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Battery Cell SOC Estimation Using Neural Network

뉴럴 네트워크를 이용한 배터리 셀 SOC 추정

  • Ryu, Kyung-Sang (Electric Power System Research Team, Korea Institute of Energy Research (KIER)) ;
  • Kim, Ho-Chan (Dept. of Electrical Engineering, Jeju National University)
  • Received : 2020.03.12
  • Accepted : 2020.03.20
  • Published : 2020.03.31

Abstract

This paper proposes a method of estimating the SOC(State of Charge) of a battery cell using a neural network algorithm. To this, we implement a battery SOC estimation simulator and derive input and output data for neural network learning through charge and discharge experiments at various temperatures. Finally, the performance of the battery SOC estimation is analyzed by comparing with the experimental value by Ah-counting using Matlab/Simulink program and confirmed that the error rate can be reduced to less than 3%.

Acknowledgement

Supported by : Korea Institute of Energy Technology Evaluation and Planning (KETEP)

References

  1. M. A. Hannan, M. S. H. Lipu, A. Hussain and A. Mohamed, "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Vol.78, pp.834-854, 2017. DOI: 10.1016/j.rser.2017.05.001 https://doi.org/10.1016/j.rser.2017.05.001
  2. K. S. Ng, C.S. Moo, Y. P. Chen and Y. C. Hsieh, "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Vol.86, pp.1506-1511, 2009. DOI: 10.1016/japenergy.2008.11.021 https://doi.org/10.1016/j.apenergy.2008.11.021
  3. Y. Jeong, Y. Cho, J. Ahn, S. Ryu and B. Lee, "Enhanced coulomb counting method with adaptive SOC reset time for estimating OCV," 2014 IEEE Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, pp.1313-1318, 2014. DOI: 10.1109/ECCE.2014.6953989
  4. M. U. Ali, A. Zafar, S. H. Nengroo, S. Hussain, M. J. Alvi and H. J. Kim, "Towards a smarter battery management system for electric vehicle applications: A critical review of lithium-Ion battery state of charge estimation," Energies, Vol.12, No.3, 2019. DOI: 10.3390/en12030446
  5. B. Enache and E. Diaconescu, "Estimating a bettery state of charge using neural networks," International Symposium on Fundamentals of Electrical Engineering, pp.1-6, 2014. DOI: 10.1109/ISFEE.2014.7050636