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Large-Scale Adaptive Electric Vehicle Charging

Lee, Zachary J. and Chang, Daniel and Jin, Cheng and Lee, George S. and Lee, Rand and Lee, Ted and Low, Steven H. (2018) Large-Scale Adaptive Electric Vehicle Charging. In: 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE , Piscataway, NJ, pp. 863-864. ISBN 978-1-7281-1295-4. http://resolver.caltech.edu/CaltechAUTHORS:20190228-160214269

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Abstract

Large-scale charging infrastructure will play an important role in supporting the adoption of electric vehicles. In this extended abstract, we describe a unique physical testbed for large-scale, high-density EV charging research which we call the Adaptive Charging Network (ACN). We describe the architecture of the ACN including its hardware and software components. We also present a practical framework for online scheduling, which is based on model predictive control and convex optimization. We use simulations based on real data collected from the ACN to illustrate the trade-offs involved in accounting for non-ideal charging behavior.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/GlobalSIP.2018.8646472DOIArticle
https://ieeexplore.ieee.org/document/8646472PublisherArticle
ORCID:
AuthorORCID
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2018 IEEE. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1745301, NSF AIR-TT under Grant No. 1602119, and NSF CTT under Grant No. 1637598.
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
NSFIIP-1602119
NSFCCF-1637598
Subject Keywords:Electric vehicles, adaptive charging, online scheduling, ACN deployment, intelligent infrastructure
Record Number:CaltechAUTHORS:20190228-160214269
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190228-160214269
Official Citation:Z. J. Lee et al., "Large-Scale Adaptive Electric Vehicle Charging," 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 863-864. doi: 10.1109/GlobalSIP.2018.8646472
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93368
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:01 Mar 2019 00:13
Last Modified:01 Mar 2019 00:13

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