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Comparative Study on Shading Database Construction for Urban Roads Using 3D Models and Fisheye Images for Efficient Operation of Solar-Powered Electric Vehicles

Author

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  • Jieun Baek

    (Energy Resources Institute, Pukyong National University, Busan 48513, Korea)

  • Yosoon Choi

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea)

Abstract

Accounting for shadows on urban roads is a complex task in the operation of solar-powered electric vehicles. There have been few opportunities to compare the methods and tools for the construction of an effective shading database for urban roads. This study quantitatively investigated and compared shading matrices generated from 3D models or fisheye images. Skymaps were formed considering the geometry of nearby shading obstructions. Sun-path diagrams tracking the position of the sun by time and season were overlaid on the skymaps, and month-by-hour shading matrices were calculated. Mean squared error (MSE) was used to clarify the quantitative differences between the shading matrices. The cases were divided into A, B, and C according to the presence of buildings and trees around the survey points. Under case A (trees), case B (buildings and trees), and case C (buildings), the average MSEs between the matrices were 24.5%, 23.9%, and 2.1%, respectively. The shading matrices using either 3D models or fisheye images provided accurate shading effects caused by buildings. In contrast, the shading effects of trees were more accurately analyzed when using fisheye images. The findings of this study provide a background for constructing shading databases of urban road environments for the optimal operation of solar-powered electric vehicles.

Suggested Citation

  • Jieun Baek & Yosoon Choi, 2022. "Comparative Study on Shading Database Construction for Urban Roads Using 3D Models and Fisheye Images for Efficient Operation of Solar-Powered Electric Vehicles," Energies, MDPI, vol. 15(21), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8228-:d:962996
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    1. Thiel, Christian & Gracia Amillo, Ana & Tansini, Alessandro & Tsakalidis, Anastasios & Fontaras, Georgios & Dunlop, Ewan & Taylor, Nigel & Jäger-Waldau, Arnulf & Araki, Kenji & Nishioka, Kensuke & Ota, 2022. "Impact of climatic conditions on prospects for integrated photovoltaics in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Kim, Hanjin & Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2022. "A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea," Renewable Energy, Elsevier, vol. 190(C), pages 713-729.
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    Cited by:

    1. Chung-Hyun Lee & Woo-Hyuk Lee & Yosoon Choi & Jangwon Suh & Sung-Min Kim, 2023. "Rapid and Effective Technology Development for 3D-Model-Based Solar Access Analysis and Comparative Study with Fish-Eye Camera," Energies, MDPI, vol. 16(7), pages 1-14, March.

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