Acknowledgement
This work was supported by the Jungwon University Research Grant (No. 2019-003).
References
- P. Mauracher, E. Karden, Dynamic modeling of Lead/acid batteries using impedance spectroscopy for parameter identification, J. Power Sources 67 (1997), 69-84. https://doi.org/10.1016/S0378-7753(97)02498-1
- S. Buller, M. Thele, E. Karden, and R.W. De Doncker, Impedance-based non-linear dynamic battery modeling for automotive applications, J. Power Sources 113 (2003), 422-430. https://doi.org/10.1016/S0378-7753(02)00558-X
- S. Sato, A. Kawamura, A new Estimation Method of State of Charge using Terminal Voltage and Internal Resistance for Lead-Acid Battery, PCC-Osaka IEEE (2002), 565-570.
- D. Dennis and A. Suleiman, A Critical Review of Using the Peukert Equation for Determining the Remaining Capacity of Lead-acid and Lithium-ion Batteries, J. of Power Sources 155 (2006), 395-400. https://doi.org/10.1016/j.jpowsour.2005.04.030
- X.G. Xu, S. Yang, and Y.F. Li, A Method of SOC-estimate Based on Forecast of Opencircuit Voltage, Electronic Design Engineering 19 (2011), 127-129.
- C. John and V. Baskar, Estimating the State of Charge of a Battery, IEEE Transactions on Control Systems Technology 13 (2005), 465-470. https://doi.org/10.1109/TCST.2004.839571
- T.O. Ting, K.L. Man, C.U. Lei, and C. Lu, State-of-charge for Battery Management System via Kalman Filter, Engineering Letters 22 (2014), 75-82.
- M.F. Tsai, Y.Y. Peng, C.S. Tseng, and N.S. Li, Modeling and Estimation of State of Charge for Lithium-Ion Batteries Using ANFIS Architecture, IEEE International Symposium on Industrial Electronics (2012), 863-868.
- K.X. Wei and Q.Y. Chen, Electric Vehicle Battery SOC Estimation Based on Multiple-model Adaptive Kalman Filter, Proceedings of the Csee 32 (2012), 19-26.
- D.D. Li, Z.C. Wang, and X.X. Guo, Estimation of SOC of Ni-MH Batteries Based on Fuzzy Adaptive Kalman Filtering for HEV, Chinese Journal of Power technology 35 (2011), 192-194.
- J.C.A. Anton, P.J.G. Nieto, F.J.D.C. Juez, F.S. Lasheras, M.G. Vega, and M.N.R. Gutierrez, Battery State-of-Charge Estimator Using the MARS Technique, IEEE Transactions on Power Electronics 28 (2013), 3798-3805. https://doi.org/10.1109/TPEL.2012.2230026
- A.K. Leros and V.C. Moussas, Performance Analysis of an Adaptive Algorithm for DOA Estimation, IAENG International J. of Computer Science 38 (2011), 309-313.
- O. Tremblay and L.A. Dessaint, Experimental Validation of Battery Dynamic Model for EV Application, World Electric Vehicle Journal 3 (2009), 0289-0298. https://doi.org/10.3390/wevj3020289