APPLICATION OF DRIVING BEHAVIOR CONTROL SYSTEM USING ARTIFICIAL NEURAL NETWORK TO IMPROVE DRIVING COMFORT BY ADJUSTING AIR-TO-FUEL RATIO

Abstract

Energy-efficient engines were introduced due to limited amount of global energy and the need for engine power to carry vehicle loads. It was discovered that the power factor of these engines was essential in developing automotive technology with subsequent significant effect on driving comfort. Moreover, it was possible to control the power and energy savings of vehicle engines by adjusting the Air to Fuel Ratio (AFR). Therefore, this study focused on achieving AFR values in the stoichiometric range of 14.7 in order to produce good emissions. The technology applied was observed to have some drawbacks, specifically in fulfilling engine power when the vehicle operates with a large load. This led to the development of a new method by designing an AFR control system with due consideration for driving behavior using an Artificial Neural Network (ANN). The aim was to overcome the problem of meeting engine power and ensuring better efficiency. The driving behavior was classified into through categories including the sporty, standard, and eco schemes. The eco scheme was the smooth behavior of a driver during the movement of the vehicle in a busy urban area, the sporty scheme was the responsive driving behavior when the vehicle operates on the highway at speeds above 80 km/h, and the standard scheme was the behavior between the eco and sporty schemes. Furthermore, the driving behavior in a sporty scheme required the addition of fuel to increase engine power while eco-scheme focused on reducing fuel to increase fuel economy. The findings showed that control system designed was able to improve driving comfort in terms of fuel economy during the eco scheme with an average AFR value of 15.68. The system further reduced the value to 13.66 during the sporty scheme. Furthermore, the AFR under stoichiometry was discovered to have produced the maximum engine power. The system was expected to be incorporated into electric, gas-fired and fuel cell vehicles in the future. ABSTRAK: Faktor kuasa enjin dan enjin cekap tenaga adalah penting dalam membangunkan teknologi automotif. Mesin penjimat tenaga diperlukan kerana jumlah tenaga global yang terhad. Manakala kuasa enjin digunakan bagi membawa muatan kenderaan. Kedua-dua faktor ini sangat mempengaruhi keselesaan pemanduan. Penjimatan kuasa dan tenaga dalam enjin kenderaan boleh dipenuhi dengan mengawal Nisbah Angin kepada Minyak (AFR). Tumpuan kajian semasa adalah berorientasikan ke arah mencapai nilai AFR dalam julat stoikiometri (14.7) atas sebab ingin mencapai pelepasan terbaik. Namun begitu, teknologi ini mempunyai kelemahan terutama dalam memenuhi kuasa enjin apabila kenderaan beroperasi dengan muatan besar. Oleh itu, kajian ini adalah berkaitan kaedah baharu bagi mengatasi masalah memenuhi kuasa enjin dan mencapai enjin cekap tenaga dengan mereka bentuk sistem kawalan AFR yang mempertimbangkan tingkah laku pemanduan menggunakan Rangkaian Neural Buatan (ANN). Tingkah laku pemanduan direka bentuk kepada tiga skim: sporty, standard dan eko. Skim eko adalah kelancaran tingkah laku pemandu apabila kenderaan bergerak di kawasan bandar yang sibuk. Skim sporty ialah tingkah laku pemanduan responsif apabila kenderaan beroperasi di lebuh raya pada kelajuan melebihi 80 km/j, dan skema standard ialah tingkah laku antara skim eko dan sporty. Tingkah laku pemanduan dalam skema sporty memerlukan penambahan bahan api bagi meningkatkan kuasa enjin. Sementara itu, tingkah laku pemanduan dalam skim eko memerlukan pengurangan bahan api bagi meningkatkan penjimatan bahan api. Hasil kajian menyatakan sistem kawalan yang direka mampu meningkatkan keselesaan pemanduan dari segi penjimatan bahan api apabila tingkah laku pemandu memasuki skim eko. AFR dicapai pada nilai purata 15.68. Apabila tingkah laku pemandu memasuki skim pemanduan sporty, sistem kawalan boleh mengurangkan AFR dengan nilai purata 13.66. AFR di bawah stoikiometri menghasilkan kuasa enjin maksimum. Pada masa hadapan, sistem ini berpotensi untuk dibangunkan pada kenderaan elektrik, menggunakan gas dan sel bahan api.