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Part of the book series: Advanced Structured Materials ((STRUCTMAT,volume 128))

Abstract

The storage in renewable energy systems especially in photovoltaic systems is still a major issue related to their unpredictable and complex working. Due to the continuous changes of the source outputs, several problems can be encountered for the sake of modeling, monitoring, control and lifetime extending of the storage devices. Therefore, several storage devices were introduced in the practice such as pumped hydro, compressed air, flywheel, super capacitors and electrochemical storage. However, the electrochemical storage especially the storage by battery bank is still the most used in PV systems. According to the performances and the features needed in such systems, two batteries types can be distinguished, namely lithium-ion and lead-acid-based batteries. Likely, there is a consensus that the lithium battery presents a better performances comparing to other types such as the high energy density, the low self-discharge current and the low maintenance. However, the major disadvantage of these batteries type is their high-cost which somewhat has slow down their progress for the large-scale applications. From there, the storage using lead-acid battery type is still widely used for the reason of its low cost and the ease of its maintenance. However, its complex electrochemical and electrical behaviors besides the random working of the PV systems make it as one of main issues for the sake of modeling, control and lifetime extending. In this chapter, we provide description of dynamic batteries behavior, encountered problems in the PV systems with solutions proposal in terms of modeling and control.

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References

  1. Blaifi, S., Moulahoum, S., Colak, I., Merrouche, W.: An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications. Appl. Energy 169, 888–898 (2016)

    Article  Google Scholar 

  2. Blaifi, S., Moulahoum, S., Colak, I., Merrouche, W.: Monitoring and enhanced dynamic modeling of battery by genetic algorithm using LabVIEW applied in photovoltaic system. Electr. Eng. 100(2), 1021–1038 (2018)

    Article  Google Scholar 

  3. Guasch, D., Silvestre, S.: Dynamic battery model for photovoltaic applications. Prog. Photovolt. Res. Appl. 11, 193–206 (2003)

    Article  CAS  Google Scholar 

  4. Copetti, J.B., Lorenzo, E., Chenlo, F.: A general battery model for PV system simulation. Prog. Photovolt. Res. Appl. 1, 283–292 (1993)

    Article  CAS  Google Scholar 

  5. Ng, K.S., Moo, C.S., Chen, Y.P., Hsieh, Y.C.: State-of-charge estimation for lead-acid batteries based on dynamic open circuit voltage. In: Proceedings of the 2nd IEEE International Power and Energy Conference (PECon’08), pp. 972–976, Johor Bahru, Malaysia, Dec 2008

    Google Scholar 

  6. Anbuky, A.H., Pascoe, P.E.: VRLA battery state-of charge estimation in telecommunication power systems. IEEE Trans. Ind. Electron. 47(3), 565–573 (2000)

    Article  Google Scholar 

  7. Sato, S., Kawamura, A.: A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. In: Proceedings of the Power Conversion Conference, pp. 565–570, Osaka, Japan, Apr 2002

    Google Scholar 

  8. Rodrigues, S., Munichandraiah, N., Shukla, A.K.: A review of state-of-charge indication of batteries by means of A.C. impedance measurements. J. Power Sources 87(1–2), 12–20 (2000)

    Article  CAS  Google Scholar 

  9. Shepherd, C.M.: Design of primary and secondary cells. J. Electrochem. Soc. 112, 657–664 (1965)

    Article  CAS  Google Scholar 

  10. Ng, K.S., Moo, C.S., Chen, Y.P., Hsieh, Y.C.: Enhanced Coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy 86(9), 1506–1511 (2009)

    Article  CAS  Google Scholar 

  11. Blaifi, S., Moulahoum, S., Benkercha, R., Taghezouit, B., Saim, A.: M5P model tree based fast fuzzy maximum power point tracker. Sol. Energy 163, 405–442 (2018)

    Article  Google Scholar 

  12. Watrin, N., Blunier, B., Miraoui, A.: Review of adaptive systems for lithium batteries state-of-charge and state-of-health estimation. In: Proceedings of IEEE Transportation Electrification Conference and Expo, pp. 1–6, Dearborn, Mich, USA, June 2012

    Google Scholar 

  13. Weigert, T., Tian, Q., Lian, K.: State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks. J. Power Sources 196(8), 4061–4066 (2011)

    Article  CAS  Google Scholar 

  14. Linda, O., William, E.J., Huf, M., et al.: Intelligent neural network implementation for SOCI development of Li/CFx batteries. In: Proceedings of the 2nd International Symposium on Resilient Control Systems (ISRCS’09), pp. 57–62, Idaho Falls, Idaho, USA, Aug 2009

    Google Scholar 

  15. Salkind, A.J., Fennie, C., Singh, P., Atwater, T., Reisner, D.E.: Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. J. Power Sources 80(1–2), 293–300 (1999)

    Article  CAS  Google Scholar 

  16. Singh, P., Vinjamuri, R., Wang, X., Reisner, D.: Design and implementation of a fuzzy logic-based state-of charge meter for Li-ion batteries used in portable defibrillators. J. Power Sources 162(2), 829–836 (2006)

    Article  CAS  Google Scholar 

  17. Singh, P., Fennie Jr., C., Reisner, D.: Fuzzy logic modeling of state-of charge and available capacity of nickel/metal hydride batteries. J. Power Sources 136(2), 322–333 (2004)

    Article  CAS  Google Scholar 

  18. Malkhandi, S.: Fuzzy logic-based learning system and estimation of state-of-charge of lead-acid battery. Eng. Appl. Artif. Intell. 19(5), 479–485 (2006)

    Article  Google Scholar 

  19. Lee, Y.S., Wang, W.Y., Kuo, T.Y.: Soft computing for battery state-of-charge (BSOC) estimation in battery string systems. IEEE Trans. Ind. Electron. 55(1), 229–239 (2008)

    Article  Google Scholar 

  20. Xu, L., Wang, J.P., Chen, Q.S.: Kalman fitering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model. Energy Convers. Manag. 53(1), 33–39 (2012)

    Article  CAS  Google Scholar 

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Blaifi, S. (2020). Energy Storage and Photovoltaic Systems. In: Mellit, A., Benghanem, M. (eds) A Practical Guide for Advanced Methods in Solar Photovoltaic Systems. Advanced Structured Materials, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-43473-1_8

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