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Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Lee, Zachary J. and Lee, George and Lee, Ted and Jin, Cheng and Lee, Rand and Low, Zhi and Chang, Daniel and Ortega, Christine and Low, Steven H. (2021) Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging. IEEE Transactions on Smart Grid, 12 (5). pp. 4339-4350. ISSN 1949-3053. doi:10.1109/TSG.2021.3074437.

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We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Lee, Zachary J.0000-0002-5358-2388
Lee, Ted0000-0002-5793-7457
Low, Zhi0000-0003-4962-7771
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see Manuscript received August 13, 2020; revised February 18, 2021; accepted April 2, 2021. Date of publication April 20, 2021; date of current version August 23, 2021. This work was supported in part by the National Science Foundation through the Graduate Research Fellowship Program under Grant 1745301; in part by NSF AIR-TT under Grant 1602119; in part by NSF Division of Electrical, Communications and Cyber Systems (ECCS) under Grant 1932611; and in part by NSF Division of Computing and Communication Foundations (CCF) under Grant 1637598; in part by the Resnick Sustainability Institute Graduate Fellowship; in part by Caltech Rocket Fund; in part by Caltech CI2 Grant; in part by the Emerging Technologies Coordinating Council of Utilities; and in part by Well Fargo/NREL IN2. Paper no. TSG-01250-2020.
Group:Resnick Sustainability Institute
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
Resnick Sustainability InstituteUNSPECIFIED
Caltech RocketFundUNSPECIFIED
Caltech Innovation Initiative (CI2)UNSPECIFIED
Emerging Technologies Coordinating Council of UtilitiesUNSPECIFIED
Wells Fargo Innovation Incubator (IN2)UNSPECIFIED
Subject Keywords:Electric vehicles (EVs), cyber-physical systems, distributed energy resources, model predictive control, smart charging
Issue or Number:5
Record Number:CaltechAUTHORS:20210503-115705210
Persistent URL:
Official Citation:Z. J. Lee et al., "Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging," in IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 4339-4350, Sept. 2021, doi: 10.1109/TSG.2021.3074437
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108942
Deposited By: George Porter
Deposited On:03 May 2021 19:48
Last Modified:24 Aug 2021 17:16

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