详情

Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic Conditions

项目名称：	Developing an Adaptive Strategy for Connected Eco-Driving Under Uncertain Traffic Conditions
摘要：	Based on emerging sensing and communication technology, connected and automated vehicles (CAVs) receive various types of information when traveling, such as geographic data, traffic condition, signal timing, vehicle dynamics and engine status. Most types of information are temporally dynamic and spatially decentralized. For example, in a connected eco-driving system, the dynamic traffic information is a key input to designing a safe and energy-efficient trajectory of the host CAV, but the acquisition of that information is constrained by the communication and sensing range. It is a great challenge to design a robust speed profile that would adapt to the uncertain downstream traffic condition. A Markov Decision Process (MDP) based approach is therefore developed in this research. Multiple decision points are distributed within the potential queuing area, so the eco-driving process is decomposed into actions with the energy consumption as the cost function. The optimal decision at each state corresponds to an adaptive and robust eco-driving strategy that minimizes the expectation of the energy consumption of all possible following actions. Numerical experiments are also conducted to validate the proposed model under different powertrain systems, such as internal combustion engine (ICE), electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV). This method provides a proactive approach rather than a passive way to adapt to the dynamic uncertainty in acquisition of the traffic information, and shows significant advantage in energy saving. A Markov Decision Process (MDP) based approach is therefore developed in this research. Multiple decision points are distributed within the potential queuing area, so the eco-driving process is decomposed into actions with the energy consumption as the cost function. The optimal decision at each state corresponds to an adaptive and robust eco-driving strategy that minimize the expectation of the energy consumption of all possible following actions. Numerical experiments are also conducted to validate the proposed model under different powertrain systems, such as ICE, EV and PHEV. This method provides a proactive approach rather than a passive way to adapt to the dynamic uncertainty in acquisition of the traffic information, and shows significant advantage in energy saving.
状态：	Completed
资金：	79671
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	University of California, Riverside
项目负责人：	Iacobucci, Lauren
执行机构：	National Center for Sustainable Transportation
主要研究人员：	Barth, M
开始时间：	20180701
预计完成日期：	20190630
实际结束时间：	20200128