Predictive energy managament strategy for a parallel hybrid electric vehicle

Abstract

A novel model-based and predictive energy supervisory controller for hybrid electric vehicles (HEVs) is presented. Its objective is to minimize the fuel consumption (FC) of HEVs using the information on the current state of charge (SoC) of the battery, data available from a standard on-board navigation system and real-time traffic data from an on board computer driver assistance systems. This objective is achieved using a predictive reference signal generator (pRSG) in combination with a non-predictive reference tracking controller for the battery SoC. The pRSG computes the desired battery SoC trajectory as a function of vehicle position such that the recuperated energy is maximized despite the constraints on the battery SoC. Simulation results of the proposed predictive strategy in hilly driving profiles are compared with non-predictive strategies to show improvements in fuel economy. A parallel HEV is analyzed. However, the proposed method is independent of the powertrain topology. Therefore, the method is applicable to all types of HEVs.