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.
arXiv Machine Learning
Jul 17
TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation
arXiv:2607. 14640v1 Announce Type: new Abstract: Battery health estimation is fundamental for battery management in battery-powered systems, where inaccurate health states may affect control, maintenance, and service life.
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.
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.
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.
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.