Market Diffusion of Autonomous Electric Vehicles and Charging Infrastructure

Date
2021
Authors
Shah, Sonali
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract

Transportation sector is the largest contributor to greenhouse gas emissions in USA, and Autonomous Electric Vehicles (AEVs) can help to mitigate this problem. AEVs can also make travel more convenient and efficient. There are three important contributions in this research. First, this research estimates the diffusion of AEVs and forecasts the AEV market penetration in USA and Texas by using Bass Diffusion (BD) and Generalized Bass Diffusion (GBD) Models. BD and GBD models are calibrated using historical sales data from similar products. Two external variables, charging station and tax are incorporated in GBD model. A sensitivity analysis assesses the impact of the variables on AEV adoption. The model results provide important insights for policy makers to prepare for AEV future. Second, potential increase in travel with AEV for the non-driving, elderly and people with travel-restrictive medical conditions is estimated using NHTS 2017 data. Travelers' groups are created assuming each person within above three groups will increase their miles traveled to a certain threshold. Study results indicate, for all three new travelers' groups, there will be an increase in travelers as well as in vehicle miles traveled in the US. Third, the need for charging infrastructure based on future market penetration of AEVs in Texas has been estimated. Study assumed market size as 80% of Texan households and Bass parameters calibrated for the US are valid for Texas. Under these assumptions, Texas's future AEVs market diffusion was forecasted, and using this market size, future charging infrastructure need for Texas was simulated using EVI-Pro Lite.

Description
This item is available only to currently enrolled UTSA students, faculty or staff.
Keywords
Charging infrastructure, Autonomous electric vehicles, Greenhouse gas emissions
Citation
Department
Civil and Environmental Engineering