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Plug-In Hybrid MDHD

Plug-in hybrid electric vehicles (PHEVs) use both an electric motor and an internal combustion engine. Batteries power the motor, and gasoline, diesel, or another fuel powers the engine or other propulsion source. For additional background, see the Alternative Fuels Data Center's Plug-In Hybrid Electric Vehicles webpage.

Explore key cost and performance metrics for plug-in hybrid electric vehicles, including modeled vehicle pricefuel economylevelized cost of driving (LCOD), and emissions. Caveats for comparing powertrains are listed on the MDHD 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 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 cost 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 following chart shows levelized cost of driving (LCOD) 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.  

The chart shows LCOD bands that include uncertainty and variation in medium- and heavy-duty (MDHD) EV charging situations. These LCOD bands incorporate scenarios of different utilization, electric demand charges, equipment costs, charging powers/fueling rates, and business models. In each calculated LCOD for each fuel pathway, the Annual Technology Baseline (ATB) combines these variations on delivery costs with its estimate of bulk energy price (electricity and hydrogen at production gate), for consistency.

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 next.

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:

  • Cost and Fuel Economy Trajectories: The cost and fuel economy trajectories are based on the analysis-year Autonomie modeling results 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 the Annual Energy Outlook (EIA, 2023a). The ATB Constant trajectory is set to the 2022 values in the Low technology progress, High-cost case and held constant through 2050. The Constant trajectory is used only where the Conservative trajectory is not available (for the following vehicle classes: Class 7, Medium Vocational; Class 8, Refuse Heavy; and Class 8, Vocational Heavy).
  • 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
  • At-Scale Manufacturing Costs: Though MDHD PHEVs and battery electric vehicles (BEVs) are not currently produced at high volume, we assume learning and scale from light-duty vehicle manufacturing of electric drivetrain components, which are produced at high volume, will lead to at-scale manufacturing costs for MDHD PHEVs and BEVs. However, we note differences in performance, durability, and warranty requirements may maintain a price premium in MDHD EV component costs compared to light-duty vehicle component costs. 
  • Vehicle Variations: The Transportation ATB presents estimates for representative vehicles in MDHD classes; 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.
  • Fuel Economy Improvements: The assumptions about fuel economy improvements reflect the adoption of lightweighting and engine efficiency technologies consistently across vehicle powertrains for a given trajectory in Islam et al. (Islam et al., 2023). However, plug-in hybrid electric vehicles use engine technologies and battery technologies that are different from those of single-fueled vehicles, so the impact of lightweighting and other technology advancements on fuel economy and modeled vehicle price may occur at different rates.
  • Efficiency Losses: The charge-depleting fuel economy estimates in Islam et al. (Islam et al., 2023)(Wang et al., 2023) are adjusted to account for battery charging efficiency losses. See Battery Electric Vehicles definition for details on efficiency losses.
  • Baseline Fuel: The baseline fuel pathways used for this powertrain in the LCOD and emissions estimates are plug-in electric vehicle charging electricity with the national grid mix and conventional E10 gasoline with starch ethanol. Additional selected fuel pathways can be displayed by choosing Lowest Cost or Lowest Emissions under Fuel Pathway filter. Additional information about these and other fuels can be found on the Gasoline and Ethanol and Electricity pages.
  • 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 effects of fuels from those of vehicle technologies because fuels remain constant whereas vehicle technologies change over time.
  • Charger Equipment and Installation Costs: The Transportation ATB includes charger equipment and installation costs for plug-in hybrid electric vehicles in the price of electricity, detailed on the electricity page.
  • Fuels References: See fuels and blendstock pages, especially for diesel bio-based blendstock and electricity, for a full description of the fuels references. Those for diesel include (EIA, 2023a)(EIA, 2024)(DOE, 2023)(Dutta et al., 2021)(Tao et al., 2017)(Tan et al., 2021)(Wang et al., 2023)(Xie et al., 2011)(Xu et al., 2022), and (Zhu et al., 2020). Those for electricity include (EIA, 2023b)(EIA, 2023c)(EIA, 2023a)(Gagnon et al., 2024), and (Wang et al., 2023).
  • Utility Factor-Weighted Average: The Fuel economy shown here is the utility-factor-weighted average, which includes the average electricity and liquid fuel consumption across charge-depleting and charge-sustaining modes. Definitions and values of the utility factors are on the Levelized Cost of Driving Assumptions page. 
  • Fuel Economy on Substitutable Fuels: The Transportation ATB assumes the charge-sustaining fuel economy remains constant when operating on substitutable fuels. 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

Plug-in hybrid electric vehicles

Scenarios

Modeled Vehicle Price

Vehicle Range

References

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

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, 2023a. https://www.eia.gov/outlooks/aeo/.

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

DOE. “Clean Cities Alternative Fuel Price Report, 2022.” Washington D.C.: U.S. Department of Energy, 2023. afdc.energy.gov/fuels/prices.html.

Dutta, Abhijit, Calvin Mukarakate, Kristiina Iisa, Huamin Wang, Michael Talmadge, Daniel Santosa, Kylee Harris, et al. “Ex Situ Catalytic Fast Pyrolysis of Lignocellulosic Biomass to Hydrocarbon Fuels: 2020 State of Technology.” National Renewable Energy Lab. (NREL), Golden, CO (United States), June 1, 2021. https://doi.org/10.2172/1805204.

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. https://doi.org/10.1186/s13068-017-0945-3.

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. https://doi.org/10.1021/acs.est.0c06141.

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. https://doi.org/10.1021/es1017703.

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. https://doi.org/10.1021/acs.est.2c00289.

Zhu, Yunhua, Susanne B. Jones, Andrew J. Schmidt, Justin M. Billing, Michael R. Thorson, Daniel M. Santosa, Richard T. Hallen, and Daniel B. Anderson. “Algae/Wood Blends Hydrothermal Liquefaction and Upgrading: 2019 State of Technology,” April 27, 2020. https://doi.org/10.2172/1616287.

EIA. “Electricity Data Browser - Average Retail Price of Electricity,” 2023b. https://www.eia.gov/electricity/data/browser/#/topic/7?agg=0,1&geo=g00080000004&endsec=vg&linechart=ELEC.PRICE.US-ALL.A&columnchart=ELEC.PRICE.US-ALL.A&map=ELEC.PRICE.US-ALL.A&freq=A&start=2021&end=2023&ctype=linechart&ltype=pin&rtype=s&pin=&rse=0&maptype=0.

EIA. “Electricity Data Browser - Net Generation for All Sectors,” 2023c. https://www.eia.gov/electricity/data/browser/#/topic/0?agg=0,1&geo=g00080000004&freq=A&start=2021&end=2023&ctype=linechart&ltype=pin&rtype=s&maptype=0&rse=0&pin=.

Gagnon, Pieter, An Pham, Wesley Cole, Sarah Awara, Anne Barlas, Maxwell Brown, Patrick Brown, et al. “2023 Standard Scenarios Report: A U.S. Electricity Sector Outlook.” National Renewable Energy Laboratory (NREL), Golden, CO (United States), January 1, 2024. https://doi.org/10.2172/2274777.

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