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System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

Author

Listed:
  • Baldwin Cortés

    (Facultad de Ingeniería Eléctrica, Universidad Michocana de San Nicolás de Hidalgo, Morelia 58030, Mexico)

  • Roberto Tapia

    (Facultad de Ingeniería Eléctrica, Universidad Michocana de San Nicolás de Hidalgo, Morelia 58030, Mexico)

  • Juan J. Flores

    (Facultad de Ingeniería Eléctrica, Universidad Michocana de San Nicolás de Hidalgo, Morelia 58030, Mexico)

Abstract

The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m 2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.

Suggested Citation

  • Baldwin Cortés & Roberto Tapia & Juan J. Flores, 2021. "System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles," Energies, MDPI, vol. 14(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4820-:d:610242
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    References listed on IDEAS

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    Cited by:

    1. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.
    2. Bryam Paúl Lojano-Riera & Carlos Flores-Vázquez & Juan-Carlos Cobos-Torres & David Vallejo-Ramírez & Daniel Icaza, 2023. "Electromobility with Photovoltaic Generation in an Andean City," Energies, MDPI, vol. 16(15), pages 1-16, July.

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