Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network

RENNY RAKHMAWATI, SUTEDJO SUTEDJO, FITROTIN NAFISA OKTAVIANI, IRIANTO IRIANTO, DIAH SEPTI YANARATRI, AHMAD FIRYAL ADILA

Sari


ABSTRAK

Penggunaan panel surya sebagai sumber energi terbarukan membutuhkan baterai sebagai tempat penyimpanan energi. Penggunaan baterai secara terus menerus, dapat menyebabkan pengurangan kapasitas dan penurunan performa. Untuk mengatasi permasalahan tersebut, diperlukan sistem estimasi nilai State of Charge (SOC) pada baterai yang berfungsi untuk mengontrol kondisi charge, agar performa baterai tetap optimal. Pada penelitian dikembangan suatu sistem estimasi SOC pada baterai jenis lead acid, dengan metode algoritma Elman Recurrent Neural Network (ERNN). Keunggulan yang terkait dengan metode ERNN meliputi proses iterasi menjadi lebih cepat, peningkatan kecepatan pembaruan parameter, dan pencapaian konvergensi yang lebih cepat. Hasil dari penelitian estimasi SOC pada baterai lead acid 12V, 12Ah dengan menggunakan algoritma ERNN sebesar 0.101% sedangkan dengan algoritma Feedforward Backpropagation sebesar 0.767%. Sehingga dapat disimpulkan bahwa algoritma ERNN lebih efisien dalam mengestimasi nilai SOC pada baterai lead acid.

Kata kunci: Baterai, Elman Recurrent Neural Network, Panel Surya, State of Charge; Lead Acid

 

ABSTRACT

Using solar panels as a renewable energy source requires batteries as energy storage. Continuous use of batteries can result in reduced capacity and performance degradation. Based on these problems, a State of Charge (SOC) estimation system is needed for the battery to control charge conditions so that battery performance remains optimal. In this research, a SOC estimation system was developed for lead acid battery using the Elman Recurrent Neural Network (ERNN) algorithm. The advantage of the ERNN method is that the iteration process is faster, the parameter update speed is increased, and convergence is faster. The results of the SOC estimation for a 12V, 12Ah lead acid battery using the ERNN algorithm were 0.101%, while the Feedforward Backpropagation algorithm resulted in 0.767%. The ERNN algorithm is more efficient in estimating the SOC value of a lead acid battery.

Keywords: Battery, Elman Recurrent Neural Network, Solar Panel, State of Charge, Lead Acid


Kata Kunci


Baterai; Elman Recurrent Neural Network; Panel Surya; State of Charge; Lead Acid

Teks Lengkap:

PDF

Referensi


Abdulrahman, M. L., Ibrahim, K. M., Gital, A. Y., Zambuk, F. U., Ja’afaru, B., Yakubu, Z. I., & Ibrahim, A. (2021). A Review on Deep Learning with Focus on Deep Recurrent Neural Network for Electricity Forecasting in Residential Building. Procedia Computer Science, 193, 141–154. https://doi.org/10.1016/j.procs.2021.10.014

Afandi, A., Sumantri, B., & Windarko, N. A. (2020). Estimation State of Charge (SOC) of Ultracapacitor Based On Classical Equivalent Circuit Using Extended Kalman Filter. 2020 International Electronics Symposium (IES), 31–36. https://doi.org/10.1109/IES50839.2020.9231736

Afandi, A., Windarko, N. A., Sumantri, B., & Fakhrudin, H. H. (2022). Estimasi State of Charge (SoC) Ultrakapasitor menggunakan Extended Kalman Filter Berbasis Ladder Equivalent Circuit Model. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 10(1), 61. https://doi.org/10.26760/elkomika.v10i1.61

Belov, M. P., Van Lanh, N., & Khoa, T. D. (2021). State Observer based Elman Recurrent Neural Network for Electric Drive of Optical-Mechanical Complexes. Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021, 802–805. https://doi.org/10.1109/ElConRus51938.2021.9396310

Damiri, D. J., Lamania, R., & Laksana, R. (2023). Design and Simulation of On-Grid Rooftop Solar Power Plant (Rooftop PV) System on Office Buildings with a PLN Grid System. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 11(1), 231. https://doi.org/10.26760/elkomika.v11i1.231

Eviningsih, R. P., Rahmadani, A., Kinasih, A. R., & Arvioneta, R. (2023). Perancangan SEPIC Converter untuk Pengisian Baterai dengan Metode Kontrol PI. Jurnal Politeknik Caltex Riau, 9(1), 75–85. https://doi.org/https://doi.org/10.35143/elementer.v9i1

Fajrianingrum, F. N., Rakhmawati, R., & Prasetyono, E. (2022). Design and Implementation MPPT-CPG for Constant Power Battery Charger. JAREE (Journal on Advanced Research in Electrical Engineering), 6(2). https://doi.org/10.12962/jaree.v6i2.319

Febian, E. B. I., Rakhmawati, R., & Suhariningsih, S. (2022). Comparison of ANFIS and FLC as Charging Battery Based on Zeta Converter. INTEK: Jurnal Penelitian, 9(1), 49. https://doi.org/10.31963/intek.v9i1.3410

Hauck, D., & Kurrat, M. (2018). Overdischarging Lithium-Ion Batteries (pp. 53–81). https://doi.org/10.1007/978-3-319-70572-9_4

Irianto, I., Eviningsih, R. P., Murdianto, F. D., & Muhyidin, A. (2022). Optimization Improvement Using Pi Controller to Reach CCCV Method in Lead Acid Battery Load. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control. https://doi.org/10.22219/kinetik.v7i4.1496

Iskak, C. A., Windarko, N. A., & Rakhmawati, R. (2019). Design and Implementation Bidirectional DC-DC Converter for Load Sharing and Charging Battery. 2019 International Seminar on Application for Technology of Information and Communication (ISemantic), 455–459. https://doi.org/10.1109/ISEMANTIC.2019.8884344

Kamanditya, B., & Kusumoputro, B. (2020). Elman Recurrent Neural Networks Based Direct Inverse Control for Quadrotor Attitude and Altitude Control. 2020 International Conference on Intelligent Engineering and Management (ICIEM), 39–43. https://doi.org/10.1109/ICIEM48762.2020.9160191

Lebkowski, A. (2017). Temperature, Overcharge and Short-Circuit Studies of Batteries used in Electric Vehicles. PRZEGLĄD ELEKTROTECHNICZNY, 1(5), 69–75. https://doi.org/10.15199/48.2017.05.13

Laily, V. O. N, Warsito, B., & Maruddani, D. A. I. (2018). Comparison of ARCH / GARCH model and Elman Recurrent Neural Network on data return of closing price stock. Journal of Physics: Conference Series, 1025(1). https://doi.org/10.1088/1742-6596/1025/1/012103

Li, S., Ju, C., Li, J., Fang, R., Tao, Z., Li, B., & Zhang, T. (2021). State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network. Energies, 14(2), 306. https://doi.org/10.3390/en14020306

Lindgren, J., & Lund, P. D. (2016). Effect of extreme temperatures on battery charging and performance of electric vehicles. Journal of Power Sources, 328, 37–45. https://doi.org/10.1016/j.jpowsour.2016.07.038

Murdianto, F. D., Nansur, A. R., Septiarini, N. A., Widarsono, K., & Purwanto, E. (2019). SEPIC converter with coupled inductor using Fuzzy Logic controller to optimized battery charging process. Journal of Physics: Conference Series, 1367(1), 012074. https://doi.org/10.1088/1742-6596/1367/1/012074

Ningrum, P., Windarko, N. A., & Suhariningsih, S. (2021). Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS). JAREE (Journal on Advanced Research in Electrical Engineering), 5(1). https://doi.org/10.12962/jaree.v5i1.150

Nurdiansyah, R., Windarko, N. A., Rakhmawati, R., & Abdul Haq, M. (2022). State of charge estimation of ultracapacitor based on equivalent circuit model using adaptive neuro-fuzzy inference system. Journal of Mechatronics, Electrical Power, and Vehicular Technology, 13(1), 60–71. https://doi.org/10.14203/j.mev.2022.v13.60-71

Ouyang, D., Chen, M., Liu, J., Wei, R., Weng, J., & Wang, J. (2018). Investigation of a commercial lithium-ion battery under overcharge/over-discharge failure conditions. RSC Advances, 8(58), 33414–33424. https://doi.org/10.1039/C8RA05564E

Pillai, P., Sundaresan, S., Kumar, P., Pattipati, K. R., & Balasingam, B. (2022). Open-Circuit Voltage Models for Battery Management Systems: A Review. Energies, 15(18), 6803. https://doi.org/10.3390/en15186803

Qays, M. O., Buswig, Y., Hossain, M. L., & Abu-Siada, A. (2022). Recent progress and future trends on the state of charge estimation methods to improve battery-storage efficiency: A review. CSEE Journal of Power and Energy Systems, 8(1), 105–114. https://doi.org/10.17775/CSEEJPES.2019.03060

Sunarno, E., Sudiharto, I., Nugraha, S. D., Murdianto, F. D., Suryono, & Qudsi, O. A. (2019). Design and implementation bidirectional SEPIC/ZETA converter using Fuzzy Logic Controller in DC microgrid application. Journal of Physics: Conference Series, 1367(1), 012058. https://doi.org/10.1088/1742-6596/1367/1/012058

Sutedjo, S., Ferdiansyah, I., Qudsi, O. A., & Setiawan, F. (2019). Design of Battery Charging System as Supply of Rice Threshers in Tractor. 2019 2nd International Conference on Applied Information Technology and Innovation (ICAITI), 32–36. https://doi.org/10.1109/ICAITI48442.2019.8982160

Sutedjo, S., Rizqi, A., & Wahjono, E. (2021). Hardware Implementation of Maximum Power Point Tracking Using Fuzzy Logic-Based Zeta Converter at PV 100Wp. CESS (Journal of Computer Engineering, System and Science), 7(1), 67. https://doi.org/10.24114/cess.v7i1.29477

Suyanto, H., Erlina, Diantari, R. A., & Al Rasyid, H. (2021). Study on Optimization of System Management Battery for Lithium Batteries and Lead Acid Batteries at the New and Renewable Energy Research Center IT PLN. 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 213–218. https://doi.org/10.1109/ICITISEE53823.2021.9655905

Trinandana, G. A., Pratama, A. W., Prasetyono, E., & Anggriawan, D. O. (2020). Real Time State of Charge Estimation for Lead Acid Battery Using Artificial Neural Network. 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA), 363–368. https://doi.org/10.1109/ISITIA49792.2020.9163692

Ula, M., & Rahmadani, A. (2023). Rancang Bangun Maximum Power Point Tracking pada Panel Surya dengan Metode Incremental Conductance Menggunakan Zeta Konverter. Techné : Jurnal Ilmiah Elektroteknika, 22(1), 1–20. https://doi.org/10.31358/techne.v22i1.334

Yolcu, O. C, Temel, F. A., & Kuleyin, A. (2021). New hybrid predictive modeling principles for ammonium adsorption: The combination of Response Surface Methodology with feedforward and Elman-Recurrent Neural Networks. Journal of Cleaner Production, 311, 127688. https://doi.org/10.1016/j.jclepro.2021.127688




DOI: https://doi.org/10.26760/elkomika.v11i4.864

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

Publisher:

Department of Electrical Engineering Institut Teknologi Nasional Bandung

Address: 20th Building  Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124

Contact: +627272215 (ext. 206)

Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________


Free counters!

Web Analytics Made Easy - StatCounter

Statistic Journal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License