Drievly: A Modular Platform for Bridging Price Forecasting and EV Fleet Charging Optimization

Abstract

In this talk, we will present the software architecture and an early prototype of a user-centric, modular platform that combines AI-driven electricity price forecasting with optimized charging schedules for electric vehicle fleets. The platform is designed to connect energy-market intelligence with fleet operational requirements and charging infrastructure, while supporting interchangeable forecasting models and optimization strategies. The talk will conclude with a live demonstration of the platform.

Bio: Pavlos Charalampidis is a Research Software Engineer at the Institute of Computer Science at FORTH. He received his M.Sc. (Hons) in Computer Science from the Computer Science Department of the University of Crete, Greece, and his M.Eng. (Hons) in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece. He has actively participated in several EU-funded and national research projects in the areas of wireless communications, networking, and the Internet of Things (IoT). His research interests include IoT architectures and applications—particularly for Smart Cities—as well as IoT security and privacy, cloud infrastructures, cyber-physical systems, and low-power, low-rate wireless sensor networks.