Trip Based Near Globally Optimal Power Management of Plug-in Hybrid Electric Vehicles Using Gas-Kinetic Traffic Flow Model

Abstract

The plug-in hybrid electric vehicle (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. However, this has so far been hampered due the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of Intelligent Transportation Systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, gas-kinetic base trip modeling approach was used for the highway portion trip and for the local road portion the traffic light sequences throughout the trip will be synchronized with the vehicle operation. Several trip models approaches were studied for a specific case. For DP based charge-depletion control of PHEV, the SOC is forced to drop to a specific terminal value at the final time of the trip. Simulation study has been performed on a hybrid SUV model from ADVISOR, for the different trip modeling approaches. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control. The gas-kinetic based trip model for the highway portion can describe the dynamics of the traffic flow on highway with on/off ramps which may be missed by the model which used only the main road detectors data. The modeling approach shows a step to the more accurate trip model prediction which can be used for the power management of PHEV.