Scaling Cleaner Freight Movement

Charting the Course for Early Truck Electrification

To gain insights into trucking electrification, RMI, Geotab and NACFE teamed to gain insights into which segments of the trucking market can be electrified first and what charging infrastructure will be required to enable that transition.

The study team used vehicle telematics data from Geotab to understand the behavior of medium- and heavy-duty trucks. Although the data provided by Geotab for this analysis is exclusively from trucks powered by internal combustion engines (ICE), the observed driving patterns of these vehicles can be used to determine which trucks could be replaced by EVs the soonest based on existing EV technology and charging infrastructure.

To estimate the charging needs and impacts of trucking electrification, the study team developed a multistep modeling framework. Two scenarios for fleet electrification were analyzed: one with an average vehicle replacement age of 15 years, and a second with an average vehicle replacement age of 10 years.

The report determined that, based on how vehicles are driven today, approximately 65% of medium-duty trucks and 49% of heavy-duty trucks (stationed in California and New York) are electrifiable, meaning they could be replaced with EVs based on current technology. These vehicles are responsible for about 30% of the vehicle miles traveled by trucks based in the two states.

Trucking electrification is only possible if vehicle charging is available. Based on the report team’s analysis, the electrifiable trucks in the dataset would require on average 50 to 400 kilowatt-hours per day — comparable to the electricity used by 2 to 14 homes. This amount of energy is generally less than or equivalent to one full charge of the truck’s battery, based on electric truck models currently on the market. Electricity demand from truck electrification may decrease as e-MHDV models begin to require fewer kilowatt-hours per mile and fleets and drivers learn to optimize regenerative braking and other driving behaviors to achieve maximum efficiency.

Key Findings

Based on results from the analysis the study team came up with some key takeaways.

Back to top