arXiv Machine Learning

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.

arXiv Machine Learning

Aug 18

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

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 AI

Jul 15

BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification

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

diffusion

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 AI

Jul 22

Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Fragmented Charging Profiles

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.

By Shuhao Chen, Tianyu Shi, Chengyi Tu

llms rag benchmarks

arXiv AI

Jul 22

Dynamic Loss Balancing for Joint SOH and RUL Prediction of Lithium-Ion Batteries via a Rotary SOH-Injected Prior Battery Transformer

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.

By Shuhao Chen, Tianyu Shi, Yiwen Huang, Chengyi Tu

llms rag benchmarks

arXiv Machine Learning

Sep 22

Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation

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...

By Amir Madmolilvand, Farzaneh Abdollahi

safety

arXiv AI

Jun 16

Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning

arXiv:2606. 16434v1 Announce Type: cross Abstract: Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management.

By Junting Wen, Dan Li, Qihao Quan, Xiwen Wang, Hang Yang, Zhaohong Meng, Zigui Jiang, Changlin Yang, Tianle Liu, Diego Mu\~noz-Carpintero, Jian Lou

agents

arXiv Machine Learning

Sep 24

Relative Discharge Stage (RDS) Classification: A Practical Indicator of Battery Discharge Progress

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 Machine Learning

Jun 19

Comparative Study of Neural Surrogate Architectures for Autoregressive Prediction of Internal Battery States

arXiv:2606. 20053v1 Announce Type: new Abstract: The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity.

By Gihyun Lee, Thorben Menne, Simon Olma, Jakob Hilgert, Sangyoung Park

efficiency safety

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 Machine Learning

Jun 29

Physics-Informed Neural Network with Transfer Learning for State Estimation in Lithium-Ion Batteries using the Single Particle Model with Electrolyte

arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.

By Gift Modekwe, Qiugang Lu

diffusion fine-tuning

Hugging Face Trending Papers

Sep 19

Intelligent Degradation Monitoring in Lithium-ion Batteries via Discharge Incremental Capacity Feature Estimation

Accurate and timely detection of degradation in lithium-ion batteries is crucial to ensure safety, reliability, and longevity in high-demand applications such as electric vehicles and energy storage s...

safety

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