基于移动充电站的施工电动车功率估计与最优作业充电调度
Power Estimation and Optimal Work-Charging Scheduling of Construction Electric Vehicles via Mobile Charging Stations
AI总结：
本研究针对施工电动车的应用限制，构建了现场数据驱动的联合优化框架，实现了CEV功率估计与移动充电站调度，可降低运营成本并高效求解最优方案。
AI中文摘要：
施工电动车（CEV）是柴油动力施工设备的极具前景的清洁替代方案，但其应用受限于现场充电基础设施稀疏、CEV机动性有限以及对其能耗的认识不足。我们通过现场数据驱动的框架解决这些缺口，该框架将CEV功率估计与考虑移动充电站的作业调度相结合。首先，利用加州大学圣地亚哥分校的真实施工示范项目，我们开发并验证了一款小型电动挖掘机的子作业功率估计模型。将手动标注的视频与粗略的电池荷电状态（SOC）远程监测数据同步，并使用约束非负最小二乘法恢复每个子作业的平均能耗。该模型对保留的测试数据的预测在17%的归一化平均绝对误差（NMAE）范围内，且配套数据集已公开。其次，利用子作业功率估计，我们构建了一个混合整数规划模型，联合优化CEV作业与充电调度，以及为CEV服务的移动充电站（MCS）的位置、时间和充放电操作。该优化考虑了能源与需求费用、碳排放、未完成作业的惩罚、MCS的移动以及CEV和MCS的物理与操作约束。在来自该示范项目的现实场景中，所提出的联合优化在所有情况下均实现了最低运营成本，比表现最佳的基准方案低7%至96%，且在一小时内即可将多数实例求解至最优性。数据集和脚本可在此https URL获取。
英文摘要：
Construction electric vehicles (CEVs) are a promising clean alternative to diesel-powered construction equipment, but their adoption is constrained by sparse onsite charging infrastructure, limited CEV mobility, and insufficient understanding of their power consumption. We address these gaps through a field-data-driven framework coupling CEV power estimation with mobile-charging-aware work scheduling. First, using a real-world construction demonstration at the University of California, San Diego, we develop and validate a per-subactivity power estimation model for a compact electric excavator. Manually labeled video is synchronized with coarse battery state-of-charge (SOC) telematics, and constrained nonnegative least squares is used to recover each subactivity's average power consumption. The model predicts held-out test data within $17\%$ normalized mean absolute error (NMAE), and the accompanying dataset is released publicly. Second, leveraging the subactivity power estimates, we formulate a mixed-integer program that jointly optimizes CEV work and charging schedules together with the location, timing, and charging/discharging of mobile charging stations (MCSs) serving the CEVs. The optimization accounts for energy and demand charges, carbon emissions, unmet work penalties, MCS travel, and the physical and operational constraints of the CEVs and MCSs. Across realistic scenarios drawn from the demonstration, the proposed co-optimization attains the lowest operating cost in every case, being $7$--$96\%$ below the best-performing baseline, while solving most instances to proven optimality within an hour. Dataset and scripts are available at https://github.com/ghosh-avik/CEV-MCS-Power-Estimation-and-Joint-Scheduling.
