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arXiv AI

Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting

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arXiv AI
Oct 9

Sera: Semantic Representation Aggregation for Reliable and Interpretable Battery Health Forecasting

Sera is a framework that augments traditional temporal models for battery state‑of‑health forecasting with semantic representations of degradation. It extracts degradation semantics from time‑series data using rule‑based knowledge and large‑language‑model interpretation, then encodes and gates these representations with the temporal model’s output. Experiments on a benchmark dataset show that Sera reduces prediction error by up to 37.3% and improves generalizability, while counterfactual analysis confirms that the model’s responses align with the meanings of key degradation descriptors.

By Jiawei Li, Fang Liu, Wei Zhang, Zuming Liu, Man-Fai Ng, Zhi Wei Seh
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arXiv AI
Aug 28

Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap

The article reviews how Large Models (LMs) based on Transformer architectures and self‑supervised pre‑training can address longstanding challenges in Battery Prognostics and Health Management (BPHM). It surveys LM applications across data scarcity, generalization, interpretability, and system automation, and outlines a roadmap for future research, including collaborative data ecosystems, validation, trustworthiness, and efficient deployment. The review aims to guide researchers and practitioners in developing next‑generation battery management systems that are safe, reliable, and autonomous throughout battery lifecycles.

By Jiale Liu, Huan Wang, Weicheng Wang, Rong Zhu, Qiqi Wang, Min Xie
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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
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arXiv Machine Learning
Aug 31

PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics

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
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arXiv AI
Oct 27

Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks

The paper presents a method for predicting the remaining useful life of lithium‑ion batteries using only the first few charge‑discharge cycles. It transforms voltage, current, and capacity data into time‑frequency representations via continuous wavelet transform, compresses the resulting scalograms to reduce redundancy, and feeds them into deep neural networks. Experiments on eight benchmark datasets show the approach outperforms previous methods across diverse chemistries and cycling protocols, and visual explanations confirm the model focuses on meaningful time‑frequency regions.

By Sungeun Kim, Mingyu Jung, Yuri Kim, Jin-Sung Park, Jongbin Ryu
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