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Diesel internal combustion vehicles have a compression-ignited injection system. In a combustion chamber, the engine piston plug compresses the injected diesel fuel, raising the temperatures until ignition occurs (DOE, 2019). For additional background, see the Alternative Fuels Data Center's How Do Diesel Vehicles Work?

On this page, explore key cost and performance metrics for diesel internal combustion engine vehicles, including modeled vehicle price, fuel economy, levelized cost of driving, and emissions. Caveats for comparing powertrains are listed on the Light-Duty Vehicle Comparison page.

Vehicle Metrics: Fuel Economy and Modeled Vehicle Price

The chart below 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 2022 Transportation ATB modeled vehicle price and fuel economy is the Argonne National Laboratory report (Islam et al., 2022); he 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 CO2e Emissions

The chart below shows levelized cost of driving and CO2emissions, 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 GREET model (Wang et al., 2022). Links to data from the Argonne National Laboratory report (Islam et al., 2022) on modeled vehicle price and fuel economy are available here.   

Select the data to display using the buttons and menus to the above the chart. Use the Metric filter to switch between levelized cost of driving 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 hyperlinked linked references—in the Key Assumptions section below.


Key Assumptions

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

  • Fuel Economy Improvements: The assumptions about fuel economy improvements reflect adoption of lightweighting and engine efficiency technologies consistently across vehicle powertrains for a given trajectory; they do not account for the trade-offs between efficiency and performance in various vehicle markets. The cost and fuel economy trajectories are based on the analysis-year Autonomie modeling results for the conventional compression ignition powertrain from Islam et al. (Islam et al., 2022). 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 Constant trajectory is set to the 2022 values in the Base performance, Low technology progress case and held constant through 2050.
  • High Production Volume: The estimates from Islam et al. (Islam et al., 2022), and those shown here, represent costs and technology performance at high production volume. Diesel 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.
  • Non-Monotonic Behavior: Modeled vehicle price trajectories may exhibit non-monotonic behavior resulting from the combination of advanced technology costs and the impact on engine efficiency. For example, though the engine cost might increase over time due to advanced technologies, the overall engine power required decreases over time due to lightweighting, improved aerodynamics, or other factors. As a result, though per unit of power engine costs might go up, lower engine power requirements decrease the total engine cost.
  • Baseline Fuel Pathway: The baseline fuel pathway used for this powertrain in the levelized cost of driving and emissions estimates is Ultra-Low-Sulfur Diesel. 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 Diesel 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 point in time that may be different for different selections. 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 effects of fuels from those of vehicle technologies, because fuels remain constant while vehicle technologies change over time.
  • Fuels References: See fuels and blendstock pages, especially for diesel bio-based blendstock, for a full description of the fuels references, which include (EIA, 2020)(EIA, 2021)(DOE, 2020)(Tao et al., 2017)(Tan et al., 2021)(Wang et al., 2022)(Xie et al., 2011), and (Xu et al., 2022).
  • Fuel Economy on Substitutable Fuels: The Transportation ATB assumes fuel economy (on a miles per gallon gasoline equivalent basis) remains constant when operating on substitutable fuels (e.g., ultra-low-sulfur diesel versus renewable diesel). In reality, fuel economy may vary because of the different energy content or engine performance.

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


For detailed definitions, see:


Fuel economy

Levelized cost of driving


Modeled Vehicle Price

Vehicle Range


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

DOE. “Alternative Fuels Data Center,” 2019.

Islam, Ehsan Sabri, Ram Vijayagopal, Benjamin Dupont, Namdoo Kim, Ayman Moawad, Daniela Nieto Prada, and Aymeric Rousseau. “A Comprehensive Simulation Study to Evaluate Future Vehicle Energy and Cost Reduction Potential.” Report to the US Department of Energy. Argonne National Laboratory, June 2022.

Wang, Michael, Amgad Elgowainy, Uisung Lee, Kwang Hoon Baek, Adarsh Bafana, Pahola Thathiana Benavides, Andrew Burnham, et al. “Summary of Expansions and Updates in GREET® 2022.” Argonne National Lab. (ANL), Argonne, IL (United States), October 1, 2022.

EIA. “U.S. Gasoline and Diesel Retail Prices,” July 13, 2020.

EIA. “Annual Energy Outlook 2021.” Washington, D.C.: U.S. Energy Information Administration, February 2021.

DOE. “Clean Cities Alternative Fuel Price Report, October 2020,” 2020.

Tao, Ling, Anelia Milbrandt, Yanan Zhang, and Wei-Cheng Wang. “Techno-Economic and Resource Analysis of Hydroprocessed Renewable Jet Fuel.” Biotechnology for Biofuels 10, no. 1 (November 9, 2017): 261.

Tan, Eric C. D., Troy R. Hawkins, Uisung Lee, Ling Tao, Pimphan A. Meyer, Michael Wang, and Tom Thompson. “Biofuel Options for Marine Applications: Technoeconomic and Life-Cycle Analyses.” Environmental Science & Technology 55, no. 11 (June 1, 2021): 7561–70.

Xie, Xiaomin, Michael Wang, and Jeongwoo Han. “Assessment of Fuel-Cycle Energy Use and Greenhouse Gas Emissions for Fischer−Tropsch Diesel from Coal and Cellulosic Biomass.” Environmental Science & Technology 45, no. 7 (April 1, 2011): 3047–53.

Xu, Hui, Longwen Ou, Yuan Li, Troy R. Hawkins, and Michael Wang. “Life Cycle Greenhouse Gas Emissions of Biodiesel and Renewable Diesel Production in the United States.” Environmental Science & Technology 56, no. 12 (June 21, 2022): 7512–21.

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