arXiv Machine Learning August 18, 2026 By Bego\~na Ispizua, Serio Gil-L\'opez, Leire Arrizabalaga, Ibai La\~na

Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 3

PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction

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 AI

1d ago

Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity

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

fine-tuning efficiency

arXiv AI

Jun 2

BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting

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

llms benchmarks

arXiv Machine Learning

Jul 21

Bridging battery design and health assessment through virtual sensing and physics-informed learning

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

rag diffusion benchmarks safety

arXiv AI

Aug 11

C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge

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).

By Khoa Tran, Tri Le, Nhu Nguyen Gia, T. Nguyen-Thoi, Vin Nguyen-Thai, Duong Tran Anh, Hung-Cuong Trinh

benchmarks safety

arXiv Machine Learning

Jul 24

AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries

arXiv:2607. 20577v1 Announce Type: new Abstract: Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs.

By Mengda Xing (CRIL, UA), Jean-Marie Lagniez (CRIL, UA), Alejandro Franco (LRCS)

llms efficiency benchmarks

Related stories

PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction

Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity

BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting

Bridging battery design and health assessment through virtual sensing and physics-informed learning

C2L-Net: A Data-Driven Model for State-of-Charge Estimation of Lithium-Ion Batteries During Discharge

AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries