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Mathematical Problems in Engineering
Volume 2016, Article ID 6180758, 8 pages
http://dx.doi.org/10.1155/2016/6180758
Research Article

An ANN-GA Framework for Optimal Engine Modeling

1Industrial Engineering Department, The University of Jordan, Amman 11942, Jordan
2Mechanical and Industrial Engineering Department, Applied Science University, Amman, Jordan

Received 2 November 2015; Accepted 11 February 2016

Academic Editor: Hung-Yuan Chung

Copyright © 2016 Khaldoun K. Tahboub et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Internal combustion engines are a main power source for vehicles. Improving the engine power is important which involved optimizing combustion timing and quantity of fuel. Variable valve timing (VVT) can be used in this respect to increase peak torque and power. In this work Artificial Neural Network (ANN) is used to model the effect of the VVT on the power and genetic algorithm (GA) as an optimization technique to find the optimal power setting. The same proposed technique can be used to improve fuel economy or a balanced combination of both fuel and power. Based on the findings of this work, it was noticed that the VVT setting is more important at high speed. It was also noticed that optimal power can be obtained by changing the VVT settings as a function of speed. Also to reduce computational time in obtaining the optimal VVT setting, an ANN was successfully used to model the optimal setting as a function of speed.