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Title: NEURAL NETWORKS AS OPTIMIZATION TOOLS FOR FUEL CONSUMPTION
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NEURAL NETWORKS AS OPTIMIZATION TOOLS FOR FUEL CONSUMPTION
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1314-2704
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English
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18
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4.2
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The paper presents a neural network based methodology for prediction and optimization of internal combustion engine vehicles' fuel consumption, as a way to reduce air pollution. As it is well demonstrated lately, the engine fuel burning is one of the main factors that generate air pollution. Its impact on environment is rapidly increasing, following the increase of vehicles numbers. The best solution to reduce the air pollution is to use electric motor driven vehicles but these are still very expensive, with a low commercial rate. So, the optimization of current engines, by tuning the main parameters like power, torque, cylinder number etc. stands for an affordable solution. Due to many influencing parameters, it is difficult to predict the fuel consumption value based only on theoretical calculus. Neural network models allow the integration of experimental acquired data, taking this way into account the mutual influences between internal combustion engine parameters, leading to a more precise estimation of fuel consumption. Taking into account that the neural network architecture is directly linked to the modelled phenomenon, several networks were tested, in order to find the one suitable for this work?s goal. Once the best model is identified, predictions and optimization procedures can be performed.
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conference
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18th International Multidisciplinary Scientific GeoConference SGEM 2018
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18th International Multidisciplinary Scientific GeoConference SGEM 2018, 02-08 July, 2018
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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531-538
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02-08 July, 2018
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website
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cdrom
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1268
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internal combustion engine; fuel consumption; neural networks
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