In this webinar, our experts present battery safety as a data-driven, real-time control challenge and introduce State of Safety (SoS) as a composite index derived from BMS measurements and advanced models. By combining electrochemical modeling, AI, and cloud analytics, potential safety risks can be predicted at an early stage, enabling proactive mitigation, adaptive control strategies, and prevention of critical failures.
Key Topics and Takeaways:
- Gain insights into advanced hybrid models that combine electrochemical, physics-based, and AI-driven approaches for battery safety assessment
- Learn how State of Safety (SoS) enables continuous, real-time evaluation of battery risks and supports proactive protective actions before critical conditions arise
Understand how aging, fast charging, and demanding operating conditions reduce safety margins and increase the need for adaptive monitoring and prediction of internal battery states
- Discover how the combination of physical and virtual sensors improves observability and enables detection of hidden degradation and failure mechanisms
Explore how cloud-connected analytics and fleet learning enhance predictive safety through early anomaly detection and continuous optimization via over-the-air (OTA) updates
Paul Schiffbänker
Director Product & Business Development Battery
AVL List GmbH
Thyagesh Sivaraman
Lead Engineer Battery Controls
AVL List GmbH