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Content Provider | ACM Digital Library |
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Author | Basmadjian, Robert Kacperski, Celina Kutzner, Florian Eider, Markus Klingert, Sonja Schulze, Thomas Sellner, Diana Berl, Andreas de Meer, Hermann Stolba, Michal |
Abstract | The existing electromobility (EM) is still in its fledgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. This straightforward goal, however, is counteracted from various sides: The range of the EV depends on the status and depletion of the EV battery which is influenced by EV use and charging characteristics. Also, most convenient charging from the user's point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV batteries are an untapped potential to store electricity from intermittent renewable energy sources. In this paper, we propose a novel approach to tackle this multi-layered problem from different perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). These components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user profile optimizing above mentioned goals. |
Starting Page | 316 |
Ending Page | 321 |
Page Count | 6 |
File Format | |
ISBN | 9781450350365 |
DOI | 10.1145/3077839.3078461 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-05-16 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Incentives User guidance Electric vehicles Routing Adas Grid prediction models Battery health Range modelling Smart charging |
Content Type | Text |
Resource Type | Article |
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