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All Journal ELKHA : Jurnal Teknik Elektro Specta Journal of Technology Power Elektronik : Jurnal Orang Elektro
Dewanto, Muhammad Ridho
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Optimalisasi Filter Sumur Bor dan Embung ITK Berbasis Tenaga Surya untuk Peningkatan Pelayanan Sarana dan Prasarana Priyanto, Yun Tonce Kusuma; Farid, Mifta Nur; Dewanto, Muhammad Ridho; Sugiarto, Kharis; saputra, Riza Hadi
Power Elektronik : Jurnal Orang Elektro Vol 14, No 1 (2025): POWER ELEKTRONIK
Publisher : Politeknik Harapan Bersama Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/polektro.v14i1.8494

Abstract

The limited supply of conventional energy poses a challenge in operating essential facilities at Institut Teknologi Kalimantan (ITK), particularly in the water filtration system and reservoir management. To address this issue, this study proposes the implementation of an off-grid Solar Power Plant as a solution to enhance energy independence and improve the quality of clean water services on campus. This research aims to design and analyze the performance of an off-grid Solar Power Plant system in supporting water pump operations while evaluating its efficiency in providing sustainable energy. The designed system utilizes three solar panels with a total capacity of 1,635 WP, which is sufficient to meet the 243 W AC pump power demand. The generated energy is regulated using a Maximum Power Point Tracker (MPPT) to optimize power conversion and minimize energy losses. Additionally, a 1,000 Watt Pure Sine Wave inverter is employed to ensure the pump operates stably, while excess energy is stored in a 24V 180 Ah battery to maintain system operation during cloudy conditions or nighttime. The calculations indicate an energy surplus of 5.33 kWh, reinforcing the system’s reliability in meeting the energy needs of the water pump. With a recorded pump efficiency of 55.5%, this study demonstrates that the designed PLTS system is effective in providing sustainable energy. The implementation of an off-grid Solar Power Plant has proven capable of supporting optimal water pump operations, enhancing campus energy independence, and reducing reliance on conventional electricity sources.
State of Charge Estimation on Lithium-Ion Batteries Using Particle Swarm Optimization Method Dewanto, Muhammad Ridho; Saputra, Riza Hadi; Sugiarto, Kharis; Saputra, Agung Adi
ELKHA : Jurnal Teknik Elektro Vol. 17 No.1 April 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i1.90020

Abstract

Lithium-ion battery management is crucial as their use grows in devices and electric vehicles. A key aspect is State of Charge (SoC) estimation, which indicates the battery's charge level at any given time. This research aims to develop a method that can provide accurate SoC estimates for Li-ion batteries using the Particle Swarm Optimization (PSO) method. In this research, a 12V 8.4 Ah Lithium-Ion battery was used as a test subject, utilizing a voltage sensor, ACS712 sensor, and LM35 temperature sensor to measure key parameters such as voltage, current, and temperature. The PSO approach was chosen because of its ability to find optimal solutions in complex search spaces, such as SoC estimation in batteries. Through a combination of the PSO algorithm and data generated from sensors, it is hoped that the SoC estimates produced can improve battery usage efficiency, extend service life, and increase the performance of systems that depend on batteries. PSO can provide more accurate predictions with smaller errors, both in terms of the RMSE value of 0.0391 and the MAPE value of 12.028%. The high accuracy of 87.972% of PSO also shows that this method is reliable for applications that require precise SoC predictions. It is hoped that the results of this research can become a basis for further research in the field of battery management and metaheuristic algorithm optimization. After all, this research aims to enhance battery management systems and deepen understanding of PSO-based SoC estimation.
State Of Charge Estimation on Lithium ION Batteries Using Quantum Neural Network Situmorang, Raftonado; Dewanto, Muhammad Ridho; Hasanah, Barokatun; Deliasgarin, Kholiq; Oktafian, Bagus Gilang
SPECTA Journal of Technology Vol. 9 No. 2 (2025): Specta Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35718/specta.v9i2.1305

Abstract

Battery applications can be found in electric vehicles, renewable energy power plants and various other portable devices. In this final project research, the author uses the Quantum Neural Network (QNN) method to estimate the State of Charge (SoC) on a lithium-ion battery designed using PYTHON. This research includes the design of a prototype SoC estimation system on lithium-ion batteries using the QNN method, real-time SoC data collection, and comparison of SoC estimation performance using QNN with real-time data. The results of real-time testing of lithium-ion batteries using ACS712 voltage and current sensors for five cycles show the following voltage results: first cycle 10.70 V to 12.68 V, second cycle 10.56 V to 12.66 V, third cycle 10.60 V to 12.69 V, fourth cycle 10.60 V to 12.00 V, and the fifth cycle 10.41 V to 12.07 V. Meanwhile, the current sensor results for five cycles showed a range of 0.1 A to 0.5 A. Each test result per cycle showed a higher increase, although there were small fluctuations, and the overall trend line showed the consistency of the voltage sensor's performance without significant degradation during repeated tests, indicating good stability of the voltage sensor. Then, methods with qubit rotation, linear entanglement, and Neural Network were tested. SoC prediction results using QNN with qubit rotation showed MAPE and RMSE values of 0.14 and 61%, respectively. Furthermore, testing the SoC prediction results on QNN with linear entanglement shows MAPE and RMSE values of 0.08 and 29%, respectively. While the SoC prediction results.
Co-Authors Barokatun Hasanah, Barokatun Deliasgarin, Kholiq Farid, Mifta Nur Oktafian, Bagus Gilang Priyanto, Yun Tonce Kusuma Saputra, Agung Adi Saputra, Riza Hadi Situmorang, Raftonado Sugiarto, Kharis