Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

Principal Investigator(s):

Will Northrop, Associate Professor, Mechanical Engineering

Project summary:

Increased electrification is a clear trend in regional trucking, with multiple vehicle manufacturers bringing to market commercially available battery and hydrogen fuel cell Class 6-8 trucks. On the fleet side, at least 70 percent of major truck fleet operators reported exploring purchase and implementation of electric vehicles for their operations as of 2018. Recently, multiple high-profile companies including PepsiCo, Walmart, Amazon, and UPS have publicly committed to the purchase of electric delivery vehicles. Despite these commitments, companies have yet to implement these vehicles at scale due to range anxiety, limited charging infrastructure availability, and sparse data from in-use operation. Further, electric vehicle implementations have been disproportionately used by larger fleets with more resources. With successful heavy-duty electric vehicle implementation being highly dependent on vehicle duty cycle--including vehicle mass and road grade, as well as external factors like climate and traffic--any electrification recommendation must be tailored to the individual fleet and vehicle.

To address these concerns and enable large-scale electric vehicle adoption, this project will develop a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool to both assess heavy-duty electric vehicle suitability and identify necessary infrastructure improvement that can be applied at a site, local, and national scale, based on in-use data vehicle data.

The developed tool will utilize existing telematics information already collected from conventionally powered, heavy-duty vehicles in regional delivery fleets, combined with a vehicle model and optimization code to predict battery size and on-route charging locations required to complete the same desired work. It will add capabilities to current tools, including the ability to take multiple vehicle data and to estimate payload mass from existing vehicle driving cycles.

Project details:

  • Project number: 2022020
  • Start date: 10/2021
  • Project status: Active
  • Research area: Environment and Energy