Reduction of the prediction horizon of predictive energy management for a plug-in HEV in hilly terrain

Due to the great weight and high costs of electric energy storage systems (ESS), the number of pure electric vehicles (EV) is increasing only slowly. As a compromise between the autonomous hybrid electric vehicle (HEV) and EV, the plug-in HEV (PHEV) allows, like the EV, the recharging of the battery by the grid but brings also a combustion engine so as not to depend on the limited electric range of the vehicle. Next to the sizing of the vehicle components, the energy management strategy has an important influence on the fuel consumption of the vehicle. To minimize fuel consumption, predictive energy management is necessary, as all stored electric energy should be consumed by the end of the trip. In this way it is possible to minimize fuel consumption by substituting as much fuel as possible by the use of electric energy. In order to reach the global optimal result, a prediction horizon of the optimization for the duration of the entire trip is necessary. However, due to model uncertainties and the limited calculation capacities of the control units in a vehicle the global optimum cannot be achieved. Therefore, measures have to be taken to reduce the computation cost on the one hand and achieve results close the global optimum on the other. One of these measures, next to an adequate optimization algorithm, is the reduction of the prediction horizon. In this study, for a real life cycle including urban and highway parts a variation of the prediction horizon is carried out and the influence on the fuel consumption is simulated. The respective results are calculated using Dynamic Programming to exclude any influence of the chosenCenergy management strategy. The results are compared to the global optimal fuel consumption of the used driving cycle.