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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 17 Documents
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Search results for , issue "Vol. 9 No. 1 (2026): Issue in Progress" : 17 Documents clear
Crashworthiness and Failure Mechanism of Polylactic Acid Multi-Cell Tubes Hybridized with Aluminum/Copper Under Axial Compression Fadly, Muhammad Syaiful; Anwar, Khairil; Hamzah, Muhammad Sadat; Antara, I Komang Diego
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14485

Abstract

This research investigates the crashworthiness performance and energy absorption behavior of crash boxes constructed from hybrid materials comprising Polylactic acid (PLA), aluminum (Al), and copper (Cu) under quasi-static axial compression. The crash box configuration comprises a PLA outer shell, an internal Al or Cu core, and PLA-based multi-cell structures with varying geometries, including circular, square, and hexagonal shapes. These components were fabricated through 3D printing and subjected to quasi-static axial compression testing. The experimental findings indicate that incorporating a hybrid core significantly enhances energy absorption capabilities. Among the tested configurations, the hexagonal multi-cell design exhibited the highest energy absorption, reaching 0.53 kJ. In addition, the CB-Al-H configuration, which uses an Al core, showed the highest specific energy absorption (SEA) of 77.77 kJ/kg and a crushing force efficiency (CFE) of 0.43%. This SEA value is approximately 69.17% higher than that of the copper-based configuration (CB-Cu-H), which recorded 45.98 kJ/kg with a CFE of 0.48%. The lower SEA observed in the copper-core configuration is primarily due to the higher Cu density, which increases the overall structural mass and consequently reduces specific energy absorption.
The Influence of Hard and Soft Magnetic Nanoparticle Additives in Magnetorheological Fluids on the Performance of Magnetorheological Dampers for Motorcycle Arifin, Rahmad Rizki Nur; Lenggana, Bhre Wangsa; Ubaidillah, Ubaidillah; Bahiuddin, Irfan; Nugroho, Kacuk Cikal; Imaduddin, Fitrian; Mazlan, Saiful Amri; Turnip, Arjon
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.14527

Abstract

Magnetorheological Fluids (MRFs) are widely utilised in semi-active suspension systems due to the ability to dynamically alter their viscosity. The frequently issue in the application of MRFs are sedimentation and aggregation stability. To address this issue, the MRFs have been modified by incorporating hard and soft magnetic nanoparticles. The MRFs investigated were MRF-carbonyl iron powder (CIP), MRF-CIP+cobalt ferrite (CoFe2O4), and MRF-CIP+manganese ferrite (MnFe2O4). The objective of this work is to evaluate the performance of these MRFs to implement the results to motorcycle dampers. To ascertain the MRFs”™ magnetic properties and viscosity (η), vibrating sample magnetometer (VSM) and rheometer testing were conducted, respectively. The performance of magnetorheological valve (MRV) was analysed using finite element method magnetics (FEMM) simulations with variations in fluid gaps and types of MRFs. The results indicated that the MRV design with a fluid gap of 0.9 mm, utilizing MRF-CIP+CoFe2O4, exhibited an appropriate combination to fulfil the damping force (Fd) requirements of the motorcycle suspension system. The damping force varied from 0.941 to 3.858 kN, indicating a growth of around 310%. This study demonstrates that the proposed MRV design and the modification of MRFs could enhance the performance of the motorcycle damping system, as evidenced by the experimental and simulation results. The house of quality (HoQ) demonstrates that the proposed magnetorheological damper (MRD) is significantly superior to its competitors in effectively fulfilling customer requirements and expectations.
Modeling Causal Analysis of Crash Severity on Indonesian Toll Road Using Integrated Z-Score and Bayesian Network Framework Istiyanto, Bambang; Pratikso; Mudiyono, Rachmat; Nurrohman, Hafidz
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Purwanto, Wawan; Koten, Hasan; Maksum, Hasan; Putra, Dwi Sudarno; Sahaq, Anang Baharuddin
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Prabowo, Wargiantoro; Yahya, Wira Jazair; Ithnin, Ahmad Muhsin; Sugeng, Dhani Avianto; Anggoro, Trisno; Saputro, Frendy Rian; Rosyadi, Erlan
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 behaviour 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 behaviour 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 Do, Tan-Thich; Dinh, Tan-Ngoc; Ly, Vinh-Dat
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Agama, Askar Adika; Auzani, Ahmad Syihan; Madsuha, Alfian Ferdiansyah; Hermawan, Hendra; Kurniawan, Ade; Mokhtar, Mokhtar; Aswin, Aswin; Nasruddin, Nasruddin; Nugroho, Yulianto Sulistyo; Harinaldi, Harinaldi
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Hidiyanto, Fitra; Fajar, Rizqon; Setiawan, Fauzi Dwi; Atmaja, Sigit Tri; Priyanto, Heru; Maarif, Muhammad Samsul; Telaumbanua, Yaaro
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Zhilejkin, Mikhail M.; Kozelkov, Oleg A.; Neverov, Vsevolod A.
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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 Abidin, Shaiful Fadzil Zainal; Sulaiman, Syabillah; Ishak, Izuan Amin; Mustafa, Mohammad Edilan; Ghazally, Saifullah Md; Azizul, Muhamad Asri
Automotive Experiences Vol. 9 No. 1 (2026): Issue in Progress
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.

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