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http://dx.doi.org/10.15207/JKCS.2019.10.11.327

Detection Method of Vehicle Fuel-cut Driving with Deep-learning Technique  

Ko, Kwang-Ho (Division of smart Automobile, Pyeongtaek University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.11, 2019 , pp. 327-333 More about this Journal
Abstract
The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.
Keywords
Fuel-cut; Eco-drive; Deep-learning; Fuel economy; GPS;
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Times Cited By KSCI : 5  (Citation Analysis)
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