Urban Global Driving Cycle Construction Method and Global Optimal Energy Management in Plug-in Hybrid Electric Vehicle

Abstract

This paper proposes a method of constructing global urban driving cycle based on real-time traffic information and applied in energy control strategy. Urban district driving cycles are collected in substantial in California through performance measure system (PeMS) to establish local speed fragment database. A real driving cycle (RDC) is selected in an urban district and established the corresponding time traffic information tensor model by using the data in PeMS. The real-time traffic information incomplete situation is solved by tensor completion algorithm. According to the average speed of road sections at different times, the kinematic fragments are stochastically selected in the speed fragment database to construct a real-time driving cycle of each section. The comparison between global urban construction driving cycle (CDC) and RDCs show that the CDCs well reflect the real road driving characteristic parameters. Compared to its application in the plug-in hybrid electric vehicle (PHEV) global energy management, CDCs, and RDCs fuel consumption are similarity within approximately 7% and the fuel consumption improved up to 17% in peak time compared with the rule-based control strategy.