# Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

arXiv: https://arxiv.org/abs/2012.02636 · HTML: https://mlsanity.com/paper/2012.02636/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [Predictability and Fairness in Load Aggregation and Operations of Virtual Power Plants](https://mlsanity.com/paper/2110.03001/) (2021)
- [Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times](https://mlsanity.com/paper/2208.14852/) (2022)
- [Out-of-Distribution-Aware Electric Vehicle Charging](https://mlsanity.com/paper/2311.05941/) (2023)
- [DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities](https://mlsanity.com/paper/2305.11195/) (2023)
- [Chargax: A JAX Accelerated EV Charging Simulator](https://mlsanity.com/paper/2507.01522/) (2025)
- [Electric Vehicle Identification from Behind Smart Meter Data](https://mlsanity.com/paper/2509.19316/) (2025)

## Abstract (arXiv)

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.