example of the variation in results from different test methods in its Regulatory Impact Analysis for Phase 1 Greenhouse Gas Rules.
  • 6x2 Axles
    A Trucking Efficiency Confidence Report published in December 2013 on 6x2 axles illustrates the difficulty inherent in comparing directly between test results rather than looking at trends.
Conclusions
  • The words accurate and precise are not interchangeable.
  • Data is available but needs to be shared.
  • The more methods used, the more confidence there is in trends.
  • Clarify objectives.
  • Adjust to operations.
  • Be comfortable with a range.

 

Recommendations

For Fleets

  • Understand scope, context, and constraints of test methods and how results will translate to real-word predictions.
  • Understand what factors are being measured, which are assumed, and which are estimated.
  • Accurately quantify the fleet’s own current performance.
  • Do not seek to determine absolute value for performance payback; instead look at the percent change.
  • Look for trends.

For Test Designers and others sharing test data

  • Achieve industry agreement on an absolute reference vehicle.
  • Initiate a concerted effort to correlate each test method to this absolute standard.
  • Continuously improve and refine measurement methods.
  • Share test results throughout the industry.
  • Work to provide better information on the potential impacts of real-word factors.