Estimasi State of Charge (SoC) Ultrakapasitor menggunakan Extended Kalman Filter Berbasis Ladder Equivalent Circuit Model

ACHMAD AFANDI, NOVIE AYUB WINDARKO, BAMBANG SUMANTRI, HANIF HASYIER FAKHRUDDIN

Sari


ABSTRAK

Penggunaan perangkat penyimpan energi semakin lama semakin meningkat pada peralatan berdaya kecil maupun besar. Baterai selama ini menjadi pilihan utama sebagai penyimpan energi. Namun akhir-akhir ini ultrakapasitor menjadi pilihan alternatif karena lifetime lebih panjang dan respon daya sesaat yang jauh lebih besar dari baterai. Pada manuskrip ini dibahas tentang estimasi nilai State of Charge (SoC) pada ultrakapasitor. Estimasi dilakukan berdasarkan rangkaian ekivalen ladder. Extended Kalman Filter adalah metode estimasi yang handal terhadap sistem dinamis dan tidak memerlukan banyak memori. Estimasi menggunakan metode Extended Kalman filter yang ditanamkan pada sistem embedded untuk mengantisipasi kondisi non-linier pada ultrakapasitor. Ultrakapasitor diuji dengan kondisi charging dan discharging. Hasil pengujian menunjukkan, kinerja metode dibandingkan antara data simulasi dan percobaan dengan perbedaan hasil sebesar 6%.

Kata kunci: State of Charge, Metode Extended Kalman Filter, Ultrakapasitor

 

ABSTRACT

The use of energy storage devices is increasing in both small and large power equipment. Batteries have been the main choice for energy storage. However, recently ultracapacitors have become an alternative choice because of a longer lifetime and a much larger instantaneous power response than batteries. This manuscript discusses the estimation of the State of Charge (SoC) value on the ultracapacitor. Estimates are made based on a ladder equivalent circuit. Extended Kalman Filter is a reliable estimation method for dynamic systems and does not require a lot of memory. The estimation uses the Extended Kalman filter method implemented in embedded system to anticipate non-linear conditions on the ultracapacitor. Ultracapacitors were tested under charging and discharging conditions. The test results show that the performance of the method is compared between simulation and experimental data with a difference of 6% in results.

Keywords: State of Charge, Metode Extended Kalman Filter, Ultrakapasitor


Kata Kunci


State of Charge; Metode Extended Kalman Filter; Ultrakapasitor

Teks Lengkap:

PDF

Referensi


Afandi, A., Sumantri, B., & Windarko, N. A. (2020). Estimation state of charge (soc) of ultracapacitor based on classical equivalent circuit using extended Kalman filter. IES 2020. Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., (pp. 31–36).

Cabrane, Z., Ouassaid, M., & Maaroufi, M. (2017). Battery and supercapacitor for photovoltaic energy storage: a fuzzy logic management. IET Renewable Power Generation, 11(8), 1157–1165.

Ceraolo, M., Lutzemberger, G., & Poli, D. (2017). State-Of-Charge Evaluation Of Supercapacitors. Journal of Energy Storage, 11, 211–218.

Chiang, C. J., Yang, J. L., & Cheng, W. C. (2013). Temperature and state of charge estimation in ultracapacitor based on extended Kalman filter. J. Power Sources, vol. 234, pp. 234–243.

Houlian, W., & Gongbo, Z. (2018). State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network . IET Electric Power Applications, 12(4), 588–594.

Mastali, M., Vazquez-Arenas, J. R., Fraser, Fowler, M., Afshar, S., & Stevens, M. (2013) Battery state of the charge estimation using Kalman filtering. J. Power Sources, vol. 239, pp. 294–307.

Pavkovic, D., Smetko, V., Hrgetic, M., & Komljenovic, A. (2014). Dual Kalman filter-based on the SoC/SoH estimator for an ultracapacitor module. 2014 IEEE Conf. Control Appl. CCA 2014, (pp. 1783–1788).

Pozo, B., Garate, J., Ferreiro, S., Fernandez, I., & Fernandez de Gorostiza, E. (2018). Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications. Electronics, 7(4), 44.

Richardson, R. R., & Howey, D. A. (2015). Sensorless Battery Internal Temperature Estimation Using a Kalman Filter With Impedance Measurement. IEEE Transactions on Sustainable Energy, 6(4), 1190–1199.

Saha, P., Dey, S., & Khanra, M. (2020). Modeling and state of charge estimation of supercapacitor considering leakge effect. IEEE Trans. Ind. Electron., 67(1), 350–357.

Sepasi, S., Ghorbani, R., & Liaw, B. Y. (2014). Improved extended Kalman filter for state of charge estimation of battery pack. Journal of Power Sources, 255, 368–376.

Tian, J., Xiong, R., Shen, W., & Wang, J. (2019). Frequency and time domain modelling and online state of charge monitoring for ultracapacitors. Energy, vol. 176, pp. 874–887.

Yan, W., Zhang, B., Zhao, G., Tang, S., Niu, G., & Wang, X. (2018). Battery Management. System with Lebesgue Sampling-Based Extended Kalman Filter. IEEE Transactions on Industrial Electronics, 1–1.

Zhang, L., Hu, X., Wang, Z., Sun, F., & Dorrell, D. G. (2018). A review of supercapacitor modeling, estimation, and applications: A control or management perspective. Renewable and Sustainable Energy Reviews, 81, 1868–1878.

Zhang, L., Wang, Z., Sun, F., & Dorrell, D. (2014). Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter. Energies, 7(5), 3204–3217.

Zhou, Y., Huang, Z., Peng, J., Li, H., & Liao, H. (2017). A generealized extended state observer for supercapacitors state of charge estimation under disturbance. Proc. Am. Control Conf., (pp. 4029–4034).




DOI: https://doi.org/10.26760/elkomika.v10i1.61

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