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
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.
By Bego\~na Ispizua, Serio Gil-L\'opez, Leire Arrizabalaga, Ibai La\~na
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.
By Raghvender Raghvender, Mahdi Abid, Ferran Brosa Planella, Charles Delacourt, Arnaud Demorti\`ere
arXiv:2607. 23482v1 Announce Type: new Abstract: Accurate State-of-Health estimation is essential for safe battery operation and cost-effective maintenance.
By Huy Hoang Le, Kim-Anh Nguyen
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.
By Sara Sameer, Yunyi Zhao, Wei Zhang, Minggang Zeng, Wenqing Li, Man-Fai Ng, Yonggang Wen
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
By Zeping Chen, Ruda Jian, Sachin Sigdel, Guoping Xiong, Jian-Xun Wang, Tengfei Luo
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.
By Ruifeng Tan, Jintao Dong, Weixiang Hong, Jia Li, Jiaqiang Huang, Tong-Yi Zhang