Artificial Intelligence based Battery Health Prediction Framework for Reliable Electric Vehicle Energy Management

Accurate prediction of battery health is critical for ensuring the reliability, safety, and efficiency of electric vehicles (EVs). This paper presents an advanced artificial intelligence (AI)-based framework for battery health prediction that integrates rare modeling techniques including manifold embedding, quaternion neural encoding, Bayesian graph neural networks, and a federated transformer ensemble. The model utilizes multimodal sensor data (voltages, currents, temperatures, and acoustic, etc.) to identify degradation signature and predict with great accuracy the state-of-health (SOH) and residual useful life (RUL). A hybrid SNN110Bio LSTM network is used to capture the dynamic characteristics over time and symbolic regression is used to increase the interpretability. The federated model proposed maintains the privacy of the users and allows cross-vehicle learning without a centralized pool of the data. Experimental validation on benchmark datasets also illustrates that the SOH prediction and RUL estimation accuracy is 96.85 and 94.27 respectively, which is notably better than the conventional CNN and LSTM bases. The framework also provides good uncertainty estimation, which provides actionable information in energy management systems. This research is not only a way to improve the battery performance monitoring, but also decreases the occurrence of unexpected failures and improves charging strategies in EVs. The methodology cracks the basis of scalable, privacy-preserving, and explainable battery health management solutions of next-generation smart mobility ecosystems.