Transferable predictive maintenance for battery energy storage systems

Reviewartikel, 2026

Battery energy storage systems are becoming multi-role infrastructures whose functions extend beyond energy storage and keep evolving over long deployment horizons. This evolution drives persistent operational drift in application requirements, duty cycles, environmental exposure and monitoring pipelines, whereas predictive maintenance still largely assumes fixed roles and stationary conditions. Drift therefore undermines the reliability of predictive maintenance, compromising system safety, asset availability and operational resilience. This Perspective proposes a system-level framework for transferable predictive maintenance under structured operational drift. We classify the sources of operational drift across evolving asset roles and organize their effects into layered functional mismatches spanning data, model and temporal layers. Each mismatch layer is linked to targeted transfer strategies, which we assess against negative transfer, catastrophic forgetting, calibrated uncertainty and sample efficiency. We position transferable predictive maintenance as a life-cycle-spanning capability connecting manufacturing, first-life operation and second-life deployment and outline the data infrastructures, foundation models, open benchmarks and regulatory recommendations needed to sustain it.