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.

Towards Balanced Three-phase Charging: Phase Optimization in Adaptive Charging Networks

“…Optimal EV charging as second-order-cone programming (SOCP) was proposed and solved in [9] and [10]. The authors of [11] illustrated detailed ACN architectures and proposed a novel model-predictive-control (MPC) algorithm that handles unbalanced three-phase infrastructure. With historical ACN charging records, reinforcement learning was used to generate penalty-based terminal functions that facilitate energy-cost efficiency [12].…”

“…In the following, we describe the constraints of the ACN circuit model depicted in Figure 2, which has been widely used to model real-world charging facilities such as the California Parking Garage [9], [11]. All charging variables and network constraints are in the unit of power (kW).…”

“…The elements of each row depend on the transformer's capacity and e thline power supply. Without loss of generality, mimicking the model used in [5], [11], we consider the effects of the transformer's capacity, i.e. for any…”

ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

“…In this section, we provide examples, including evaluating (1) possible infrastructure solutions, (2) the effect of unbalance on oversubscribing infrastructure, (3) time-series of EV charging profiles, and (4) the effect of large-scale EV charging on a distribution feeder. In addition, ACN-Sim has been used to design dynamic pricing schemes and cost-optimal scheduling [28], train reinforcement learning agents for EV charging systems [29], and examine the effect of non-ideal batteries and EVSE pilot quantization on model predictive control and baseline algorithms [30]. The code for all case studies presented here is available at [31].…”

Analysis of AC/DC/DC Converter Modules for Direct Current Fast-Charging Applications

“…Additional functional guidelines are also specified by charging station operators and installers [19][20][21][22]. Due to the still small number of vehicles, their price is a very important criterion related to the development of charging stations.…”