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

Authors

DOI:

https://doi.org/10.31436/iiumej.v24i2.2781

Keywords:

Driving behavior, AFR, ANN, Engine power, Fuel economy

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.

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References

Burdzik R. (2022) A comprehensive diagnostic system for vehicle suspensions based on a neural classifier and wavelet resonance estimators. Measurement, 200: 111602 https://doi.org/10.1016-/j.measurement.2022.111602. DOI: https://doi.org/10.1016/j.measurement.2022.111602

Uslu S, Celik MB.(2020) Performance and exhaust emission prediction of a si engine fueled with i amyl alcohol-gasoline blends?: An ANN coupled RSM based optimization. Fuel, 265: 116922. https://doi.org/10.1016/j.fuel.2019.116922. DOI: https://doi.org/10.1016/j.fuel.2019.116922

Al-fattah SM. (2020) Non-OPEC conventional oil?: Production decline, supply outlook and key implications. Journal of Petroleum Science and Engineering 189: 107049. DOI: https://doi.org/10.1016/j.petrol.2020.107049

https://doi.org-/10.1016/j.petrol.2020.1070.

Kutlu O. (2020) Global oil production declines in June 2020. Energy. Available: https://www.-aa.com.tr/en/energy/international-organization/global-oil-production-declines-in-june-2020-/29901.

Wang Y, Fan Y, Wang D, Liu Y, Qiu Z, Liu J. (2020) Optimization of the areas of solar collectors and photovoltaic panels in liquid desiccant air-conditioning systems using solar energy in isolated low-latitude islands. Energy, 198: 117324. https://doi.org/10.1016/j.energy.-2020.117324. DOI: https://doi.org/10.1016/j.energy.2020.117324

Gagliardi G, Mari D, Tedesco F, Casavola A. (2021) An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers. Control Engineering Practice, 107: 104694. DOI: https://doi.org/10.1016/j.conengprac.2020.104694

doi:10.1016-/j.conengprac.2020.104694.

Ahmed SFA. (2019) Analyzing and predicting the relation between air – fuel ratio (AFR), lambda (?) and the exhaust emissions percentages and values of gasoline ? fueled vehicles using versatile and portable emissions measurement system tool. SN Applied Science, 1(11): 1-12. https://doi.org/10.1007/s42452-019-1392–5. DOI: https://doi.org/10.1007/s42452-019-1392-5

Zhao X, Wang Z, Xu Z, Wang Y, Li X, Qu X. (2020) Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle. Transportation Research Part C: Emerging Technologies, 114: 205-224. DOI: https://doi.org/10.1016/j.trc.2020.02.018

https://doi.org/10.1016-/j.trc.2020.02.018. DOI: https://doi.org/10.1088/1475-7516/2020/02/018

Sharma A, Zheng Z, Bhaskar A, Haque M. (2019) Modelling car-following behaviour of connected vehicles with a focus on driver compliance. Transportation Research Part B, 126: 256-279. https://doi.org/10.1016/j.trb.2019.06.008. DOI: https://doi.org/10.1016/j.trb.2019.06.008

Fadhloun K, Rakha H. (2020) Novel vehicle dynamics and human behavior car-following model?: Model development and preliminary testing. International Journal of Transportation Science and Technology, 9: 14-28. https://doi.org/10.1016/j.ijtst.2019.05.004. DOI: https://doi.org/10.1016/j.ijtst.2019.05.004

Grove K, Soccolich S, Engström J, Hanowski R. (2019) Driver visual behavior while using adaptive cruise control on commercial motor vehicles. Transportation Research Part F, 60: 343-352. https://doi.org/10.1016/j.trf.2018.10.013. DOI: https://doi.org/10.1016/j.trf.2018.10.013

Fung KC, Dick TJ.(2016) System and method for responding to driver behavior. https://patents.google.com/patent/US9440646B2.

Martinelli F, Mercaldo F, Orlando A, Nardone V, Santone A, Kumar A. (2020) Human behavior characterization for driving style recognition in vehicle system. Computers & Electrical Engineering, 83: 02504. https://doi.org/10.1016/j.compeleceng.2017. DOI: https://doi.org/10.1016/j.compeleceng.2017.12.050

Yuan Y, Lu Y, Wang Q. (2020) Adaptive forward vehicle collision warning based on driving behavior. Neurocomputing, 408: 64-71. https://doi.org/10.1016/j.neucom.2019.11.02. DOI: https://doi.org/10.1016/j.neucom.2019.11.024

Raz O, Fleishman H, Mulchadsky I. (2008) System and method for vehicle driver behavior analysis and evaluation. https://patents.google.com/patent/US7389178B/en.

Hong Z, Chen Y, Wu Y. (2020) A driver behavior assessment and recommendation system for connected vehicles to prod Prevention, 139: 105460. DOI: https://doi.org/10.1016/j.aap.2020.105460

https://doi.org/10.1016/j.aap.2020.-105460.

Mafeni J, Majid S, Mesgarpour M, Torres M, Figueredo GP, Chapman P. (2020) Evaluating the impact of Heavy Goods Vehicle driver monitoring and coaching to reduce risky behaviour, Accident Analysis & Prevention, 146: 105754. https://doi.org/10.1016/j.aap.2020.105754. DOI: https://doi.org/10.1016/j.aap.2020.105754

Bando T. (2015) System for detecting abnormal driving behavior. https://patents.google.com-/patent/US9111400B2/en.

Hongbo G, Guotao X, Hongzhe L, Xinyu Z. (2017) Lateral control of autonomous vehicles based on learning driver behavior via cloud model. The Journal of China Universities of Posts and Telecommunications, 24(2): 10-17. http://dx.doi.org/10.1016/S1005-8885(17)601. DOI: https://doi.org/10.1016/S1005-8885(17)60194-8

Ashkrof P, Homem G, Correia DA, Van Arem B. (2020) Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour?: A case study in the Netherlands. Transportation Research Part D, 78: 102206. https://doi.org/10.1016/j.trd.2019.-102206. DOI: https://doi.org/10.1016/j.trd.2019.102206

Silver A, Lewis L. (2015) Automatic identification of a vehicle driver based on driving behavior. https://patents.google.com/patent/US9201932B2/en.

Yansong R, O’Gorman L, Wood TL. (2019) Driver behavior monitoring systems and methods for driver behavior monitoring.

https://patents.google.com/patent/US9201932B2/en.

Julian DJ and Agrawal A. (2017) Driver behavior monitoring. https://patents.google.com-/patent/US10460600B2/en.

Stogios C, Kasraian D, Roorda MJ, and Hatzopoulou M. (2019) Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions. Transportation Research Part D, 76: 176-192. https://doi.org/10.1016/j.trd.2019.09.020. DOI: https://doi.org/10.1016/j.trd.2019.09.020

Kohl J, Gross A, Henning M, Baumgarten T. (2020) Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions. Transportation Research Part F: Traffic Psychology and Behaviour, 70: 163-174. https://doi.org/10.1016/j.trf.2020.01.017. DOI: https://doi.org/10.1016/j.trf.2020.01.017

Xing Y, Lv C, Cao D, Lu C. (2020) Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling. Applied Energy, 261: 114471. https://doi.org/10.1016/j.apenergy.2019.114. DOI: https://doi.org/10.1016/j.apenergy.2019.114471

Vaezipour A, Rakotonirainy A, Haworth N. (2018) A simulator evaluation of in-vehicle human machine interfaces for eco-safe driving. Transportation Research Part A: Policy and Practice, 118: 696-713. https://doi.org/10.1016/j.tra.2018.10.022. DOI: https://doi.org/10.1016/j.tra.2018.10.022

Reinolsmann N, Alhajyaseen W, Brijs T, Pirdavani A, Hussain Q, Brijs K. (2019) Investigating the impact of dynamic merge control strategies on driving behavior on rural and urban expressways – A driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour, 65: 469-484. https://doi.org/10.1016/j.trf.2019.08.010. DOI: https://doi.org/10.1016/j.trf.2019.08.010

Kiat D, Poh H, Yee C, Zhen R, Markus C, Ping T. (2021) Internal quality control?: Moving average algorithms outperform westgard rules. Clinical Biochemistry, 98: 63–69. https://doi.org/10.1016/j.clinbiochem.2021.09.007. DOI: https://doi.org/10.1016/j.clinbiochem.2021.09.007

Maverick JB. (2022) Advantages and disadvantages of the Simple Moving Average (SMA)?, Investopedia. Available: https://www.investopedia.com/ask/answers/013015/what-are-main-advantages-and-disadvantages-using-simple-moving-average-sma.asp. [Accessed: 05-May-2022].

Zhu C, Wang P, Wang P, Liu Z, Wang P, Liu Z. (2018) Model Prediction Control of Fuel-Air Ratio for Lean-Burn Spark Ignition Gasoline. IFAC-PapersOnLine, 51(31): 640–645. https://doi.org/10.1016/j.ifacol.2018.10.150. DOI: https://doi.org/10.1016/j.ifacol.2018.10.150

Sardarmehni T, Aghili AA, Menhaj MB. (2019) Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines. Soft Computing, 23(15): 6169-6182. https://doi.org/10.1007/s00500-018–3270, 2019. DOI: https://doi.org/10.1007/s00500-018-3270-2

Martyr A and Plint M. (2007) Third Edition Engine Testing Theory and Practice. Elsevier Ltd.

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Published

2023-07-04

How to Cite

Triwiyatno, A., Munahar, S., Munadi, M., & SETIAWAN, J. D. (2023). APPLICATION OF DRIVING BEHAVIOR CONTROL SYSTEM USING ARTIFICIAL NEURAL NETWORK TO IMPROVE DRIVING COMFORT BY ADJUSTING AIR-TO-FUEL RATIO . IIUM Engineering Journal, 24(2), 337–353. https://doi.org/10.31436/iiumej.v24i2.2781

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Section

Mechatronics and Automation Engineering

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