Assess Electric Vehicle Adoption Through an Agent Based Approach

Abstract

With technological innovations and support from government agencies, the auto industry for electric vehicle (EVs) has now gained a renowned popularity among consumers who are in search for emission-free substitutes to replace diesel and petrol-fueled vehicles. This paper employs an Agent-Based Modeling (ABM) technique, providing us the opportunity to capture interactions among agents (consumers), each with different socio-economic levels and unique utility function. The simulated result allows us to analyze micro-level EV adoption and macro-level impact from changing environment. The aim of our research is to investigate interconnected factors, especially on decision making process, behind the evolving preference towards EVs. Under each tick (tick = day), agents are designated to move from home, to work and have the opportunity to other locations of their choice. Hence, each agent has distinct daily travel distance to mimic reality. In addition, we input data collect from official government agencies including average gas price, EVs rebate program offering and vehicle range per charge to empirically analyze the comparison between electric and gas vehicles. Furthermore, we leverage cost benefit analysis using willingness to pay (WTP) and affordability as key thresholds in the decision-making process; only when both affordability and willingness thresholds are met, agents are then able to consider adopting the optimal vehicle type to maximize their utilities. In three scenario analysis, external macro-level shocks that can impact consumer’s affordability or/and willingness: number of available public charging stations, price of EVs, and EV rebate programs are considered. Simulated result presents the aggregated emergent behaviors and illustrates a calibrated multi-agent framework that enables us to address issues related to purchase incentives and consumer price sensitivity in decision-making process.