Dynamic Deployment of Mobile Charging Trucks During Natural Disaster Evacuation: An Offline-to-Online Framework

2026-05-15

AI Summary

This study addresses the challenge of fixed charging station overload during large-scale evacuations, which exacerbates waiting times and risk exposure for electric vehicles. To mitigate this, the authors propose an adaptive framework that dynamically deploys mobile charging units through a hybrid offline–online approach. In the offline phase, a multi-agent proximal policy optimization (MAPPO) algorithm performs risk-aware resource allocation under a decentralized partially observable Markov decision process. The online phase employs receding horizon optimization for dynamic routing, integrated with spatiotemporal travel time predictions. Simulations based on real-world hurricane evacuation data demonstrate that the proposed method reduces risk exposure by up to 71.1% compared to a baseline without mobile charging. Moreover, under infrastructure failure scenarios, it consistently achieves 39.3%–60.5% risk reduction, with robustness improving as perturbation intensity increases.

Abstract

During large-scale evacuations, concentrated electric vehicle (EV) charging demand can overload fixed charging stations (FCSs), leading to prolonged waiting time and increased risk exposure. To address this challenge, this study proposes dynamically deploying mobile charging trucks (MCTs) to complement FCSs, and develops an Adaptive Risk-aware MCT Deployment (ARMD) framework for real-time operation. It divides the MCT deployment into two problems: risk-aware allocation of MCTs among FCSs and dynamic routing of MCTs to the assigned FCSs, and solves them under an offline-to-online paradigm. The resource allocation problem is formulated as a decentralized partially observable Markov decision process, and a multi-agent proximal policy optimization (MAPPO)-based policy is developed to coordinate multiple MCTs under decentralized observations. The policy is pre-trained offline in an evacuation simulator and adaptively refined online according to current evacuation context. For routing, a spatio-temporal travel time predictor is developed to support rolling-horizon route updates. The proposed framework is evaluated in a simulated hurricane evacuation environment built using real-world data from Hillsborough County, Florida. Experiments show that ARMD consistently outperforms offline optimization, online heuristic dispatch, and rolling-horizon optimization in reducing risk exposure. For demand perturbation scenarios, ARMD reduces average risk exposure by up to 71.1%, relative to the baseline without MCTs. In the case of fixed e-vehicle charging infrastructure or road link failures, ARMD achieves 39.3% to 60.5% reduction in average risk exposure, with its advantages becoming more pronounced as the severity of disruption increases. These results demonstrate the effectiveness and robustness of ARMD in enhancing mobile charging operations for realistic scenarios of uncertain evacuation conditions.

Problem

• electric vehicle charging
• natural disaster evacuation
• mobile charging trucks
• risk exposure
• charging infrastructure overload

Innovation

• mobile charging trucks
• offline-to-online learning
• multi-agent reinforcement learning
• risk-aware deployment
• evacuation logistics