Time and Money Matters for Sustainability: Insights on User Preferences on Renewable Energy for Electric Vehicle Charging Stations

TL;DR

This paper explores how EV users' preferences for renewable energy at charging stations are influenced by time and cost considerations, providing insights for designing navigation systems that promote sustainable energy use.

Contribution

It introduces a user-centered approach to integrating dynamic renewable energy data into EV charging station navigation systems, highlighting user preferences and decision factors.

Findings

EV users prioritize either time or money savings when choosing stations.

Renewable-powered stations are preferred when aligned with user priorities.

User preferences vary based on driving scenarios and individual values.

Abstract

Charging electric vehicles (EVs) with renewable energy can lessen their environmental impact. However, the fluctuating availability of renewable energy affects the sustainability of public EV charging stations. Nearby public charging stations may utilize differing energy sources due to their microgrid connections - ranging from exclusively renewable to non-renewable or a combination of both - highlighting the substantial variability in energy supply types within short distances. This study investigates the near-future scenario of integrating dynamic renewable energy availability in charging station navigation to impact the choices of EV users towards renewable sources. We conducted a within-subjects design survey with 50 car users and semi-structured interviews with 10 EV users from rural, suburban, and urban areas. The results show that when choosing EV charging stations, drivers often prioritize either time savings or money savings based on the driving scenarios that influence drivers' consumer value. Notably, EV users tend to select renewable-powered stations when they align with their main priority, be it saving money or time. This study offers end-user insights into the front-end graphic user interface and the development of the back-end ranking algorithm for navigation recommender systems that integrate dynamic renewable energy availability for the sustainable use of electric vehicles.