Performance Guarantees for Electric Vehicle Fast Charging Station Management

Clemson University, Clemson SC

Investigators

Abstract

This grant will fund research that enables the development of effective solutions for the management of electric vehicle charging stations that reduce operating costs, shorten queuing time, and lessen battery degradation, thereby promoting the progress of science and advancing national prosperity. Existing fast charging station management solutions are based on conservatively forecasted charging demand and do not leverage power control optimization for vehicle charging. Given typical constraints on the availability of grid-supplied power, conventional charging solutions may result in undesirable performance, including longer charging/queuing times and faster battery degradation. Such outcomes are expected to be exacerbated as projected increases in the charging needs of battery and hybrid electric vehicles, in terms of both number and diversity, significant spatial and temporal variability at geographically distributed charging stations, and intermittency of renewable power resources impose significant stresses on the electric power grid. This project will address these challenges by applying dynamic systems, control, and optimization techniques to derive new charging station management solutions that guarantee performance in terms of reduced charging time under limited power supplies, increased battery life and user satisfaction, and improved grid support for a secure power supply. Industry outreach will be conducted to present outcomes, refine research directions, and seek commercialization of new technology. Summer workshops on electric vehicle charging for high school and undergraduate students will be used to promote engagement with STEM, including of individuals from currently underrepresented groups. This research aims to develop the foundations of a compartmentalization approach to charging power management at electric vehicle fast charging stations under maximum power restrictions and grid integration constraints. It accomplishes this outcome by modeling fast charging station management as a multi-objective optimization problem in terms of charging protocols, charging power allocation, charging pricing, and power grid interactions, constrained by the dynamics of electrochemical battery degradation, electricity market pricing, local photovoltaic power generation, vehicle to grid service, and demand response. A critical challenge is the construction of a Lyapunov function that enables a decomposition of the long-period optimization horizon into temporally queued, short-term and small-scale subproblems with guaranteed asymptotic convergence to the optimal solution of the original problem. A systematic verification and validation framework for virtual prototyping and hardware-in-the-loop testing will be implemented to investigate the performance of fast charging station management solutions under real-world conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.