Wireless Mobile Charging for Emergency Electric Vehicle Routing: A Mixed-Integer and Metaheuristic Framework for In-Motion Energy Transfer

TL;DR

This paper presents a novel framework for wireless in-motion energy transfer to EVs, optimizing routing and charging via a mixed-integer model and metaheuristics, demonstrated on real-world logistics data.

Contribution

It introduces the Wireless Mobile Charging EV Routing Problem and develops a hybrid algorithm to optimize in-motion wireless energy transfer for emergency logistics.

Findings

Significant runtime improvements over commercial solvers.

Higher-quality solutions for mobile energy transfer routing.

Enhanced planning for wireless charging infrastructure in urban logistics.

Abstract

As electric vehicles (EVs) become central to decarbonization efforts, the need for uninterrupted power supply in time-critical logistics, particularly in medical transportation, poses unique challenges for power systems integration. Conventional fixed or mobile charging infrastructure requires vehicle downtime, which makes them unsuitable for nonstop operations such as organ delivery. This work introduces the Wireless Mobile Charging Electric Vehicle Routing Problem, a novel framework in which mobile charging trucks wirelessly transfer energy to moving EVs via inductive coupling, eliminating the need for stationary charging stops. We formulate a mixed-integer programming model that co-optimizes routing and in-motion energy transfer between heterogeneous vehicle fleets under temporal and spatial alignment constraints. To address computational complexity, we develop a hybrid Bitmask Dynamic Programming and Large Neighborhood Search algorithm, tailored to arc-level charging decisions and dual-fleet synchronization. Experiments using real-world hospital logistics data from Singapore demonstrate significant runtime improvements and higher-quality solutions compared with commercial solvers. This study advances computational power system modeling by incorporating dynamic, motion-based energy delivery that is both time- and space-dependent. The results provide actionable insights into planning mobile energy infrastructure, enhancing demand-side flexibility, strengthening resilience in emergency logistics, and integrating wireless charging into urban power systems to support flexible and zero-carbon electrified mobility.