Journal of Computational Electronics

, Volume 18, Issue 1, pp 260–270 | Cite as

Fast and accurate PV model for SPICE simulation

  • Hamdy AbdelhamidEmail author
  • Ahmed Edris
  • Amr Helmy
  • Yehea Ismail


This paper presents a complete model for photovoltaic modules able to accurately predict the IV characteristics at different levels of temperature and irradiance. The model greatly reduces the computational effort needed to extract the five parameters of the one-diode model by applying five boundary conditions based on data provided by the manufacturer only. The model equations are reduced to only two simultaneous equations of two unknowns (series resistance, \(R_\mathrm{s}\), and shunt resistance, \(R_\mathrm{sh}\)), which converge in five iterations on average. The model parameters are extrapolated to account for temperature and irradiance variations. The model is matched very well with the experimental data obtained from different commercial PV modules. The proposed IV model has least root mean square error of (0.0031) compared to other works. The model is implemented in Verilog-A to be used inside SPICE simulators. The model in Verilog-A is integrated in Cadence-SPECTRE circuit simulator and tested with a boost converter.


Cadence Newton–Raphson PV model Shunt resistance Series resistance SPICE Verilog-A 



This research was partially funded by Zewail City of Science and Technology (ZCST), ITIDA ITAC program, Academy of Scientific Research and Technology in Egypt (ASRT), Deepen Local Manufacturing in Electronics Industry Alliance (DLMEI).


  1. 1.
    Ishaque, K., Salam, Z., Taheri, H.: Simple, fast and accurate two-diode model for photovoltaic modules. Solar Energy Mater. Solar Cells 95(2), 586–594 (2011)CrossRefGoogle Scholar
  2. 2.
    Saloux, E., Teyssedou, A., Sorin, M.: Explicit model of photovoltaic panels to determine voltages and currents at the maximum power point. Solar Energy 85, 713–722 (2011)CrossRefGoogle Scholar
  3. 3.
    Khezzar, R., Zereg, M., Khezzar, A.: Modeling improvement of the four parameter model for photovoltaic modules. Solar Energy 110, 452–462 (2014)CrossRefGoogle Scholar
  4. 4.
    Laudani, A., Fulginei, F.R., Salvini, A.: Identification of the one-diode model for photovoltaic modules from datasheet values. Solar Energy 108, 432–446 (2014)CrossRefGoogle Scholar
  5. 5.
    Cubas, J., Pindado, S., Victoria, M.: On the analytical approach for modeling photovoltaic systems behavior. J. Power Sources 247, 467–474 (2014)CrossRefGoogle Scholar
  6. 6.
    Carrero, C., Ramírez, D., Rodríguez, J., Platero, C.: Accurate and fast convergence method for parameter estimation of PV generators based on three main points of the i–v curve. Renew. Energy 36, 2972–2977 (2011)CrossRefGoogle Scholar
  7. 7.
    Ghani, F., Duke, M., Carson, J.: Numerical calculation of series and shunt resistances and diode quality factor of a photovoltaic cell using the lambert w-function. Solar Energy 91, 422–431 (2013)CrossRefGoogle Scholar
  8. 8.
    Lun, S. Du, C., Xu, C.: An explicit i–v model of solar cells based on padé approximants. In 2016 Chinese Control and Decision Conference (CCDC). IEEE (2016)Google Scholar
  9. 9.
    Peng, L., Sun, Y., Meng, Z.: An improved model and parameters extraction for photovoltaic cells using only three state points at standard test condition. J. Power Sources 248, 621–631 (2014)CrossRefGoogle Scholar
  10. 10.
    Mahmoud, Y.A., Xiao, W., Zeineldin, H.H.: A parameterization approach for enhancing PV model accuracy. IEEE Trans. Ind. Electron. 60, 5708–5716 (2013)CrossRefGoogle Scholar
  11. 11.
    Mahmoud, Y., El-Saadany, E.F.: A photovoltaic model with reduced computational time. IEEE Trans. Ind. Electron. 62(6), 3534–3544 (2015)Google Scholar
  12. 12.
    Brano, V.L., Orioli, A., Ciulla, G., Gangi, A.D.: An improved five-parameter model for photovoltaic modules. Solar Energy Mater. Solar Cells 94, 1358–1370 (2010)CrossRefGoogle Scholar
  13. 13.
    Orioli, A., Gangi, A.D.: A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data. Appl. Energy 102, 1160–1177 (2013)CrossRefGoogle Scholar
  14. 14.
    de Blas, M., Torres, J., Prieto, E., Garcia, A.: Selecting a suitable model for characterizing photovoltaic devices. Renew. Energy 25, 371–380 (2002)CrossRefGoogle Scholar
  15. 15.
    Khan, F., Baek, S.-H., Park, Y., Kim, J.H.: Extraction of diode parameters of silicon solar cells under high illumination conditions. Energy Convers. Manag. 76, 421–429 (2013)CrossRefGoogle Scholar
  16. 16.
    Khalifa, A.E., Elhamid, H.A., Swillam, M.A.: Optimal design of intermediate reflector layer in micromorph silicon thin-film solar cells. J. Nanophotonics 10, 046006 (2016)CrossRefGoogle Scholar
  17. 17.
    Lim, L.H.I., Ye, Z., Ye, J., Yang, D., Du, H.: A linear identification of diode models from single i–v characteristics of PV panels. IEEE Trans. Ind. Electron. 62, 4181–4193 (2015)CrossRefGoogle Scholar
  18. 18.
    Soon, J.J., Low, K.-S.: Photovoltaic model identification using particle swarm optimization with inverse barrier constraint. IEEE Trans. Power Electron. 27, 3975–3983 (2012)CrossRefGoogle Scholar
  19. 19.
    Jordehi, A.R.: Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Solar Energy 159, 78–87 (2018)CrossRefGoogle Scholar
  20. 20.
    Jadli, U., Thakur, P., Shukla, R.D.: A new parameter estimation method of solar photovoltaic. IEEE J. Photovolt. 8, 239–247 (2018)CrossRefGoogle Scholar
  21. 21.
    Hachana, O., Hemsas, K.E., Tina, G.M., Ventura, C.: Comparison of different metaheuristic algorithms for parameter identification of photovoltaic cell/module. J. Renew. Sustain. Energy 5, 053122 (2013)CrossRefGoogle Scholar
  22. 22.
    Zhu, H., Yu, C., Lu, L., Lian, W., Yao, J., Hu, Y.: Research on parameter distribution features of photovoltaic array under the cover and shadow shading conditions. Int. J. Photoenergy 2018, 1–14 (2018)CrossRefGoogle Scholar
  23. 23.
    De Soto, W., Klein, S., Beckman, W.: Improvement and validation of a model for photovoltaic array performance. Solar Energy 80, 78–88 (2006)CrossRefGoogle Scholar
  24. 24.
    Batzelis, E.I., Papathanassiou, S.A.: A method for the analytical extraction of the single-diode PV model parameters. IEEE Trans. Sustain. Energy 7, 504–512 (2016)CrossRefGoogle Scholar
  25. 25.
    Sera, D., Teodorescu, R., Rodriguez, P.: PV panel model based on datasheet values. In: 2007 IEEE International Symposium on Industrial Electronics. IEEE (2007)Google Scholar
  26. 26.
    Ding, K., Zhang, J., Bian, X., Xu, J.: A simplified model for photovoltaic modules based on improved translation equations. Solar Energy 101, 40–52 (2014)CrossRefGoogle Scholar
  27. 27.
    Chatterjee, A., Keyhani, A., Kapoor, D.: Identification of photovoltaic source models. IEEE Trans. Energy Convers. 26, 883–889 (2011)CrossRefGoogle Scholar
  28. 28.
    Park, J.-Y., Choi, S.-J.: Datasheet-based circuit parameter extraction method for maximum power point simulation of photovoltaic array. In: 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia). IEEE (2015)Google Scholar
  29. 29.
    Easwarakhanthan, T., Bottin, J., Bouhouch, I., Boutrit, C.: Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int. J. Solar Energy 4, 1–12 (1986)CrossRefGoogle Scholar
  30. 30.
    Phang, J., Chan, D., Phillips, J.: Accurate analytical method for the extraction of solar cell model parameters. Electron. Lett. 20(10), 406 (1984)CrossRefGoogle Scholar
  31. 31.
    Bouzidi, K., Chegaar, M., Bouhemadou, A.: Solar cells parameters evaluation considering the series and shunt resistance. Solar Energy Mater. Solar Cells 91, 1647–1651 (2007)CrossRefGoogle Scholar
  32. 32.
    AlHajri, M., El-Naggar, K., AlRashidi, M., Al-Othman, A.: Optimal extraction of solar cell parameters using pattern search. Renew. Energy 44, 238–245 (2012)CrossRefGoogle Scholar
  33. 33.
    El-Naggar, K., AlRashidi, M., AlHajri, M., Al-Othman, A.: Simulated annealing algorithm for photovoltaic parameters identification. Solar Energy 86, 266–274 (2012)CrossRefGoogle Scholar
  34. 34.
    Peng, L., Sun, Y., Meng, Z., Wang, Y., Xu, Y.: A new method for determining the characteristics of solar cells. J. Power Sources 227, 131–136 (2013)CrossRefGoogle Scholar
  35. 35.
    Rezk, A.A., Helmy, A., Ismail, Y.: VHDL implementation of a power management algorithm for PV-battery system. In: 5th International Conference on Energy Aware Computing Systems & Applications. IEEE (2015)Google Scholar
  36. 36.
    Rezk, A.A., Helmy, A., Abdallah, A., Ismail, Y.: VHDL implementation of maximum power point tracking algorithms. In: 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS). IEEE (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Nano-electronics and Devices, Center for Nano-TechnologyZewail City of Science and Technology6th of October, GizaEgypt
  2. 2.Center for Nano-electronics and DevicesZewail City of Science and Technology6th of October, GizaEgypt
  3. 3.University of Science and TechnologyZewail City of Science and Technology6th of October, GizaEgypt
  4. 4.Center for Nano-electronics and DevicesAmerican University in Cairo/Zewail City of Science and Technology6th of October, GizaEgypt

Personalised recommendations