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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Jurnal Teknik Mesin Indonesia Mechanical Engineering for Society and Industry Automotive Experiences
Putra, Mufti Reza Aulia
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Analysis of lithium-ion indirect liquid cooling battery thermal management system with high discharge rate Nizam, Muhammad; Putra, Mufti Reza Aulia
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1414-1420

Abstract

Electric vehicles are developing rapidly and require technological support. Electric vehicles require good power storage. One of the reasonable parameters of a battery pack is its high discharge capability. A high discharge rate requires suitable cell and heat management capabilities in the battery pack. When discharging, it produces heat energy and needs to be released. The battery thermal management system (BTMS) is a method used to maintain battery heat. BTMS using liquid has a better performance compared to phase-change memory (PCM) and air cooling. The use of liquid coolers still has limitations. Namely, the weight of the cooling system is quite large because of a large amount of liquid which increases the weight of the battery. This study offers the potential to use mini channels mounted on cooling plates for application as BTMS. This research used the finite element method (FEM) process by simulating the process of fluid flow that occurs when the battery is used at various C rates. The results of this study indicate that the type of BTMS can keep the battery hot at working temperatures below 40 ºC.
Robust SVM optimization using PSO and ACO for accurate lithium-ion battery health monitoring Putra, Mufti Reza Aulia; Nizam, Muhammad; Mujianto, Agus; Adriyanto, Feri; Santoso, Henry Probo; Afandi, Arif Nur; Gunadin, Indar Chaerah
Mechanical Engineering for Society and Industry Vol 5 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.12280

Abstract

The increasing demand for reliable lithium-ion battery in various applications is focused on the need for accurate State of Health (SOH) predictions to prevent performance degradation and potential safety risks. Therefore, this research aimed to improve the accuracy of SOH prediction by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Support Vector Machine (SVM) to overcome the overfitting problem in traditional machine learning models. The dataset used consisted of data from 1000 cycles of lithium-ion battery, collected under laboratory conditions. Data from lithium-ion battery cycles were analyzed using optimized PSO-SVM and ACO-SVM models. These models were evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) metrics, showing significant improvements in prediction accuracy and model generalization. The results showed that although both optimized models were superior to the baseline SVM, PSO-SVM had higher generalization performance during testing. The higher performance was due to the effective balance between exploring the search space and exploiting optimal solutions, making it more suitable for real-world applications. In comparison, ACO-SVM showed superior performance in training data accuracy but was more prone to overfitting, suggesting the potential for scenarios prioritizing high training accuracy. These results could be applied to extend the lifespan of lithium-ion battery, contributing to enhanced reliability and cost-effectiveness in applications.
Pengujian dan Pengembangan Driving Cycle di Area Solo untuk Simulasi Kinerja Baterai Pack Kendaraan Listrik Putra, Mufti Reza Aulia; Setiawan, Bagas; Arifwardana, Julian Fikri
Jurnal Teknik Mesin Indonesia Vol. 20 No. 1 (2025): Vol. 20 No. 1 (2025): Jurnal Teknik Mesin Indonesia
Publisher : Badan Kerja Sama Teknik Mesin Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36289/jtmi.v20i1.864

Abstract

Driving cycle testing is a crucial step in measuring the performance of battery electric vehicles (BEVs), especially in terms of energy efficiency and battery design optimization. This article discusses the driving cycle testing conducted in the Solo area, Central Java, to obtain a route that can be used in battery pack testing. The proposed driving cycle testing data shows good results, where the generated route pattern closely resembles the data in the model, with a difference of less than 3% between the field data and simulator data. The testing scheme using a 14.8 A load has met the applicable testing standards. Field test data recordings show an energy consumption value of 22.3 Ah, while simulation data shows a value of 22.8 Ah, with a difference of 2.2%. These recorded results provide consistent and relevant data to be used as input in electric vehicle simulators, allowing for more accurate simulations of battery performance under various real-world operational conditions. Therefore, this driving cycle data serves not only as a measure of vehicle efficiency but also as a valid basis for evaluating battery performance in simulator-based testing.
Hybrid Catenary-Battery Trains for Non-Electrified Sections and Emergency Use Nizam, Muhammad; Maghfiroh, Hari; Putra, Mufti Reza Aulia; Jamaluddin, Anif; Inayati, Inayati
Automotive Experiences Vol 8 No 2 (2025)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.13440

Abstract

The hybrid catenary–battery system offers a promising solution for railways operating in non-electrified sections and during emergencies, ensuring uninterrupted operation, enhanced safety, environmental sustainability, and cost efficiency. This study addresses the challenge of determining an appropriate battery size and introduces a novel rule-based Energy Management Strategy (EMS) with coasting mode to minimize energy consumption while meeting operational requirements. The novelty of this work lies in (i) a straightforward sizing method based on worst-case emergency scenarios and (ii) the integration of coasting-mode operation into a rule-based EMS for hybrid catenary–battery trains. Simulation results show that the proposed approach achieves up to 12.56% energy savings on 3% gradient tracks while fully supplying auxiliary loads, compared with baseline operation that provides only partial coverage. These results demonstrate a practical and scalable framework for designing efficient, reliable, and resilient railway transport systems.
Co-Authors A.N. Afandi Adriyanto, Feri Anif Jamaluddin Arifwardana, Julian Fikri Bagas Setiawan, Bagas Gunadin, Indar Chaerah Inayati Inayati Maghfiroh, Hari Muhammad Nizam Mujianto, Agus Santoso, Henry Probo