论文标题
电动汽车需求响应式运输的动态充电管理
Dynamic charging management for electric vehicle demand responsive transport
论文作者
论文摘要
由于气候变化的挑战，运输网络公司开始使其机队电气化以减少二氧化碳排放。但是，这种生态过渡给不确定的动态电动车队充电管理带来了新的研究挑战。在这项研究中，我们解决了通过公共充电站对共享乘车服务的动态充电计划管理。提出了在滚动范围框架下采用两阶段的收费调度优化方法，以最大程度地降低车队的总体充电运营成本，包括车辆的访问时间，充电时间和等待时间，通过预测未来的公共充电站可用性。充电站占用预测基于混合LSTM（长期短期内存）网络方法，并将其集成到拟议的在线车辆授权任务中。所提出的方法应用于英国邓迪市的一项现实模拟研究。数值研究表明，与基于需求的收费参考政策相比，提出的方法可以将车队的总收费等待时间减少48.3％，总充电量增加了35.3％。
With the climate change challenges, transport network companies started to electrify their fleet to reduce CO2 emissions. However, such an ecological transition brings new research challenges for dynamic electric fleet charging management under uncertainty. In this study, we address the dynamic charging scheduling management of shared ride-hailing services with public charging stations. A two-stage charging scheduling optimization approach under a rolling horizon framework is proposed to minimize the overall charging operational costs of the fleet, including vehicles' access times, charging times, and waiting times, by anticipating future public charging station availability. The charging station occupancy prediction is based on a hybrid LSTM (Long short-term memory) network approach and integrated into the proposed online vehicle-charger assignment. The proposed methodology is applied to a realistic simulation study in the city of Dundee, UK. The numerical studies show that the proposed approach can reduce the total charging waiting times of the fleet by 48.3% and the total charged the amount of energy of the fleet by 35.3% compared to a need-based charging reference policy.
