datafev—A Python framework for development and testing of management algorithms for electric vehicle charging infrastructures

• Published in: Software impacts Vol. 15; p. 100467
• Main Authors: Gümrükcü, E., Ahmadifar, A., Yavuzer, A., Ponci, F., Monti, A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023
• Subjects: Dynamic simulations; Electric vehicle charging infrastructure; Optimization; Electric vehicle charging infrastructure; Optimization; Dynamic simulations
• ISSN: 2665-9638; 2665-9638
• DOI: 10.1016/j.simpa.2023.100467

datafev is an open-source Python framework for developing and testing management strategies for electric vehicle (EV) charging. It includes several algorithms related to EV charging such as schedule optimization, and reference routines to generate charging events based on statistical inputs such as conditional probability distributions of arrival/departure times. datafev provides reference dynamic simulation routines to represent the temporal and logical sequence of the events taking place in an EV charging environment including EV drivers, aggregators, and charger cluster operators. An illustrative code example demonstrates the framework’s use for testing user-defined energy management strategies. •The global electric vehicle (EV) stock is expected to reach 125 million by 2030.•Smart charging is required for the efficient integration of EVs into power grids.•datafev is an open-source Python library to develop and test EV charging strategies.•datafev provides reference algorithms, simulation, and scenario generation routines.