Self-Learning Control Strategy for Electrified Off-Highway Machines to Optimize Energy Efficiency 2015-01-2831
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.
Citation: Pohlandt, C. and Geimer, M., "Self-Learning Control Strategy for Electrified Off-Highway Machines to Optimize Energy Efficiency," SAE Int. J. Commer. Veh. 8(2):513-518, 2015, https://doi.org/10.4271/2015-01-2831. Download Citation
Author(s):
Christian Pohlandt, Marcus Geimer
Affiliated:
Karlsruhe Institute of Technology
Pages: 6
Event:
SAE 2015 Commercial Vehicle Engineering Congress
ISSN:
1946-391X
e-ISSN:
1946-3928
Also in:
SAE International Journal of Commercial Vehicles-V124-2EJ, SAE International Journal of Commercial Vehicles-V124-2
Related Topics:
Energy conservation
Machine learning
Neural networks
Machining processes
Mathematical models
Optimization
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