Jack Ferrari
Papers
1
Total Citations
3
H-Index
1
About
Jack Ferrari is a rising researcher at the forefront of embedded intelligence for battery management systems. His work centers on developing practical, scalable algorithms for real-time State of Charge (SoC) and State of Health (SoH) estimation, critical for the safe and efficient operation of modern battery-powered systems. Ferrari’s most cited paper, “Embedded strategy for battery module states estimation using tiny machine learning models” (2026), introduces a novel approach that leverages tiny machine learning models to perform accurate state estimation directly on low-power microcontrollers. This work directly addresses the industry’s need for cost-effective, on-device intelligence, moving beyond traditional cloud-dependent methods. By demonstrating that sophisticated battery diagnostics can be embedded within the battery module itself, Ferrari’s research has significant implications for electric vehicles, portable electronics, and grid storage. His contributions are already gaining traction, with 3 citations for this foundational paper, and his approach is poised to influence the next generation of Battery Management System (BMS) design, making battery systems safer, more reliable, and more autonomous.
Research Focus
Key Achievements
Top Papers
- 1