A Proactive, AI-Enhanced Safety Framework for Emerging Long-Duration Energy Storage Technologies

Safety for Long-Duration Energy Storage (LDES) is governed by NFPA 855 (installation), UL 9540/9540A (listing and large-scale fire tests), and IFC §1207. While these have improved, they remain largely incident-driven, creating a “reactive gap” for emerging chemistries and non-battery modalities. We present ProActive-SAFE, a five-stage, assistive framework that (i) anticipates hazards with QSAR-like screening, AI-enhanced FMEA/HAZOP, and physics-informed machine learning (PIML); (ii) enforces a NIST AI RMF-aligned TEVV gate for auditable data/models; (iii) translates results into code-facing artifacts mapped to NFPA 855, UL 9540/9540A, and IFC §1207; and (iv) sustains learning via a neutral, confidential $S^{3} R S$ reporting backbone for near-misses and micro-events. ProActive-SAFE does not replace manufacturer evidence, accredited testing, or AHJ authority; it focuses test plans, clarifies submittals, and enables standardized leading indicators (CII, RRE, HEI, PIF, SOE). We outline pilot KPIs and dissemination to help avoid incident-driven delays while accelerating safe inclusion of new LDES technologies in existing codes and listings.