Helmus, J.R.; van den Hoed, R. Link to publication Helmus, J., & van den Hoed, R. (2015). Unraveling user type characteristics: towards a taxonomy for charging infrastructure. Abstract from The 28th International Electric Vehicle Symposium and Exhibition, Kintex, Goyang, Korea, Republic of. It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please contact the library: http://www.hva.nl/bibliotheek/contact/contactformulier/contact.html, or send a letter to: University Library (Library of the University of Amsterdam and Amsterdam University of Applied Sciences), Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  Many European cities with air quality concerns struggle how to further roll out charging infrastructure in the coming years in a cost effective manner. Typical concerns include charging point under-capacity (scarcity of charging points due to high demand) as well as over-capacity (placement of charging points in less prevalent locations). Being able to predict future utilization of charging stations would provide a great deal to policy makers in order to achieve more optimized infrastructure planning and roll out. This paper argues that in order to make solid predictions on the future use of charging stations one needs to acknowledge significant differences in charging behavior amongst user types, be it residents, commuters, city visitors or users of electric car sharing schemes. This paper sets out to unravel the particular charging patterns of different user types in terms of timing, charging amount and location preferences. By combining the specific user patterns with probabilities on which locations these users are likely to charge, the user-based charging patterns provide a powerful starting point to make predictions how charging points are likely to be used, as well as provide policy makers tools to make strategic decisions how to optimize the roll out of new charging infrastructure.
For example, Helmus et al. REF characterize different user types in terms of EV charging session start and end times (with associated charged energy), as well as the time between subsequent charging sessions.
Unraveling User Type Characteristics: Towards a Taxonomy for Charging Infrastructure
World Electric Vehicle Journal
Computer Science