2024 AIChE Annual Meeting
(364a) Development of Fast-Charging Protocol Considering Cell-to-Cell Variability of Lithium-Ion Batteries
Here, polynomial chaos expansion (PCE) was employed to identify parameters contributing to degradation acceleration factors and quantify associated uncertainties. The propagation of uncertainty suggests that charging strategies designed based on nominal parameters can probabilistically accelerate degradation. The confidence intervals for the degradation factors identified through PCE offer quantitative insights into conservative charging conditions (e.g., C-rate) that can minimize degradation. Our approach was applied to two state-dependent protocols for fast charging of LiC6/LiCoO2, achieving approximately 10% of the computational budget compared to the Monte Carlo simulation.
Research Interests
My research background focuses on first principles-based process design and optimization using data-driven approaches. Primarily, I concentrate on constructing high-fidelity process models through parameter identification, analyzing systems via uncertainty quantification, and performing robust optimization. My work spans various battery fields, including not only the development of fast charging protocols but also battery model parameter identification and Bayesian active learning-based electrolyte discovery.