Joint optimization of electric bus charging infrastructure, vehicle scheduling, and charging management
Joint optimization of electric bus charging infrastructure, vehicle scheduling, and charging management
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DOI：
10.1016/j.trd.2023.103653
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发表时间：
2023-03-02
影响因子：
7.6
通讯作者：
Song, Ziqi
中科院分区：
文献类型：
--
作者：
He, Yi;Liu, Zhaocai;Song, Ziqi
车辆和充电基础设施的前期成本高，以及缺乏与基础设施规划和电动公交系统运营相关的知识，是实施电池电动公交车（BEB）的主要障碍。为了解决这些障碍并促进BEB的采用，开发了一个全面的优化框架，以解决BEB系统的充电基础设施规划，车辆调度和充电管理问题，目标是最大限度地降低总拥有成本。该问题被表述为一个混合整数非线性问题。然后提出了一种基于遗传算法的方法来解决这个问题。最后，以犹他州湖城的一个城市公交网络为例，分析了三种备选方案，并与最优方案的数值试验结果进行了比较。比较结果表明，所提出的模型和求解算法的有效性，在确定一个具有成本效益的规划策略BEB系统。
High upfront costs of vehicles and charging infrastructure as well as the lack of knowledge related to infrastructure planning and electric bus system operation are major obstacles to the implementation of battery electric buses (BEBs). To tackle the obstacles and promote BEB adoption, a comprehensive optimization framework was developed to address the combined charging infrastructure planning, vehicle scheduling, and charging management problem for BEB systems, with the goal to minimize the total cost of ownership. The problem was formulated as a mixed-integer non-linear problem. A genetic algorithm-based approach was then proposed to solve the problem. Last, three alternative scenarios based on a sub-transit network in Salt Lake City, Utah, were analyzed and compared with the optimal scenario results in the numerical experiments. The comparison results demonstrate the effectiveness of the proposed model and solution algorithm in determining a cost-efficient planning strategy for BEB systems.