Project number: PSR-22-06

Funding source: U.S Department of Transportation

Contract number: 69A3551747109

Total cost: $100,000.00

Performance period: 8/15/2022 to 8/14/2023

In this project the analysis of the commercial fleet demand for Battery Electric Vehicles (BEV) and Plugin-Hybrid Electric  Vehicles (PHEVs) is approached in a tripartite way based on an overall conceptual framework developed within the project. Commercial fleets are vehicle fleets of large corporations, rental car companies, utilities, and government agencies. The data used here are the California Energy Commission (CEC) vehicle surveys of 2017 and 2019. The first part analyzes these data on commercial fleet composition in terms of vehicle types, fuel types and annual miles to understand commercial fleet priorities and use intensity. The second part studies the correlation between the fleet owner characteristics, vehicle experience, and future vehicle purchase intentions. The third part using choice experiment data identifies significant attributes underlying the choice of fleet managers in selecting new vehicles and related technologies.  Then, estimates the BEV and PHEV fleet vehicle ownership demand together with willingness to pay for vehicle types and  fuel attributes. At the end, a synthesis of the findings and recommendations for policy action concludes the  project. The  project tasks are: Task 1 Conceptual Framework: In this task the conceptual framework of commercial fleet demand for  electric vehicles is finalized to guide the data analysis and choice model specifications in the other tasks; Task 2 Current Fleet Ownership: In this task a state of the art model called the Multiple Discrete Continuous Extreme Value model is estimated using the data on annual miles, vehicle type, and fuel type of the current fleet for each commercial fleet in the data as a function of business type, size of fleet, and vehicle attributes among other explanatory factors; Task 3 Purchase  Intentions: In this task survey data are analyzed to understand the types of vehicles commercial fleets plan to purchase and the attributes and other determinants of these stated intentions using a Structural Equation Model with latent variables; Task 4 Hybrid Choice Models: Using the CEC survey data in the discrete choice experiments a suite of advanced discrete choice models aree stimated to find optimal combinations of incentives and willingness to pay by commercial fleets; and Task 5 Findings and Policy Recommendations: Summaries of findings and policy recommendations for public agencies and private car manufacturers are provided in this task to support an accelerated electric vehicle market penetration. Lessons learned from the computation and data analysis guidelines will also be provided in a final report and three refereed journal papers.