Upcoming YP Webinar: Mobile Charging Stations for Electric Vehicle Charging: Optimal Routing, Scheduling, and Grid-Aware Operation
Join the IEEE VTS Young Professionals for a new webinar!
Topic: Mobile Charging Stations for Electric Vehicle Charging: Optimal Routing, Scheduling, and Grid-Aware Operation
Speaker: Dr. Ubaid Qureshi, University of Kashmir, India
Date: 29 January 2026
Time: 9:00 UTC
Abstract
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The large-scale adoption of electric vehicles is increasingly constrained by limitations in fixed charging infrastructure, including congestion, long waiting times, and inadequate coverage during peak demand and emergency scenarios. Mobile Charging Stations (MCS) have emerged as a flexible and scalable solution to address these challenges by dynamically delivering energy to EVs when and where it is needed.
This webinar presents a comprehensive overview of mobile charging stations for electric vehicles, focusing on optimal routing, scheduling, and real-time operational decision-making. The talk will explore how data-driven optimisation, reinforcement learning, and uncertainty-aware models can be employed to coordinate MCS fleets under dynamic traffic conditions, stochastic charging demand, and power system constraints. Grid interaction aspects such as congestion alleviation, energy pricing, and integration with renewable sources will also be discussed.
Through illustrative case studies and recent research outcomes, the session will highlight how intelligent mobile charging can improve system reliability, reduce charging delays, and enhance user experience. The webinar aims to provide young professionals with practical insights and emerging research directions in mobile charging technologies within the broader context of vehicular technology.
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Short Bio
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Dr. Ubaid Qureshi is an Assistant Professor in the Department of Electrical Engineering at the University of Kashmir, India. He received his PhD in Electrical Engineering from IIT Delhi, where his research focused on data-driven optimisation, uncertainty quantification, and reinforcement learning for energy and transportation systems. His research interests include electric vehicles, mobile charging infrastructure, digital twins, smart grids, and AI-enabled predictive maintenance. Dr. Qureshi has published in leading conferences and journals, received multiple academic awards, and actively contributes to IEEE activities, particularly within the power, energy, and vehicular technology communities.