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Enhanced Methodologies in Photovoltaic Production with Energy Storage Systems Integrating Multi-cell Lithium-Ion Batteries

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Intelligent Systems: Theory, Research and Innovation in Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 864))

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Abstract

The increasing world’s energy demand and the emerging environmental concerns are encouraging the search for clean energy solution. Renewable sources are free, clean and virtually limitless and for those reasons they present a great potential. This chapter addresses two concerns related to photovoltaic (PV) production with energy storage system integrating multi-cell Lithium-ion batteries. To increase the efficiency of a PV production, a Maximum Power Point Tracking (MPPT) method is proposed based on the particle swarm optimization (PSO) algorithm. The proposed PSO-based MPPT is able to avoid the oscillations around the maximum power point (MPP) and the convergence to a local maximum under partial shading conditions. Also, it exhibits an excellent tracking under rapid variation in the environment conditions (irradiance and temperature). Additionally, a new charging method was developed based on the parameters of the battery pack in real time, extending the battery lifespan, improving the capacity usage and the performance of the Energy Storage System (ESS). The proposed Lithium Ion (Li-ion) battery charging method analyses at each moment the difference between the desired voltage and the mean voltage of the cells, the temperature of the pack and the difference of voltages between cells. Based on the obtained information, the algorithm calculates the charging current through trilinear interpolation. It should also be noted that the proposed charging method combines a balancing method and a state of charge determination method based on the Coulomb counting method, which represents an innovation when compared to the existing methods in the literature. The experimental results of both methods demonstrate excellent performance allowing to, on the one hand, achieve an optimized PV production, and on the other hand, make the ESS more effective and efficient.

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Author information

Authors and Affiliations

  1. Department of Electromechanical Engineering, Universidade da Beira Interior, Calçada Fonte do Lameiro, Covilhã, Portugal

    J. B. L. Fermeiro, J. A. N. Pombo, R. L. Velho, G. Calvinho, M. R. C. Rosário & S. J. P. S. Mariano

  2. Instituto de Telecomunicações, Covilhã, Portugal

    J. B. L. Fermeiro, M. R. C. Rosário & S. J. P. S. Mariano

Authors
  1. J. B. L. Fermeiro
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  2. J. A. N. Pombo
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  3. R. L. Velho
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  4. G. Calvinho
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  5. M. R. C. Rosário
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  6. S. J. P. S. Mariano
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Corresponding author

Correspondence to J. B. L. Fermeiro .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computers, Faculdade de Ciências e Tecnologia, UNINOVA-CTS Centre of Technology and Systems, Universidade Nova de Lisboa, Caparica, Portugal

    Ricardo Jardim-Goncalves

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev

  3. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Vladimir Jotsov

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Fermeiro, J.B.L., Pombo, J.A.N., Velho, R.L., Calvinho, G., Rosário, M.R.C., Mariano, S.J.P.S. (2020). Enhanced Methodologies in Photovoltaic Production with Energy Storage Systems Integrating Multi-cell Lithium-Ion Batteries. In: Jardim-Goncalves, R., Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Intelligent Systems: Theory, Research and Innovation in Applications. Studies in Computational Intelligence, vol 864. Springer, Cham. https://doi.org/10.1007/978-3-030-38704-4_11

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