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All Journal Mechatronics, Electrical Power, and Vehicular Technology
Aam Muharam
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Electrical & Electronics Engineering

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LSTM-based forecasting on electric vehicles battery swapping demand: Addressing infrastructure challenge in Indonesia Muhammad Zakiyullah Romdlony; Rashad Abul Khayr; Aam Muharam; Eka Rakhman Priandana; Sudarmono Sasmono; Muhammad Ridho Rosa; Irwan Purnama; Amin Amin; Ridlho Khoirul Fachri
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.72-79

Abstract

This article aims to design a model for forecasting the number of vehicles arriving at the battery swap station (BSS). In our case, we study the relevance of the proposed approach given the rapid increase in electric vehicle users in Indonesia. Due to the vehicle electrification program from the government of Indonesia and the lack of supporting infrastructure, forecasting battery swap demands is very important for charging schedules. Forecasting the number of vehicles is done using machine learning with the long short-term memory (LSTM) method. The method is used to predict sequential data because of its ability to review previous data in addition to the current input. The result of the forecasting using the LSTM method yields a prediction score using the root-mean-square error (RMSE) of 2.3079 x 10-6 . The forecasted data can be combined with the battery charging model to acquire predicted hourly battery availability that can be processed further for optimization and scheduling.
Co-Authors Amin Amin Eka Rakhman Priandana Irwan Purnama Muhammad Ridho Rosa Muhammad Zakiyullah Romdlony Rashad Abul Khayr Ridlho Khoirul Fachri Sudarmono Sasmono