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，由于其环保特性，电动出租车、电动公交车、电动卡车、个人共享租赁电动汽车等现代交通系统中迅速采用了电动汽车。由于电动汽车充电频繁且耗时长，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，必须对电动汽车的调度和充电活动进行高效管理。因此，一个有效的电动汽车管理框架有可能(i)减少到充电站的行驶距离，（ii）减少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，目前的电动汽车管理策略主要是基于同质化的电动汽车，忽视了异质化电动汽车带来的挑战和机遇，例如电动私家车、电动出租车、电动公交车。在本项目中，研究团队将设计并实施一套异构电动汽车车队管理策略，利用各种电动汽车的实时数据来提高异构电动汽车车队的整体性能。如果成功，研究小组将从车队导向的角度清楚地了解如何管理大规模异构电动汽车，以提高城市交通效率，并有可能应用于未来的自动驾驶电动汽车。这种对异构电动汽车的理解将提高日常生活的质量，例如更高效的通勤和更低的能源使用，这将有利于环境。迄今为止，研究人员已经积累了大量的知识，如何管理单个电动汽车，甚至是同质电动汽车车队，基于精确的数学模型。然而，这些模型过于简化，因为它们没有考虑异构电动汽车车队的网络物理混合状态空间或模型不确定性。异构电动汽车车队是具有异构特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构移动网络物理系统做出网络控制决策的理解有限。本项目以电动车为例平台，研究异构移动网络物理系统管理的基础理论与应用。具体而言，研究团队将(i)开发一套数据驱动的网络和物理模型，以预测异构电动汽车车队的基本状态，例如移动模式和能耗率；（ii）通过开发数据驱动的混合动力系统分布式鲁棒优化方法，建立分层控制框架，以实现异构电动汽车调度和充电管理的性能保证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.