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.

翻译：在大规模疏散过程中，集中式电动汽车充电需求可能使固定充电站过载，导致等待时间延长及风险暴露增加。为应对这一挑战，本研究提出动态部署移动充电车以补充固定充电站，并开发了一种自适应的风险感知移动充电车部署框架用于实时操作。该框架将移动充电车部署分为两个问题：充电站间的风险感知资源分配与移动充电车到指定充电站的动态路径规划，并通过离线到在线范式进行求解。资源分配问题被建模为分散式部分可观测马尔可夫决策过程，并基于多智能体近端策略优化开发了协调多辆移动充电车的策略，该策略在分散观测条件下运行。策略先在疏散模拟器中离线预训练，再根据当前疏散情境在线自适应优化。路径规划方面，开发了时空旅行时间预测器以支持滚动时域路径更新。基于佛罗里达州希尔斯伯勒县真实数据构建的模拟飓风疏散环境评估显示：ARMD框架在降低风险暴露方面始终优于离线优化、在线启发式调度和滚动时域优化。在需求扰动场景中，与未部署移动充电车的基线相比，ARMD平均风险暴露降低高达71.1%。针对固定充电基础设施或道路链路故障情况，ARMD实现了39.3%至60.5%的平均风险暴露降低，且其优势随中断严重程度增加而愈发显著。这些结果验证了ARMD在不确定疏散条件下增强移动充电操作的有效性和鲁棒性。