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: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:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
arXiv:2605. 08653v2 Announce Type: replace Abstract: Accurate state-of-charge (SOC) estimation is critical for the safe and efficient operation of lithium-ion batteries in battery management systems (BMS).
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: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:2607. 18329v1 Announce Type: cross Abstract: The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity.
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.
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.
Accurate remaining discharge time (RDT) prediction is challenging in real-world battery applications because future load profiles are unknown and highly dynamic. To address the uncertainty of continuo...
arXiv:2607. 18330v1 Announce Type: cross Abstract: The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity.
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.
PhyMamba is a two‑stage physics‑modulated Mamba framework designed for robust battery health prognostics. The first stage uses a lightweight Mamba encoder to process BMS signals and generate latent representations, which are then transformed into physics‑informed aging features via an aging parameterization module. The second stage employs a customized Mamba forecasting backbone that integrates physics to guide temporal updates, enabling multi‑cycle predictions that outperform diverse baselines with a 31.8% mean error reduction across three public datasets.