Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms

In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms...

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Bibliographic Details
Published in:Journal of Zhejiang University. A. Science 2013-09, Vol.14 (9), p.657-670
Main Author: Jose D. MARTINEZ-MORALES Elvia R. PALACIOS-HERNANDEZ Gerardo A. VELAZQUEZ-CARRILLO
Format: Article
Language:English
Subjects:
Algorithms
Carbon monoxide
Civil Engineering
Classical and Continuum Physics
Engineering
Engines
Exhaust emission
Gasoline engines
Industrial Chemistry/Chemical Engineering
Mathematical models
Mechanical Engineering
Optimization
Pareto解
Reduction
人工神经网络
多目标优化
废气排放量
汽油发动机
粒子群优化算法
进化算法
非支配排序遗传算法
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Summary:In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
ISSN:1673-565X
1862-1775
DOI:10.1631/jzus.A1300010