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20-07-2023 | Technical Paper

Design of solar battery swapping station for EV using LSTM-assisted solar power forecasting

Authors: S. K. Chawrasia, D. Hembram, D. Bose, C. K. Chanda

Published in: Microsystem Technologies | Issue 9/2024

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Abstract

Electric vehicles (EVs) are gaining importance nowadays as these are the best option for green transportation. But the charging of the electric vehicle is again a challenging task as it takes hours to charge the EV which is not acceptable to the customers. Also, if we charge the EV using a conventional grid it is again an extra burden on the grid. So, we need to find some solution for these issues and the best solution is using a battery swapping station instead of a battery charging station which will take just 2 min to swap the battery instead of charging. And to reduce the burden on the grid we can use solar or other renewable energies to charge the batteries at swapping stations. In this paper, solar power is used to charge the battery packs and if in an emergency situation solar power is not sufficiently available to ensure the availability of fully charged battery packs then the power can be taken from the grid or other renewable energy sources. Solar power is forecasted using the long short-term memory (LSTM) model for better power scheduling to the grid or other renewable energies.

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Literature
go back to reference Shalaby AA, Shaaban MF, Mokhtar M, Zeineldin HH, El-Saadany EF (2021) Optimal day-ahead operation for a PV-based battery swapping station for electric vehicles. In: 2021 6th international symposium on environment-friendly energies and applications (EFEA), pp. 1–8, doi: https://doi.org/10.1109/EFEA49713.2021.9406274 Shalaby AA, Shaaban MF, Mokhtar M, Zeineldin HH, El-Saadany EF (2021) Optimal day-ahead operation for a PV-based battery swapping station for electric vehicles. In: 2021 6th international symposium on environment-friendly energies and applications (EFEA), pp. 1–8, doi: https://​doi.​org/​10.​1109/​EFEA49713.​2021.​9406274
Metadata
Title
Design of solar battery swapping station for EV using LSTM-assisted solar power forecasting
Authors
S. K. Chawrasia
D. Hembram
D. Bose
C. K. Chanda
Publication date
20-07-2023
Publisher
Springer Berlin Heidelberg
Published in
Microsystem Technologies / Issue 9/2024
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI
https://doi.org/10.1007/s00542-023-05510-0