Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
Abstract
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.
Additional Information
© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. 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.Attached Files
Published - Adaptive_Charging_Networks_A_Framework_for_Smart_Electric_Vehicle_Charging.pdf
Submitted - 2012.02636.pdf
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Additional details
- Eprint ID
- 108942
- Resolver ID
- CaltechAUTHORS:20210503-115705210
- NSF Graduate Research Fellowship
- DGE-1745301
- NSF
- IIP-1602119
- NSF
- ECCS-1932611
- NSF
- CCF-1637598
- Resnick Sustainability Institute
- Caltech RocketFund
- Caltech Innovation Initiative (CI2)
- Emerging Technologies Coordinating Council of Utilities
- Wells Fargo Innovation Incubator (IN2)
- Created
-
2021-05-03Created from EPrint's datestamp field
- Updated
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2021-08-24Created from EPrint's last_modified field
- Caltech groups
- Resnick Sustainability Institute