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

查看全文 作　　者：JoséD. MARTíNEZ-[1]MORALES;Elvia R. PALACIOS-[2]HERNáNDEZ;Gerardo A. VELáZQUEZ-[3]CARRILLO

机构地区：[1]Faculty of Engineering, Autonomous University of San Luis Potosi,San Luis Potosi 78290, Mexico;[2]Faculty of Science, Autonomous University of San Luis Potosi,San Luis Potosi 78290, Mexico;[3]Mechatronics Department, Monterrey Institute of Technology and Higher Education,Mexico D. F. 01389, Mexico

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》索引2013年第14卷第9期,共14页

摘　　要：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. 关键词：人工神经网络汽油发动机多目标优化进化算法非支配排序遗传算法粒子群优化算法 PARETO解废气排放量

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

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