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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 only the information on the current state of charge (SoC) of the battery and data available from a standard onboard navigation system. This objective is achieved using a predictive reference signal generator (pRSG) in combination with a nonpredictive 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. To compute the SoC reference trajectory, only the topographic profile of the future road segments and the corresponding average traveling speeds must be known. Simulation results of the proposed predictive strategy show substantial improvements in fuel economy in hilly driving profiles, compared with nonpredictive strategies. A parallel HEV is analyzed in this paper. However, the proposed method is independent of the powertrain topology. Therefore, the method is applicable to all types of HEVs.