2014
DOI: 10.4028/www.scientific.net/amm.492.3
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Prediction of Vehicle Fuel Consumption Model Based on Artificial Neural Network

Abstract: With the increasing cost of fuel price minimizing fuel consumption is a major concern as far as sustainable engineering is concerned It is apparent that effective techniques for estimating fuel consumption costs are essential in order to avoid unnecessary fuel wastage and make use the most out of it In this paper an Artificial Neural Network (ANN) 2approach is used to fuel consumption model was proposed First few estimation calculator techniques have2 been briefly described Second the proposed optimization obj… Show more

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Cited by 8 publications
(3 citation statements)
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“…Artificial neural networks have wide application variety in automation problems including adaptive control, automotive, industry, medical diagnosis, electronics, finance, as well as information and signal processing [20][21][22][23][24][25][26].…”
Section: Artificial Neural Network(ann) Methodsmentioning
confidence: 99%
“…Artificial neural networks have wide application variety in automation problems including adaptive control, automotive, industry, medical diagnosis, electronics, finance, as well as information and signal processing [20][21][22][23][24][25][26].…”
Section: Artificial Neural Network(ann) Methodsmentioning
confidence: 99%
“…Although these models can obtain reasonable results, they require second-by-second speed-fuel data, which is often unavailable with current connectedvehicle technology, and the calibration of coefficients is tedious. Artificial neural network models have been applied to establish the fuel consumption model of the tractor [5], vehicle [6] and hauling trucks in surface mines [7]. Xu et al [8] developed a generalized regression neural network (GRNN) model to establish implicitly the relationship between truck fuel consumption and the truck driver's driving behavior obtained from the Internet of Vehicles.…”
Section: Introductionmentioning
confidence: 99%
“…They are used in many engineering fields because of their nonlinearity, robustness and tolerance to errors, attitude to learn from examples, high parallelism and ability to process noisy information. Neural networks have been successfully used in automotive engine problems (Ramli and Morris, 1993), for prediction of engine fuel consumption (Amer et al, 2014;Bekir and Demirtas, 2014;Parlak et al, 2006;Gokalp et al, 2010) and to forecast the fuel consumption for special application vehicles such JEDT 18,3 as mining dump trucks (Siami-Irdemoosa and Dindarloo, 2015). The use of various artificial intelligence models for diesel engine modelling in literature has been discussed with a systematic review by Sujesha and Ramesh (2018).…”
Section: Introductionmentioning
confidence: 99%