Impact of Aggressive and Moderate Driving Intensity on Vehicle Air–Fuel Ratio Stability

Mohamad Norsyafiq Ab Hamid; Muhammad Yusri Bin Ismail; Najmi Haziq Badrulhisam

doi:10.31436/iiumej.v27i2.4062

Authors

Mohamad Norsyafiq Ab Hamid Universiti Malaysia Pahang Al-Sultan Abdullah
Muhammad Yusri Bin Ismail Universiti Malaysia Pahang Al-Sultan Abdullah https://orcid.org/0000-0002-3427-0047
Najmi Haziq Badrulhisam Universiti Malaysia Pahang Al-Sultan Abdullah https://orcid.org/0000-0002-0556-154X

DOI:

https://doi.org/10.31436/iiumej.v27i2.4062

Keywords:

Driving analysis, aggressive, moderate, air fuel ratio

Abstract

Aggressive driving behavior occurs when drivers neglect traffic rules, leading to violations, accidents, and increased road hazards. Such behavior endangers the driver and places other road users at risk. In contrast, moderate driving reflects disciplined patterns that reduce risks and improve vehicle efficiency. This study examines the influence of aggressive and moderate driving behaviors on engine performance, with a focus on air–fuel ratio (AFR) stability. The investigation used On-Board Diagnostics (OBD-II) data collected during 11.7 km driving sessions under two conditions: aggressive and moderate. The test vehicle, equipped with a 1.8-liter engine, was driven at controlled speeds. Aggressive driving was defined as driving in the 60–90 km/h range, while moderate driving occurred below 60 km/h, representing typical urban operation. Results show distinct differences in AFR behavior. Aggressive driving produced greater instability, with richer AFR outliers near 12.5, higher global wavelet spectrum (GWS) peaks of about 40 units compared to 20, and longer time in fuel-rich regions (12–17% versus 5–14%). These fluctuations indicate unstable combustion, increased fuel use, and higher emissions. In contrast, moderate driving maintained a more stable AFR distribution, with a greater proportion of leaner readings (~36% versus 30%) and closer alignment with stoichiometric values. This stability supports improved combustion efficiency, reduced emissions, and better fuel economy. Overall, the findings show that driving intensity significantly influences AFR stability, with moderate driving promoting cleaner and more efficient engine operation.

ABSTRAK: Tingkah laku pemanduan agresif berlaku apabila pemandu mengabai peraturan lalu lintas, menyebabkan pelanggaran, kemalangan dan peningkatan bahaya di jalan raya. Tingkah laku sedemikian membahayakan pemandu dan membahayakan pengguna jalan raya lain. Sebaliknya, pemanduan sederhana mengurangkan risiko dan meningkatkan kecekapan kenderaan. Kajian ini mengkaji pengaruh tingkah laku pemanduan agresif dan sederhana terhadap prestasi enjin, dengan memberi tumpuan kepada kestabilan nisbah udara-bahan api (AFR). Kajian ini menggunakan data Diagnostik Atas Papan (OBD-II) yang dikumpul semasa sesi pemanduan 11.7 km di bawah dua keadaan: agresif dan sederhana. Kenderaan ujian, dilengkapi enjin 1.8 liter, dipandu pada kelajuan terkawal. Pemanduan agresif ditakrifkan pada julat 60–90 km/j, manakala pemanduan sederhana berlaku di bawah 60 km/j, mewakili pemanduan tipikal dalam bandar. Dapatan kajian menunjukkan perbezaan ketara dalam tingkah laku AFR. Pemanduan agresif menghasilkan ketidakstabilan terbesar, dengan AFR terpisah yang lebih tinggi iaitu hampir 12.5, puncak spektrum gelombang global (GWS), iaitu 40 unit lebih tinggi berbanding 20, dan masa lebih lama di kawasan kaya bahan api (12–17% berbanding 5–14%). Turun naik ini menunjukkan pembakaran yang tidak stabil, peningkatan penggunaan bahan api dan pelepasan karbon lebih tinggi. Sebaliknya, pemanduan sederhana mengekalkan taburan AFR lebih stabil, dengan kadar bacaan lebih rendah (~36% berbanding 30%) dan penjajaran hampir pada nilai stoikiometrik. Kestabilan ini menyokong kecekapan pembakaran yang lebih baik, mengurangkan pelepasan dan penjimatan bahan api yang lebih baik. Secara keseluruhan, dapatan kajian menunjukkan bahawa pemanduan yang baik mempengaruhi kestabilan AFR dengan ketara. Pemanduan sederhana menggalakkan operasi enjin yang lebih bersih dan cekap.

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References

A. Boretti, “Advancing sustainable mobility: Integrating flywheel kinetic energy recovery systems with high-efficiency hydrogen internal combustion engines,” Int. J. Hydrogen Energy, vol. 125, pp. 354–363, 2025.

N. Venkatachalam, A. P. V. Manoharan, K. Ravichandran, P. V. Aravinth, and T. H. Sardar, “Speed Breaker and Vehicle Accident Detection with Alert Sensors,” in Applications of Computational Learning and IoT in Smart Road Transportation System, Springer, 2025, pp. 17–36.

C. A. MacCarley, “Engines and Fuels,” in Non-Petroleum Automotive Transportation, Springer, 2025, pp. 61–96.

Y. Ling, Z. Ma, Y. Song, Q. Zhang, X. Weng, and X. Ma, “Bus driver deceleration behavior modeling at intersections using multi-source on-board sensor data,” J. Public Trans., vol. 27, p. 100123, 2025.

F. Haque and M. A. Abas, “Review of Driving Behavior Towards Fuel Consumption and Road Safety,” Jurnal Mekanikal, 2018.

E. G. Giakoumis, Driving and engine cycles, vol. 1. Springer, 2017.

T. Wei, T. Zhu, and M. Zhang, “Impact of driving style on rear-end crash risk in leading vehicle emergency braking situations,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, p. 09544070251323635, 2025.

A. Moawad, M. Zebiak, J. Han, D. Karbowski, Y. Zhang, and A. Rousseau, “Effect of adaptive cruise control on fuel consumption in real-world driving conditions,” Nat. Commun., vol. 15, no. 1, p. 10016, 2024.

M. S. H. Nahid, T. Z. Tila, and T. S. Seecharan, “Detecting emotional arousal and aggressive driving using neural networks: A pilot study involving young drivers in Duluth,” Sensors, vol. 24, no. 22, p. 7109, 2024.

M. Kumar and T. Shen, “Estimation and feedback control of air-fuel ratio for gasoline engines,” Control Theory and Technology, vol. 13, no. 2, pp. 151–159, 2015.

B. Daham, H. Li, G. E. Andrews, J. Tate, K. Ropkins, and M. Bell, “Driver Variability Influences on Real World Emissions at a Road Junction using a PEMS,” SAE Technical Paper, 2010.

D. Rimpas, A. Papadakis, and M. Samarakou, “OBD-II sensor diagnostics for monitoring vehicle operation and consumption,” Energy Reports, vol. 6, pp. 55–63, Feb. 2020, doi: 10.1016/j.egyr.2019.10.018.

A. Zeb et al., “ON-BOARD DIAGNOSTIC (OBD-II) BASED CYBER PHYSICAL SYSTEM FOR ROAD BOTTLENECKS DETECTION,” 2022.

G. Padmanaban, F. Feng, E. Dai, A. Saini, G. Hu, and Y. Zhao, “A Comparative Analysis of Acceleration and Deceleration Profiles for Aggressive Driving Styles and Fuel Economy Test Cycles,” SAE Technical Paper, 2025.

B. Li et al., “Representing-behavior-based online driving style recognition and its road verification,” Eng. Appl. Artif. Intell., vol. 156, p. 111241, 2025.

M. S. Labbo, X. Jiang, S. A. Wada, G. J. de Dieu, and N. D. Bala, “Road rage patterns in Nigeria: A multi-level latent class analysis,” Case Stud. Transp. Policy, p. 101471, 2025.

K. ?LUSARCZYK and R. Jurecki, “The use of a vehicle simulator for eco-driving research,” Combustion Engines, 2025.

L. Zhang, Z. Zhu, Z. Zhang, G. Song, Z. Zhai, and L. Yu, “An improved method for evaluating eco-driving behavior based-on speed-specific vehicle-specific power distributions,” Transp. Res. D Transp. Environ., vol. 113, p. 103476, 2022.

A. Adavikottu and N. R. Velaga, “Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections,” Accid. Anal. Prev., vol. 187, p. 107088, 2023.

A. Adavikottu, N. R. Velaga, and S. Mishra, “Modelling the effect of aggressive driver behavior on longitudinal performance measures during car-following,” Transp. Res. Part F Traffic Psychol. Behav., vol. 92, pp. 176–200, 2023.

Z. Ma and Y. Zhang, “Driver-automated vehicle interaction in mixed traffic: Types of interaction and drivers’ driving styles,” Hum. Factors, vol. 66, no. 2, pp. 544–561, 2024.

A. Adavikottu and N. R. Velaga, “Modeling the impact of driving aggression on lane change performance measures: steering compensatory behavior, lane change execution duration and crash probability,” Transp. Res. Part F Traffic Psychol. Behav., vol. 103, pp. 526–553, 2024.

T. Kerwin and B. J. Bushman, “Measuring the perception of aggression in driving behavior,” Accid. Anal. Prev., vol. 145, p. 105709, 2020.

M. R. Saxena and R. K. Maurya, “Characterization of cycle-to-cycle variations in conventional diesel engine using wavelets,” in Advances in internal combustion engine research, Springer, 2017, pp. 135–155.

C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bull. Am. Meteorol. Soc., vol. 79, no. 1, pp. 61–78, 1998.

Mashruk et al., “Perspectives on NO X Emissions and Impacts from Ammonia Combustion Processes,” Energy & Fuels, vol. 38, no. 20, pp. 19253–19292, 2024.

M. Batallas and P. Molina, “Developing a Methodology to Reduce Fuel Consumption and Classify Driving Styles for a Fleet of Vehicles,” in International Conference on Science, Technology and Innovation for Society, Springer, 2024, pp. 185–194.

H. Abediasl, A. Ansari, V. Hosseini, C. R. Koch, and M. Shahbakhti, “Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 238, no. 12, pp. 3779–3793, 2024.

S. Yoo, J. Shin, and S.-H. Choi, “Machine learning vehicle fuel efficiency prediction,” Sci. Rep., vol. 15, no. 1, pp. 1–20, 2025.