Data-Driven Pathways to a Self-Improving Battery Health Ecosystem: A Perspective on State of Health and Remaining Useful Life Prediction

Accurate battery state-of-health and remaining-useful-life estimation faces three challenges: sparse data, poor cross-condition generalization, and stringent hardware constraints. We review current models and public data sets, then propose advances toward field-ready prognostics: a standardized test matrix quantifying performance across data sets, chemistries, and usage patterns; physics-informed constraints (monotonic capacity fade, energy consistency) integrated into learning pipelines; and a pretraining framework for multimodal foundation models. The integration of these three pillars, i.e., physics-informed learning for robust model design, standardized benchmarking for transparent evaluation, and foundation models for scalable knowledge transfer, forms the core of our proposed ecosystem, where each component reinforces the others to enable continuous improvement through feedback loops between deployed models and centralized learning systems. We present a five-year roadmap with targets for data standards, validation protocols, and pilot deployments. We further discuss practical implementation pathways, including the use of existing public data sets for validating the proposed generalization test matrix. This framework aims to transform predictive battery management from laboratory demonstrations into trusted, deployable technology for electric vehicles, grids, and sustainable battery lifecycles.