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A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems

  • Muhammad Talha
  • Furqan Asghar
  • Sung Ho Kim
Research Article - Electrical Engineering
  • 43 Downloads

Abstract

Sealed-type lead–acid batteries are most common energy storage devices used with renewable systems. Battery state of charge (SOC) and state of health (SOH) estimation is a crucial part which requires maximum possible accuracy to ensure a secure and long-lasting battery energy storage system by cutting off charging and discharging processes at right time (100–40%). In this research work, a neural network (NN)-based simplified SOC and SOH estimation technique is proposed. Proposed technique provides an accurate online SOC and SOH estimation without battery internal parameter information. This technique only requires real-time battery voltage and current information and very simple mathematical calculations to estimate SOC and SOH, which makes it easy to implement at any low-cost micro-controller unit (MCU). Training data for NN have been acquired by using Arduino mega MCU, voltage sensor circuit, and current sensor. The NN program is designed and trained by backpropagation technique in Arduino mega. The calculated weights are further used for estimation. Terminal voltage \((V_{\mathrm{t}})\) and open-circuit voltage \((V_{\mathrm{oc}})\) are measured for different charging currents \((I_{\mathrm{ch}})\) and discharging currents \((I_{\mathrm{dch}})\). Later on, these data are used for training the NN. Experimental results are provided to prove the preeminence of proposed SOC and SOH estimation technique.

Keywords

Battery energy storage system (BESS) State of charge (SOC) estimation State of health (SOH) estimation Neural network (NN) Arduino mega 

Abbreviation

SOC

State of charge

SOH

State of health

\(\mathrm{SOC}^{{\prime }}\)

SOC slope

BESS

Battery energy storage system

\(V_{\mathrm{t}}\)

Battery terminal voltage.

\(V_{\mathrm{oc}}\)

Battery open-circuit voltage

\(I_{\mathrm{ch}}\)

Charging current

\(I_{\mathrm{dch}}\)

Discharging current

MCU

Micro-controller unit

EKF

Extended Kalman filter

CKF

Cubature Kalman filter

ACKF

Adaptive cubature Kalman filter

\(Q_{\mathrm{m}}\)

Maximum battery capacity at time

\(Q_{\mathrm{nom}}\)

Nominal battery capacity

\(V_{\mathrm{Bn}\,\mathrm{actual}}\)

Actual battery voltage

\(V_{\mathrm{Bn measured}}\)

Measured battery voltage

\(\hbox {weights}_{\mathrm{hidden}}\)

Hidden weights

\(\hbox {weights}_{\mathrm{output}}\)

Output weights

\(\hbox {weights}_{\mathrm{Bhidden}}\)

Bias hidden weights

\(\hbox {weights}_{\mathrm{Boutput}}\)

Bias output weights

\(\mathrm{Hidden}_{\mathrm{act}}\)

Hidden activation value

\(\hbox {Output}_{\mathrm{act}}\)

Output activation value

\(\nabla _{\mathrm{output}}\)

Output delta

\(\nabla _{\mathrm{hidden}}\)

Hidden delta

\(\hbox {Change}_{\mathrm{Whidden}}\)

Change in hidden weights

\(\hbox {Change}_{\mathrm{Woutput}}\)

Change in output weights

LR

Learning rate

M

Momentum

SSE

Sum square error

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References

  1. 1.
    Choi, Y.; Kim, H.: Optimal scheduling of energy storage system for self-sustainable base station operation considering battery wear-out cost. Energies (2016)Google Scholar
  2. 2.
    Li, Y.; Shen, Z.; Ray, A.; Rahn, C.D.: Real-time estimation of lead-acid battery parameters: a dynamic data-driven approach. J. Power Sour. 268, 758–764 (2014)CrossRefGoogle Scholar
  3. 3.
    Han, J.; Kim, D.; Sunwoo, M.: State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter. J. Power Sour. 188, 606–612 (2008)CrossRefGoogle Scholar
  4. 4.
    Seonwoo, J.; Jae-Jung, Y.; Sungwoo, B.: Comparative study on battery state-of-charge estimation method. Indian J. Sci. Technol. 8 (2015)Google Scholar
  5. 5.
    Zhang, C.; Jiang, J.; Zhang, W.; Sharkh, S.M.: Estimation of state of charge of lithium-ion batteries used in HEV using robust extended kalman filtering. Energies (2012)Google Scholar
  6. 6.
    Xia, B.; Wang, H.; Tian, Y.; Wang, M.; Sun, W.; Xu, Z.: State of charge estimation of lithium-ion batteries using an adaptive cubature kalman filter. Energies (2015)Google Scholar
  7. 7.
    Chen, S.; Kang, C.; Zhang, Z.; Zhu, H.: A method for SOC estimation for lead-acid battery based on multi-model adaptive extended kalman filtering estimation. IEEE Ind. Electron. Soci. IECON2016 (2016)Google Scholar
  8. 8.
    Windarko, N.A.; Choi, J.: LiPB battery SOC estimation using extended kalman filter improved with variation of single dominant parameter. J. Power Electron. 12(1), 40–48 (2012)CrossRefGoogle Scholar
  9. 9.
    Wu, S.-L.; Chen, H.-C.; Chou, S.-R.: Fast estimation of charge for lithium-ion batteries. Energies (2014)Google Scholar
  10. 10.
    Sato, S.; Kawamura, A.: A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. IEEE Power Convers. Conf. (2002)Google Scholar
  11. 11.
    Yang, Y.; Liang, Z.; Gu, D.-J.; Cheng, X.: Balancing strategy of lithium-ion batteries based on the change rate of SOC. IEEE Appl. Power Conf. Expos. (APEC) (2017)Google Scholar
  12. 12.
    Eifert, M.: A discrete battery state monitoring algorithm for lead-acid batteries. IEEE Control Appl. (CCA) (2014)Google Scholar
  13. 13.
    Hu, X.; Sun, F.; Zou, Y.: Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive Luenberger observer. Energies (2010)Google Scholar
  14. 14.
    Wang, T.-W.; Yang, M.-J.; Shyu, K.-K.; Lai, C.-M.: Design fuzzy SOC estimation For sealed lead-acid batteries of electric vehicles in reflex. IEEE Ind. Electron. (2017)Google Scholar
  15. 15.
    Yin, H.; Zhou, W.; Zhao, C.: An adaptive fuzzy logic based energy management strategy on battery/ultracapacitor hybrid electric vehicles. IEEE Trans. Transp. Electrif. (2016)Google Scholar
  16. 16.
    Hussain, A.; Bui, V.-H.; Kim, H.-M.: Fuzzy logic-based operation of battery energy storage systems (BESSs) for enhancing the resiliency of hybrid microgrids. Energies (2017)Google Scholar
  17. 17.
    Hussein, A. A.: Kalman filter versus NNs in Battery state-of-charge estimation: a comparative study. Int. J. Modern Nonlinear Theory Appl. (2014)Google Scholar
  18. 18.
    Liu, F.; Liu, T.; Fu, Y.: An improved SoC estimation algorithm based on artificial NN. IEEE ISCID 2015 (2016)Google Scholar
  19. 19.
    Moo, C.S.; Ng, K.S.; Chen, Y.P.; Hsieh, Y.C.: State-of-charge estimation with open-circuit-voltage for lead-acid batteries. IEEE Power Convers. Conference-Nagoya. (2007)Google Scholar
  20. 20.
    Chaui, H.; Miah, S.; Oukaour, A.; Gualous, H.: State-of-charge and state-of-health prediction of lead-acid batteries with genetic algorithms. IEEE ITEC (2015)Google Scholar
  21. 21.
    Li, X.; Shu, X.; Shen, J.; Xiao, R.; Yan, W.; Chen, Z.: An on-board remaining useful life estimation algorithm for lithium-ion batteries of electrical vehicles. Energies (2017)Google Scholar
  22. 22.
    Marchildon, J.; Doumbia, M.L.; Agbossou, K.: SOC and SOH characterization of lead acid batteries. IEEE IECON (2016)Google Scholar
  23. 23.
    Zou, Y.; Xiaosong, H.; Ma, H.; Li, S.E.: Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. J. Power Sour. 273, 793–803 (2014)CrossRefGoogle Scholar
  24. 24.
    Huang, S.-C.; Tseng, K.-H.; Liang, J.-W.; Chang, C.-L.; Pecht, M.G.: An online SOC and SOH estimation model for lithium-ion batteries. Energies (2017)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  1. 1.Kunsan National UniversityGunsanRepublic of Korea
  2. 2.School of electrical engineering, The University of FaisalabadFaisalabadPakistan

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