Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods

Technology forecasting is a valuable tool for the technology level analyzing and it shows the technological effects for the long term on the new products. Quantitative and qualitative methods are the two main parts of technology forecasting, but they are further divided into several subgroups. This...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Koten, Hasan
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Automobile industry Automobiles Electric vehicles Forecasting Rechargeable batteries Subgroups Technological forecasting Technology assessment
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue	1
container_start_page
container_title
container_volume	2689
creator	Koten, Hasan
description	Technology forecasting is a valuable tool for the technology level analyzing and it shows the technological effects for the long term on the new products. Quantitative and qualitative methods are the two main parts of technology forecasting, but they are further divided into several subgroups. This study aims to get the forecasting of electric vehicle batteries technology that would help the automotive industry. They can estimate the future of the electric vehicle batteries technology that they are planning to design on their vehicles and govern their resources and investments properly. In this research, technology forecasting methods are explained and compared, and new electric source technologies for the automotive industry are analyzed. Different types of electric batteries in the automotive industry are analyzed using forecasting methods. In the forecasting methods, patent analysis is used, and the forecasts are performed using the pearl curve. Lastly, a technology substitution model created from the patent analysis is employed and discussed.
doi_str_mv	10.1063/5.0114117
format	Conference Proceeding
fullrecord	<record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_proquest_journals_2840399054</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2840399054</sourcerecordid><originalsourceid>FETCH-LOGICAL-p961-264dd9f9fa0aa699ab13f78b212bd980c83e5e7c119290e71f3cb46dc935111a3</originalsourceid><addsrcrecordid>eNpF0NFKwzAUBuAgCs7phW8Q8E7ozGnatLmUMacw8GYX3pU0PVkzuqYm6WCP5Tv4YG5s4NX_X3ycAz8hj8BmwAR_yWcMIAMorsgE8hySQoC4JhPGZJakGf-6JXchbBlLZVGUEzIs9qobVfz9cT11hjbWGPTYR4od6uitprWKEf2BRtRt7zq3OVDjPI0t_ps9tlZ3SG3fjCEe8Rhsvzk51CrEU99hbF0T7smNUV3Ah0tOyfptsZ6_J6vP5cf8dZUMUkCSiqxppJFGMaWElKoGboqyTiGtG1kyXXLMsdAAMpUMCzBc15lotOQ5ACg-JU_ns4N33yOGWG3d6PvjxyotM8alZHl2VM9nFbSNKlrXV4O3O-UPFbDqNGiVV5dB-R9caWqY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2840399054</pqid></control><display><type>conference_proceeding</type><title>Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods</title><source>AIP Journals Complete</source><creator>Koten, Hasan</creator><contributor>Gunawan ; Adanta, Dendy ; Oemar, Barlin ; Zulkarnain ; Arifin, Amir ; Saputra, M. A. Ade</contributor><creatorcontrib>Koten, Hasan ; Gunawan ; Adanta, Dendy ; Oemar, Barlin ; Zulkarnain ; Arifin, Amir ; Saputra, M. A. Ade</creatorcontrib><description>Technology forecasting is a valuable tool for the technology level analyzing and it shows the technological effects for the long term on the new products. Quantitative and qualitative methods are the two main parts of technology forecasting, but they are further divided into several subgroups. This study aims to get the forecasting of electric vehicle batteries technology that would help the automotive industry. They can estimate the future of the electric vehicle batteries technology that they are planning to design on their vehicles and govern their resources and investments properly. In this research, technology forecasting methods are explained and compared, and new electric source technologies for the automotive industry are analyzed. Different types of electric batteries in the automotive industry are analyzed using forecasting methods. In the forecasting methods, patent analysis is used, and the forecasts are performed using the pearl curve. Lastly, a technology substitution model created from the patent analysis is employed and discussed.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0114117</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Automobile industry ; Automobiles ; Electric vehicles ; Forecasting ; Rechargeable batteries ; Subgroups ; Technological forecasting ; Technology assessment</subject><ispartof>AIP conference proceedings, 2023, Vol.2689 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0114117$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,790,4498,23909,23910,25118,27901,27902,76126</link.rule.ids></links><search><contributor>Gunawan</contributor><contributor>Adanta, Dendy</contributor><contributor>Oemar, Barlin</contributor><contributor>Zulkarnain</contributor><contributor>Arifin, Amir</contributor><contributor>Saputra, M. A. Ade</contributor><creatorcontrib>Koten, Hasan</creatorcontrib><title>Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods</title><title>AIP conference proceedings</title><description>Technology forecasting is a valuable tool for the technology level analyzing and it shows the technological effects for the long term on the new products. Quantitative and qualitative methods are the two main parts of technology forecasting, but they are further divided into several subgroups. This study aims to get the forecasting of electric vehicle batteries technology that would help the automotive industry. They can estimate the future of the electric vehicle batteries technology that they are planning to design on their vehicles and govern their resources and investments properly. In this research, technology forecasting methods are explained and compared, and new electric source technologies for the automotive industry are analyzed. Different types of electric batteries in the automotive industry are analyzed using forecasting methods. In the forecasting methods, patent analysis is used, and the forecasts are performed using the pearl curve. Lastly, a technology substitution model created from the patent analysis is employed and discussed.</description><subject>Automobile industry</subject><subject>Automobiles</subject><subject>Electric vehicles</subject><subject>Forecasting</subject><subject>Rechargeable batteries</subject><subject>Subgroups</subject><subject>Technological forecasting</subject><subject>Technology assessment</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpF0NFKwzAUBuAgCs7phW8Q8E7ozGnatLmUMacw8GYX3pU0PVkzuqYm6WCP5Tv4YG5s4NX_X3ycAz8hj8BmwAR_yWcMIAMorsgE8hySQoC4JhPGZJakGf-6JXchbBlLZVGUEzIs9qobVfz9cT11hjbWGPTYR4od6uitprWKEf2BRtRt7zq3OVDjPI0t_ps9tlZ3SG3fjCEe8Rhsvzk51CrEU99hbF0T7smNUV3Ah0tOyfptsZ6_J6vP5cf8dZUMUkCSiqxppJFGMaWElKoGboqyTiGtG1kyXXLMsdAAMpUMCzBc15lotOQ5ACg-JU_ns4N33yOGWG3d6PvjxyotM8alZHl2VM9nFbSNKlrXV4O3O-UPFbDqNGiVV5dB-R9caWqY</recordid><startdate>20230721</startdate><enddate>20230721</enddate><creator>Koten, Hasan</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20230721</creationdate><title>Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods</title><author>Koten, Hasan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p961-264dd9f9fa0aa699ab13f78b212bd980c83e5e7c119290e71f3cb46dc935111a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Automobile industry</topic><topic>Automobiles</topic><topic>Electric vehicles</topic><topic>Forecasting</topic><topic>Rechargeable batteries</topic><topic>Subgroups</topic><topic>Technological forecasting</topic><topic>Technology assessment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Koten, Hasan</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Koten, Hasan</au><au>Gunawan</au><au>Adanta, Dendy</au><au>Oemar, Barlin</au><au>Zulkarnain</au><au>Arifin, Amir</au><au>Saputra, M. A. Ade</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods</atitle><btitle>AIP conference proceedings</btitle><date>2023-07-21</date><risdate>2023</risdate><volume>2689</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Technology forecasting is a valuable tool for the technology level analyzing and it shows the technological effects for the long term on the new products. Quantitative and qualitative methods are the two main parts of technology forecasting, but they are further divided into several subgroups. This study aims to get the forecasting of electric vehicle batteries technology that would help the automotive industry. They can estimate the future of the electric vehicle batteries technology that they are planning to design on their vehicles and govern their resources and investments properly. In this research, technology forecasting methods are explained and compared, and new electric source technologies for the automotive industry are analyzed. Different types of electric batteries in the automotive industry are analyzed using forecasting methods. In the forecasting methods, patent analysis is used, and the forecasts are performed using the pearl curve. Lastly, a technology substitution model created from the patent analysis is employed and discussed.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0114117</doi><tpages>9</tpages></addata></record>
fulltext	fulltext
identifier	ISSN: 0094-243X
ispartof	AIP conference proceedings, 2023, Vol.2689 (1)
issn	0094-243X 1551-7616
language	eng
recordid	cdi_proquest_journals_2840399054
source	AIP Journals Complete
subjects	Automobile industry Automobiles Electric vehicles Forecasting Rechargeable batteries Subgroups Technological forecasting Technology assessment
title	Evaluatıon of different electric battery technology for the electric vehicle industry using forecasting methods
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T00%3A33%3A14IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Evaluat%C4%B1on%20of%20different%20electric%20battery%20technology%20for%20the%20electric%20vehicle%20industry%20using%20forecasting%20methods&rft.btitle=AIP%20conference%20proceedings&rft.au=Koten,%20Hasan&rft.date=2023-07-21&rft.volume=2689&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0114117&rft_dat=%3Cproquest_scita%3E2840399054%3C/proquest_scita%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2840399054&rft_id=info:pmid/&rfr_iscdi=true