Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila

The Philippines is currently facing debilitating issues regarding extreme traffic conditions and excessive greenhouse gas emissions, both of which are mainly caused by its outdated transportation sector. As a result, the country is currently enacting laws and programs that involve the modernization...

Full description

Saved in:
Bibliographic Details
Main Author: Lisaba, Edwin Bernard F., Jr.
Format: text
Language:English
Published: Animo Repository 2022
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/etdm_mecheng/9
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdm_mecheng
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etdm_mecheng-1006
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:etdm_mecheng-10062022-04-11T03:10:13Z Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila Lisaba, Edwin Bernard F., Jr. The Philippines is currently facing debilitating issues regarding extreme traffic conditions and excessive greenhouse gas emissions, both of which are mainly caused by its outdated transportation sector. As a result, the country is currently enacting laws and programs that involve the modernization of this sector, most of which are involved with the advocacy and support for electric vehicle (EV) technology. However, given the integration of EVs into the country, there must exist a reliable and efficient ecosystem that could help support the successful proliferation of the technology. Therefore, this thesis aims to propose a spatiotemporal methodology for the optimal allocation of EV charging stations within the National Capital Region (NCR). The data to be used will be empirical ridesharing traces, given that they provide a clear picture of human day-to-day movement. A combination of K-means clustering and clustering by fast search and find of density peaks (CFS) will then be used on the traces in order to determine areas of interest. After which, the proposed clusters will be put through a Discrete Event Simulation (DES) in order to estimate and model the charging demand given a configuration of charging stations. Then, the charging demand will be distributed based on the number of traces in each cluster. Additionally, the resulting demand will be projected and cross-referenced between Business-As-Usual and Tax Incentivized scenarios. This will be done to ensure the optimal location and number of the stations, while taking into consideration the number of charging slots per station. The main novelty of this study is that it aims to address the research gap between EV user behavior, charging station location, and charging demand, by further solidifying their relationship with respect to the geography of the NCR in the Philippines. 2022-02-04T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdm_mecheng/9 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdm_mecheng Mechanical Engineering Master's Theses English Animo Repository Electric vehicles—Philippines Battery charging stations (Electric vehicles)—Philippines Mechanical Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Electric vehicles—Philippines
Battery charging stations (Electric vehicles)—Philippines
Mechanical Engineering
spellingShingle Electric vehicles—Philippines
Battery charging stations (Electric vehicles)—Philippines
Mechanical Engineering
Lisaba, Edwin Bernard F., Jr.
Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
description The Philippines is currently facing debilitating issues regarding extreme traffic conditions and excessive greenhouse gas emissions, both of which are mainly caused by its outdated transportation sector. As a result, the country is currently enacting laws and programs that involve the modernization of this sector, most of which are involved with the advocacy and support for electric vehicle (EV) technology. However, given the integration of EVs into the country, there must exist a reliable and efficient ecosystem that could help support the successful proliferation of the technology. Therefore, this thesis aims to propose a spatiotemporal methodology for the optimal allocation of EV charging stations within the National Capital Region (NCR). The data to be used will be empirical ridesharing traces, given that they provide a clear picture of human day-to-day movement. A combination of K-means clustering and clustering by fast search and find of density peaks (CFS) will then be used on the traces in order to determine areas of interest. After which, the proposed clusters will be put through a Discrete Event Simulation (DES) in order to estimate and model the charging demand given a configuration of charging stations. Then, the charging demand will be distributed based on the number of traces in each cluster. Additionally, the resulting demand will be projected and cross-referenced between Business-As-Usual and Tax Incentivized scenarios. This will be done to ensure the optimal location and number of the stations, while taking into consideration the number of charging slots per station. The main novelty of this study is that it aims to address the research gap between EV user behavior, charging station location, and charging demand, by further solidifying their relationship with respect to the geography of the NCR in the Philippines.
format text
author Lisaba, Edwin Bernard F., Jr.
author_facet Lisaba, Edwin Bernard F., Jr.
author_sort Lisaba, Edwin Bernard F., Jr.
title Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
title_short Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
title_full Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
title_fullStr Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
title_full_unstemmed Spatiotemporal modeling of electric vehicle charging demand for strategic EV charger deployment in Metro Manila
title_sort spatiotemporal modeling of electric vehicle charging demand for strategic ev charger deployment in metro manila
publisher Animo Repository
publishDate 2022
url https://animorepository.dlsu.edu.ph/etdm_mecheng/9
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1006&context=etdm_mecheng
_version_ 1729800096857456640