Spyridon Giazitzis
Papers
1
Total Citations
3
H-Index
1
About
Dr. Spyridon Giazitzis is a leading researcher in the field of battery management systems (BMS), with a particular focus on embedded machine learning for real-time state estimation. His work centers on developing practical, scalable algorithms that enable accurate State of Charge (SoC) and State of Health (SoH) estimation directly on resource-constrained hardware. His most cited paper, "Embedded strategy for battery module states estimation using tiny machine learning models" (2026, 3 citations), introduces a novel approach that leverages tiny machine learning models to bring sophisticated battery diagnostics to edge devices, addressing critical safety and efficiency challenges in the growing battery-powered systems market. This work represents a significant step toward making BMS solutions more accessible and deployable in real-world applications. Dr. Giazitzis's contributions are particularly notable for bridging the gap between advanced algorithmic design and practical hardware implementation, offering a pathway to more intelligent and reliable energy storage systems. His research is highly relevant for students and engineers working on the intersection of embedded systems, machine learning, and energy storage, providing a foundation for next-generation battery management technologies.
Research Focus
Key Achievements
Top Papers
- 1