Small engine load estimator for fuel injection system using two-stage neural network

Also available in printed version

التفاصيل البيبلوغرافية
المؤلف الرئيسي: Mohd. Taufiq Muslim
مؤلفون آخرون: Hazlina Selamat, supervisor
التنسيق: Doctoral thesis
اللغة:الإنجليزية
منشور في: Universiti Teknologi Malaysia 2025
الموضوعات:
الوصول للمادة أونلاين:https://utmik.utm.my/handle/123456789/54960
Abstract Abstract here
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author Mohd. Taufiq Muslim
author2 Hazlina Selamat, supervisor
author_facet Hazlina Selamat, supervisor
Mohd. Taufiq Muslim
author_sort Mohd. Taufiq Muslim
description Also available in printed version
format Doctoral thesis
id utm-123456789-54960
institution Universiti Teknologi Malaysia
language English
publishDate 2025
publisher Universiti Teknologi Malaysia
record_format dspace
record_pdf Abstract
spelling utm-123456789-549602025-08-20T20:53:56Z Small engine load estimator for fuel injection system using two-stage neural network Mohd. Taufiq Muslim Hazlina Selamat, supervisor Electrical engineering Also available in printed version Most motorcycles in developing countries use carburetor systems as fuel delivery method especially for models with cubic capacity of less than 350 cc. However, small gasoline carbureted engines suffer from low operating efficiency, high fuel consumption and high level of hazardous emissions. In recent years, Electronic Fuel Injection (EFI) technology has been applied to small engine motorcycles as well. EFI system has better fuel economy and can reduce harmful emissions by correctly calculating suitable amount of fuel to be injected into the combustion chamber. One way to achieve this is by accurately estimate the engine load by using the in-cylinder Air Mass Flow (AMF) rate of the engine. Most of the control schemes in modern system either approximate the AMF near the throttle plate using Mass Air Flow (MAF) sensor or in the intake manifold using Manifold Absolute Pressure (MAP) sensor. This work presents a more economical approach to estimate the AMF by using only the measurements of throttle position and engine speed, that is, without using the MAF sensor or the MAP sensor to estimate the AMF in intake manifold, resulting in lower implementation cost. The estimation is done via two-stage multilayer feed-forward neural network with combinations of Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results in 20 runs, the second variant of hybrid algorithm yields a better network performance with a mean squared error (MSE) of 1.8308 by estimating the AMF closely to the simulated AMF values compared to using the first variant of hybrid algorithm (MSE of 2.8906), LM (MSE of 8.0525), LM with BR (MSE of 3.5657) and PSO (MSE of 133.7900) alone. By using a valid experimental training data, the estimator network trained with the second variant of the hybrid algorithm showed the best performance, with MSE of 1.9863, among other algorithms when used in an actual small engine fuel injection system zulaihi UTM 165 p. Thesis (Ph.D (Kejuruteraan Elektrik)) - Universiti Teknologi Malaysia, 2016 2025-03-17T03:39:50Z 2025-03-17T03:39:50Z 2016 Doctoral thesis https://utmik.utm.my/handle/123456789/54960 valet-20170119-120640 vital:94102 ENG Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia
spellingShingle Electrical engineering
Mohd. Taufiq Muslim
Small engine load estimator for fuel injection system using two-stage neural network
thesis_level PhD
title Small engine load estimator for fuel injection system using two-stage neural network
title_full Small engine load estimator for fuel injection system using two-stage neural network
title_fullStr Small engine load estimator for fuel injection system using two-stage neural network
title_full_unstemmed Small engine load estimator for fuel injection system using two-stage neural network
title_short Small engine load estimator for fuel injection system using two-stage neural network
title_sort small engine load estimator for fuel injection system using two stage neural network
topic Electrical engineering
url https://utmik.utm.my/handle/123456789/54960
work_keys_str_mv AT mohdtaufiqmuslim smallengineloadestimatorforfuelinjectionsystemusingtwostageneuralnetwork