Joint charging mode and location choice model for battery electric vehicle users
•A mixed logit model for the choice analysis of charging mode and location.•A tangible procedure to perform data processing and cleaning.•An appropriate instrumental variable to correct the endogeneity issue.•Far-reaching implications for operation and deployment of charging stations, etc. This pape...
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| Veröffentlicht in: | Transportation research. Part B: methodological 2017-09, Vol.103, p.68-86 |
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| container_title | Transportation research. Part B: methodological |
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| creator | Xu, Min Meng, Qiang Liu, Kai Yamamoto, Toshiyuki |
| description | •A mixed logit model for the choice analysis of charging mode and location.•A tangible procedure to perform data processing and cleaning.•An appropriate instrumental variable to correct the endogeneity issue.•Far-reaching implications for operation and deployment of charging stations, etc.
This paper aims to investigate the choice for charging mode and location with the revealed preference data of battery electric vehicle (BEV) users in Japan. Three alternatives including the normal charging at home (for private BEVs)/company premise (for commercial BEVs), normal charging at public charging stations and fast charging at public charging stations are defined. A mixed logit model is developed to investigate what and how factors influence BEV users’ choice of charging mode (normal or fast) and location (home/company or public stations), by identifying an appropriate instrumental variable to correct the serious endogeneity problem caused by the midnight indicator. The parameters estimation and results interpretation are conducted for private and commercial BEVs respectively. They suggest that the battery capacity, midnight indicator, initial state of charge (SOC) and number of past fast charging events are the main predictors for users’ choice of charging mode and location, that the day interval between current charging and next trip positively affects the normal charging at home/company. In addition, with the increasing of vehicle-kilometres of travel (VKT)/travel duration on former/next travel day, the probability of normal charging at home/company is increased for commercial BEVs, while is decreased for private BEVs. The findings obtained herein have provided new insights into the realization of power peak-load shifting and operation strategy for public charging stations, as well as inspired the development and application of new models and methodologies to determine the density and deployment of public charging stations. |
| doi_str_mv | 10.1016/j.trb.2017.03.004 |
| format | Article |
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This paper aims to investigate the choice for charging mode and location with the revealed preference data of battery electric vehicle (BEV) users in Japan. Three alternatives including the normal charging at home (for private BEVs)/company premise (for commercial BEVs), normal charging at public charging stations and fast charging at public charging stations are defined. A mixed logit model is developed to investigate what and how factors influence BEV users’ choice of charging mode (normal or fast) and location (home/company or public stations), by identifying an appropriate instrumental variable to correct the serious endogeneity problem caused by the midnight indicator. The parameters estimation and results interpretation are conducted for private and commercial BEVs respectively. They suggest that the battery capacity, midnight indicator, initial state of charge (SOC) and number of past fast charging events are the main predictors for users’ choice of charging mode and location, that the day interval between current charging and next trip positively affects the normal charging at home/company. In addition, with the increasing of vehicle-kilometres of travel (VKT)/travel duration on former/next travel day, the probability of normal charging at home/company is increased for commercial BEVs, while is decreased for private BEVs. The findings obtained herein have provided new insights into the realization of power peak-load shifting and operation strategy for public charging stations, as well as inspired the development and application of new models and methodologies to determine the density and deployment of public charging stations.</description><identifier>ISSN: 0191-2615</identifier><identifier>EISSN: 1879-2367</identifier><identifier>DOI: 10.1016/j.trb.2017.03.004</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Alternating current ; Batteries ; Battery electric vehicles ; Charging ; Charging mode and location ; Consumer behavior ; Data processing ; Electric vehicles ; Electrical loads ; Electricity consumption ; Endogeneity ; Location analysis ; Logit models ; Mixed logit model ; Parameter estimation ; Peak load ; Service stations ; State of charge ; Stations ; Travel ; Trip estimation</subject><ispartof>Transportation research. Part B: methodological, 2017-09, Vol.103, p.68-86</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Sep 2017</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c477t-51ee4352c6aa533cfba721a7e8e763295b51237996d6fb1b9ecd8e710c57d4d93</citedby><cites>FETCH-LOGICAL-c477t-51ee4352c6aa533cfba721a7e8e763295b51237996d6fb1b9ecd8e710c57d4d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.trb.2017.03.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids></links><search><creatorcontrib>Xu, Min</creatorcontrib><creatorcontrib>Meng, Qiang</creatorcontrib><creatorcontrib>Liu, Kai</creatorcontrib><creatorcontrib>Yamamoto, Toshiyuki</creatorcontrib><title>Joint charging mode and location choice model for battery electric vehicle users</title><title>Transportation research. Part B: methodological</title><description>•A mixed logit model for the choice analysis of charging mode and location.•A tangible procedure to perform data processing and cleaning.•An appropriate instrumental variable to correct the endogeneity issue.•Far-reaching implications for operation and deployment of charging stations, etc.
This paper aims to investigate the choice for charging mode and location with the revealed preference data of battery electric vehicle (BEV) users in Japan. Three alternatives including the normal charging at home (for private BEVs)/company premise (for commercial BEVs), normal charging at public charging stations and fast charging at public charging stations are defined. A mixed logit model is developed to investigate what and how factors influence BEV users’ choice of charging mode (normal or fast) and location (home/company or public stations), by identifying an appropriate instrumental variable to correct the serious endogeneity problem caused by the midnight indicator. The parameters estimation and results interpretation are conducted for private and commercial BEVs respectively. They suggest that the battery capacity, midnight indicator, initial state of charge (SOC) and number of past fast charging events are the main predictors for users’ choice of charging mode and location, that the day interval between current charging and next trip positively affects the normal charging at home/company. In addition, with the increasing of vehicle-kilometres of travel (VKT)/travel duration on former/next travel day, the probability of normal charging at home/company is increased for commercial BEVs, while is decreased for private BEVs. The findings obtained herein have provided new insights into the realization of power peak-load shifting and operation strategy for public charging stations, as well as inspired the development and application of new models and methodologies to determine the density and deployment of public charging stations.</description><subject>Alternating current</subject><subject>Batteries</subject><subject>Battery electric vehicles</subject><subject>Charging</subject><subject>Charging mode and location</subject><subject>Consumer behavior</subject><subject>Data processing</subject><subject>Electric vehicles</subject><subject>Electrical loads</subject><subject>Electricity consumption</subject><subject>Endogeneity</subject><subject>Location analysis</subject><subject>Logit models</subject><subject>Mixed logit model</subject><subject>Parameter estimation</subject><subject>Peak load</subject><subject>Service stations</subject><subject>State of charge</subject><subject>Stations</subject><subject>Travel</subject><subject>Trip estimation</subject><issn>0191-2615</issn><issn>1879-2367</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPAzEQhC0EEiHwA-gsUd_hx9mORYUinooEBdSWz7eXOLqcg-1Eyr_HEGqqLWZmd-dD6JqSmhIqb9d1jm3NCFU14TUhzQma0JnSFeNSnaIJoZpWTFJxji5SWhNCeEPoBL2_Bj9m7FY2Lv24xJvQAbZjh4fgbPZhLFLwDn6FAfch4tbmDPGAYQCXo3d4DyvvBsC7BDFdorPeDgmu_uYUfT4-fMyfq8Xb08v8flG5RqlcCQrQcMGctFZw7vrWKkatghkoyZkWraCMK61lJ_uWthpcVyRKnFBd02k-RTfHvdsYvnaQslmHXRzLScOIJoLNuJbFRY8uF0NKEXqzjX5j48FQYn7AmbUp4MwPOEO4KeBK5u6YgfL-3kM0yXkYHXQ-lsamC_6f9DcmInX2</recordid><startdate>20170901</startdate><enddate>20170901</enddate><creator>Xu, Min</creator><creator>Meng, Qiang</creator><creator>Liu, Kai</creator><creator>Yamamoto, Toshiyuki</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>SOI</scope></search><sort><creationdate>20170901</creationdate><title>Joint charging mode and location choice model for battery electric vehicle users</title><author>Xu, Min ; Meng, Qiang ; Liu, Kai ; Yamamoto, Toshiyuki</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c477t-51ee4352c6aa533cfba721a7e8e763295b51237996d6fb1b9ecd8e710c57d4d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Alternating current</topic><topic>Batteries</topic><topic>Battery electric vehicles</topic><topic>Charging</topic><topic>Charging mode and location</topic><topic>Consumer behavior</topic><topic>Data processing</topic><topic>Electric vehicles</topic><topic>Electrical loads</topic><topic>Electricity consumption</topic><topic>Endogeneity</topic><topic>Location analysis</topic><topic>Logit models</topic><topic>Mixed logit model</topic><topic>Parameter estimation</topic><topic>Peak load</topic><topic>Service stations</topic><topic>State of charge</topic><topic>Stations</topic><topic>Travel</topic><topic>Trip estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Min</creatorcontrib><creatorcontrib>Meng, Qiang</creatorcontrib><creatorcontrib>Liu, Kai</creatorcontrib><creatorcontrib>Yamamoto, Toshiyuki</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><jtitle>Transportation research. Part B: methodological</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Min</au><au>Meng, Qiang</au><au>Liu, Kai</au><au>Yamamoto, Toshiyuki</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint charging mode and location choice model for battery electric vehicle users</atitle><jtitle>Transportation research. Part B: methodological</jtitle><date>2017-09-01</date><risdate>2017</risdate><volume>103</volume><spage>68</spage><epage>86</epage><pages>68-86</pages><issn>0191-2615</issn><eissn>1879-2367</eissn><abstract>•A mixed logit model for the choice analysis of charging mode and location.•A tangible procedure to perform data processing and cleaning.•An appropriate instrumental variable to correct the endogeneity issue.•Far-reaching implications for operation and deployment of charging stations, etc.
This paper aims to investigate the choice for charging mode and location with the revealed preference data of battery electric vehicle (BEV) users in Japan. Three alternatives including the normal charging at home (for private BEVs)/company premise (for commercial BEVs), normal charging at public charging stations and fast charging at public charging stations are defined. A mixed logit model is developed to investigate what and how factors influence BEV users’ choice of charging mode (normal or fast) and location (home/company or public stations), by identifying an appropriate instrumental variable to correct the serious endogeneity problem caused by the midnight indicator. The parameters estimation and results interpretation are conducted for private and commercial BEVs respectively. They suggest that the battery capacity, midnight indicator, initial state of charge (SOC) and number of past fast charging events are the main predictors for users’ choice of charging mode and location, that the day interval between current charging and next trip positively affects the normal charging at home/company. In addition, with the increasing of vehicle-kilometres of travel (VKT)/travel duration on former/next travel day, the probability of normal charging at home/company is increased for commercial BEVs, while is decreased for private BEVs. The findings obtained herein have provided new insights into the realization of power peak-load shifting and operation strategy for public charging stations, as well as inspired the development and application of new models and methodologies to determine the density and deployment of public charging stations.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.trb.2017.03.004</doi><tpages>19</tpages></addata></record> |
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| identifier | ISSN: 0191-2615 |
| ispartof | Transportation research. Part B: methodological, 2017-09, Vol.103, p.68-86 |
| issn | 0191-2615 1879-2367 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Alternating current Batteries Battery electric vehicles Charging Charging mode and location Consumer behavior Data processing Electric vehicles Electrical loads Electricity consumption Endogeneity Location analysis Logit models Mixed logit model Parameter estimation Peak load Service stations State of charge Stations Travel Trip estimation |
| title | Joint charging mode and location choice model for battery electric vehicle users |
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