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Universitas Muhammadiyah Magelang, Jl. Bambang Soegeng KM. 4 Mertoyudan Magelang, Telp/Faks : (0293) 326945
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Automotive Experiences
Published by Universitas Muhammadiyah Magelang
ISSN : 26156202     EISSN : 26156636     DOI : 10.31603/ae
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering
Automotive experiences invite researchers to contribute ideas on the main scope of Emerging automotive technology and environmental issues; Efficiency (fuel, thermal and mechanical); Vehicle safety and driving comfort; Automotive industry and supporting materials; Vehicle maintenance and technical skills; and Transportation policies, systems, and road users behavior.
Arjuna Subject : Teknik (semua kategori) - Teknik Otomotif
Articles 262 Documents
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Modeling Causal Analysis of Crash Severity on Indonesian Toll Road Using Integrated Z-Score and Bayesian Network Framework Bambang Istiyanto; Pratikso; Rachmat Mudiyono; Hafidz Nurrohman
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15445

Abstract

Traffic crashes remain a critical safety challenge, with Indonesia experiencing 73,446 fatalities annually. This study develops an integrated Z-Score and Bayesian Network framework to analyze causal interactions between human and environmental factors influencing crash severity on toll roads. Z-Score analysis of 450 crash records (2022”“2025) identified five statistically significant blackspot segments, with KM 430”“431 exhibiting the highest concentration (Z = 4.036, n = 91). A Bayesian Network model constructed using K2 structure learning and Expectation-Maximization parameter estimation achieved 86.2% classification accuracy, surpassing previous international applications (78”“82%). Conditional probability analysis revealed that straight-downhill segments exhibited 3.3-fold higher fatal crash probability than straight-level segments (0.083 vs. 0.025), while night-time conditions increased fatal risk by 57%. Sensitivity analysis demonstrated that crash type (weighted index = 0.282) and accident cause (0.214) exerted strongest influence on severity outcomes. Human error constituted 83% of crashes but showed moderate sensitivity, indicating that severe outcomes emerge from interactions between human factors and adverse conditions rather than isolated factors. Findings support prioritizing enhanced lighting and speed management on curved-downhill segments during night-time, alongside rear-end collision prevention strategies. This validated framework enables evidence based, proactive crash management and intervention prioritization for toll road safety in developing countries.
Integrated Examines of Hydrolyzers, Compression Ratio, Spark Plugs, and Ethanol Gasoline in Four Stroke Spark Ignition Engine for Potentially Application of Higher Ethanol Application Wawan Purwanto; Hasan Koten; Hasan Maksum; Dwi Sudarno Putra; Anang Baharuddin Sahaq
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15499

Abstract

Optimizing combustion parameters by incorporating alternative fuels and modifying the engine's mechanical properties is essential to improving the thermal efficiency and performance of modern internal combustion engines. This study examines the impact of HHO gas utilization, variations in compression ratios, various types of spark plugs, and ethanol gasoline blends on the torque and other characteristics of a 4-stroke fuel-injected single cylinder engine. Hydrogen is generated via electrolysis and used as a supplementary fuel. The Taguchi method was employed to create tests involving four variables: HHO percentage, compression ratio, spark plug type, and ethanol mixture. Testing occurred at 5000 RPM under a load of 1800 Watts. The findings indicated that the combination of 20% HHO, a compression ratio of 16.9:1, platinum spark plugs, and E-80 ethanol yielded optimal engine performance, with thermal efficiency reaching 60% at 7500 rpm. Moreover, the results of deposit content analysis after 50 hours of operation indicated that the ideal design produced fewer deposits than RON 92 gasoline.
Real-Time Surfactant-Free Emulsification of Plastic-Derived Diesel Oil: Combustion and Emission Characteristics Wargiantoro Prabowo; Wira Jazair Yahya; Ahmad Muhsin Ithnin; Dhani Avianto Sugeng; Trisno Anggoro; Frendy Rian Saputro; Erlan Rosyadi
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15504

Abstract

Plastic waste pyrolysis has emerged as a promising strategy for converting non-recyclable plastics into plastic-derived diesel oil (PDDO), providing a pathway for both waste valorization and alternative fuel production. However, the direct utilization of PDDO in diesel engines remains constrained by suboptimal combustion behavior and elevated exhaust emissions. While real-time non-surfactant emulsion fuel supply systems (RTES) have been widely investigated for conventional diesel fuels, their application to PDDO has not yet been systematically evaluated in engine operation. This study presents the first implementation of a real-time non-surfactant emulsification system to generate surfactant-free water-in-PDDO emulsions containing 5–15% water by volume. Engine performance and exhaust emissions were experimentally assessed using a 4.5 kW single-cylinder compression-ignition generator at low and high loads. The results indicate that controlled water addition modifies combustion behavior by improving spray atomization and secondary droplet breakup associated with micro-explosion phenomena. Among the tested blends, the 15% water emulsion (EPO15) provided the most balanced performance, improving brake thermal efficiency by 6.48% while reducing NOx emissions by up to 47.06% compared with the baseline fuel. Exhaust gas temperature was consistently reduced, without substantial deterioration in fuel consumption. These findings demonstrate that RTES can enhance the combustion and emission characteristics of PDDO, supporting its potential application in small-scale compression ignition engine systems.
Modeling, Simulation, and Assessment of Electric Motorcycle and Battery Characteristics under the Driving Cycle Test Tan-Thich Do; Tan-Ngoc Dinh; Vinh-Dat Ly
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14012

Abstract

Global warming, increasing temperatures, and air pollution have become significant challenges in the past decade due to traditional emissions. Therefore, using green energy, especially electric vehicles and electric motorcycles, is the key solution to protecting the environment. Electric motorcycles are widely used in many countries due to their convenience, ease of use, and flexibility. Thus, modeling and simulating electric motorcycles are crucial for accurately calculating and designing the battery pack energy requirements. In this study, electric motorcycles were modeled and simulated to investigate energy characteristics under driving cycle test using Matlab/Simulink software. The results show the electric motorcycle dynamics and energy consumption, the influence of electric motorcycle mass, aerodynamic drag, the quality of the road, road slope angle on the electric motor power, and operating ambient temperature on the battery behavior in the heat generation. In addition, the characteristics of batteries and suitability for selecting of battery required power were compared under various batteries and proposed the best battery for the electric motorcycle. The battery trademark of the A123 (pouch) model was selected as the most suitable for the required battery pack owing to superior characteristics compared to other batteries, with the insight characteristics of high capacity of 19.5 Ah, continuous current of 19.5 A, mass of battery pack of 9.45 kg, and number of cells of 19, with total average energy consumption of 28.23 Wh km−1. This study is significant for the design and precise calculation of the battery's required power for new electric motorcycles.
Effect of Graphene Oxide Addition on Spark Ignition Engine Performance and Cycle-to-cycle Variation with Gasoline-ethanol Fuel Askar Adika Agama; Ahmad Syihan Auzani; Alfian Ferdiansyah Madsuha; Hendra Hermawan; Ade Kurniawan; Mokhtar Mokhtar; Aswin Aswin; Nasruddin Nasruddin; Yulianto Sulistyo Nugroho; Harinaldi Harinaldi
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14237

Abstract

A fuel blend of gasoline and ethanol increases octane, meets air quality standards, and satisfies renewable fuel mandates, but the blend does not always result in perfect bonding, causing fuel separation and increasing cyclic variation. To overcome these limitations, up to 60 ppm graphene oxide (GO) nanoparticles were added into an 80:20 gasoline-ethanol blend (E20) and tested for the first time on a spark-ignition (SI) engine. The engine performance was evaluated by measuring cyclic variation, combustion stability and pressure, torque and power, specific fuel consumption, and CO2 emission. The acquired data were then statistically treated by using a coefficient of variation (COV) and then evaluated with Response Surface Methodology (RSM) in order to demonstrate a strong ability to accurately predict the optimization. Results show that the addition of GO nanoparticles into the E20 reduced the COV by up to 19.54% at an engine speed of 8000 rpm when compared to E20 alone, while the torque and power both increased by 5% at 5500 rpm. The specific fuel consumption of the GO-E20 blend was up to 15% higher than that of E20, with a decrease in CO emission but an increase in CO2 emission. Generally, the E20GO blend positively impacts the SI engine’s cyclic stability and performance, but its potential adverse effects on the environment and health must be carefully considered.
Integrating Synthetic Data with Deep Learning for Predictive Modelling and Optimization of Diesel Engine Performance on Waste Plastic Oil Blends Fitra Hidiyanto; Rizqon Fajar; Fauzi Dwi Setiawan; Sigit Tri Atmaja; Heru Priyanto; Muhammad Samsul Maarif; Yaaro Telaumbanua
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14360

Abstract

The scarcity of experimental data for diesel engines fueled by waste plastic oil (WPO) is a critical obstacle to optimizing engine performance. In this study, only 42 experimental data points covering six blend ratios and seven load conditions were available. To overcome this limitation, 121 synthetic data points were generated by training a suite of machine‑learning models—Random Forest, Gradient Boosting, and AdaBoost—on the original dataset and then predicting outputs across a grid of WPO blend ratios (0–50% in 5% increments) and engine loads (0–100% in 10% increments). The synthetic data were rigorously validated using Kolmogorov–Smirnov tests, kernel density estimation, and principal component analysis to ensure statistical similarity with the original measurements. Subsequently, a Multi‑Input Multi‑Output (MIMO) deep neural network was trained on the combined real and synthetic dataset to predict four key performance metrics—power, torque, specific fuel consumption (SFC) and brake thermal efficiency (BTE)—and its hyperparameters were fine‑tuned using Bayesian optimization via Optuna, achieving coefficients of determination (R²) above 0.95. Optimization analysis indicated that a 17% WPO blend at 82% load delivers the best trade‑off between power, efficiency and fuel consumption for non‑road applications. This integrated framework demonstrates how synthetic data generation, rigorous validation and deep‑learning modelling can effectively mitigate data scarcity and provide actionable insights for performance optimization of plastic pyrolysis oil in diesel engines.  
Lyapunov-based Model for Modern Stabilization Systems of All-wheel Drive Vehicles Mikhail M. Zhilejkin; Oleg A. Kozelkov; Vsevolod A. Neverov
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14855

Abstract

The study develops a mathematical model of vehicle stability with torque redistribution, aimed at ensuring guaranteed stabilization under non-stationary conditions. Unlike existing methods, the approach combines Lyapunov functions with bifurcation analysis to derive analytical stability criteria for vehicles with mechanical differentials and enables the synthesis of adaptive control strategies that integrate differential locking, wheel braking, and dynamic torque redistribution with formal stability guarantees. The model provides accurate calculations of torque redistribution to the inner or outer wheels during vehicle oversteer or understeer, respectively, ensuring motion stabilization and preserves stability even under sharp steering inputs, as confirmed by phase portraits and transient response analyses. The proposed model was implemented and verified. The model can be incorporated into active safety systems of wheeled vehicles to enhance stability on complex surfaces, reduce computational requirements, and ensure compatibility with existing mechanical drivetrains.
A Comprehensive Study of Electric Vehicle Performance under Diverse Powertrain Architecture using 1D Simulation Approach Shaiful Fadzil Zainal Abidin; Syabillah Sulaiman; Izuan Amin Ishak; Mohammad Edilan Mustafa; Saifullah Md Ghazally; Muhamad Asri Azizul
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15151

Abstract

Electric vehicles (EVs) are becoming popular because of their potential for reducing carbon emissions and promoting sustainable transportation. However, the driving range and energy consumption performance could be a limitation on EVs' performance, which are influenced by various technical and environmental factors. This study investigates the effects of key powertrain parameters of EVs, such as battery capacity, motor efficiency, motor power, and transmission setup, on the driving range and energy consumption of EVs through simulation analysis. The Nissan Leaf and Hyundai Kona, two different EV model categories from the hatchback and Sport Utility Vehicle (SUV), were selected for analysis using 1D simulation method. The models were tested under two standardized driving cycles, which are the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicles Test Cycle (WLTC). The validation results showed that the absolute percentage error is less than 10 % against the key technical specifications provided by the EV manufacturers. This study considered variations in battery capacity (±30%), motor power (±30%), motor efficiency (-15% to 5%), and transmission configurations. The outcomes from this study showed that battery capacity performance, motor efficiency, and transmission gear ratio configuration significantly impacted the driving range performance. In contrast, only motor efficiency and transmission gear ratio configuration significantly contributed to energy consumption performance. This research can be considered a benchmark in optimizing EV powertrain design, which can contribute to EV development in terms of cost and productivity.
Probabilistic Performance Prediction of a Hydrogen-Converted SI Engine Using a Markov-Chain-Wiebe Framework Purnami; Willy Satrio Nugroho; Lilis Yuliati; Fikrul Akbar Alamsyah; Abdul Mudjib Sulaiman Wahid; I Nyoman Gede Wardana
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15544

Abstract

This study employs a novel Markov-chain modeling framework to analyze the combustion-performance interaction in a hydrogen-fueled spark-ignition engine. The methodology integrates a Wiebe heat-release model within a Markov-chain state-transition framework, where each discrete engine state defines combustion parameters and probabilistic transitions capture cycle-to-cycle variability. Results demonstrate that engine behavior is dominantly governed by combustion phasing, with spark timing exerting primary control over torque, efficiency, and brake-specific fuel consumption (BSFC). Sensitivity analysis confirms spark timing produces the steepest performance gradients, while ignition voltage offers secondary benefits and engine speed exhibits minimal influence. The model reveals a highly nonlinear torque response to spark advance, characterized by a rapid rise culminating in a narrow maximum brake torque (MBT) plateau at 8°–10° BTDC, corresponding to a distinct BSFC minimum. Significant data scatter underscores the stochastic nature of hydrogen combustion, arising from multidomain interactions between air-fuel ratio, ignition strength, and phasing. The Markov-chain approach successfully captures this coupled deterministic-probabilistic behavior, highlighting the critical need for precise spark-timing control to optimize performance in hydrogen applications.
Numerical Study of Hydrogen Enrichment on Stoichiometric DME–Air Premixed Flames Aris Purwanto; Herman Saputro; Akhmad Faruq Alhikami; Riyadi Muslim; Eka Dwi Ariyanto; Fudhail Abdul Munir
Automotive Experiences Vol. 9 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.15766

Abstract

Dimethyl ether (DME), an alternative fuel lacking carbon–carbon bonds, offers the potential for clean combustion with minimal soot emissions. Despite this advantage, DME exhibits relatively low initial reactivity and flame-propagation velocity under premixed conditions, which constrains its stability and operational flexibility. This study presents a numerical investigation of hydrogen enrichment effects on DME–air combustion characteristics and mechanisms, with emphasis on microkinetic behavior and flame structure. The investigation employs one-dimensional (1D) and two-dimensional (2D) simulations to assess adiabatic flame temperature, laminar flame propagation velocity, elementary reaction rates, dominant reaction pathways, and distributions of temperature and OH radicals. Results from 1D simulations indicate that introducing hydrogen at low fractions (approximately 5%) markedly increases both flame temperature and propagation velocity by enhancing the H–O–OH radical pool. When hydrogen fractions exceed 10%, further improvements in combustion performance plateau as the system nears chemical equilibrium. Kinetic analysis reveals that hydrogen acts as a key modulator, shifting DME oxidation from initiation-dominated reactions to hydrogen-abstraction and chain-branching regimes. Two-dimensional simulations corroborate that this mechanistic shift produces a more compact flame, advances heat release, and increases the concentration of OH radicals by an order of magnitude. Collectively, these results demonstrate that hydrogen functions as a microkinetic enhancer rather than merely a fuel additive and indicate that moderate enrichment (5–10%) is sufficient to optimize DME combustion.

Page 24 of 27 | Total Record : 262

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