Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (5): 1360-1365.doi: 10.13229/j.cnki.jdxbgxb20170904

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Idling time prediction method based on least square support vector machine

KUI Hai-lin1, BAO Cui-zhu1, LI Hong-xue1, LI Ming-da1,2   

  1. 1.College of Transportation, Jilin University, Changchun 130022, China;
    2.School of Mechatronics Engineering, Changchun Institute of Technology, Changchun 130012, China
  • Received:2017-09-01 Online:2018-09-20 Published:2018-12-11

Abstract: Engine start-stop system is widely used as it can reduce fuel consumption by turning off the engine while idling. But in traffic congestion conditions, the engine start-stop system increases fuel consumption because of turning off engine during short time idling. To solve this problem, Least Square Support Vector Machine (LS-SVM) is applied to predict the idling time, that the engine will not be turned off during short time idling. Experiment results show that the accuracy of idling time prediction using LS-SVM can reach 80%. The engine start-stop system using this method can reduce fuel consumption by about 0.2% during smooth hour. Meanwhile the driving comfort can be improved by reducing more than 50% start-stop numbers during rush hour and more than 15% during smooth hour.

Key words: vehicle engineering, start-stop system, idling time prediction, time and space learning, least squares support vector machines(LS-SVM), fuel saving control

CLC Number: 

  • U471.1
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