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Machine Learning Methods to Predict Vehicle Fuel Consumption

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(9), pp.13-20
  • DOI : 10.9708/jksci.2022.27.09.013
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 26, 2022
  • Accepted : September 5, 2022
  • Published : September 30, 2022

Kwang-Ho Ko 1

1평택대학교

Accredited

ABSTRACT

It’s proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It’s for the coefficient of determination (R2) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

Citation status

* References for papers published after 2022 are currently being built.