CPS: Small: Collaborative Research: Improving Efficiency of Electric Vehicle Fleets: A Data-Driven Control Framework for Heterogeneous Mobile Cyber Physical Systems

随着电动汽车技术的成熟，由于其环保的性质，它们已迅速被应用于现代交通系统，如电动出租车、电动公交车、电动卡车和共享个人租赁电动汽车。由于电动汽车需要频繁但耗时的充电，考虑到大型电动汽车的高充电需求和有限的充电基础设施，必须对其调度和充电活动进行有效管理。因此，高效的电动汽车管理框架有可能(I)缩短到充电站的行驶距离，(Ii)缩短电动汽车充电的等待时间，以及(Iii)平衡充电基础设施的需求和供应。然而，目前电动汽车的管理策略主要是基于同质电动汽车，而忽视了异质电动汽车带来的挑战和机遇，如电动个人汽车、电动出租车和电动公交车。在本项目中，研究团队将设计并实施一套针对异质电动汽车车队的管理策略，利用来自各种电动汽车的实时数据来提高异质电动汽车车队的整体性能。如果成功，研究团队将从车队导向的角度对如何管理大规模异质电动汽车以提高城市交通效率产生清晰的认识，并可能应用于未来的自动驾驶电动汽车。对异质电动汽车的这种理解将提高日常生活的质量，如更高效的通勤和更低的能源使用，这将有利于环境。到目前为止，研究人员已经积累了丰富的知识，关于如何管理单个电动汽车，甚至同质电动汽车车队，基于精确的数学模型。然而，这些模型过于简化，因为它们没有考虑网络-物理混合状态空间或不同类型电动汽车车队的模型不确定性。异类电动汽车车队是具有异质属性的移动网络物理系统，例如，移动模式、能源消耗和接受控制决策的激励。然而，研究界对如何在现实世界中大规模地为异类移动网络物理系统做出网络控制决策的理解有限。在这个项目中，研究团队以电动汽车为例，研究了管理异质移动网络物理系统的基础理论和应用。具体地说，研究团队将(I)开发一套数据驱动的网络和物理模型，以预测异类电动汽车车队的基本状态，例如移动性模式和能源消耗率，以及(Ii)建立分层控制框架，通过开发用于混合动力系统的数据驱动的分布式稳健优化方法，实现异类电动汽车调度和充电管理的性能保证。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As electric vehicle technologies become mature, they have been rapidly adopted in modern transportation systems, such as electric taxis, electric buses, electric trucks, and shared-personal rental electric vehicles, due to their environment-friendly nature. Since electric vehicles require frequent yet time-consuming recharges, their dispatching and charging activities have to be managed efficiently considering the high charging demand of large-scale electric vehicles and the limited charging infrastructure. Therefore, an efficient electric vehicle management framework has the potential to (i) reduce the traveling distance to a charging station, (ii) reduce the wait time for electric vehicles to charge, and (iii) balance the demand and supply for charging infrastructure. However, current management strategies for electric vehicles are mainly based on homogeneous electric vehicles, ignoring challenges and opportunities introduced by heterogeneous electric vehicles, for example, electric personal vehicles, electric taxis, and electric buses. In this project, the research team will design and implement a set of management strategies for heterogeneous electric vehicle fleets, which utilize real-time data from various electric vehicles to improve the overall performance of heterogeneous electric vehicle fleets. If successful, the research team will develop a clear understanding of how to manage large-scale heterogeneous electric vehicles to improve urban mobility efficiency from a fleet-oriented perspective, with potential applications to future autonomous electric vehicles. Such an understanding of heterogeneous electric vehicles will improve the quality of every-day life such as more efficient commutes and lower energy usage, which will benefit the environment.To date, researchers have accumulated abundant knowledge on how to manage individual electric vehicles, even homogeneous electric vehicle fleets, based on precise mathematical models. Nevertheless, such models are over-simplified as they do not consider the cyber-physical hybrid state space or model uncertainties for heterogeneous electric vehicle fleets. Heterogeneous electric vehicle fleets are mobile cyber-physical systems with heterogeneous properties, for example, mobility patterns, energy consumption, and incentives to accept control decisions. However, the research community has a limited understanding of how to make network control decisions for heterogeneous mobile cyber-physical systems at large scale in a real-world setting. In this project, the research team aims to investigate the fundamental theories and applications to manage heterogeneous mobile cyber-physical systems by utilizing electric vehicles as an example platform. Specifically, the research team will (i) develop a set of data-driven cyber and physical models to predict the essential status of heterogeneous electric vehicle fleets, for example, mobility patterns and energy consumption rates and (ii) establish a hierarchical control framework to achieve performance guarantees for heterogeneous electric vehicle dispatching and charging management by developing data-driven distributionally robust optimization methods for hybrid systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.