A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety

Electric vehicle (EV) owners enjoy many positive aspects when driving their cars, including low running costs and zero tailpipe gas emissions, which makes EVs a clean technology provided that they are sourced through renewable sources, e.g., biomass, solar power, or wind energy. However, their driving behaviour is often negatively affected by the so-called range anxiety phenomenon, i.e., a concern that an EV might not have enough driving range to reach the desired destination due to its limited battery size. The perception of range anxiety may also affect potential buyers in their decisions on whether to purchase an internal combustion engine vehicle as opposed to an EV. This paper investigates some factors that influence range anxiety through a comparative analysis of two target groups: (i) existing EV owners, and (ii) non-EV owners (i.e., potential EV owners). The specially crafted survey was used to collect range anxiety data from more than 200 participants. In particular, participants provided their perceptions on (i) the potential relationship between existing gas station infrastructure and the desired EV charging station infrastructure, and (ii) the potential relationship between range anxiety and two influencing variables, namely the current state of charge and remaining range. Concerning the existing gas station infrastructure, evidence suggests that both target groups think that the distances between gas stations could be increased. Moreover, our analysis shows that the desired distances between charging stations correspond to the distances between the existing gas stations, which indicates that both EV owners and non-EV owners have a common view on the optimal gas station and charging station topology. Furthermore, we find that the type of settlement (urban vs rural) influences preferred distances, where both target groups living in cities desire shorter distances, and that non-EV owners, as opposed to EV owners, are more prone to be affected by the state of charge and remaining range. Quantitatively, we are able to define a measure for range anxiety, which is connected with the preferred distance between two neighbouring charging stations. Throughout our analyses, we find that the mean preferred distance between two neighbouring charging stations is 7 km, but this value significantly differs based on the settlement type of a (potential) EV owner

A Data-Driven Approach to Manage Charging Infrastructure for Electric Vehicles in Parking Lots

The ever-increasing number of electric vehicles (EV) on the road is in line with many governments' efforts to tackle urgent environmental challenges. This inherently means that there is a growing need for charging infrastructure as well. A potential solution to address the need for charging stations is to transform traditional parking lots into EV-enabled parking lots (EVPLs), in a sense that EVPLs provide not only parking services, but also the possibility for EV owners to charge their cars for a price. Due to the inherently complex and dynamic environment, a potential obstacle, from a business perspective, to the process of transforming parking lots into EVPLs is the complexity of estimating the EVPL's profitability and, consequently, the length of time required to recover the cost of the investment in the EV charging infrastructure. We propose a novel framework based on discrete-event simulations to estimate an EVPL's profit during a certain period of time. Our framework relies on historical data from parking lots and electricity markets as well as behavioral data related to EV owners. We illustrate the use of our framework in a real-world setting involving the city of Melbourne in Australia. In particular, we show how our framework can determine the profitability of an EVPL and, consequently, the payback period for transforming traditional parking lots into parking lots with EV chargers. To obtain these results, our framework first determines the ideal number of EV chargers to be installed and the underlying charging tariff so as to maximize profitability. We also discuss how our framework allows for what-if analyses that can provide valuable managerial and policy-making insights