针对高速公路配电网承载力不足以及可再生能源出力与服务区用能负荷不匹配的问题，利用移动储能车辆（MESV）进行服务区之间的能量互济，能够促进可再生能源消纳。为提高移动储能的实时响应能力，提出一种在电动汽车（EV）换电服务平均损失率约束下，计及交通状态的MESV调度优化策略。首先，结合高速公路交通自由流特性，将车辆行驶速度设置为服从截断正态分布的随机变量，并离散为多个速度区间以表征不同交通状态及其发生概率；同时，考虑EV换电需求的随机性，采用多状态伯努利分布描述单位时隙内的换电需求，建立移动储能运输时间成本模型与换电站（BSS）能量状态更新模型。其次，根据服务区之间交通状态和换电站能量状态构建马尔可夫链，描述系统状态的一步转移概率。在此基础上，构建以最小化移动储能长期平均运输时间成本为目标、换电服务平均损失率为约束的受限马尔可夫决策优化模型，并求解获得最优调度参数与稳态概率分布。基于蔚来第4代EV换电站实际运行参数开展仿真验证，结果表明：所提策略具有交通状态和能量状态的双门限结构，可根据服务区之间交通状态与BSS能量储备水平自适应调整MESV发车频次；与贪婪策略和Q-learning方法相比，移动储能平均运输时间成本分别降低17.23%和8.89%，能够在满足服务质量约束的前提下，实现运输成本与换电服务性能的最优权衡。

To address the issues of insufficient distribution network capacity in highways and the mismatch between renewable energy output and service area energy loads, utilizing mobile energy storage vehicles (MESVs) for energy mutual support between service areas can enhance renewable energy consumption. To enhance the real-time response capability of mobile energy storage, this paper proposes an MESV dispatch optimization strategy that considers traffic state, under the constraint of the average loss rate for electric vehicle (EV) battery swap services. First, leveraging the characteristics of free-flow traffic on highways, vehicle travel speed is modeled as a random variable following a truncated normal distribution and discretized into multiple speed intervals to represent different traffic states and their probabilities. Concurrently, considering the randomness of EV battery swap demand, a multi-state Bernoulli distribution is adopted to describe the swap demand within each time slot, establishing a transportation time cost model for MESVs and an energy state update model for battery swap stations (BSSs). Second, a Markov chain is constructed based on the traffic states between service areas and the energy states of BSSs to characterize the one-step transition probabilities of the system state. Building on this, a constrained Markov decision-making optimization model is then formulated, aiming to minimize the long-term average transportation time cost for mobile energy storage, subject to the constraints of the average loss rate for battery swap services. The model is solved to obtain optimal dispatch parameters and steady-state probability distributions. Simulations based on the actual operational parameters of NIO’s fourth-generation EV BSSs were conducted for validation. The results show that the proposed strategy exhibits a dual-threshold structure based on traffic condition and energy state, allowing adaptive adjustment of MESV dispatch frequency according to traffic conditions between service areas and the energy reserve levels of BSSs. Compared with greedy strategy and Q-learning method, the average transportation time cost was reduced by 17.23% and 8.89%, respectively, achieving an optimal trade-off between transportation cost and battery swap service performance while meeting service quality constraints.