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

CPS：小型：协作研究：提高电动汽车车队的效率：异构移动网络物理系统的数据驱动控制框架

• 批准号: 1932223
• 负责人: Desheng Zhang
• 金额: $ 29.97万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932223&HistoricalAwards=false
• 关键词: CPS; Small; Collaborative; Research; Improving

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.

随着电动汽车技术的成熟，由于其环境友好的性质，它们已被迅速应用于现代交通系统，例如电动出租车、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而耗时的充电，因此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，必须有效地管理其调度和充电活动。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基于同质电动汽车，忽视了异质电动汽车带来的挑战和机遇，例如，电动个人汽车，电动出租车和电动公交车。在这个项目中，研究团队将设计和实施一套针对异构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构电动汽车车队的整体性能。如果成功，研究团队将从车队的角度对如何管理大规模异构电动汽车以提高城市交通效率有清晰的认识，并有可能应用于未来的自动驾驶电动汽车。对异构电动汽车的这种了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丰富的知识，了解如何管理单个电动汽车，甚至是异构电动汽车车队，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。异构电动车辆车队是具有异构属性的移动的网络物理系统，例如，移动模式、能量消耗和接受控制决策的激励。然而，研究界有一个有限的理解如何在大规模的异构移动的网络物理系统在现实世界中的设置网络控制决策。在这个项目中，研究小组的目的是调查的基本理论和应用程序，以管理异构移动的网络物理系统，利用电动汽车作为一个例子平台。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，移动模式和能源消耗率，以及（ii）建立分层控制框架，通过开发数据来实现异构电动车辆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。

项目成果

Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets

• DOI: 10.1109/icde51399.2021.00108
• 发表时间: 2021-04
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Guang Wang;Shuxin Zhong;Shuai Wang;Fei Miao;Zheng Dong;Desheng Zhang

MoCha: Large-Scale Driving Pattern Characterization for Usage-based Insurance

MoCha：基于使用的保险的大规模驾驶模式表征

• DOI: 10.1145/3447548.3467114
• 发表时间: 2021
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Fang, Zhihan;Yang, Guang;Zhang, Dian;Xie, Xiaoyang;Wang, Guang;Yang, Yu;Zhang, Fan;Zhang, Desheng
• 通讯作者: Zhang, Desheng

Extending Coverage of Stationary Sensing Systems with Mobile Sensing Systems for Human Mobility Modeling

• DOI: 10.1145/3411827
• 发表时间: 2020-09
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Yu Yang;Zhihan Fang;Xiaoyan Xie;Fan Zhang;Yunhuai Liu;Desheng Zhang

TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge

TransRisk：基于转移知识的移动隐私风险预测

• DOI: 10.1145/3534581
• 发表时间: 2022
• 期刊: Wearable and Ubiquitous Technologies
• 作者: Xie, Xiaoyang;Hong, Zhiqing;Qin, Zhou;Fang, Zhihan;Tian, Yuan;Zhang, Desheng
• 通讯作者: Zhang, Desheng

Towards Accessible Shared Autonomous Electric Mobility With Dynamic Deadlines

• DOI: 10.1109/tmc.2022.3213125
• 发表时间: 2024-01
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Guang Wang;Zhou Qin;Shuai Wang;Huijun Sun;Zheng Dong;Desheng Zhang