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All Journal Jurnal EECCIS JAREE (Journal on Advanced Research in Electrical Engineering)
Rahmawan, Hanif Adi
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Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Power Allocation based on ANN for Hybrid Battery and Supercapacitor Storage System in EV Rahmawan, Hanif Adi; Lystianingrum, Vita; Adityanugraha, Dimas Febry; Pamuji, Feby Agung
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 2 (2024): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i2.385

Abstract

The paper focuses on presented an ArtificialNeural Network (ANN) approach to allocate power for a hybridenergy storage system (HESS) in an Electric Vehicle (EV). TheHESS is comprised of a battery and supercapacitor, and theANN algorithm aims to optimize power allocation between thesetwo energy storage devices. The data for ANN training wasbased on cost optimization-based power allocation fromprevious research. While optimization can often take highcomputational resource and time, it is expected that a welltrained ANN can allocate power for the EV HESS more quickly.In this research, the inputs to the ANN are the required powerderived from the drive cycle, energy and power capacity of thebattery and supercapacitor, and state of charge (SoC) of thebattery and supercapacitor. The trained ANN was trained withvarious inputs not used in the training and it shows satisfactoryperformance.
Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks Rahmawan, Hanif Adi; Widjianto, Bambang Lelono; Indriawati, Katherin; Ariefianto, Rizki Mendung
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 17 No. 2 (2023)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v17i2.1655

Abstract

Maintenance of machines is highly necessary to prolong the operational lifespan of induction motors. Prioritizing preventive measures is crucial in order to prevent more significant damage to the machinery. One of these measures includes detecting abnormalities, such as misalignment, in the motor shaft. This research is aimed to detect the misalignment of induction motor experimentally by varying the coupling between normal and parallel misalignment. The signal readings were analyzed in the frequency domain using Fast Fourier Transform (FFT). The results revealed that in the case of coupling misalignment, a peak appeared at f = 13.5 Hz, whereas in the parallel misalignment condition with a 1 cm misalignment, a peak was found at f+fr = 20 Hz. By utilizing the Convolutional Neural Network (CNN) system, normal and parallel conditions can be detected with an accuracy level of 87.5%.
Co-Authors Adityanugraha, Dimas Febry Ariefianto, Rizki Mendung Feby Agung Pamuji Katherin Indriawati Lystianingrum, Vita Widjianto, Bambang Lelono