Determinants of battery electric vehicle adoption concerns: insights from commercial fleet owners

Abstract

Light-duty commercial vehicles contribute to urban transportation challenges, including traffic congestion and pollution, with these issues expected to worsen as commercial vehicle activity increases. Commercial Battery Electric Vehicles (BEVs) offer a potential solution by reducing fossil fuel dependency, but there is limited research on the concerns commercial entities have about adopting BEVs. Using the latest survey data from California, the study's goal is to apply both machine learning and econometric models to understand the factors associated with BEV adoption concerns among commercial entities. Specifically, the study identifies factor chains linked to concerns through association rule mining (ARM) and explores how various factors influence concern probabilities using a random parameters logit regression framework. The study focuses on the top five concerns regarding BEVs: limited driving range, hauling capacity, battery life uncertainty, cost, and charging infrastructure. ARM results indicate that construction industries and companies using pickups or trucks are particularly concerned about hauling capacity, while companies without BEV ownership are more likely to worry about high costs. Additionally, those without EVs in their fleet tend to focus on limited charging infrastructure and driving range. The regression models reinforce these findings but also reveal significant variability in how factors such as BEV ownership experience, industry type, and fleet composition influence concern probabilities. This variability offers insights beyond those provided by a fixed regression approach. The study concludes that targeted interventions addressing these key concerns could significantly facilitate BEV adoption in commercial fleets, helping to alleviate urban transportation and environmental issues.