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arxiv: 2604.23561 · v1 · submitted 2026-04-26 · 🧮 math.OC

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

Jingyi Zhao , Haoxiang Yang , Youxuan Pan , Yang Liu This is my paper

Pith reviewed 2026-05-08 05:50 UTC · model grok-4.3

classification 🧮 math.OC
keywords electric vehicle routingwireless chargingmixed-integer programmingmobile chargingemergency logisticsdynamic programminglarge neighborhood searchin-motion energy transfer
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The pith

A mixed-integer model co-optimizes routes for emergency EVs and mobile chargers so wireless energy transfers occur while both fleets keep moving.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines a new routing problem in which mobile charging trucks deliver energy to electric vehicles through inductive coupling without requiring either vehicle to stop. It builds a mixed-integer program that jointly selects routes, schedules, and energy-transfer arcs for the two heterogeneous fleets while enforcing that the vehicles occupy the same location at the same time. A hybrid solver that pairs bitmask dynamic programming with large-neighborhood search is introduced to handle the resulting computational burden. When tested on real hospital logistics instances from Singapore, the method returns higher-quality solutions in far less time than commercial solvers.

Core claim

By treating wireless in-motion charging as an arc-level decision synchronized across two fleets, the Wireless Mobile Charging Electric Vehicle Routing Problem removes the need for stationary charging stops in time-critical operations such as medical transport, and the proposed hybrid algorithm solves the resulting mixed-integer program at practical scale on real data.

What carries the argument

The Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP) model, which co-optimizes dual-fleet routing decisions together with time- and space-aligned inductive energy transfers on individual arcs.

If this is right

  • Emergency medical fleets can complete missions without inserting charging dwell times into their schedules.
  • Planning of mobile energy infrastructure becomes part of the same optimization that produces vehicle routes.
  • Dynamic, location-dependent charging loads can be incorporated directly into urban power-system models.
  • Demand-side flexibility in electrified logistics increases because energy supply moves with the vehicles.
  • Zero-carbon mobility targets become more attainable in nonstop operations that previously required downtime.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same alignment constraints could be adapted to other paired-fleet problems such as drone-assisted delivery or convoy logistics.
  • Battery-capacity requirements for emergency EVs might decrease if reliable on-the-move charging is confirmed.
  • Grid operators would need new forecasting tools to manage the spatially and temporally varying loads created by moving chargers.
  • Regulatory approval for in-motion inductive charging would be required before widespread deployment.

Load-bearing premise

Inductive wireless charging can transfer usable amounts of energy safely and efficiently between two vehicles traveling at ordinary traffic speeds.

What would settle it

A controlled road test that records actual kilowatt-hours delivered and safety metrics when a charging truck drives alongside an EV on the same Singapore hospital routes at recorded speeds and traffic densities.

Figures

Figures reproduced from arXiv: 2604.23561 by Haoxiang Yang, Jingyi Zhao, Yang Liu, Youxuan Pan.

Figure 1
Figure 1. Figure 1: MCT Pattern B. Novel Contributions This paper makes several contributions at the intersection of electric vehicle routing, wireless energy transfer, and power system flexibility. Compared with existing EV routing for￾mulations that assume charging at static facilities, our work extends the modelling, algorithmic, and system-integration frontier in the following ways: 1) Problem formulation. We introduce th… view at source ↗
Figure 3
Figure 3. Figure 3: Visual example of state transitions for a 5-edge route. view at source ↗
Figure 4
Figure 4. Figure 4: First five BDP transitions. Red denotes infeasible states; numbers view at source ↗
Figure 5
Figure 5. Figure 5: Impact of different unit costs of MCTs ( view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces the Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP) for time-critical logistics such as medical transport. It formulates a mixed-integer programming model that jointly optimizes routing decisions and in-motion inductive energy transfer between heterogeneous fleets, subject to temporal and spatial alignment constraints. A hybrid algorithm combining Bitmask Dynamic Programming with Large Neighborhood Search is proposed to handle the computational complexity of arc-level charging and fleet synchronization. Experiments on real-world hospital logistics data from Singapore are reported to show runtime gains and improved solution quality relative to commercial solvers.

Significance. If the alignment constraints prove to represent physically feasible energy delivery, the framework could support planning of mobile charging infrastructure for nonstop EV operations in emergency settings, advancing demand-side flexibility and resilience in electrified logistics. The hybrid algorithm's design for dual-fleet synchronization offers a reusable approach for similar synchronized routing problems with dynamic resource transfer.

major comments (2)
  1. [Model formulation] Model formulation section: The MIP encodes only discrete time windows and position matching for in-motion energy transfer. It does not embed continuous electromagnetic coupling efficiency, lateral misalignment tolerances, speed-dependent transfer rates, or thermal/safety limits from inductive charging literature. This abstraction is load-bearing for the central claim that the model co-optimizes feasible routing and energy transfer; without these parameters the reported routes may be infeasible under real traffic dynamics.
  2. [Computational experiments] Computational experiments section: The abstract asserts significant runtime improvements and higher-quality solutions on Singapore hospital data, yet no quantitative metrics (objective values, runtimes, optimality gaps, or statistical validation) are supplied. This prevents assessment of whether the hybrid algorithm's gains are robust or merely solver-specific.
minor comments (2)
  1. [Model formulation] Notation consistency: Ensure that all decision variables for alignment (e.g., time and position matching) are defined with explicit units and ranges before their first use in the MIP constraints.
  2. [Introduction] The abstract claims the approach 'advances computational power system modeling,' but the manuscript should include a brief literature comparison table distinguishing WMC-EVRP from prior EVRP variants with mobile charging.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, with clear indications of planned revisions.

read point-by-point responses

  1. Referee: [Model formulation] Model formulation section: The MIP encodes only discrete time windows and position matching for in-motion energy transfer. It does not embed continuous electromagnetic coupling efficiency, lateral misalignment tolerances, speed-dependent transfer rates, or thermal/safety limits from inductive charging literature. This abstraction is load-bearing for the central claim that the model co-optimizes feasible routing and energy transfer; without these parameters the reported routes may be infeasible under real traffic dynamics.

    Authors: We agree that the MIP formulation relies on discrete temporal and spatial alignment constraints rather than embedding continuous electromagnetic parameters such as coupling efficiency, misalignment tolerances, speed-dependent rates, or thermal limits. This design choice prioritizes tractability for the joint routing and synchronization problem while using alignment as a necessary condition for feasible energy transfer. The abstraction is explicitly noted in the model assumptions. In the revised manuscript we will add a new subsection under Model Formulation that discusses these modeling choices in relation to inductive charging literature, states the limitations clearly, and outlines how continuous physics-based extensions could be incorporated in future refinements without altering the core combinatorial structure. revision: partial

  2. Referee: [Computational experiments] Computational experiments section: The abstract asserts significant runtime improvements and higher-quality solutions on Singapore hospital data, yet no quantitative metrics (objective values, runtimes, optimality gaps, or statistical validation) are supplied. This prevents assessment of whether the hybrid algorithm's gains are robust or merely solver-specific.

    Authors: The computational experiments section contains tables reporting objective values, runtimes, optimality gaps, and direct comparisons against commercial solvers on the Singapore hospital instances. To ensure these metrics are immediately visible and to strengthen support for the abstract claims, we will revise the section to explicitly reference and interpret the quantitative results in the main text, include averaged performance statistics across instances, and add a short discussion on robustness relative to solver-specific behavior. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in model formulation or algorithmic development

full rationale

The paper introduces a novel Wireless Mobile Charging Electric Vehicle Routing Problem as a new formulation, defines an MIP that co-optimizes routing and energy transfer via temporal/spatial alignment constraints, and develops a hybrid Bitmask DP + LNS algorithm for it. Experiments compare the approach to commercial solvers on external Singapore hospital data. No load-bearing step reduces by construction to a fitted parameter, self-definition, or self-citation chain; the derivation from problem statement to model to solver to empirical comparison remains independent and self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Based on abstract only; the model relies on standard optimization assumptions for routing and energy transfer. No specific free parameters or invented physical entities are detailed.

axioms (1)
  • domain assumption Standard mixed-integer programming assumptions hold for vehicle routing, including non-negative flows, feasible temporal-spatial alignments, and linear energy transfer constraints.
    Invoked when formulating the co-optimization model for heterogeneous fleets.
invented entities (1)
  • Wireless Mobile Charging Electric Vehicle Routing Problem no independent evidence
    purpose: To model in-motion wireless energy transfer between moving EVs and charging trucks
    New problem class introduced to capture dynamic, motion-based charging.

pith-pipeline@v0.9.0 · 5533 in / 1350 out tokens · 55860 ms · 2026-05-08T05:50:11.785101+00:00 · methodology

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Reference graph

Works this paper leans on

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