EconPapers    
Economics at your fingertips  
 

Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine

Necla Togun and Sedat Baysec

Applied Energy, 2010, vol. 87, issue 11, 3401-3408

Abstract: This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R2Â =Â 0.9878 for gasoline engine torque and R2Â =Â 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical.

Keywords: Gasoline; engine; Torque; Brake; specific; fuel; consumption; Genetic; programming; Explicit; solution; Modeling; engine (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306-2619(10)00134-0
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:87:y:2010:i:11:p:3401-3408

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Series data maintained by Dana Niculescu ().

 
Page updated 2017-09-29
Handle: RePEc:eee:appene:v:87:y:2010:i:11:p:3401-3408