fig5

From: Uncertainty-aware physics-guided digital twins for lithium-ion batteries: toward integrated health, safety, and fast-charging management

Figure 5. Digital-twin-enabled health management beyond scalar SOH. (A) Multimodal feature engineering from EV field data; (B) Deep-learning SOH-estimation framework using 2D voltage, 1D sequence, and point-feature domains. Figure 5A and B are reprinted with permission from Ref.^[12]. Copyright © 2025 Springer Nature; (C) Power-autocorrelation profiles calculated from discharge data; (D) Relation between power-autocorrelation loss and capacity loss. Figure 5C and D is reprinted with permission from Ref.^[67]. Copyright © 2024 Springer Nature; (E) Available-capacity degradation under fragmented charging windows. Figure 5E is reprinted with permission from Ref.^[82]. Copyright © 2025 Springer Nature; (F) Diverse ageing-factor coverage for inter-cell lifetime prediction; (G) Long- and short-term degradation behaviors across ageing conditions. Figure 5F and G are reprinted from Ref.^[86], under the CC BY 4.0 license; (H) BatteryGPT pipeline linking early-cycle data to full-lifecycle SOH, knee point, and EOL prediction. Figure 5H is reprinted with permission from Ref.^[27]. Copyright © 2025 Springer Nature. SOH: state of health; MATR: MIT-Accelerated Technology Readiness; HUST: Huazhong University of Science and Technology; SNL: Sandia National Laboratories; CALCE: Center for Advanced Life Cycle Engineering; EOL: end-of-life; EV: electric vehicle.