Date of Award
8-2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Automotive Engineering
Committee Chair/Advisor
Jiangfeng Zhang
Committee Member
Venkat Krovi
Committee Member
Benjamin Lawler
Committee Member
Zheyu Zhang
Abstract
The transportation sector is responsible for 28% of global energy-related emissions, with road transport accounting for most of these emissions. While electric vehicles (EVs) are essential for decarbonizing transportation, widespread adoption depends on fast, efficient charging. Fast charging reduces charging time but accelerates battery degradation, whereas slow charging increases range anxiety. In addition, fast charging stations (FCSs) impose significant stress on distribution grids, making their operation a critical challenge. Optimizing FCS operation can conflict with EV fast-charging objectives, yet existing research largely treats these problems independently despite their strong interdependence.
Current fast-charging methods focus on individual cells or small battery modules, neglecting battery pack behavior and FCS constraints. Likewise, FCS management relies on stochastic optimization over fixed planning horizons, requiring unrealistic forecasts of uncertain user behavior.
This dissertation addresses these limitations through a unified framework based on Lyapunov optimization (LO), which decomposes large optimization problems into real-time per-time-step decisions using only current information. This approach naturally accommodates the different time scales of EV charging and FCS operation, eliminates the need for demand forecasting, reduces computational complexity, and provides provable near-optimal performance. The dissertation reformulates EV fast charging, FCS management, and electricity market participation within the LO framework and establishes convergence guarantees for each formulation. It also develops a reinforcement learning (RL)-based battery pack fast-charging strategy and introduces an RL-augmented LO framework that combines RL's ability to capture complex nonlinear battery dynamics with LO's constraint satisfaction and optimality guarantees, enabling practical, real-time optimization of EV charging, FCS operation, and market participation.
Recommended Citation
Abbasi, Mohammad Hossein, "Optimal Management of Electric Vehicle Fast Charging Stations via Lyapunov Optimization and Reinforcement Learning" (2026). All Dissertations. 4352.
https://open.clemson.edu/all_dissertations/4352