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Gasoline

Gasoline internal combustion vehicles typically use a spark-ignited internal combustion engine. In a combustion chamber, injected fuel is combined with air. The mixture of air and fuel ignites from a spark from the spark plug (DOE, 2024). For additional background, see the Alternative Fuels Data Center's How Do Gasoline Cars Work? webpage.

On this page, explore key cost and performance metrics for gasoline internal combustion vehicles, including modeled vehicle pricefuel economylevelized cost of driving (LCOD), and emissions. Caveats for comparing powertrains are listed on the Light-Duty Vehicle Comparison page.

Vehicle Metrics: Fuel Economy and Modeled Vehicle Price

The following chart shows fuel economy and modeled vehicle price, metrics associated with the vehicle. Fuel economy represents how efficiently a vehicle converts fuel during operation. Modeled vehicle price represents an estimated cost to the consumer to purchase a new vehicle, based on modeling that includes manufacturing costs and profit.

The source of the 2024 Transportation Annual Technology Baseline (ATB) modeled vehicle price and fuel economy is the Argonne National Laboratory (ANL) report (Islam et al., 2023); the original data are available here. These data are developed using ANL's Autonomie simulation tool.

Select the data to display using the menus above the chart. Use the Metric filter to switch between fuel economy and modeled vehicle price data. Select the vehicle class, powertrain, and other powertrain details using the additional filters.

Vehicle and Fuel Metrics: Levelized Cost of Driving and CO2 Emissions

The following chart shows levelized cost of driving and CO2e emissions in addition to the associated fuel data. Levelized cost of driving is a metric that combines modeled vehicle price, fuel economy, fuel cost, and other assumptions for the selected fuel. CO2e emissions represents the emissions for the fuel well-to-wheels portion of the life cycle for the selected fuel. Emissions associated with vehicle life cycles are not included here.  

These calculations use data from Argonne National Laboratory, which develops and applies the Autonomie simulation tool and R&D GREET model (Wang et al., 2023). Links to data from the ANL report (Islam et al., 2023) on modeled vehicle price and fuel economy are available here.

Select the data to display using the buttons and menus above the chart. Use the Metric filter to switch between LCOD and CO2e emissions data. Select the pathway, scenario, vehicle class, and powertrain details using the additional filters. Clicking the black arrows on the top right of the figure shows additional details of the selected fuel pathways. The underlying source for a data point in the chart can be seen by placing your mouse cursor over that data point. The data sources are also cited—with linked references—in the Key Assumptions section that follows.

Notes:

  • The levelized cost of driving in the ATB includes vehicle, fuel, and maintenance costs. See the levelized cost of driving definition for details of what is included in and excluded from LCOD in the ATB.
  • Changes over time are attributable only to projected vehicle cost and performance; the fuel cost and emissions are constant over time.
  • The Fuel Pathway filter displays the selected fuel pathways for the Baseline FuelLowest Cost Fuel, and Lowest CO2e Emissions Fuel. The full set of fuel pathways is available in the data download.
  • Emissions references and fuels costs and prices references do not always use the same data source for a given pathway. We recommend caution in interpretation of combined sources of information.

Key Assumptions

The data and estimates presented here are based on the following key assumptions:

  • Fuel Economy Improvements: The cost and fuel economy trajectories are based on the analysis year Autonomie modeling results for the Conventional Turbo powertrain from Islam et al. (Islam et al., 2023). The ATB Advanced trajectory corresponds to the Base performance, High technology progress case. The ATB Mid trajectory corresponds to the Base performance, Low technology progress case. The ATB conservative trajectory is based on relative changes in the Annual Energy Outlook (EIA, 2023).
  • Powertrain Details Filter: The Powertrain Details filter allows for selection of multiple powertrain configurations. For full descriptions of alternative configurations, refer to documentation by Islam et al. (Islam et al., 2023).
  • High Production Volume: The estimates from Islam et al. (Islam et al., 2023), and those shown here, represent costs and technology performance at high production volume. Gasoline internal combustion engine vehicles are currently manufactured at high volume, and the high-volume estimates should therefore reflect the current state of technology.
  • Vehicle Variations: The Transportation ATB presents estimates for a representative light-duty vehicle; we do not account for variations in make, model, and trim or for pricing incentives or geographic heterogeneity that influence prices in the market. As a result, representative values shown here may differ from specific models available on the market.
  • Technology Advances: Technology advances include changes that may reduce costs or may increase costs while improving performance, which implies costs do not always decline between less- and more-advanced scenarios.
  • Nonmonotonic Behavior: Modeled vehicle price trajectories may exhibit nonmonotonic behavior resulting from the combination of advanced technology costs and the impact on engine efficiency. An example of this behavior can occur when engine cost is assumed to increase over time because of advanced technologies, but engine power is assumed to decrease over time because of lightweighting, improved aerodynamics, or other factors. This combination results in an example of potentially counterintuitive trends in which engine costs per unit of power increase over time but the total engine cost decreases because of the decreasing power requirements.
  • Baseline Fuel Pathway: The baseline fuel pathway used for this powertrain in the LCOD and emissions estimates is conventional E10 gasoline with starch ethanol. Additional selected fuel pathways can be displayed by choosing Lowest Cost or Lowest Emissions under the Fuel Pathway filter. Additional information about these and other fuels can be found on the Gasoline and Ethanol page.
  • Frozen Fuel Price Level: The fuel price and emissions of the selected fuel pathways (e.g., Baseline, Lowest Cost, and Lowest Emissions) are associated with a single year. Because we do not provide a time-series trajectory, here we show fuel price at a frozen level for all years so we can offer a range of fuel price values. In the levelized cost of driving and emissions charts, this approach clearly distinguishes the effects of fuels from those of vehicle technologies because fuels remain constant whereas vehicle technologies change over time.
  • Fuels References: See fuels and blendstock pages, especially for ethanol and BOB for a full description of fuels references. References for ethanol include (EIA, 2023)(Elgowainy et al., 2016)(Dutta et al., 2011)(Wang et al., 2023)(Lee et al., 2021)(Humbird et al., 2011)(Tao et al., 2014)(Dunn et al., 2013), and (Dunn et al., 2018). References for blendstocks for oxygenate blending (BOB) include (EIA, 2024)(EIA, 2023), and (Wang et al., 2023).
  • Fuel Economy on Substitutable Fuels: The Transportation ATB assumes the fuel economy (on a miles per gallon gasoline equivalent basis) remains constant when operating on substitutable fuels (e.g., conventional E10 gasoline versus reformulated E15 gasoline). In reality, fuel composition may affect engine performance.

The data downloads include additional details of assumptions and calculations for each metric.

Definitions

For detailed definitions, see:

Emissions

Fuel economy

Levelized cost of driving

Scenarios

Modeled Vehicle Price

Vehicle Range

References

The following references are specific to this page; for all references in this ATB, see References.

DOE. “Alternative Fuels Data Center,” 2024. https://afdc.energy.gov/.

Islam, Ehsan Sabri, Daniela Nieto Prada, Ram Vijayagopal, Charbel Mansour, Paul Phillips, Namdoo Kim, Michel Alhajjar, and Aymeric Rousseau. “Detailed Simulation Study to Evaluate Future Transportation Decarbonization Potential.” Report to the US Department of Energy, Contract ANL/TAPS-23/3. Argonne National Laboratory (ANL), Argonne, IL (United States), October 2023. https://anl.app.box.com/s/hv4kufocq3leoijt6v0wht2uddjuiff4.

Wang, Michael, Amgad Elgowainy, Uisung Lee, Kwang Hoon Baek, Sweta Balchandani, Pahola Thathiana Benavides, Andrew Burnham, et al. “Summary of Expansions and Updates in R&D GREET® 2023.” Argonne National Lab. (ANL), Argonne, IL (United States), December 1, 2023. https://doi.org/10.2172/2278803.

EIA. “Annual Energy Outlook 2023.” Washington D.C.: U.S. Energy Information Administration, March 16, 2023. https://www.eia.gov/outlooks/aeo/.

Elgowainy, Amgad, Jeongwoo Han, Jacob Ward, Fred Joseck, David Gohlke, Alicia Lindauer, Todd Ramsden, et al. “Cradle-to-Grave Lifecycle Analysis of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions and Economic Assessment of Current (2015) and Future (2025–2030) Technologies,” September 1, 2016. https://doi.org/10.2172/1324467.

Dutta, A., M. Talmadge, J. Hensley, M. Worley, D. Dudgeon, D. Barton, P. Groendijk, et al. “Process Design and Economics for Conversion of Lignocellulosic Biomass to Ethanol: Thermochemical Pathway by Indirect Gasification and Mixed Alcohol Synthesis.” Golden, CO (United States): National Renewable Energy Laboratory, May 1, 2011. https://doi.org/10.2172/1015885.

Lee, Uisung, Hoyoung Kwon, May Wu, and Michael Wang. “Retrospective Analysis of the U.S. Corn Ethanol Industry for 2005–2019: Implications for Greenhouse Gas Emission Reductions.” Biofuels, Bioproducts, and Biorefining 15, no. 5 (2021): 1318–31. https://doi.org/10.1002/bbb.2225.

Humbird, D, R Davis, L Tao, C Kinchin, D Hsu, A Aden, P Schoen, et al. “Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover,” March 1, 2011. https://doi.org/10.2172/1013269.

Tao, L., D. Schell, R. Davis, E. Tan, R. Elander, and A. Bratis. “NREL 2012 Achievement of Ethanol Cost Targets: Biochemical Ethanol Fermentation via Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover,” April 1, 2014. https://doi.org/10.2172/1129271.

Dunn, Jennifer, Michael Johnson, Zhichao Wang, Michael Wang, Kara Cafferty, Jake Jacobson, Erin Searcy, et al. “Supply Chain Sustainability Analysis of Three Biofuel Pathways: Biochemical Conversion of Corn Stover to Ethanol Indirect Gasification of Southern Pine to Ethanol Pyrolysis of Hybrid Poplar to Hydrocarbon Fuels.” Argonne, IL (United States): Argonne National Laboratory, November 2013. https://publications.anl.gov/anlpubs/2014/07/78878.pdf.

Dunn, Jennifer B., Mary Biddy, Susanne Jones, Hao Cai, Pahola Thathiana Benavides, Jennifer Markham, Ling Tao, et al. “Environmental, Economic, and Scalability Considerations and Trends of Selected Fuel Economy-Enhancing Biomass-Derived Blendstocks.” ACS Sustainable Chemistry & Engineering 6, no. 1 (January 2, 2018): 561–69. https://doi.org/10.1021/acssuschemeng.7b02871.

EIA. “U.S. Gasoline and Diesel Retail Prices,” 2024. https://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_a.htm.

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