Impact of stochastic driving range on the optimal charging infrastructure expansion planning

Abstract This paper presents the impact of the stochastic electric-drive vehicles' driving range on the charging reliability of charging infrastructure. For this purpose, it incorporates an additional uncertainty distance in addition to the initial driving range of the electric vehicle to address all probabilistic occurrences that can affect the range, such as the battery charge level, driving style and mobility behaviour, road configuration, air-conditioning, etc. The analysis is performed based on the proposed optimisation model on a test road network applied for different stochastic driving range scenarios, Quality of Service, electric vehicles' trajectories and the types of charging technologies. In general, a dependency is observed where a shorter uncertainty distance increases the number of candidate locations included in the charging reliability criterion resulting in higher overall charging infrastructure placement costs and vice-versa. By becoming familiar with the uncertainty distance impact and its probability of occurrence, charging infrastructure planners could decide in which optimal solution they would invest to both perceive beneficial gains and engage unlimited mobility for electric vehicle users. Above all, planners can use the model as a foundation for future investment incentives in technological development or easier decision making for the adoption of the final charging infrastructure expansion plan.

[1]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[2]  Ali Elkamel,et al.  Optimal Transition to Plug-In Hybrid Electric Vehicles in Ontario, Canada, Considering the Electricity-Grid Limitations , 2010, IEEE Transactions on Industrial Electronics.

[3]  Miloš Pantoš,et al.  Stochastic optimal charging of electric-drive vehicles with renewable energy , 2011 .

[4]  Yueyue Fan,et al.  Infrastructure planning for fast charging stations in a competitive market , 2016 .

[5]  Ali Emadi,et al.  Modern electric, hybrid electric, and fuel cell vehicles : fundamentals, theory, and design , 2009 .

[6]  Ziyou Gao,et al.  Charging station location problem of plug-in electric vehicles , 2016 .

[7]  Pandian Vasant,et al.  Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures , 2016 .

[8]  Tony Markel,et al.  Connectivity and Convergence: Transportation for the 21st Century , 2014, IEEE Electrification Magazine.

[9]  Nizar Zorba,et al.  Analysis and quality of service evaluation of a fast charging station for electric vehicles , 2016 .

[10]  Gonçalo Homem de Almeida Correia,et al.  A MIP model for locating slow-charging stations for electric vehicles in urban areas accounting for driver tours , 2015 .

[11]  Magdy M. A. Salama,et al.  Optimal allocation for electric vehicle charging stations using Trip Success Ratio , 2017 .

[12]  Yuhe Zhang,et al.  Remaining driving range estimation of electric vehicle , 2012, 2012 IEEE International Electric Vehicle Conference.

[13]  Furong Li,et al.  Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates , 2016 .

[14]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

[15]  Sreten Davidov,et al.  Planning of electric vehicle infrastructure based on charging reliability and quality of service , 2017 .

[16]  Xiaozhou Zhang,et al.  The design of electric vehicle charging network , 2016 .

[17]  Yu Nie,et al.  A corridor-centric approach to planning electric vehicle charging infrastructure , 2013 .

[18]  Xiaowen Chu,et al.  Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions , 2013, IEEE Transactions on Smart Grid.