{"type":"ScholarlyArticle","title":"Optimal Electric Vehicle Charging Station Placement","authors":[{"name":"Yanhai Xiong","slug":"yanhai-xiong","given":"Yanhai","family":"Xiong","affiliation":"Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly , NTU , Singapore; ,"},{"name":"Jiarui Gan","slug":"jiarui-gan","given":"Jiarui","family":"Gan","affiliation":"The Key Lab of Intelligent Information Processing , ICT , CAS; University of Chinese Academy of Sciences , Beijing , China"},{"name":"Bo An","slug":"bo-an","given":"Bo","family":"An","affiliation":"School of Computer Engineering , Nanyang Technological University , Singapore"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"School of Computer Engineering , Nanyang Technological University , Singapore"},{"name":"Ana L. C. Bazzan","slug":"ana-l-c-bazzan","given":"Ana L. C.","family":"Bazzan","affiliation":"Universidade Federal do Rio Grande do Sul , Brazil"}],"abstract":"Many countries like Singapore are planning to introduce Electric Vehicles (EVs) to replace traditional vehicles to reduce air pollution and improve energy efficiency. The rapid development of EVs calls for efficient deployment of charging stations both for the convenience of EVs and maintaining the efficiency of the road network. Unfortunately, existing work makes unrealistic assumption on EV drivers' charging behaviors and focus on the limited mobility of EVs. This paper studies the Charging Station PLacement (CSPL) problem, and takes into consideration 1) EV drivers' strategic behaviors to minimize their charging cost, and 2) the mutual impact of EV drivers' strategies on the traffic conditions of the road network and service quality of charging stations. We first formulate the CSPL problem as a bilevel optimization problem, which is subsequently converted to a single-level optimization problem by exploiting structures of the EV charging game played by EV drivers. Properties of CSPL problem are analyzed and an algorithm called OCEAN is proposed to compute the optimal allocation of charging stations. We further propose a heuristic algorithm OCEAN-C to speed up OCEAN. Experimental results show that the proposed algorithms significantly outperform baseline methods.","doi":null,"url":"https://ijcai.org/proceedings/2015/377","pdf":"https://ijcai.org/proceedings/2015/0377.pdf","markdown":"https://ijcai.org/proceedings/2015/377.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/377","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Computational Sustainability","track":"Special Track on Computational Sustainability","number":377,"pages":{"first":2662,"last":2667},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Banner and Orda, 2007] R. 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