The transition to electric freight trucking is essential for building a sustainable logistics sector, yet it introduces a range of new challenges. Compared to conventional fleets, electric trucks have limited range and depend on charging stations that are often capacity-constrained, while electricity prices fluctuate over time and grid capacity is restricted. Without careful coordination, these factors create inefficiencies that increase costs and slow the adoption of electric fleets.
This PhD project investigates how logistics and energy systems can be jointly optimized to overcome these challenges. The first study focuses on redesigning freight pickup-and-delivery operations by incorporating charging station capacity, nonlinear charging efficiency, and time-of-use pricing into exact optimization models. The second study aims to develop robust planning methods for fleet and infrastructure investment under uncertainty. The third study will address real-time coordination of routing and charging through reinforcement learning, enabling adaptive control under dynamic electricity prices and operational disruptions. Together, these studies aim to provide both methodological advances and practical decision-support tools that accelerate the transition toward cost-effective and reliable electric trucking.
