LITHIUM BATTERY

Lithium-ion batteries presence today as cornerstone power solutions for all scales of energy management from consumer electronic devices to electric vehicles and renew able systems. The high energy density together with long life cycle of lithium-ion batteries exists with reliability and safety risks due to thermal runaway and internal short circuits and capacity degradation faults. A new generative deep learning framework emerged for lithium-ion battery fault diagnosis through advanced machine learning implementations which improved anomaly detection alongside predictive fault analysis capabilities. Reliability measurements in batteries could be assessed through a method which integrates Graph Neural Networks (GNNs) and attention mechanisms plus spiral correlation detection for analysing complex non-linear characteristics. The model required data processing and temporal sequence generation as well as feature graph construction steps to ensure accuracy and resistance to external conditions. The system utilized synthetic and real data sets to reach an accuracy level of 95.6% and achieved 92.8% recall performance together with 0.98 AUC- ROC score. These metrics show that the framework successfully detects minor anomalies in addition to making exact judgments about normal and faulty battery states. Analysis demonstrated that GNNs work well for parameter relationship modelling and attention methods direct the model toward significant features like temperature and voltage variations. Spiral correlation detection as a method delivered ground breaking understanding of nonlinear fault patterns especially in cases of capacity fade and electrode degradation. These study findings show how integrating deep learning methods with specialized domain understanding allows the resolution of essential battery diagnostic problems.