A STUDY ON THE METHODS OF ESTIMATING THE STATE OF CHARGE (SOC) OF BATTERY

Authors

  • Abu Salman Khan Dept. of Electrical and Electronics Engineering, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh
  • Tapas Kumar Singh Dept. of Electrical and Electronics Engineering, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh
  • Md. Muhibul Islam Dept. of Electrical and Electronics Engineering, Shahjalal University of Science & Technology, Sylhet-3114, Bangladesh

DOI:

https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0034-se

Keywords:

State of charge (SoC), Electric Vehicle, Battery management system, Kalman Filter (EKF), Coulomb Counting Method, MATLAB

Abstract

Batteries are a popular and important item that are utilized as energy sources in a variety of applications. The rise of electric vehicles in the twenty-first century has increased its importance. A battery's state of charge (SoC) is critical information. It is vital to estimate SoC with a reasonable degree of precision. During charge and discharge cycles, lithium-ion batteries change their internal state. The level of charge of a lithium-ion battery changes throughout the charging cycle and is dependent on the internal structure of the components, which can degrade over time. Battery management systems that depend on charge counting and cell voltage monitoring frequently estimate the status of charge. It's difficult to tell what state the battery components are in physically. This work will focus on different methods of estimating SoC. Basically, this study will cover terminal Voltage method, Open Circuit Voltage Method, Coulomb Counting Method and a dynamic method based on Unscented Kalman Filter (EKF). The study will be based on simulation on MATLAB and it will also cover a practical experiment by charging and discharging a battery multiple time. A comparative study of the advantages and drawbacks of the existing methods will be discussed in this work.

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References

Ng, Kong-Song, Huang, Yao-Feng, Moo, Chin-Sien, Hsieh, Yao-ching,(2009) An enhanced coulomb counting method for estimating state-of-charge and state-of-health of lead-acid batteries. https://doi.org/10.1109/ INTLEC.2009.5351796

J. Du, Z. Liu, Y. Wang, C. Wen, (2014) A fuzzy logic-based model for li-ion battery with SoC and temperature effect. https://doi.org/10.1109/ICCA.2014.6871117

P. Spagnol, S. Rossi, S.M. Savaresi,(2011) Kalman Filter SoC Estimation for li-ion batteries. https://doi.org/10.1109/ CCA.2011.6044480

C. Florin, R. Comeliu, N. Marian, G. Catalin, (2017) Extended KAlman Filter for State of charge estimation in electric vehicle battery packs. https://doi.org/10.1109/OPTIM.2017.7975036

H. Zhai, (2017) Modeling of Lithium-ion Battery for Charging/Discharging Characteristics Based on Circuit Model. https://doi.org/10.3991/ijoe.v13i06.6799

N. Shahab, (2016) Adaptive Techniques for Estimation and Online Monitoring of Battery Energy Storage Devices.

Sepasi, Saeed. (2014) Adptive State of Charge estimation for battery packs. https://doi.org/10.13140/ RG.2.1.4737.5209

Linden, David. Handbook of Batteries, 3rd ed., McGraw-Hill., New York, 2001.

Piller S., Perrin M., Jossen, (2001) A methods of State of Charge determination and their application. https://doi.org/10.1016/S0378-7753(01)00560-2

M.A.U.S NAvaratne, R.V. Koswatta, S.G. Abeyratne, (2009) A simple battery chemistry identification and implementation technique for a self-adaptable charger. https://doi.org/10.1109/ICIINFS. 2009.5429872

M. Urbain, S.Rael, B.Davat, P.Desprez, (2007) State Estimation of a Lithium-Ion Battery Through Kalman Filter. https://doi.org/10.1109/PESC.2007.4342463

Ng, Kong-Song, Huang, Yao-Feng, Moo, Chin-Sien, Hsieh, Yao-ching, (2009) State-ofcharge estimation with open-circuit voltage for lead-acid batteries. https://doi.org/10.1109/PECON.2008.4762614

S. Sindhuja, K.Vasanth, (2015) Modified coulomb counting method of SoC estimation for uninterruptible power supply system’s battery management system. https://doi.org/10.1109/ICCICCT.2015. 7475275

R.G. Brown, Patrick Y.C. Hwang, (2012) Introduction to Random Signals and Applied Kalman Filtering. Malkhandi, Souradip, (2006) Fuzzy logic-based learning system and estimation of state-of charge of lead-acid battery. https://doi.org/10.1016/j.engappai.2005.12.005

Kang L, Zhao X, Ma J. (2014) A new neural network model for the state-of-charge estimation in the battery degradation process. Appl. Energy 2014; 121:20–7. https://doi.org/10.1016/j.apenergy.2014.01.066

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Published

16-11-2022

How to Cite

[1]
A. S. . Khan, T. K. Singh, and M. M. . Islam, “A STUDY ON THE METHODS OF ESTIMATING THE STATE OF CHARGE (SOC) OF BATTERY”, Khulna Univ. Stud., pp. 417–431, Nov. 2022.

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