Reducing greenhouse gas emissions will require light-duty vehicle manufacturers to implement strategies such as aerodynamic drag improvements, reduced tire rolling resistance, and mass optimization. In support of the California Air Resources Board Advanced Clean Cars program, these vehicle load attributes were assessed for the potential to reduce carbon dioxide (CO2) emissions from the future light-duty vehicle fleet.
Every model year 2014 vehicle available in the California fleet was studied to determine the extent to which vehicle load reduction technologies have already been applied. In total, 1358 individual model variants from 23 manufacturers were assessed. Using manufacturer-reported information, including certification data, the aerodynamic drag and tire rolling resistance were estimated for each vehicle model. Vehicle curb weight was obtained from the same sources and, combined with other vehicle specifications, used to evaluate mass efficiency. The distributions of these vehicle load parameters across each vehicle class were evaluated to select values that were representative of the best available. Recognizing measurement and analysis variability, the best-in-class performance was defined as a specific percentile of each distribution, not simply the most extreme value. These analyses were then used to identify vehicles achieving best-in-class performance for each load attribute and the results cross-referenced to load reduction technologies.
The best-in-class-performance of each load attribute was then applied to all vehicle models in the same class to generate projections of fuel consumption and tailpipe CO2 emissions, which determined the potential benefit of vehicle load reduction. Assuming that all current vehicles adopt similar amounts of load reduction technologies and strategies already available in today’s better performing vehicles, and that the powertrains are re-optimized to the lower loads, it is estimated that a reduction in tailpipe CO2 emissions of up to 10.4% is achievable.
For questions regarding this research project, including available data and progress status, contact: Research Division staff at (916) 445-0753
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