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Energy yield estimation of on-vehicle photovoltaic systems in urban environments

Author

Listed:
  • Rigogiannis, Nick
  • Perpinias, Ioannis
  • Bogatsis, Ioannis
  • Roidos, Ioannis
  • Vagiannis, Nick
  • Zournatzis, Athanasios
  • Kyritsis, Anastasios
  • Papanikolaou, Nick
  • Kalogirou, Soteris

Abstract

Greenhouse gases from the propulsion systems of road transportations constitute a significant obstacle to achieve the Paris Agreement objectives. Nowadays, the substitution of conventional internal combustion engines with electric motors, along with electrochemical storage systems are the leading efforts to reduce the use of fossil fuels in road transportations. However, their limited driving range and the long charging times are the main technical factors that hinder the development of electromobility. Thus, energy harvesters and regeneration systems are increasingly incorporated in road vehicles, in order to increase their driving range. In this context, Vehicle Integrated and Applied Photovoltaics (VIAPVs) constitute an attractive prospect. The electricity yield for VIAPVs depends strongly on the route, the shadings due to the urban environment, the applied Maximum Power Point (MPPT) algorithm and the traffic conditions. In this paper, four commonly used commercial MPPT algorithms are experimentally evaluated, regarding their ability to extract the maximum available power simulating realistic city routes. The results show notable discrepancies in the performance of the studied algorithms, between terrestrial and VIAPV applications, highlighting the impact of poor MPPT performance in terms of power generation in moving vehicles.

Suggested Citation

  • Rigogiannis, Nick & Perpinias, Ioannis & Bogatsis, Ioannis & Roidos, Ioannis & Vagiannis, Nick & Zournatzis, Athanasios & Kyritsis, Anastasios & Papanikolaou, Nick & Kalogirou, Soteris, 2023. "Energy yield estimation of on-vehicle photovoltaic systems in urban environments," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123009047
    DOI: 10.1016/j.renene.2023.118998
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    References listed on IDEAS

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    1. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
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    3. Oh, Myeongchan & Kim, Sung-Min & Park, Hyeong-Dong, 2020. "Estimation of photovoltaic potential of solar bus in an urban area: Case study in Gwanak, Seoul, Korea," Renewable Energy, Elsevier, vol. 160(C), pages 1335-1348.
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