Adaptive Monitoring and Control for Lithium-Sulfur Separator Engineering: Failure Modes across the Evidence Chain

Abstract

Adaptive monitoring of lithium-sulfur cells must react to evolving shuttle behavior and separator degradation without learning unsafe responses from sparse failures. This review organizes the control problem around the full chain from material batch and cell assembly to sensing, AI inference, charge protocol, and maintenance action. Attention-based sequence models, graph representations of interacting cell variables, and Bayesian uncertainty are examined for detecting precursors in voltage, current, impedance, temperature, and cycling history. The synthesis stresses that an alert should lead to a defined action—additional diagnosis, derating, human review, or shutdown—and that every action changes the data available for later learning. Rare-event imbalance, delayed labels, sensor drift, and policy feedback are therefore treated as primary evaluation concerns. A constrained update scheme freezes safety limits while allowing bounded recalibration within known regimes. No new battery experiment or accuracy claim is introduced. The review provides a failure-aware design for adaptive AI that keeps the polysulfide-barrier mechanism visible, records the provenance of updates, and preserves operator control when evidence becomes uncertain.