Co-optimization of charging scheduling and platooning for long-haul electric freight vehicles
Co-optimization of charging scheduling and platooning for long-haul electric freight vehicles
复制标题
长途电动货运车辆充电调度与队列协同优化
DOI：
10.1016/j.trc.2022.104009
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Guo, Zhaomiao
中科院分区：
文献类型：
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作者：
Alam, Md Rakibul;Guo, Zhaomiao
货运里程在过去十年中显著增加，占全球温室气体（GHG）排放量的11%。货运电气化和车队是两种很有前途的技术，可以减轻货运对能源和环境的影响。考虑到当前充电基础设施不足、充电持续时间长、能源消耗高以及长途电动货车（efv）交付时间紧迫等问题，迫切需要有效协调充电和排队，以最大限度地发挥这两种技术的效益。在本研究中，我们的目标是共同优化电动汽车的排队和充电策略。为了实现这一目标，我们开发了一个混合整数线性规划模型，以最小化系统总成本，包括途中充电成本、交付延迟成本和集线器充电成本。以595英里的佛罗里达州际高速公路为例，对所提出的模型进行了测试，并通过最先进的分支切断算法进行了求解，通过该算法，我们证明了使用我们的模型来确定最佳充电和队列调度，量化充电需求的空间分布和队列节能的有效性。我们还进行了敏感性分析，以更好地了解一些关键因素的影响，包括EFV模型、充电站（CS）容量、CS数量、充电速度和离开时间窗口。
Freight mileages have been significantly increasing over the past decade, accounting for 11% of global greenhouse gas (GHG) emissions. Freight electrification and platooning are two promising technologies to mitigate the energy and environmental impacts of freight transportation. Effective coordination of charging and platooning are urgently needed to maximize the benefits of these two technologies, especially considering the current inadequate charging infrastructure, long charging duration, higher energy usage, and tight delivery schedules for long-haul electric freight vehicles (EFVs). In this study, we aim to co-optimize the platooning and charging strategies of EFVs. To achieve this goal, we developed a mixed-integer linear programming model to minimize the total system costs, including en-route charging cost, delivery delay cost, and hub charging cost. The proposed model was tested using a case study of a 595-mile Florida interstate highway route and solved by the state-of-the-art branch-and-cut algorithms, through which we demonstrated the effectiveness of using our model to identify the optimal charging and platooning schedules and quantify the spatial distribution of charging demand and platoon energy savings. We also performed sensitivity analyses to better understand the impacts of some critical factors, including the EFV models, charging station (CS) capacity, number of CSs, charging speed, and departure-time windows.