  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.
