Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

TL;DR

This paper presents the design and deployment of the Adaptive Charging Network (ACN), a scalable, real-time EV charging system using convex optimization and predictive control, improving efficiency and operator profit.

Contribution

It introduces a novel adaptive scheduling algorithm for EV charging that handles real-world challenges and demonstrates significant performance improvements over baseline methods.

Findings

Improves operator profit by 3.4 times over uncontrolled charging.

Outperforms baseline algorithms in congested systems.

Successfully manages practical challenges like infrastructure imbalance and non-ideal battery behavior.

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.