Power Electronics
- DC-DC converter topologies
- High-efficiency design trade-offs
- Wide-band-gap devices including SiC and GaN
Research Profile
My research interests lie at the intersection of converter design, battery management, control, and data-driven modeling, with attention to how systems behave beyond ideal laboratory assumptions.
I am exploring high-efficiency converter design for battery-connected systems, with emphasis on realistic operating conditions, thermal and reliability limits, and system-level trade-offs.
I am interested in machine learning and deep learning approaches for battery SOC/SOH estimation, especially where traditional physics-based models struggle with aging, noise, and operating variation.
A comparative study of deep learning architectures for State-of-Charge estimation in lithium-ion battery systems, with emphasis on robustness, generalization, and practical applicability.
PDF to be added.
Explores bio-inspired optimization algorithms applied to State-of-Health estimation in lithium-ion batteries, focusing on adaptive behavior, convergence characteristics, and long-term degradation modeling.
Paper PDFA study of active cell balancing techniques in BMS, highlighting energy redistribution, efficiency improvement, lifespan impact, safety, and system reliability.
Slides PDF to be added.
Opportunities and constraints in SiC and GaN based power converters, including switching frequency, efficiency, and practical non-idealities.
Notes PDF to be added.
Reflections on evaluating energy systems beyond short-term efficiency metrics and early prototype performance.
Notes on control theory, uncertainty, prediction limits, and the gap between models and deployed systems.