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.
Self-Learning Control Strategy for Electrified Off-Highway Machines to Optimize Energy Efficiency
Sae Int. J. Commer. Veh
SAE 2015 Commercial Vehicle Engineering Congress ; 2015
Sae International Journal of Commercial Vehicles ; 8 , 2 ; 513-518
2015-09-29
6 pages
Aufsatz (Konferenz)
Englisch
Self-learning control strategy of electrified off-highway machines to optimize energy efficiency
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