In this free webinar, IAV’s Reik Laubenstein provides a clear vision of risk mitigation in lithium-ion batteries using various fault detection algorithms for Battery Management Systems (BMS). We sidestep resource-consuming physical tests by employing electro-physicochemical models to simulate different fault scenarios.

Using readily available sensor data, we aim for simple, cost-effective detection methods. Our research offers essential insights into enhancing battery safety and efficiency, paving the way for leveraging AI and machine learning in future battery development.

Key topics and takeaways:

• Learn about increasing battery safety by employing new functions in the BMS
• Discover IAV’s capabilities to couple electro-physicochemical modeling with various simulation environments for model-based development
• Find out about the influence of the internal short circuit resistance on cell behavior and the detection of this anomaly using different algorithms
• Gain insights on how machine learning supports early detection of cell anomalies