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Modeling and Estimation of Lithium-ion Battery State of Charge Using Intelligent Techniques

  • S. HemavathiEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 609)

Abstract

The Li-ion battery is an energy storage system in consumer and industrial applications. Because of their cell and pack level protection, the Li-ion battery requires a battery management system. The important function of the battery management system is to monitor the Li-ion battery state of charge (SOC), to indicate the charge level of the battery. In this research article, efficient intelligent techniques-based SOC estimation algorithm is presented. The proposed techniques are feedforward neural network and layer recurrent neural network with a Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) training methods. The proposed estimators are applied on 18650 single-cell Li-ion battery to test the performance of the neural networks to estimate the SOC. A real-time experiment carried out on 18650 single-cell Li-ion battery, and experimental results and characteristics are analyzed. The battery cell voltage and current obtained from experimental results are used as the input parameter to proposed networks and battery SOC as the output. The proposed estimation is carried out in the MATLAB. The simulation results show that layer recurrent neural network with LM training method has the best performance to estimate the Li-ion battery SOC in terms of accurate measurement with actual SOC and highest convergence speed.

Keywords

Feedforward neural network Levenberg-Marquardt Li-ion battery Recurrent neural network Scaled conjugate gradient State of charge 

Notes

Acknowledgements

Author would like to thank the Research and development in battery division of Central Electrochemical Research Institute, Council of Scientific and Industrial Research in India, for the financial assistance. The work presented in this paper is a part of the In House Project (IHP) “Design and Development of 1 kWh Solar Energy Storage System using Li-ion Battery cells,” funded by Central Electrochemical Research Institute, India.

References

  1. 1.
    Corno M, Bhatt N, Savaresi SM, Verhaegen M (2015) Electrochemical model-based state of charge estimation for Li-ion cells. IEEE Trans Control Syst Technol 23(1):117–127CrossRefGoogle Scholar
  2. 2.
    Chang WY (2013) The state of charge estimating methods for battery: a review. ISRN Appl MathGoogle Scholar
  3. 3.
    Pang S, Farrell J, Du J, Barth M (2001) Battery state of charge estimation. In: Proceedings of the 2001 American control conference, pp 1644–1649. June 2001Google Scholar
  4. 4.
    Xiong R, Cao J, Yu Q, He H, Sun F (2018) Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6:1832–1843CrossRefGoogle Scholar
  5. 5.
    Berrueta A, San Martin I, Sanchis P, Ursua A (2016) Comparison of state-of-charge estimation methods for stationary Lithium-ion batteries. In: 42nd annual conference of the IEEE industrial electronics society, pp 2010–2015. Dec 2016Google Scholar
  6. 6.
    Kribsky P, Krivka J, Valda L, Zahour J (2014) Li-ion state of charge estimation methods. In: 22nd telecommunications forum, pp 649–651. Nov 2014Google Scholar
  7. 7.
    Chiasson J, Vairamohan B (2005) Estimating the state of charge of a battery. IEEE Trans Control Syst Technol 13(3):465–470CrossRefGoogle Scholar
  8. 8.
    Parthiban T, Ravi R, Kalaiselvi N (2007) Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of Lithium-ion cells. Electrochimica Acta 53:1877–1882CrossRefGoogle Scholar
  9. 9.
    Jeon S, Yun JJ, Bae S (Oct 2015) Comparative study on the battery state-of-charge estimation method. Indian J Sci Technol 8(26)Google Scholar
  10. 10.
    Chaoui H, Mandalapu S (2017) Comparative study of online open circuit voltage estimation techniques for state of charge estimation of Lithium-ion batteries. Batteries 3(2):12CrossRefGoogle Scholar
  11. 11.
    Lee S, Kim L, Lee J, Cho BH (2008) State-of-charge and capacity estimation of Lithium-ion battery using a new open-circuitvoltage versus state-of-charge. J Power Sour 185(2):1367–1373CrossRefGoogle Scholar
  12. 12.
    Ma Yan, Li Bingsi, Xie Y, Chen H (2016) Estimating the state of charge of Lithium-ion battery based on sliding mode observer. IFAC-Papers OnLine 49(11):54–61CrossRefGoogle Scholar
  13. 13.
    Yadaiah N, Sowmya G (2006) Neural network based state estimation of dynamical systems. In: International joint conference on neural networks, pp 1042–1049. July 2006Google Scholar
  14. 14.
    Qazi A, Fayaz H, Wadi A, Raj RG, Rahim NA, Khan WA (2015) The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. J Clean Prod 104:1–12CrossRefGoogle Scholar
  15. 15.
    Yu Z, Huai R, Xiao L (2015) State-of-charge estimation for Lithium-ion batteries using a Kalman filter based on local linearization. Energies 8:7854–7873CrossRefGoogle Scholar
  16. 16.
    Hussein AA (2014) Kalman filters versus neural networks in battery state-of-charge estimation: a comparative study. Int J Mod Nonlinear Theory Appl 3:199–209CrossRefGoogle Scholar
  17. 17.
    Youssef C, Omar D, Ahmed G, Fatima E, Najia ES (2017) Designand simulation of an accurate neural network state-of-charge estimator for Lithium ion battery pack. Int Rev Autom Control 10(2):186–192Google Scholar
  18. 18.
    Jimenez-Bermejo D, Fraile-Ardanuy J, Castano-Solis S, Merino J, Alvaro-Hermana R (2018) Using dynamic neural networks for battery state of charge estimation in electric vehicles. Procedia Comput Sci 130:533–540CrossRefGoogle Scholar
  19. 19.
    Narang SB, Singh M, Pubby K (2015) Comparison of feed forward neural network training methods for visual character recognition. Adv Comput Sci Inf Technol 2(4):366–369Google Scholar
  20. 20.
    Prerana ParveenSehgal (2015) Comparative study of GD, LM and SCG method of neural network for thyroid disease diagnosis. Int J Appl Res 1(10):34–39Google Scholar
  21. 21.
    Cetisli B, Barkana A (Mar 2009) Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput, pp 365–378Google Scholar
  22. 22.
    Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:523–533CrossRefGoogle Scholar
  23. 23.
    Lv, Xing Y, Zhang J, Na X, Li Y, Liu T, Cao D, Wang FY (2017) Levenberg-marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system. IEEE Trans Ind InfGoogle Scholar
  24. 24.
    Ismail M, Dlyma R, Elrakaybi A, Ahmed R, Habibi S (2017) Battery state of charge estimation using an artifical neural network. IEEE Transp Electrif Conf Expo, pp 342–349Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Battery DivisionCentral Electrochemical Research InstituteChennaiIndia

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