Real-Time Energy Management Strategy Based on Velocity Forecasts Using V2V and V2I Communications

Abstract

The performance of energy management in hybrid electric vehicles is highly dependent on the forecasted velocity. To this end, a new velocity-prediction approach utilizing the concept of chaining neural network (CNN) is introduced. This velocity forecasting approach is subsequently used as the basis for an equivalent consumption minimization strategy (ECMS). The CNN is used to predict the velocity over different temporal horizons, exploiting the information provided through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication channels. In addition, a new adaptation law for the so-called equivalent factor (EF) in ECMS is devised to investigate the effects of future velocity on fuel economy and to impose charge sustainability. Compared with traditional adaptation law, this paper considers the impact of predicted velocity on EF. The control objective is to improve the fuel economy relative to the ECMS without considering predicted velocity. Finally, simulations are conducted in three cases over different prediction horizons to demonstrate the performance of the proposed velocity-prediction method and ECMS with adaptation law. Simulation results confirm that ECMS with EF adjusted by the proposed adaptation law produces between 0.2% and 5% improvements in fuel economy relative to ECMS with traditional adaptation law. In addition, better charge sustainability is achieved as well.