Issue |
E3S Web of Conf.
Volume 465, 2023
8th International Conference on Industrial, Mechanical, Electrical and Chemical Engineering (ICIMECE 2023)
|
|
---|---|---|
Article Number | 02042 | |
Number of page(s) | 10 | |
Section | Symposium on Electrical, Information Technology, and Industrial Engineering | |
DOI | https://doi.org/10.1051/e3sconf/202346502042 | |
Published online | 18 December 2023 |
An Optimization Model for Electric Motorcycle Battery Swapping Station in Indonesia: A Case Study
Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia
* Corresponding author: iqbalsp72@student.uns.ac.id
† Corresponding author: hisjam@staff.uns.ac.id
‡ Corresponding author: wakhidjauhari@gmail.com
Battery Electric Vehicle (BEV) has become a new technological innovation that effectively accelerates electrification due to their short charging and swapping processes. On the other hand, developing charging infrastructure, such as charging stations, still needs to be improved in Indonesia. The absence of government policies regulating charging station design has led each electric motorcycle company to innovate with their own standards. This research aims to implement a proposed Battery Swap Station (BSS) model to minimize swapping costs by optimizing the charging schedule for each exchanged battery based on a case study of one electric motorcycle service provider in Solo. Three factors are considered in searching for the objective function: the number of batteries taken from the stock during daily operations, battery degradation resulting from high charging scheme usage, and the electricity cost used at the station. Genetic Algorithms are proposed to determine the optimal charging scheduling, resulting in the minimum objective function. A series of battery swapping demand case studies are simulated to assess the feasibility of the proposed model, which can serve as a policy reference for the government and companies.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.