Real-world data-driven decision support system for electric vehicle charging infrastructure development : doctoral thesis - Pevec, Dario (D. Pevec ; Fakultet elektrotehnike i računarstva, 2020)
Trends show that the popularity of electric vehicles (EVs) has increased significantly in recent years, bringing about changes not only in the transport industry but also in the economy and society. There are many obstacles for EVs to reach their full market potential, among which the underdevelopment of the charging station infrastructure is clearly highlighted. The main objective of this PhD thesis is to provide a data-driven approach for an intelligent management of the infrastructure of EV charging stations using data science methods in the interdisciplinary field of sustainable transport, business and energy informatics. This dissertation answers three research questions: (i) "Where should a provider of EV charging station infrastructure install / remove / relocate a charging station?", (ii) "How can the infrastructure management approach be determined in view of the development of the existing infrastructure?" and (iii) "How can the range anxiety in the use of electric vehicles be defined and measured?" using practical methods. The scientific contributions of the dissertation are: a model and computing system based on real-world data intended for the management of charging station infrastructure for electric vehicles, and a techno-economic framework that allows a technological action plan and the analysis of what-if scenarios for the development of charging stations for electric vehicles. Keywords: Electric Vehicles, Charging Station, Range Anxiety, Data Science