AI-Based Digital Twin-Anomaly Detection and Diagnostics for HV Battery Behavior and Performance

In the rapidly evolving automotive industry, HV batteries play a pivotal role, demanding a focused effort on safety and failure prevention. Conventional methods for health monitoring fall short due to their supervised nature, relying on historical fault data. This paper proposes an innovative approach involving the implementation of an AI-based digital twin leveraging a graph neural network for unsupervised anomaly detection in fleet data. Furthermore, our approach incorporates domain knowledge to proactively prevent HV battery failure. The results demonstrate state-of-the-art performance for enhancing the reliability and safety of EV power systems.