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The electrification of off-highway machines are increasing significantly. Advanced functionalities, beneficial energy efficiency and effectiveness are only a few advantages of electric propulsion systems. To control these complex systems in varying environments intelligent algorithms at system level are needed. This paper addresses the topic of machine learning algorithms applied to off-highway machines and presents a methodology based on artificial neural networks to identify and recognize recurrent load cycles and work tasks. To gain efficiency and effectiveness benefits the recognized pattern settings are applied to the electric propulsion system to adjust relevant parameters online. A dynamic adaption of the DC-link voltage based on the operating points of the machine processes is identified as such a parameter.

As a result of the Kyoto Protocol [1], the European Union's legislation demands higher saving rates for the total energy consumption of technical equipment. Heavy Equipment, such as construction- and agricultural machines, contributes over 80% of the total off-road diesel fuel consumption in Germany per annum. It is therefore necessary to provide helpful solutions in order to reach this ambitious aim. The German Federal Ministry of Education and Research cooperates with machine manufacturers, component suppliers and research institutes in the area of heavy equipment. Under the project name TEAM [2] a three year project has been started, which is focused on the development and integration of new propulsion and steering systems for heavy equipment. One task within the project is finding an appropriate way of evaluating the energy efficiency of the enhanced machines, after the powertrain modifications have been applied to it.