Combustion model and control parameter optimization methods for single cylinder diesel engine
type: Item
instance of: scholarly article
title: Combustion model and control parameter optimization methods for single cylinder diesel engine
zbMATH Open document ID: 1460.80024
DOI: 10.1155/2014/135163
author: Bambang Wahono
author: Harutoshi Ogai
published in: Journal of Optimization
publication date: 2019-02-18T00:00:00Z
review text: Summary: This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NO\(_x\), and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO) was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.
Mathematics Subject Classification ID: 80A25
Mathematics Subject Classification ID: 93C95
zbMATH DE Number: 7024753
Wikidata QID: Q59060734
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/135163
OpenAlex ID: W2030505344
cites work: Q4330499
cites work: Applied Regression Analysis
description: scientific article; zbMATH DE number 7024753