DESAIN DETEKSI KESALAHAN BATTERY MANAGEMENT SYSTEM MENGGUNAKAN ALGORITMA KALMAN FILTER PADA MOBIL LISTRIK NASIONAL

Authors

  • Lora Khaula Amifia Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya
  • Moch. Iskandar Riansyah Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya
  • Isa Hafidz Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya
  • Dimas Adiputra Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya
  • Anifatul Faricha Program Studi Teknik Elektro, Fakultas Teknik Elektro, Institut Teknologi Telkom Surabaya

DOI:

https://doi.org/10.0301/jttb.v2i1.63

Keywords:

Battery Management System, Fault Detection, Kalman Filter

Abstract

Electric cars are currently being developed by many people because of low pollution and many countries used them in their daily activity. One of the important and main component is a battery, especially the Battery Management System (BMS) which can optimize the implementation of electric cars. BMS can protect and maintain the battery performance efficiently and at the same time can be a fault detection. Basically, It has three important parameters, there are current, voltage, and temperature that must be maintained and there is no overcurrent, overcharging, and discharging for too long because it can cause a fire. The protection of the BMS on electric cars need battery testing and done by taking current and voltage data, which prioritizes discharging and overdischarging test with a capacity of 2,2 Ah or a maximum capacity of 4,2 Volt. This research optimizes the work of BMS when experiencing faults/errors in order to work properly. The battery is modelled with a simple battery model (Rint) which previously identified parameters and formed a state space that aims to make fault detection. The results showed that fault detection using the Kalman Filter algorithm is very efficient and reliable in improving readings of overcurrent and overdischarge data so as to maintain security and extend/lifetime battery so that it can be implemented safely by the public

Downloads

Download data is not yet available.

References

Rajesh Rajamani., "Observers for Lipschitz Nonlinear Systems", ZEEE Trans. on Aut. Control, pp. 397-400, V. 43, No. 3, March 1998.
Frank P.M, "Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey", Automatica, pp. 459-474, Vol. 26, 1990.
Frank P.M, "Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey", Automatica, pp. 459-474, Vol. 26, 1990.
Frank P.M, "Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey", Automatica, pp. 459-474, Vol. 26, 1990.
Schreier G, Kratz F, and Frank P.M, "Observer design for a class of non-linear systems with stability discussion: Application to fault diagnosis", 5th European Control Conference ECC'99 Karlsruhe,1999.
Shumsky A.Y, "Robust residual generation for diagnosis of nonlinear systems: parity relation approach", Ifac Symp. Safeprocess, Hull, U.K., 5, pp. July 5-9, 1999.
Zhuang 2 and Frank P.M, "Qualitative observer and its application to FDI systems", Proc. Znstn. Mech. Engrs, 211(4), pp. 253-262, 1997.
Patton, R.J.: ‘Robust model-based fault diagnosis: the state of art’. Proc. iFAC Symp. Fault Detection, Supervision and Safety for Process (SAFE-PROCESS), Espoo, Finland, 1994, pp. 1–24.
Yang, H., and Saif, M.: ‘Nonlinear adaptive observer design for fault detection’. Proc. American Control Conf., Seattle, 1995, pp. 1136–1139

Sreedhar, R., Fernandez, B., and Masada, G.Y.: ‘Robust fault detection in nonlinear systems using sliding mode observers’. Proc. 2nd IEEE Conf. Control Applications, Vancouver, BC, Canada,
1993, pp. 716–721
Seliger R. anf Frank P.M, " Fault diagnosis bydisturbance decoupled nonlinear observers", IEEE CDC, England, pp 2248-2253,1991.
Saif, M., and Guan, Y.: ‘A new approach to robust fault detection and identification’, IEEE Trans. Aero. Electron. Syst., 1993, 29, (3), pp. 685–695
Edwards, C., and Spurgeon, S.K.: ‘Sliding-mode stabilization of uncertain systems using only output information’, Int. J. Control, 1995, 62, pp. 1129–1144
Jorge-Zavala, M.F., and Alcorta-Garcia, E.: ‘Detection of internafaults in transformers using nonlinear observers’. Proc. 2nd IEEE Conf. Control Applications, Houston, TX, 2003, pp. 195–199
Thau, F.E.: ‘Observing the state of nonlinear dynamic systems’, Int. J. Control, 1973, 17, (3), pp. 471–479
Garg, V., and Hedrick, J.K.: ‘Fault detection filters for a class of nonlinear systems’. Proc. American Control Conf., Seattle, WA, 1995, pp. 1647–1651
Mohamed AH, Schwarz KP. Adaptive Kalman filtering for INS/GPS. J Geod 1999;73:193–203.
Eykeren V, Chu Quping L, Mulder JA. Sensor fault detection and isolation using adaptive extended Kalman filter. Fault Detect Superv Saf Tech Process 2012;8.
Plett GL. Extended Kalman filtering for battery management systems of LiPBbased HEV battery packs – Part 3. State and parameter estimation. J Power Sources 2004;134:277–92.

Published

2019-03-28

How to Cite

Khaula Amifia, L. ., Iskandar Riansyah, M. ., Hafidz, I. ., Adiputra, D. ., & Faricha, A. . (2019). DESAIN DETEKSI KESALAHAN BATTERY MANAGEMENT SYSTEM MENGGUNAKAN ALGORITMA KALMAN FILTER PADA MOBIL LISTRIK NASIONAL . Jurnal Teknologi Dan Terapan Bisnis, 2(1), 65-70. https://doi.org/10.0301/jttb.v2i1.63