[ { "key": "N3M9PQHA", "version": 76, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/N3M9PQHA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/N3M9PQHA", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/WRNCRZFR", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "N3M9PQHA", "version": 76, "parentItem": "WRNCRZFR", "itemType": "attachment", "linkMode": "imported_url", "title": "arXiv:1604.05472 PDF", "accessDate": "2017-07-13T15:25:32Z", "url": "http://www.arxiv.org/pdf/1604.05472.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Gopalakrishnan et al. - 2016 - Demand Prediction and Placement Optimization for E.pdf", "md5": "ce7218288f55b9cdef69410275dbd4d4", "mtime": 1499959536000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:25:32Z", "dateModified": "2017-07-13T15:25:36Z" } }, { "key": "5K6455B2", "version": 75, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/5K6455B2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/5K6455B2", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/WRNCRZFR", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "5K6455B2", "version": 75, "parentItem": "WRNCRZFR", "itemType": "attachment", "linkMode": "imported_url", "title": "arXiv.org Snapshot", "accessDate": "2017-07-13T15:25:35Z", "url": "https://arxiv.org/abs/1604.05472", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "1604.html", "md5": "d814bc25b019e1befd98d637432135ee", "mtime": 1499959535000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:25:35Z", "dateModified": "2017-07-13T15:25:35Z" } }, { "key": "9Z9ZIPX9", "version": 74, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/9Z9ZIPX9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/9Z9ZIPX9", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/WRNCRZFR", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "9Z9ZIPX9", "version": 74, "parentItem": "WRNCRZFR", "itemType": "note", "note": "

Comment: Published in the proceedings of the 25th International Joint Conference on Artificial Intelligence IJCAI 2016

", "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:25:32Z", "dateModified": "2017-07-13T15:25:32Z" } }, { "key": "WRNCRZFR", "version": 74, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/WRNCRZFR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/WRNCRZFR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Gopalakrishnan et al.", "parsedDate": "2016-04-19", "numChildren": 3 }, "data": { "key": "WRNCRZFR", "version": 74, "itemType": "journalArticle", "title": "Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations", "creators": [ { "creatorType": "author", "firstName": "Ragavendran", "lastName": "Gopalakrishnan" }, { "creatorType": "author", "firstName": "Arpita", "lastName": "Biswas" }, { "creatorType": "author", "firstName": "Alefiya", "lastName": "Lightwala" }, { "creatorType": "author", "firstName": "Skanda", "lastName": "Vasudevan" }, { "creatorType": "author", "firstName": "Partha", "lastName": "Dutta" }, { "creatorType": "author", "firstName": "Abhishek", "lastName": "Tripathi" } ], "abstractNote": "Effective placement of charging stations plays a key role in Electric Vehicle (EV) adoption. In the placement problem, given a set of candidate sites, an optimal subset needs to be selected with respect to the concerns of both (a) the charging station service provider, such as the demand at the candidate sites and the budget for deployment, and (b) the EV user, such as charging station reachability and short waiting times at the station. This work addresses these concerns, making the following three novel contributions: (i) a supervised multi-view learning framework using Canonical Correlation Analysis (CCA) for demand prediction at candidate sites, using multiple datasets such as points of interest information, traffic density, and the historical usage at existing charging stations; (ii) a mixed-packing-and- covering optimization framework that models competing concerns of the service provider and EV users; (iii) an iterative heuristic to solve these problems by alternately invoking knapsack and set cover algorithms. The performance of the demand prediction model and the placement optimization heuristic are evaluated using real world data.", "publicationTitle": "arXiv:1604.05472 [cs]", "publisher": "", "place": "", "date": "2016-04-19", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://arxiv.org/abs/1604.05472", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv: 1604.05472", "tags": [ { "tag": "Computer Science - Artificial Intelligence", "type": 1 }, { "tag": "Computer Science - Data Structures and Algorithms", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T15:25:32Z", "dateModified": "2017-07-13T15:25:32Z" } }, { "key": "TH999KFB", "version": 76, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/TH999KFB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/TH999KFB", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/3W67W4M9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "TH999KFB", "version": 76, "parentItem": "3W67W4M9", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2017-07-13T15:05:08Z", "url": "http://www.sciencedirect.com/science/article/pii/S030626191631087X", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S030626191631087X.html", "md5": "45a43d6b627044bec35e89f9b86a77a9", "mtime": 1499958307000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:05:08Z", "dateModified": "2017-07-13T15:05:08Z" } }, { "key": "3W67W4M9", "version": 73, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/3W67W4M9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/3W67W4M9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Andrenacci et al.", "parsedDate": "2016-11-15", "numChildren": 1 }, "data": { "key": "3W67W4M9", "version": 73, "itemType": "journalArticle", "title": "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas", "creators": [ { "creatorType": "author", "firstName": "N.", "lastName": "Andrenacci" }, { "creatorType": "author", "firstName": "R.", "lastName": "Ragona" }, { "creatorType": "author", "firstName": "G.", "lastName": "Valenti" } ], "abstractNote": "Despite all the acknowledged advantages in terms of environmental impact reduction, energy efficiency and noise reduction, the electric mobility market is below expectations. In fact, electric vehicles have limitations that pose several important challenges for achieving a sustainable mobility system: among them, the availability of an adequate charging infrastructure is recognized as a fundamental requirement and appropriate approaches to optimize public and private investments in this field are to be delineated. In this paper we consider actual data on conventional private vehicle usage in the urban area of Rome to carry out a strategy for the optimal allocation of charging infrastructures into portions (subareas) of the urban area, based on an analysis of a driver sample under the assumption of a complete switch to an equivalent fleet of electric vehicles. Moreover, the energy requirement for each one of the subareas is estimated in terms of the electric energy used by the equivalent fleet of electric vehicles to reach their destination. The model can be easily generalized to other problems regarding facility allocation based on user demand.", "publicationTitle": "Applied Energy", "publisher": "", "place": "", "date": "November 15, 2016", "volume": "182", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "39-46", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Applied Energy", "DOI": "10.1016/j.apenergy.2016.07.137", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S030626191631087X", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0306-2619", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Big data analysis", "type": 1 }, { "tag": "Cluster analysis", "type": 1 }, { "tag": "Electric charging station deployment", "type": 1 }, { "tag": "Electric mobility", "type": 1 }, { "tag": "Electric vehicle energy requirement", "type": 1 }, { "tag": "Electric vehicle simulation", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T15:05:06Z", "dateModified": "2017-07-13T15:05:06Z" } }, { "key": "72SZTWQH", "version": 76, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/72SZTWQH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/72SZTWQH", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/PXAMB45S", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "72SZTWQH", "version": 76, "parentItem": "PXAMB45S", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2017-07-13T15:03:49Z", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X17300542", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S0968090X17300542.html", "md5": "ff3c2f5244bafa529142bf488d0022c2", "mtime": 1499958228000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:03:49Z", "dateModified": "2017-07-13T15:03:49Z" } }, { "key": "PXAMB45S", "version": 72, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/PXAMB45S", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/PXAMB45S", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Yang et al.", "parsedDate": "2017-04-01", "numChildren": 1 }, "data": { "key": "PXAMB45S", "version": 72, "itemType": "journalArticle", "title": "A data-driven optimization-based approach for siting and sizing of electric taxi charging stations", "creators": [ { "creatorType": "author", "firstName": "Jie", "lastName": "Yang" }, { "creatorType": "author", "firstName": "Jing", "lastName": "Dong" }, { "creatorType": "author", "firstName": "Liang", "lastName": "Hu" } ], "abstractNote": "This paper presents a data-driven optimization-based approach to allocate chargers for battery electric vehicle (BEV) taxis throughout a city with the objective of minimizing the infrastructure investment. To account for charging congestion, an M/M/x/s queueing model is adopted to estimate the probability of BEV taxis being charged at their dwell places. By means of regression and logarithmic transformation, the charger allocation problem is formulated as an integer linear program (ILP), which can be solved efficiently using Gurobi solver. The proposed method is applied using large-scale GPS trajectory data collected from the taxi fleet of Changsha, China. The key findings from the results include the following: (1) the dwell pattern of the taxi fleet determines the siting of charging stations; (2) by providing waiting spots, in addition to charging spots, the utilization of chargers increases and the number of required chargers at each site decreases; and (3) the tradeoff between installing more chargers versus providing more waiting spaces can be quantified by the cost ratio of chargers and parking spots.", "publicationTitle": "Transportation Research Part C: Emerging Technologies", "publisher": "", "place": "", "date": "April 1, 2017", "volume": "77", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "462-477", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part C: Emerging Technologies", "DOI": "10.1016/j.trc.2017.02.014", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X17300542", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0968-090X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Charging infrastructure planning", "type": 1 }, { "tag": "Electric taxis", "type": 1 }, { "tag": "GPS trajectory data", "type": 1 }, { "tag": "Integer programing", "type": 1 }, { "tag": "Queueing model", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T15:03:47Z", "dateModified": "2017-07-13T15:03:47Z" } }, { "key": "VDGFHVEV", "version": 76, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/VDGFHVEV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/VDGFHVEV", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/4FMX258W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "VDGFHVEV", "version": 76, "parentItem": "4FMX258W", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2017-07-13T15:01:27Z", "url": "http://www.sciencedirect.com/science/article/pii/S187775031530034X#bib0005", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S187775031530034X.html", "md5": "867ecd59fbdb5c8fbdbe9b3334084547", "mtime": 1499958086000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T15:01:27Z", "dateModified": "2017-07-13T15:01:27Z" } }, { "key": "4FMX258W", "version": 71, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/4FMX258W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/4FMX258W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Viswanathan et al.", "parsedDate": "2016-01-01", "numChildren": 1 }, "data": { "key": "4FMX258W", "version": 71, "itemType": "journalArticle", "title": "Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments", "creators": [ { "creatorType": "author", "firstName": "Vaisagh", "lastName": "Viswanathan" }, { "creatorType": "author", "firstName": "Daniel", "lastName": "Zehe" }, { "creatorType": "author", "firstName": "Jordan", "lastName": "Ivanchev" }, { "creatorType": "author", "firstName": "Dominik", "lastName": "Pelzer" }, { "creatorType": "author", "firstName": "Alois", "lastName": "Knoll" }, { "creatorType": "author", "firstName": "Heiko", "lastName": "Aydt" } ], "abstractNote": "High population densities in today's cities are leading to increasing congestion and air pollution. Sustainable cities of the future will require a large scale transition to electro-mobility. The development of electric vehicle charging infrastructure is necessary to enable this transition. Existing methods for determining charging infrastructure take an optimization approach that ignores existing traffic demands and infrastructure. Moreover, the dynamics of vehicle movement like stop-and-go traffic, congestion and the effect of traffic lights are not considered in determining energy consumption. In this paper, we propose a novel nanoscopic city-scale traffic simulation based method for determining charging infrastructure locations; subsequently, we demonstrate its usefulness in spatio-temporal planning through a case-study of Singapore. Through this method, existing traffic and road network data and the dynamics of individual vehicle movement can be taken into consideration in planning.", "publicationTitle": "Journal of Computational Science", "publisher": "", "place": "", "date": "January 1, 2016", "volume": "12", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1-10", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Journal of Computational Science", "DOI": "10.1016/j.jocs.2015.10.012", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S187775031530034X", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1877-7503", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Agent based modelling", "type": 1 }, { "tag": "Charging station placement", "type": 1 }, { "tag": "Data driven computing", "type": 1 }, { "tag": "Nanoscopic simulation", "type": 1 }, { "tag": "Traffic simulation", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T15:01:25Z", "dateModified": "2017-07-13T15:01:25Z" } }, { "key": "KSDVIZCC", "version": 76, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/KSDVIZCC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/KSDVIZCC", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/ASIKKZ75", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "KSDVIZCC", "version": 76, "parentItem": "ASIKKZ75", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2017-07-13T14:57:17Z", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X15003538", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S0968090X15003538.html", "md5": "14a0e1cc483917c56bb9ffd5b2855d99", "mtime": 1499957837000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T14:57:17Z", "dateModified": "2017-07-13T14:57:17Z" } }, { "key": "ASIKKZ75", "version": 70, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/ASIKKZ75", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/ASIKKZ75", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Tu et al.", "parsedDate": "2016-04-01", "numChildren": 1 }, "data": { "key": "ASIKKZ75", "version": 70, "itemType": "journalArticle", "title": "Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach", "creators": [ { "creatorType": "author", "firstName": "Wei", "lastName": "Tu" }, { "creatorType": "author", "firstName": "Qingquan", "lastName": "Li" }, { "creatorType": "author", "firstName": "Zhixiang", "lastName": "Fang" }, { "creatorType": "author", "firstName": "Shih-lung", "lastName": "Shaw" }, { "creatorType": "author", "firstName": "Baoding", "lastName": "Zhou" }, { "creatorType": "author", "firstName": "Xiaomeng", "lastName": "Chang" } ], "abstractNote": "Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.", "publicationTitle": "Transportation Research Part C: Emerging Technologies", "publisher": "", "place": "", "date": "April 1, 2016", "volume": "65", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "172-189", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part C: Emerging Technologies", "DOI": "10.1016/j.trc.2015.10.004", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X15003538", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0968-090X", "archive": "", "archiveLocation": "", "shortTitle": "Optimizing the locations of electric taxi charging stations", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Big data", "type": 1 }, { "tag": "Electric vehicle", "type": 1 }, { "tag": "Facility location", "type": 1 }, { "tag": "Maximum coverage", "type": 1 }, { "tag": "Spatial–temporal demand", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T14:57:15Z", "dateModified": "2017-07-13T14:57:15Z" } }, { "key": "23MVBW3Z", "version": 75, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/23MVBW3Z", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/23MVBW3Z", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/645348/items/TUA35IV9", "type": "application/json" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } } }, "data": { "key": "23MVBW3Z", "version": 75, "parentItem": "TUA35IV9", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2017-07-13T14:41:39Z", "url": "http://www.sciencedirect.com/science/article/pii/S0306261916311084#b0040", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S0306261916311084.html", "md5": "38017d8d8051e4d55692114d3950bef5", "mtime": 1499956899000, "tags": [], "relations": {}, "dateAdded": "2017-07-13T14:41:39Z", "dateModified": "2017-07-13T14:41:39Z" } }, { "key": "TUA35IV9", "version": 68, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/TUA35IV9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/TUA35IV9", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "He et al.", "parsedDate": "2016-10-15", "numChildren": 1 }, "data": { "key": "TUA35IV9", "version": 68, "itemType": "journalArticle", "title": "Individual trip chain distributions for passenger cars: Implications for market acceptance of battery electric vehicles and energy consumption by plug-in hybrid electric vehicles", "creators": [ { "creatorType": "author", "firstName": "Xiaoyi", "lastName": "He" }, { "creatorType": "author", "firstName": "Ye", "lastName": "Wu" }, { "creatorType": "author", "firstName": "Shaojun", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Michael A.", "lastName": "Tamor" }, { "creatorType": "author", "firstName": "Timothy J.", "lastName": "Wallington" }, { "creatorType": "author", "firstName": "Wei", "lastName": "Shen" }, { "creatorType": "author", "firstName": "Weijian", "lastName": "Han" }, { "creatorType": "author", "firstName": "Lixin", "lastName": "Fu" }, { "creatorType": "author", "firstName": "Jiming", "lastName": "Hao" } ], "abstractNote": "The energy and environmental benefits of electric vehicles (EVs) are highly dependent on individual driving patterns. To characterize individual driving patterns in Beijing, a populated megacity in East Asian, GPS-based travel data from 459 private passenger vehicles were gathered covering nearly 17,000 sampling days in 2013–2015. The data were analyzed using a statistical model to produce 0.5h, 4h, 8h and daily individual trip chain distributions, which were used to evaluate customer acceptance for battery electric vehicles (BEVs) based on inconvenience thresholds and to assess the energy consumption for plug-in hybrids (PHEVs). The mean daily distances travelled on weekdays and weekends in Beijing were found to be 44.6km and 51.4km respectively. In Beijing the mean habitual travel distance (40.4km) is modest, the random component of travel distance is lower, and the fraction of habitual travel is higher than for cities in the U.S. and in Germany. These factors make EV deployment in Beijing more favorable than in the U.S. or Germany. We show that the estimated acceptance rate for BEVs is very sensitive to the predetermined inconvenience threshold level. The abundant public transportation alternatives and traffic management in Beijing are factors which reduce the inconvenience of BEVs and may make them acceptable without substantially increased cost for larger battery capacity. PHEVs with all-electric ranges of 50km (PHEV50) have an ensemble utility factor (UF) and equivalent gasoline consumption estimated to be 0.55 and 4.39L/100km. However, for 50% of vehicle owners PHEV50s would have a UF of 0.94 and equivalent gasoline consumption of 3.03L/100km. Our results show that attention to heterogeneity among individuals instead of analysis at the ensemble level is essential to understanding the real-world acceptance and benefits of EVs.", "publicationTitle": "Applied Energy", "publisher": "", "place": "", "date": "October 15, 2016", "volume": "180", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "650-660", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Applied Energy", "DOI": "10.1016/j.apenergy.2016.08.021", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0306261916311084", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0306-2619", "archive": "", "archiveLocation": "", "shortTitle": "Individual trip chain distributions for passenger cars", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Acceptance", "type": 1 }, { "tag": "Battery electric vehicle", "type": 1 }, { "tag": "Energy consumption", "type": 1 }, { "tag": "Individual trip chain distribution", "type": 1 }, { "tag": "Plug-in hybrid vehicle", "type": 1 }, { "tag": "Vehicle usage", "type": 1 } ], "collections": [ "SAQV842J" ], "relations": {}, "dateAdded": "2017-07-13T14:41:37Z", "dateModified": "2017-07-13T14:41:37Z" } }, { "key": "MFS6XPS2", "version": 66, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/MFS6XPS2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/MFS6XPS2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Hidalgo et al.", "parsedDate": "2016-04", "numChildren": 0 }, "data": { "key": "MFS6XPS2", "version": 66, "itemType": "conferencePaper", "title": "Optimizing the charging station placement by considering the user's charging behavior", "creators": [ { "creatorType": "author", "firstName": "P. A. L.", "lastName": "Hidalgo" }, { "creatorType": "author", "firstName": "M.", "lastName": "Ostendorp" }, { "creatorType": "author", "firstName": "M.", "lastName": "Lienkamp" } ], "abstractNote": "The successful introduction of electromobility relies considerably on the implementation of the charging stations. This implementation, should be cost effective and satisfy the energy demand of the electric vehicle users. This article presents a tool that computes the optimal charging infrastructure, by considering the placement and type of charging stations. To achieve this, we first calculate the spatiotemporal energy demand to account for the specific demand of each user. Next, we conduct a preselection step, where the locations and station types of little relevance are identified and excluded from optimization. The actual optimization step uses a multi-objective genetic algorithm with two objectives: minimizing the total installation costs of the infrastructure and minimizing the amount of trips that fail due to insufficient energy in the vehicles. Finally, the study analyzes two factors, which possibly influence the optimization algorithm: the user's charging behavior and developments of the battery energy efficiency.", "proceedingsTitle": "2016 IEEE International Energy Conference (ENERGYCON)", "conferenceName": "2016 IEEE International Energy Conference (ENERGYCON)", "publisher": "", "place": "", "date": "April 2016", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "1-7", "series": "", "seriesNumber": "", "DOI": "10.1109/ENERGYCON.2016.7513920", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T17:42:02Z", "dateModified": "2016-08-23T17:42:02Z" } }, { "key": "SR5EDSBA", "version": 65, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/SR5EDSBA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/SR5EDSBA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Shahraki et al.", "parsedDate": "2015-12", "numChildren": 0 }, "data": { "key": "SR5EDSBA", "version": 65, "itemType": "journalArticle", "title": "Optimal locations of electric public charging stations using real world vehicle travel patterns", "creators": [ { "creatorType": "author", "firstName": "Narges", "lastName": "Shahraki" }, { "creatorType": "author", "firstName": "Hua", "lastName": "Cai" }, { "creatorType": "author", "firstName": "Metin", "lastName": "Turkay" }, { "creatorType": "author", "firstName": "Ming", "lastName": "Xu" } ], "abstractNote": "We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quickly regardless of charging speed.", "publicationTitle": "Transportation Research Part D: Transport and Environment", "publisher": "", "place": "", "date": "December 2015", "volume": "41", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "165-176", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part D: Transport and Environment", "DOI": "10.1016/j.trd.2015.09.011", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1361920915001352", "accessDate": "2016-08-23T17:40:36Z", "PMID": "", "PMCID": "", "ISSN": "1361-9209", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T17:40:36Z", "dateModified": "2016-08-23T17:40:36Z" } }, { "key": "ZMEAFDIS", "version": 64, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/ZMEAFDIS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/ZMEAFDIS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Huang and Zhou", "parsedDate": "2015-03", "numChildren": 0 }, "data": { "key": "ZMEAFDIS", "version": 64, "itemType": "journalArticle", "title": "An optimization framework for workplace charging strategies", "creators": [ { "creatorType": "author", "firstName": "Yongxi", "lastName": "Huang" }, { "creatorType": "author", "firstName": "Yan", "lastName": "Zhou" } ], "abstractNote": "The workplace charging (WPC) has been recently recognized as the most important secondary charging point next to residential charging for plug-in electric vehicles (PEVs). The current WPC practice is spontaneous and grants every PEV a designated charger, which may not be practical or economic when there are a large number of PEVs present at workplace. This study is the first research undertaken that develops an optimization framework for WPC strategies to satisfy all charging demand while explicitly addressing different eligible levels of charging technology and employees’ demographic distributions. The optimization model is to minimize the lifetime cost of equipment, installations, and operations, and is formulated as an integer program. We demonstrate the applicability of the model using numerical examples based on national average data. The results indicate that the proposed optimization model can reduce the total cost of running a WPC system by up to 70% compared to the current practice. The WPC strategies are sensitive to the time windows and installation costs, and dominated by the PEV population size. The WPC has also been identified as an alternative sustainable transportation program to the public transit subsidy programs for both economic and environmental advantages.", "publicationTitle": "Transportation Research Part C: Emerging Technologies", "publisher": "", "place": "", "date": "March 2015", "volume": "52", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "144-155", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part C: Emerging Technologies", "DOI": "10.1016/j.trc.2015.01.022", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X15000303", "accessDate": "2016-08-23T17:37:17Z", "PMID": "", "PMCID": "", "ISSN": "0968-090X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T17:37:17Z", "dateModified": "2016-08-23T17:37:17Z" } }, { "key": "9XNV3MNP", "version": 63, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/9XNV3MNP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/9XNV3MNP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Fetene et al.", "parsedDate": "2016-06", "numChildren": 0 }, "data": { "key": "9XNV3MNP", "version": 63, "itemType": "journalArticle", "title": "The economics of workplace charging", "creators": [ { "creatorType": "author", "firstName": "Gebeyehu M.", "lastName": "Fetene" }, { "creatorType": "author", "firstName": "Georg", "lastName": "Hirte" }, { "creatorType": "author", "firstName": "Sigal", "lastName": "Kaplan" }, { "creatorType": "author", "firstName": "Carlo G.", "lastName": "Prato" }, { "creatorType": "author", "firstName": "Stefan", "lastName": "Tscharaktschiew" } ], "abstractNote": "To overcome the range-anxiety problem and further shortcomings associated with electric vehicles, workplace charging (WPC) is gaining increasing attention. We propose a microeconomic model of WPC and use the approach to shed light on the incentives and barriers employees and employers face when deciding on demand for and supply of WPC. It is shown that under market conditions there is no WPC contract an employer is willing to offer and at the same time the majority of employees is willing to accept. To overcome the lack of demand or underprovision of WPC we discuss various ‘remedies’, involving subsidies to charging facility costs and adjustments in electricity tariffs or loading technologies. We find that direct subsidies to WPC facilities or subsidies combined with specific energy price policies could be a way to foster WPC provision. In contrast measures on the employee side that may help to stimulate the demand for WPC turn out to be less feasible. Hence, our results suggest that in order to promote WPC it is more promising to support employers in offering WPC contracts than to provide employees an incentive to accept WPC contracts. The study therefore gives a rationale for public initiatives being undertaken to boost WPC provision, as e.g. in the case of the US.", "publicationTitle": "Transportation Research Part B: Methodological", "publisher": "", "place": "", "date": "June 2016", "volume": "88", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "93-118", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part B: Methodological", "DOI": "10.1016/j.trb.2016.03.004", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0191261515302009", "accessDate": "2016-08-23T17:36:50Z", "PMID": "", "PMCID": "", "ISSN": "0191-2615", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T17:36:50Z", "dateModified": "2016-08-23T17:36:50Z" } }, { "key": "4GC9V4M2", "version": 62, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/4GC9V4M2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/4GC9V4M2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Bonges III and Lusk", "parsedDate": "2016-01", "numChildren": 0 }, "data": { "key": "4GC9V4M2", "version": 62, "itemType": "journalArticle", "title": "Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation", "creators": [ { "creatorType": "author", "firstName": "Henry A.", "lastName": "Bonges III" }, { "creatorType": "author", "firstName": "Anne C.", "lastName": "Lusk" } ], "abstractNote": "Electric Vehicles (EV) are highly beneficial due to their reliance on electricity and Climate Change response yet EV sales are lower than would be expected due to range anxiety. If a potential buyer cannot be assured of having constantly-available and compatible charging stations, they will not purchase an EV. To increase the sales of EVs through improved charger availability, this paper examines parking configurations, charger design, convenient “EV only” parking, free charging, etiquette in unplugging another’s vehicle, and legislation. Data were derived from academic publications, trade market press, conversations, personal observations, and laws. The results show that chargers are often in a lot’s corner and thus accessible only to one vehicle, EV owners leave their charged car in the space, drivers use EV spaces for parking, etiquette cards are not understood, and legislation makes it illegal to unplug another’s EV. Improvements include less convenient charger spots, an octopus charger in the middle of the parking lot, modest charging fees to foster turnover, chargers that indicate an EV is charged, education and legislation about etiquette cards, and legislation that allows an individual to unplug another’s charged EV. Improvements to charging should be implemented simultaneously to lessen range anxiety and realize the environmental benefits from reductions in gasoline consumption and mobile source air pollution.", "publicationTitle": "Transportation Research Part A: Policy and Practice", "publisher": "", "place": "", "date": "January 2016", "volume": "83", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "63-73", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part A: Policy and Practice", "DOI": "10.1016/j.tra.2015.09.011", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0965856415002451", "accessDate": "2016-08-23T17:35:55Z", "PMID": "", "PMCID": "", "ISSN": "0965-8564", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T17:35:55Z", "dateModified": "2016-08-23T17:35:55Z" } }, { "key": "CPNV2ZD6", "version": 61, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/CPNV2ZD6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/CPNV2ZD6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Dong et al.", "parsedDate": "2014-01", "numChildren": 0 }, "data": { "key": "CPNV2ZD6", "version": 61, "itemType": "journalArticle", "title": "Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data", "creators": [ { "creatorType": "author", "firstName": "Jing", "lastName": "Dong" }, { "creatorType": "author", "firstName": "Changzheng", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Zhenhong", "lastName": "Lin" } ], "abstractNote": "This paper studies electric vehicle charger location problems and analyzes the impact of public charging infrastructure deployment on increasing electric miles traveled, thus promoting battery electric vehicle (BEV) market penetration. An activity-based assessment method is proposed to evaluate BEV feasibility for the heterogeneous traveling population in the real world driving context. Genetic algorithm is applied to find (sub)optimal locations for siting public charging stations. A case study using the GPS-based travel survey data collected in the greater Seattle metropolitan area shows that electric miles and trips could be significantly increased by installing public chargers at popular destinations, with a reasonable infrastructure investment.", "publicationTitle": "Transportation Research Part C: Emerging Technologies", "publisher": "", "place": "", "date": "January 2014", "volume": "38", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "44-55", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part C: Emerging Technologies", "DOI": "10.1016/j.trc.2013.11.001", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0968090X13002283", "accessDate": "2016-08-23T16:32:03Z", "PMID": "", "PMCID": "", "ISSN": "0968-090X", "archive": "", "archiveLocation": "", "shortTitle": "Charging infrastructure planning for promoting battery electric vehicles", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:32:03Z", "dateModified": "2016-08-23T16:32:03Z" } }, { "key": "RU6VEBBS", "version": 60, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/RU6VEBBS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/RU6VEBBS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Guo and Zhao", "parsedDate": "2015-11-15", "numChildren": 0 }, "data": { "key": "RU6VEBBS", "version": 60, "itemType": "journalArticle", "title": "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective", "creators": [ { "creatorType": "author", "firstName": "Sen", "lastName": "Guo" }, { "creatorType": "author", "firstName": "Huiru", "lastName": "Zhao" } ], "abstractNote": "Selecting the most sustainable site plays an important role in the life cycle of electric vehicle charging station (EVCS), which needs to consider some conflicting criteria. Different from the previous studies which mostly utilize programming (optimization) models, this paper employed a multi-criteria decision-making (MCDM) method to consider some subjective but important criteria for EVCS site selection. To reflect the ambiguity and vagueness due to the subjective judgments of decision makers, fuzzy TOPSIS method was applied to select the optimal EVCS site. Based on academic literatures, feasibility research reports and expert opinions in different fields, the evaluation index system for EVCS site selection was built from sustainability perspective, which consists of environmental, economic and social criteria associated with a total of 11 sub-criteria. Then, the criteria performances of different alternatives and criteria weights were judged by five groups of expert panels in the fields of environment, economy, society, electric power system and transportation system. Finally, the EVCS site alternatives were ranked by employing fuzzy TOPSIS method. The result shows EVCS site A2 located at Changping district in Beijing obtains the highest ranking score and should be selected as the optimal site. Meanwhile, the environmental and social criteria are paid more attentions from decision makers than economic criteria. The sensitivity analysis results indicate the alternative A2 always secures its top ranking no matter how sub-criteria weights change. It is effective and robust to apply fuzzy TOPSIS method into EVCS site selection. This paper provides a new research perspective for site selection and also extends the application domains of fuzzy TOPSIS method.", "publicationTitle": "Applied Energy", "publisher": "", "place": "", "date": "November 15, 2015", "volume": "158", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "390-402", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Applied Energy", "DOI": "10.1016/j.apenergy.2015.08.082", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0306261915010181", "accessDate": "2016-08-23T16:28:12Z", "PMID": "", "PMCID": "", "ISSN": "0306-2619", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:28:12Z", "dateModified": "2016-08-23T16:28:12Z" } }, { "key": "5IEZX9XA", "version": 59, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/5IEZX9XA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/5IEZX9XA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Wu et al.", "parsedDate": "2016-03-03", "numChildren": 0 }, "data": { "key": "5IEZX9XA", "version": 59, "itemType": "journalArticle", "title": "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method", "creators": [ { "creatorType": "author", "firstName": "Yunna", "lastName": "Wu" }, { "creatorType": "author", "firstName": "Meng", "lastName": "Yang" }, { "creatorType": "author", "firstName": "Haobo", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Kaifeng", "lastName": "Chen" }, { "creatorType": "author", "firstName": "Yang", "lastName": "Wang" } ], "abstractNote": "The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper for EVCS site selection. Firstly, the use of the PROMETHEE method can bolster the confidence and visibility for decision makers. Secondly, the cloud model is recommended to describe the fuzziness and randomness of linguistic terms integrally and accurately. Finally, the Analytical Network Process (ANP) method is adopted to measure the correlation of the indicators with a greatly simplified calculation of the parameters and the steps required.", "publicationTitle": "Energies", "publisher": "", "place": "", "date": "2016-03-03", "volume": "9", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "157", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3390/en9030157", "citationKey": "", "url": "http://www.mdpi.com/1996-1073/9/3/157", "accessDate": "2016-08-23T16:27:47Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.mdpi.com", "callNumber": "", "rights": "http://creativecommons.org/licenses/by/3.0/", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:27:47Z", "dateModified": "2016-08-23T16:27:47Z" } }, { "key": "TGH344CE", "version": 58, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/TGH344CE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/TGH344CE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Kazemi et al.", "parsedDate": "2016-10-01", "numChildren": 0 }, "data": { "key": "TGH344CE", "version": 58, "itemType": "journalArticle", "title": "Optimal siting and sizing of distribution system operator owned EV parking lots", "creators": [ { "creatorType": "author", "firstName": "Mohammad Amin", "lastName": "Kazemi" }, { "creatorType": "author", "firstName": "Mostafa", "lastName": "Sedighizadeh" }, { "creatorType": "author", "firstName": "Mohammad Javad", "lastName": "Mirzaei" }, { "creatorType": "author", "firstName": "Omid", "lastName": "Homaee" } ], "abstractNote": "In this paper, a new approach is presented to determine the optimal number, location and capacity of each Electric Vehicle (EV) parking lot to maximize the profit of electrical distribution companies. In the proposed approach, while considering the term of “EV owners’ welfare”, the effect of this term on optimal location and capacity of the EV parking lots is also investigated. Moreover, the expected growth percentage of the EVs in the upcoming years is presented as a probabilistic parameter. The way of affecting the planning of the parking lots is analyzed. Also, an approach has been presented based on K-means clustering method to estimate the number of EVs approaching for a parking lot which can lead to a more accurate prediction of the costs and incomes of establishing a parking lot. The presented approach is performed on the 69 node radial distribution system and the results are analyzed.", "publicationTitle": "Applied Energy", "publisher": "", "place": "", "date": "October 1, 2016", "volume": "179", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "1176-1184", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Applied Energy", "DOI": "10.1016/j.apenergy.2016.06.125", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S030626191630900X", "accessDate": "2016-08-23T16:26:23Z", "PMID": "", "PMCID": "", "ISSN": "0306-2619", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:26:23Z", "dateModified": "2016-08-23T16:26:23Z" } }, { "key": "5S4N34HV", "version": 57, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/5S4N34HV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/5S4N34HV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Xiang et al.", "parsedDate": "2016-09-15", "numChildren": 0 }, "data": { "key": "5S4N34HV", "version": 57, "itemType": "journalArticle", "title": "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates", "creators": [ { "creatorType": "author", "firstName": "Yue", "lastName": "Xiang" }, { "creatorType": "author", "firstName": "Junyong", "lastName": "Liu" }, { "creatorType": "author", "firstName": "Ran", "lastName": "Li" }, { "creatorType": "author", "firstName": "Furong", "lastName": "Li" }, { "creatorType": "author", "firstName": "Chenghong", "lastName": "Gu" }, { "creatorType": "author", "firstName": "Shuoya", "lastName": "Tang" } ], "abstractNote": "This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.", "publicationTitle": "Applied Energy", "publisher": "", "place": "", "date": "September 15, 2016", "volume": "178", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "647-659", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Applied Energy", "DOI": "10.1016/j.apenergy.2016.06.021", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0306261916307966", "accessDate": "2016-08-23T16:25:58Z", "PMID": "", "PMCID": "", "ISSN": "0306-2619", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:25:58Z", "dateModified": "2016-08-23T16:25:58Z" } }, { "key": "48X3DUM6", "version": 56, "library": { "type": "group", "id": 645348, "name": "electric_vehicle_equipment", "links": { "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/645348/items/48X3DUM6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/electric_vehicle_equipment/items/48X3DUM6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 250494, "username": "tsandberg", "name": "Tami Sandberg", "links": { "alternate": { "href": "https://www.zotero.org/tsandberg", "type": "text/html" } } }, "creatorSummary": "Carley et al.", "parsedDate": "2013-01", "numChildren": 0 }, "data": { "key": "48X3DUM6", "version": 56, "itemType": "journalArticle", "title": "Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites", "creators": [ { "creatorType": "author", "firstName": "Sanya", "lastName": "Carley" }, { "creatorType": "author", "firstName": "Rachel M.", "lastName": "Krause" }, { "creatorType": "author", "firstName": "Bradley W.", "lastName": "Lane" }, { "creatorType": "author", "firstName": "John D.", "lastName": "Graham" } ], "abstractNote": "This paper examines consumer stated intent to purchase plug-in electric vehicles and assesses the factors that increase or decrease interest. We surveyed adult drivers in large US cities in early fall 2011, before vehicle manufacturers and dealers began marketing campaigns. The survey responses thus document early impressions of this transport technology. We find that, given current battery technology and public perceptions, overall stated intent to purchase or lease electric vehicles is low. Interest in plug-in hybrid technology is somewhat greater than interest in all-electric technology. Consumers who express early interest in adopting electric vehicles are typically highly educated, previous owners of conventional hybrids, environmentally sensitive, and concerned about dependence on foreign oil. Enhanced fuel economy, the primary tangible advantage of plug-in technology, is recognized as favorable by respondents but fails to exert a strong influence on purchasing intentions. Interest in plug-in electric vehicles is shaped primarily by consumers’ perceptions of electric vehicle disadvantages.", "publicationTitle": "Transportation Research Part D: Transport and Environment", "publisher": "", "place": "", "date": "January 2013", "volume": "18", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "39-45", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Transportation Research Part D: Transport and Environment", "DOI": "10.1016/j.trd.2012.09.007", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S1361920912001095", "accessDate": "2016-08-23T16:22:39Z", "PMID": "", "PMCID": "", "ISSN": "1361-9209", "archive": "", "archiveLocation": "", "shortTitle": "Intent to purchase a plug-in electric vehicle", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-08-23T16:22:39Z", "dateModified": "2016-08-23T16:22:39Z" } } ]