Modeling and Multi-Objective Optimization of Engine Performance and Hydrocarbon Emissions via the Use of a Computer Aided Engineering Code and the NSGA-II Genetic Algorithm

Sustainability

Richard Fiifi Turkson¹  Fuwu Yan¹  Mohamed Kamal Ahmed Ali¹  Bo Liu¹  Jie Hu¹  Muge Mukaddes Darwish² 

[1] School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China;;School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

关键词: engine modeling;    NSGA-II genetic algorithm;    optimization;    emissions;   

DOI  :  10.3390/su8010072

来源: mdpi

【 摘 要 】

It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmental pollution issues, the high cost of fossil-based fuels and the increasing demand for fossil fuels has led to the search for environmentally friendly, cheaper and efficient fuels. In the search for these alternatives, liquefied petroleum gas (LPG) has been identified as one of the viable alternatives that could be used in place of gasoline in spark-ignition engines. The objective of the study was to present the modeling and multi-objective optimization of brake mean effective pressure and hydrocarbon emissions for a spark-ignition engine retrofitted to run on LPG. The use of a one-dimensional (1D) GT-Power™ model, together with Group Method of Data Handling (GMDH) neural networks, has been presented. The multi-objective optimization was implemented in MATLAB^® using the non-dominated sorting genetic algorithm (NSGA-II). The modeling process generally achieved low mean squared errors (0.0000032 in the case of the hydrocarbon emissions model) for the models developed and was attributed to the collection of a larger training sample data using the 1D engine model. The multi-objective optimization and subsequent decisions for optimal performance have also been presented.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.