Development of a predictive system for car fuel consumption using an artificial neural network

Jian Da Wu, Jun Ching Liu

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


A predictive system for car fuel consumption using a back-propagation neural network is proposed in this paper. The proposed system is constituted of three parts: information acquisition system, fuel consumption forecasting algorithm and performance evaluation. Although there are many factors which will effect the fuel consumption of a car in a practical drive procedure, however, in the present system the impact factors for fuel consumption are simply decided as make of car, engine style, weight of car, vehicle type and transmission system type which are used as input information for the neural network training and fuel consumption forecasting procedure. In the fuel consumption forecasting, to verify the effect of the proposed predictive system, an artificial neural network with back-propagation neural network has a learning capability for car fuel consumption prediction. The prediction results demonstrated that the proposed system using neural network is effective and the performance is satisfactory in fuel consumption prediction.

Original languageEnglish
Pages (from-to)4967-4971
Number of pages5
JournalExpert Systems with Applications
Issue number5
Publication statusPublished - 2011 May 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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