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Figure 2. Closed-loop architecture of uncertainty-aware physics-guided digital twins. (A) Digital twins-assisted equalization strategy of the batteries; (B) Battery Digital twins concept in COMSOL Multiphysics®. Figure 2A and B is reprinted from Ref.^[50], under the CC BY 4.0 license; (C) Multimodal field-data feature construction for EV SOH estimation, including voltage maps, charge-capacity and temperature sequences, and point features. Figure 2C is reprinted with permission from Ref.^[12]. Copyright © 2025 Springer Nature; (D) Multi-contributor federated learning framework for retired-battery sorting without raw-data exchange; (E) Privacy-preserving collaboration compared with the conventional data-islanding paradigm. Figure 2D and E is reprinted from Ref.^[49], under the CC BY 4.0 license; (F) Personalized federated fault-warning architecture using a central hyper-model and local owner-specific models for massive EV fleets. Figure 2F is reprinted with permission from Ref.^[48]. Copyright © 2025 Springer Nature; (G) Closed-loop residual-learning demonstration for battery monitoring; (H) Mechanistically inspired SOC/SOH monitoring pipeline for lifelong state correction. Figure 2G and H is reprinted with permission from Ref.^[18]. Copyright © 2026 Springer Nature; (I) Cloud-edge-fleet updating and personalization loop for battery digital twins. BMS: Battery management system; SOC: state of change; SOH: state of health; EV: electric vehicle.