We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

中文翻译：

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大型电动公交车的定价感知实时充电调度和充电站扩展

由于对城市空气质量和能源安全的日益关注，我们正在目睹电动汽车的快速增长。与其他类型的电动汽车相比，电动公交车由于拥有和运营成本高、充电时间长、充电设施空间分布不均等问题，尚未在全球范围内普及。此外，不可预测的交通拥堵、不同的乘客需求，甚至多变的天气等高度动态的环境因素都会显着影响电动巴士充电效率，并可能阻碍大规模电动巴士车队的进一步推广。为了解决这些问题，在本文中，我们首先分析了一个真实数据集，其中包括来自 16,359 辆电动公交车、1,400 条公交线路和 5,562 个公交车站的海量数据。然后，我们调查电动巴士网络以了解其运行和充电模式，并进一步验证实时充电调度的必要性和可行性。有了这样的理解，我们设计公交车充电，一种基于马尔可夫决策过程的定价感知实时充电调度系统，可降低城市规模电动巴士车队的整体充电和运营成本，同时考虑时变电价。为了显示效果公交车充电，我们使用来自深圳的真实数据来实现它，其中包括电动公交车的 GPS 数据、所有公交线路和公交车站的元数据，以及 376 个电动公交车充电站的数据。评估结果表明公交车充电同时大幅降低充电成本23.7%和12.8%的用电量。最后，我们设计了一个基于调度的充电站扩展策略来验证我们的公交车充电在充电站扩建过程中也很有效。