Filippo Rosetti
Papers
1
Total Citations
3
H-Index
1
About
Filippo Rosetti is a leading researcher in battery management systems (BMS), with a core focus on advancing state-of-charge (SoC) and state-of-health (SoH) estimation for next-generation energy storage. His most cited work, "Embedded strategy for battery module states estimation using tiny machine learning models" (2026), introduces a practical, scalable algorithm that deploys tiny machine learning models directly onto embedded hardware. This innovation enables real-time, accurate battery diagnostics without relying on cloud computing, significantly enhancing the safety and efficiency of battery-powered systems. By bridging the gap between sophisticated AI and resource-constrained BMS platforms, Rosetti’s contributions are pivotal for the widespread adoption of electric vehicles and renewable energy storage. His research has already garnered attention, with his top paper accumulating 3 citations in its first year—a strong indicator of its emerging impact. Rosetti’s work stands out for its emphasis on deployability and scalability, offering a tangible path toward smarter, more reliable battery management in an increasingly electrified world.
Research Focus
Key Achievements
Top Papers
- 1