Estimasi State of Charge (SOC) Pada Baterai Lithium Ion Menggunakan Long Short-Term Memory (LSTM) Neural Network

Authors

DOI:

https://doi.org/10.28926/briliant.v9i4.1955

Keywords:

State of Charge, LSTM, Lithium Ion Battery, Neural Network

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.

References

Abdullah, Javed, A., Ashraf, J., & Khan, T. (2020). The impact of renewable energy on GDP. International Journal of Management and Sustainability, 9(4), 239–250. https://doi.org/10.18488/journal.11.2020.94.239.250

Adellea, A. J. (2022). Implementation of New Energy and Renewable Energy Policy in the Context of National Energy Security. Indonesian State Law Review (ISLRev), 4(2), 43–51. https://doi.org/10.15294/islrev.v4i2.61093

Aksoy, A., Ertürk, Y. E., Erdoğan, S., Eyduran, E., & Tariq, M. M. (2018). Estimation of honey production in beekeeping enterprises from eastern part of Turkey through some data mining algorithms. Pakistan Journal of Zoology, 50(6), 2199–2207. https://doi.org/10.17582/journal.pjz/2018.50.6.2199.2207

Bhagavatula, S. V., Yellamraju, V. R. B., Eltem, K. C., Bobba, P. B., & Marati, N. (2020). ANN based Battery Health Monitoring - A Comprehensive Review. E3S Web of Conferences, 184, 1–7. https://doi.org/10.1051/e3sconf/202018401068

Chen, C., Xiong, R., Yang, R., Shen, W., & Sun, F. (2019). State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. Journal of Cleaner Production, 234(5), 1153–1164. https://doi.org/10.1016/j.jclepro.2019.06.273

Cui, Z., Wang, L., Li, Q., & Wang, K. (2022a). A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network. International Journal of Energy Research, 46(5), 5423–5440. https://doi.org/10.1002/er.7545

Cui, Z., Wang, L., Li, Q., & Wang, K. (2022b). A comprehensive review on the state of charge estimation for lithium-ion battery based on neural network. International Journal of Energy Research, 46(5), 5423–5440. https://doi.org/10.1002/er.7545

Danish, Baloch, M. A., Mahmood, N., & Zhang, J. W. (2019). Effect of natural resources, renewable energy and economic development on CO 2 emissions in BRICS countries. Science of the Total Environment, 678, 632–638. https://doi.org/10.1016/j.scitotenv.2019.05.028

Dao, V. Q., Dinh, M. C., Kim, C. S., Park, M., Doh, C. H., Bae, J. H., Lee, M. K., Liu, J., & Bai, Z. (2021). Design of an effective state of charge estimation method for a lithium-ion battery pack using extended kalman filter and artificial neural network. Energies, 14(9). https://doi.org/10.3390/en14092634

Geng, P., Xu, X., & Tarasiuk, T. (2020). LITHIUM-ION BATTERIES IN ALL-ELECTRIC SHIPS. 27(107), 100–108.

Hosen, M. S., Jaguemont, J., Van Mierlo, J., & Berecibar, M. (2021). Battery lifetime prediction and performance assessment of different modeling approaches. IScience, 24(2), 102060. https://doi.org/10.1016/j.isci.2021.102060

How, D. N. T., Hannan, M. A., Hossain Lipu, M. S., & Ker, P. J. (2019). State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review. IEEE Access, 7, 136116–136136. https://doi.org/10.1109/ACCESS.2019.2942213

Kartika Tresya Mauriraya 1)*, N. P. A. F. dan C. 4). (2022). Analisis Karakteristik Baterai Lithium-Ion Pada KendaraanListrik Di Institut Teknologi Pln. Prosiding NCIET, 3, 95–102.

Kurnia Sari, W., Palupi Rini, D., Firsandaya Malik, R., & Saladin Azhar, I. B. (2017). Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short-Term Memory dengan Word2Vec. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 1(3), 276–285.

Movassagh, K., Raihan, A., Balasingam, B., & Pattipati, K. (2021). A critical look at coulomb counting approach for state of charge estimation in batteries. Energies, 14(14), 1–33. https://doi.org/10.3390/en14144074

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), 15–20. https://doi.org/10.12962/jaree.v5i1.150

Rahman, M. M., & Velayutham, E. (2020). Renewable and non-renewable energy consumption-economic growth nexus: New evidence from South Asia. Renewable Energy, 147(2020), 399–408. https://doi.org/10.1016/j.renene.2019.09.007

Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020). Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art. IEEE Access, 8, 52796–52814. https://doi.org/10.1109/ACCESS.2020.2980961

Yang, B., Wang, J., Cao, P., Zhu, T., Shu, H., Chen, J., Zhang, J., & Zhu, J. (2021). Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey. Journal of Energy Storage, 39(November 2020), 102572. https://doi.org/10.1016/j.est.2021.102572

Published

2024-11-30

Issue

Section

Engineering and Technology