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All Journal Saintika Medika: Jurnal Ilmu Kesehatan dan Kedokteran Keluarga. Mechatronics, Electrical Power, and Vehicular Technology AGRITEPA: Jurnal Ilmu dan Teknologi Pertanian ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology Briliant: Jurnal Riset dan Konseptual Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis AGRISAINTIFIKA Jurnal Ilmu-ilmu Pertanian JEPA (Jurnal Ekonomi Pertanian dan Agribisnis) Jurnal Agribest Jurnal Kesmas dan Gizi (JKG) JURNAL FARMASIMED (JFM) Jurnal Review Pendidikan dan Pengajaran (JRPP) Dinasti International Journal of Management Science Convergence : The Journal of Economic Development Ainara Journal (Jurnal Penelitian dan PKM Bidang Ilmu Pendidikan) Journal of Agri Socio Economics and Business MAHATANI: Jurnal Agribisnis (Agribusiness and Agricultural Economics Journal) KARYA: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengmas Kestra (JPK) Jurnal AGRISEP: Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis Agricultural Review Agrifo: Jurnal Agribisnis Universitas Malikussaleh Medistra Medical Journal (MMJ)
Bambang Sumantri
Staff Pengajar Jurusan Sosial Ekonomi Pertanian Fak.Pertanian UNIB
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Journal : Briliant: Jurnal Riset dan Konseptual

 1 
Estimasi Kecepatan Motor Brushless DC dengan Menggunakan Metode Sliding Mode Observer Abdurrahman, Rizqy; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 6 No 3 (2021): Volume 6 Nomor 3, Agustus 2021
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1572.293 KB) | DOI: 10.28926/briliant.v6i3.700

Abstract

Pada dasarnya motor brusless DC  (BLDC) atau yang biasa juga disebut permanent magnet synchronous motor (PMSM) menggunakan hall-sensor untuk mengetahui posisi dan kecepatan dari motor tersebut. Data nilai arus (I) dan tegangan (V) pada pemodelan dasar dari motor BLDC sebagai masukan dari metode sliding mode observer (SMO). Metode sensorless yang didasarkan pada SMO diajukan untuk menggantikan perangkat hall-sensor untuk mengestimasi posisi rotor dan kecepatan motor BLDC. Pengujian akan dilakukan menggunakan aplikasi power simulator (PSim). Untuk mendapatkan error estimasi kecepatan pengujian dilakukan dengan membandingkan kecepatan aktual dengan kecepatan estimasi. Pengujian dilakukan dengan dua (2) nilai kecepatan yang berbeda yaitu sebesar 1000 r/min dan 1200 r/min dan dua (2) beban mekanik yang berbeda yaitu sebesar 0.1 Nm dan 0.5 Nm. Hasil dari simulasi yang telah dilakukan dengan kecepatan motor BLDC sebesar 1000 r/min dan beban mekanik sebesar 0.1 Nm, didapatkan nilai error estimasi kecepatan sebesar 6,7%, dengan kecepatan sebesar 1000 r/min dan beban sebesar 0.5 Nm, didapatkan nilai error estimasi sebesar 7,2%, dengan kecepatan motor sebesar 1200 r/min dan beban sebesar 0.1 Nm, didapatkan nilai error estimasi sebesar 9,5%, dengan kecepatan motor sebesar 1200 r/min dan beban sebesar 0.5 Nm, didapatkan nilai error estimasi sebesar 9,8%. Dari pengujian tersebut membuktikan sliding mode observer dapat bekerja dengan baik karena nilai error estimasi kurang dari 10% dan merupakan metode yang robust.
Estimasi State-of-Charge Pada Baterai Lithium-Ion Menggunakan Deep Neural Network Amrullah, Haniifan Patra; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 9 No 3 (2024): Volume 9 Nomor 3, Agustus 2024
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v9i3.1874

Abstract

As electric vehicles (EV) become increasingly popular in the automotive world, an accurate State-of-Charge (SoC) estimation is critical to optimizing energy utilization, increasing driving range and ensuring long-lasting battery system. This research focuses on the application of Deep Neural Networks (DNN) as an SoC estimation method in EV, exploiting the inherent capacity of DNN to learn complex relationships in vast data sets. The results of the performed simulations show that the proposed DNN-based SoC estimation method achieves a high level of accuracy, outperforming traditional estimation techniques, especially in scenarios involving rapid changes in driving conditions. This research also explores the impact of Neural Networks architecture and hyperparameter tuning on overall performance and provides insights for optimizing DNN-based SoC estimation systems. From the tests that have been carried out, an error value of 1.3% is obtained from the results of the training carried out on the DNN structure that has been prepared.
Estimasi State Of Charge (Soc) Pada Baterai Lithium Ion Menggunakan Long Short-Term Memory (LSTM) Neural Network Husien.R, Alwi Azis; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 9 No 4 (2024): Volume 9 Nomor 4, November 2024
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v9i4.1955

Abstract

Lithium-ion batteries have become one of the top choices for efficient and environmentally friendly mobility in today's era. Batteries play an important role in our digital lifestyles, from smartphones to electric cars. The use of this battery is inseparable from the challenge of estimating the State of Charge (SOC), which is a key parameter to monitor the availability of energy remaining in the battery. Therefore, an accurate SOC Estimation method is needed, which is important for efficient energy management and safe battery use. The Long Short-Term Memory (LSTM) model was chosen because of its ability to handle complex time series data and nonlier patterns in battery performance. This study provides the application of LSTM for SoC estimation and shows that LSTM is superior to the Feed Neural Network (FNN) method as evidenced by the simulation results that show that the LSTM model produces an RMSE of 4.92%, while the FNN model produces an RMSE of 7.82. From all the tests that have been carried out, the best RMSE value of 3.53% was obtained at a temperature of 25°C epoch 100.
Rancang Bangun Adaptive Neuro-Fuzzy Inference System (ANFIS) untuk Estimasi State-of-Charge (SOC) Baterai Salsabila, Regina; Windarko, Novie Ayub; Sumantri, Bambang
BRILIANT: Jurnal Riset dan Konseptual Vol 10 No 1 (2025): Volume 10 Nomor 1, Februari 2025
Publisher : Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/briliant.v10i1.2148

Abstract

The growing demand for energy around the world is driving the development of renewable resources, and batteries are the primary choice for energy storage. To carry out effective energy management, State of Charge (SOC) estimation of Lithium-ion batteries is essential. The development of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for SOC estimation using LG 18650 HG2 battery dataset is the objective of this research. It is tested with two parameters, namely two inputs consisting of voltage and current; and three inputs consisting of voltage, current, and temperature. The shape of the membership function, number of nodes, and epochs are some of the indicators tested to find the best configuration. The results show that the three-input configuration with generalized-bell membership function (Gbell MF), five nodes, and 100 epochs has the smallest Root Mean Square Error (RMSE), which is 0.0317, compared to the best two-input configuration, which has an RMSE of 0.0527. Since the three-input configuration takes longer to train, further improvements are needed for real-time implementations such as in electric vehicle battery management systems.
Co-Authors . Sriyoto, . Abdilla, Moch Rafi Damas Abdillah Aziz Muntashir Abdurrahman, Rizqy Achmad Afandi, Achmad Afiffah, Shofaul Agus Purwoko Ahdiati, Laila Alfayanti Alfayanti, Alfayanti Almunawar, Anggel Amrullah, Haniifan Patra Anastasia Surbakti, Anastasia angga wahyu aditya Anggraini, Zulfa Galuh Anna Suwarni, Anna APRIYANTO, RADEN AKBAR NUR Basuki Sigit Priyono BASUKI, GAMAR Bian Suwarli, Bian Bima Sena Bayu Dewantara Br Ginting, Luci Riani Budi Ansori, Budi Cahyati, Ainun Damayanti, Destin Dedid Cahya Happyanto Edi Paris, Edi Ellys Yuliarti, Ellys Emlan Fauzi, Emlan Era Purwanto Fakhruddin, Hanif Hasyier Fauzi, Moh. Hilal Felix Kasim Felycia Tiera Kencana, Felycia Tiera Ferdiansyah, Indra Hanita, Erma Harry Tsaputra, Harry Hasanah, Nursyara Hidayatullah, Samudra Syarif Wahyu Husien.R, Alwi Azis Indra Cahyadinata, Indra Indriani, Desy Ira Primalasari Irnad, Irnad Ismail, Ahmad Yusuf Kartini . Ketut Sukiyono Kiasati, Kanaya Zharfa Krisdianilo, Visensius Kurnia, Anggia M. Mustopa Romdhon, M. Mustopa Maemunah Manalu, Arta Santrina MARTINI, NI PUTU DEVIRA AYU Meiliza Cecilia, Meiliza Mentari Putri Jati MOCHAMAD ARI BAGUS NUGROHO Naisyah, Tiara Ni Luh Putu Hariastuti Nola Windirah Novie Ayub Windarko Nusril Nusril Nyayu Neti Arianti, Nyayu Neti Prawito, Priyono Pujiati, Herni Putri Suci Asriani Putri, Marthalinda Dwi R. Oktav Yama Hendra Redy Badrudin Reswita, reswita wita Salsabila, Regina SAPUTRI, ANGGUN Sari, Jarmi Puspita Sari, Poppy Antika Satria Putra Utama Septarena, Refi Sidabutar, Rimayani Sirait, Reni Aprinawaty Sriyoto Sriyoto, Sriyoto Suda, Kadek Reda Setiawan Sumantri Maria, Sumantri Syamsuri trisusilo, agung Wati, Nisya Nur Choirunia Wiryanto, Ibnu Yeni Marlina Yulian Apriansyah, Yulian Yulius Budiman, Yulius Yuningsih, Windi Tetra Yurida Safitri, Yurida Yuristia, Rahmi Zizou Aly, M.Fadhlullah Prasetyo