The paper proposes a ridge regression scheme to address parameter confounding in phenomenological models, such as those used for state‑of‑health prediction in lithium‑ion batteries. An automated, information‑theoretic method optimises the ridge hyper‑parameter at each iteration, converging rapidly via fixed‑point iteration. The resulting regularised iterative generalised least squares framework can fit heteroscedastic and serially correlated data, with simulations confirming its effectiveness.
By Mark Cary, Charles Bokor
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.
By Khoa Tran, Tri Le, Hung-Cuong Trinh, Hung Tran-Nam
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.
By Jiaqi Yao, Julia Kowal
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.
By Wendi Guo, S{\o}ren Byg Vilsen, Daniel Ioan Stroe, Yaqi Li, Yicun Huang, Ashima Verma, Daniel Brandell
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:2608. 14637v1 Announce Type: new Abstract: Long-duration stationary energy storage requires batteries whose degradation can be detected before substantial capacity loss has accumulated.
By Suyang Zhuang, Zekun Jiang, Tianhang Zhou