Estimasi SOC Saat Discharging pada Baterai VRLA Berbasis Elman Backpropagation

DIAH SEPTI YANARATRI, SUTEDJO SUTEDJO, ACHMAD DICKY FIRMANSYAH, IRIANTO IRIANTO, RENNY RAKHMAWATI, AHMAD FIRYAL ADILA

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ABSTRAK

Penurunan performa baterai terjadi akibat siklus pengisian dan pengosongan berulang yang melebihi batas, mempercepat degradasi. Penelitian ini bertujuan untuk meningkatkan akurasi estimasi State of Charge (SOC) baterai menggunakan Artificial Neural Network (ANN) dengan algoritma Elman Backpropagation. Metode digunakan karena menambahkan lapisan context neuron yang mampu menangkap pola dinamis pada data baterai. Pengujian dilakukan dengan membandingkan hasil estimasi SOC dari metode ini dengan metode Coulomb Counting. SOC baterai diestimasi dari 100% hingga 60%, dan hasil menunjukkan bahwa meskipun Coulomb Counting awalnya memberikan SOC lebih tinggi, estimasi dari kedua metode menjadi lebih mirip seiring waktu. Error estimasi berkisar antara 0,1% hingga 14,7%. Algoritma Elman Backpropagation terbukti mampu memberikan estimasi SOC yang lebih akurat, namun masih memerlukan kalibrasi lebih lanjut untuk meningkatkan presisi.

Kata kunci: Artificial Neural Network, Baterai, Coulumb Counting, Elman Backpropagation, State of Charge.

 

ABSTRACT

The decline in battery performance occurs due to repeated charge and discharge cycles that exceed limits, accelerating degradation. This study aimed to improve the accuracy of State of Charge (SOC) estimation using an Artificial Neural Network (ANN) with the Elman Backpropagation algorithm. The method used was unique in adding a context neuron layer capable of capturing dynamic patterns in battery data. Testing was conducted by comparing SOC estimates from this method with the Coulomb Counting method. The battery's SOC was estimated from 100% to 60%, and the results showed that although Coulomb Counting initially provided higher SOC estimates, the estimates from both methods became more similar over time. Estimation errors ranged from 0.1% to 14.7%. The Elman Backpropagation algorithm proved to provide more accurate SOC estimates, although further calibration is needed to improve precision.

Keywords: Artificial Neural Network, Battery, Coulumb Counting, Elman Backpropagation, State of Charge.


Kata Kunci


Artificial Neural Network; Baterai; Coulumb Counting; Elman Backpropagation; State of Charge

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Referensi


Afandi, A., Windarko, N. A., Sumantri, B. & Fakhuruddin, 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), pp. 61-75.

Baronti, F. (2015). State of charge estimation enhancing of supercapacitors in electric vehicles. IEEE Transactions on Industrial Electronics, 62(12), 7699-7708.

Chen, J., Wang, T., Wu, Y. & Xu, Z. (2021). Short-term load forecasting based on Elman neural network optimized by genetic algorithm. IEEE Transactions on Smart Grid, 12(2), 1341-1350.

Daud, A., Wibawa, K. P. & Rachmawati, E. (2019). Lead Acid Battery: Advances and Applications. s.l.:Elsevier.

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), 49-57.

Gayatri, M. (2020). Implementation of Epileptic EEG using Recurrent Neural Network. International Journal of Computer Science and Network Security, 10.

Hu, X., Li, S. & Peng, H., (2014). A comparative study of equivalent circuit models for Li-ion batteries. Journal of Power Sources, 198(15), 359-367.

Kumar, S. & Patra, A. (2017). Advances in Battery Technologies for Electric Vehicles. s.l.:Woodhead Publishing.

Kusumoputro, Kamanditya, B. & Benyamin, (2020). Elman Recurrent Neural Networks Based Direct Inverse Control for Quadrotor Attitude and Altitude Control. London, United Kingdom, International Conference on Intelligent Engineering and Management (ICIEM).

Li, J., Zhang, T. & Chen, X., 2021. Improved Coulomb counting method for state of charge estimation of lithium-ion batteries in electric vehicles. Journal of Cleaner Production, 310, 127408.

Li, M. (2021). Research on electric vehicle state of health estimation model based on improved Elman neural network. Energy Reports, Osa/vuosikerta, 7, 279-288.

Rahmawan, Z., (2018). Estimasi State of Charge (SOC) pada Baterai Lead-Acid dengan Menggunakan Metode Coulomb Counting pada PV Hybrid, s.l.: Institut Teknologi Sepuluh Nopember.

Rakhmawati, R., Sutedjo, Oktaviani, F. N. & Irianto. (2023). Estimasi State of Charge pada Baterai Lead Acid menggunakan Elman Recurrent Neural Network.. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika., 11(4), 864-874.

Samoto. (2024). SAMOTO Battery & Digital Stabilizer. [Online]

Available at: https://samoto.co.id/products/smt-1218/

[Haettu 1 Juni 2024].

Wang, D., Liu, G. & Sun, B., (2017). A comprehensive review on the key technologies for state of charge estimation of lithium-ion batteries. Renewable and Sustainable Energy Reviews, 79, 1341-1352.

Wang, Q., Jiang, B. & Li, B. (2016). Battery Management Systems: Modeling, Integration, and Optimization. 1 toim. s.l.:Wiley.

Yang, X., Zhang, C. & Xie, W. (2018). State-of-charge estimation of lithium-ion batteries using a neural network based on an improved genetic algorithm. Journal of Energy Storage, 15, 1-11.

Yu, L. (2021). Financial time series prediction using a novel Elman neural network with multi-indicators. Expert Systems with Applications, 171, 114663.

Zeng, W., Huang, C., Zhang, X. & Wang, Y. (2023). Short-term traffic flow prediction based on hybrid Elman neural network with improved deep learning. Journal of Advanced Transportation, 2023(1), pp. 1-12.

Zhang, T. & Tang, Y. (2020). Advances in Lead-Acid Batteries. s.l.:CRC Press.

Zhang, X., Zhang, X., Zhang, L. & Zhang, Y. (2020). A novel chaotic Elman neural network with application in time series prediction. Neurocomputing, 408(1), pp. 82-92.

Zhou, Z., Yin, G. & Liu, W. (2014). Battery Technology Handbook. s.l.:Springer.




DOI: https://doi.org/10.26760/elkomika.v12i4.862

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

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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________________________________________________________________________________________________________________________


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