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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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):117–127
Chang WY (2013) The state of charge estimating methods for battery: a review. ISRN Appl Math
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 2001
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 6:1832–1843
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 2016
Kribsky P, Krivka J, Valda L, Zahour J (2014) Li-ion state of charge estimation methods. In: 22nd telecommunications forum, pp 649–651. Nov 2014
Chiasson J, Vairamohan B (2005) Estimating the state of charge of a battery. IEEE Trans Control Syst Technol 13(3):465–470
Parthiban T, Ravi R, Kalaiselvi N (2007) Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of Lithium-ion cells. Electrochimica Acta 53:1877–1882
Jeon S, Yun JJ, Bae S (Oct 2015) Comparative study on the battery state-of-charge estimation method. Indian J Sci Technol 8(26)
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):12
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):1367–1373
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):54–61
Yadaiah N, Sowmya G (2006) Neural network based state estimation of dynamical systems. In: International joint conference on neural networks, pp 1042–1049. July 2006
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 104:1–12
Yu Z, Huai R, Xiao L (2015) State-of-charge estimation for Lithium-ion batteries using a Kalman filter based on local linearization. Energies 8:7854–7873
Hussein AA (2014) Kalman filters versus neural networks in battery state-of-charge estimation: a comparative study. Int J Mod Nonlinear Theory Appl 3:199–209
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):186–192
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 130:533–540
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):366–369
Prerana ParveenSehgal (2015) Comparative study of GD, LM and SCG method of neural network for thyroid disease diagnosis. Int J Appl Res 1(10):34–39
Cetisli B, Barkana A (Mar 2009) Speeding up the scaled conjugate gradient algorithm and its application in neuro-fuzzy classifier training. Soft Comput, pp 365–378
Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:523–533
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 Inf
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 342–349
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hemavathi, S. (2020). Modeling and Estimation of Lithium-ion Battery State of Charge Using Intelligent Techniques. In: Singh, S., Pandey, R., Panigrahi, B., Kothari, D. (eds) Advances in Power and Control Engineering. Lecture Notes in Electrical Engineering, vol 609. Springer, Singapore. https://doi.org/10.1007/978-981-15-0313-9_12
Download citation
DOI: https://doi.org/10.1007/978-981-15-0313-9_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0312-2
Online ISBN: 978-981-15-0313-9
eBook Packages: EngineeringEngineering (R0)