Modelling and prediction of diesel engine performance using relevance vector machine

Ka In Wong, Pak Kin Wong, Chun Shun Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.
Original languageEnglish
Pages (from-to)265-271
Number of pages7
JournalInternational Journal of Green Energy
Volume12
Issue number3
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Artificial neural network
  • Data scarcity
  • Diesel engine modelling
  • Engine performance
  • Relevance vector machine

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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