PhD Thesis Defense
Millions of electric vehicles (EVs) will enter service in the next decade, generating gigawatt-hours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging. While researchers have explored smart charging for over a decade, very few of these systems have been deployed in practice, leaving a sizeable gap between the research literature and the real world.
The goal of this thesis is to develop systems, tools, and algorithms to bridge these gaps between theory and practice.
First, we describe the architecture of a first-of-its-kind smart charging system we call the Adaptive Charging Network (ACN). Next, we use data and models from the ACN to develop a suite of tools to help researchers. These tools include ACN-Data, a public dataset of over 80,000 charging sessions; ACN-Sim, an open-source simulator based on realistic models; and ACN-Live, a platform for field testing algorithms on the ACN.
Finally, we describe the algorithms we have developed using these tools. For example, we propose a practical and robust algorithm based on model predictive control, which can reduce infrastructure requirements by over 75%, increase operator profits by up to 3.4 times, and significantly reduce strain on the electric power grid. Other examples include a pricing scheme that fairly allocates costs to users considering time-of-use tariffs and demand charges and a data-driven approach to optimally size on-site solar generation with smart EV charging systems.