Advanced Data-Driven Diagnostics and Prognostics for Next-Generation Battery Systems

Battery diagnostics and prognostics support safe electrified transport and grid storage, yet comparison across model families is hindered by inconsistent notation, validation, and uncertainty reporting. This review organizes state-of-health, state-of-charge, and remaining-useful-life estimation in a common mathematical framework, drawing primarily on lithium-ion evidence and examining conditions for transfer to sodium-ion, lithium–sulfur, and solid-state systems. Deterministic degradation laws, Wiener and Gamma processes, reduced-order electrochemical models, statistical learning, deep architectures, Kalman and particle filters, and physics-informed methods are expressed in common notation and compared by assumptions, identifiability, data requirements, computational cost, and uncertainty treatment. The synthesis shows that stochastic and Bayesian formulations are well suited to probabilistic life prediction, feature-based models remain competitive in small-data regimes, and physics-informed approaches improve extrapolation only when governing models and parameters remain valid. Cross-chemistry transfer requires target-system evidence that the model assumptions and input features remain appropriate. Priorities include cell-wise and cross-condition validation, calibrated prediction intervals, pack-level inference, interpretable models, and privacy-preserving multi-fleet learning.