One of the main issues in the actual automotive research, development and legislative discussion is the development of intelligent energy-efficient automotive drive trains. Hybrid powertrains use a combination of combustion engines and electric engines as one approach to fulfill the high demands for fuel-efficient and low emission vehicles. Besides the drive train component design including the operational mode management of the hybrid system in accordance with the driver's behavior plays a big role for the engineering of those vehicles. This paper is dealing with new control strategies and the usability of vehicle-internal status sensors and surround information sensors to predict the driving progress within a certain horizon. Those predicted driving data is utilized as input value for a model-predictive control algorithm that determines the optimal balance between the power torques of the combustion and electrical engines according to the current driving situation. As a result of the simulation conclusions on fuel efficiency and information on the required prediction range of the assistance systems are given.
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