The paper presents a data‑efficient method for quantifying cracks in lithium‑ion cathodes using a frozen self‑supervised vision‑transformer encoder, a lightweight decoder, and iterative model‑assisted annotation. Applied to three 120‑megapixel NMC cathode cross‑sections, the framework distinguishes intragranular from early and late intergranular cracks, providing per‑particle distributions of crack width, tortuosity, and area fraction. Late intergranular crack coverage reaches 4.6% in cycled samples versus 0.5% in initial and calendar‑aged samples, indicating degradation primarily from electrochemical cycling.
arXiv:2607. 16864v1 Announce Type: new Abstract: Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows.
arXiv:2608. 16612v1 Announce Type: cross Abstract: An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system.
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
arXiv:2608. 14764v1 Announce Type: new Abstract: With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical.
arXiv:2607. 11943v1 Announce Type: cross Abstract: Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for dense exploration of operating conditions over extended cycle life.
arXiv:2608. 14637v1 Announce Type: new Abstract: Long-duration stationary energy storage requires batteries whose degradation can be detected before substantial capacity loss has accumulated.
arXiv:2609.21932v1 Announce Type: new Abstract: Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. T...
arXiv:2605. 27044v2 Announce Type: replace Abstract: Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment.
arXiv:2609.22843v1 Announce Type: cross Abstract: Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applicat...
The paper presents a super‑resolution generative adversarial network (SRGAN) that boosts the throughput of electron backscatter diffraction (EBSD) for Li‑ion battery electrode materials. By training on LiNixMnyCozO2 cathode data, the SRGAN outperforms classical interpolation across upscaling factors of 2× to 12×, especially preserving small grains and realistic boundaries. A 5× upscaling yields a 25× speed‑up or larger field of view with acceptable accuracy, reducing grain‑size errors to within ±15%.
The paper presents a super‑resolution generative adversarial network (SRGAN) that boosts electron backscatter diffraction (EBSD) throughput for lithium‑ion battery electrode materials. Trained on LiNixMnyCozO2 cathode data, the SRGAN outperforms classical interpolation across 2×–12× upscaling, especially preserving small grains and realistic boundaries. A 5× upscaling yields a 25× speed‑up or larger field of view with acceptable errors in grain size and shape metrics.
The paper introduces Relative Discharge Stage (RDS), a battery‑management indicator that classifies remaining discharge condition into five interpretable classes—Normal, Good, Moderate, Low, and Recharge Required—without needing future load information. It combines physics‑based SOC estimation with a lightweight temporal convolutional network that processes measured current, voltage, temperature, and SOC over a sliding window. Experiments on two public lithium‑ion datasets show RDS classification accuracy above 80% across varied load and thermal conditions.