Emanuèle Ogliari
Papers
1
Total Citations
3
H-Index
1
About
Emanuèle Ogliari is a leading researcher in energy systems and battery management, with a focus on advancing the reliability and intelligence of battery-powered technologies. Her work centers on developing innovative algorithms for Battery Management Systems (BMS), particularly for accurate State of Charge (SoC) and State of Health (SoH) estimation—critical for ensuring safe and efficient operation in applications ranging from electric vehicles to grid storage. Among her notable contributions is the development of embedded strategies that leverage tiny machine learning models for real-time battery module state estimation, a scalable and practical approach that bridges the gap between computational efficiency and predictive accuracy. Her most cited paper, "Embedded strategy for battery module states estimation using tiny machine learning models" (2026), has already garnered 3 citations, reflecting its early impact in the field. Ogliari’s work is distinguished by its emphasis on deploying advanced analytics directly onto hardware, enabling smarter, more autonomous energy systems. Her research not only advances the theoretical foundations of battery diagnostics but also offers tangible solutions for next-generation energy storage, making her a key figure in the transition toward sustainable and intelligent power management.
Research Focus
Key Achievements
Top Papers
- 1