Real-Time Global Optimal Energy Management Strategy for Connected PHEVs Based on Traffic Flow Information
Yi Zhang, Shiyu Xu, Yize Song, Wenjie Qi, Qiang Guo, Xu Li, Linli Kong, Jiahao Chen
Abstract
This paper proposes a two-layer structure Internet-distributed energy management strategy (ID-EMS) for connected plug-in hybrid electric vehicles (PHEVs) to deal with two significant challenges in the real-time global optimization process. One is that the traffic flow information from the commercial intelligent transportation system (ITS) is insufficient to accurately predict future driving conditions, which is alleviated by introducing the computer vision-based detection method for traffic flow density. The other is the conflict between global optimality and real-time capability, which the algorithm complexity analysis solves. Namely, the maximum problem size of the global optimization under a given computing power is derived to ensure real-time capability. Finally, an Internet-distributed vehicle-in-the-loop (ID-VIL) simulation platform is introduced to evaluate the proposed ID-EMS’s feasibility through an on-road driving experiment. Some extreme conditions, such as heavy calculation load and network failure, are also tested.