Ershun Pan

Joey Chan, Zhen Chen, Ershun Pan

With the rapid proliferation of electric vehicles, the safety and reliability of lithium-ion batteries have become critical concerns. Effective anomaly detection is essential for ensuring safe battery operation. However, as battery systems and operating scenarios become increasingly complex, battery fault diagnosis and maintenance require stronger cross-domain adaptability and human-AI collaboration. Traditional fault detection and diagnosis methods are usually designed for specific scenarios and predefined workflows, making them less effective in complex real-world applications. To address the scarcity of open-source battery fault report corpora and the lack of unified maintenance knowledge representation, this study proposes a descriptive text modeling approach for battery signal reports. Monitoring signals, statistical features, anomaly records, and state assessment results are transformed into structured and readable natural language descriptions, forming a language corpus for battery health diagnosis and maintenance. Based on this corpus, we propose VBFDD-Agent, a vehicle battery fault detection and diagnosis agent for automotive-grade battery systems. VBFDD-Agent integrates descriptive battery-state texts, historical case retrieval, local maintenance manuals, and large language model reasoning to generate structured diagnostic results and maintenance recommendations. Experiments show that the proposed framework can accurately perform anomaly monitoring based on descriptive textual representations and provide flexible, efficient, and actionable maintenance suggestions. Expert evaluation further confirms the practical value of the generated recommendations. Overall, VBFDD-Agent extends traditional battery diagnosis from label prediction to interpretable and maintenance-oriented decision support.

Joey Chan, Huan Wang, Haoyu Pan et al.

Accurate forecasting of battery capacity fade is essential for the safety, reliability, and long-term efficiency of energy storage systems. However, the strong heterogeneity across cell chemistries, form factors, and operating conditions makes it difficult to build a single model that generalizes beyond its training domain. This work proposes a unified capacity forecasting framework that maintains robust performance across diverse chemistries and usage scenarios. We curate 20 public aging datasets into a large-scale corpus covering 1,704 cells and 3,961,195 charge-discharge cycle segments, spanning temperatures from $-5\,^{\circ}\mathrm{C}$ to $45\,^{\circ}\mathrm{C}$, multiple C-rates, and application-oriented profiles such as fast charging and partial cycling. On this corpus, we adopt a Time-Series Foundation Model (TSFM) backbone and apply parameter-efficient Low-Rank Adaptation (LoRA) together with physics-guided contrastive representation learning to capture shared degradation patterns. Experiments on both seen and deliberately held-out unseen datasets show that a single unified model achieves competitive or superior accuracy compared with strong per-dataset baselines, while retaining stable performance on chemistries, capacity scales, and operating conditions excluded from training. These results demonstrate the potential of TSFM-based architectures as a scalable and transferable solution for capacity degradation forecasting in real battery management systems.

Joey Chan, Zhen Chen, Ershun Pan

Accurate estimation of lithium-ion battery capacity degradation is critical for enhancing the reliability and safety of battery operations. Traditional expert models, tailored to specific scenarios, provide isolated estimations. With the rapid advancement of data-driven techniques, a series of general-purpose time-series foundation models have been developed. However, foundation models specifically designed for battery capacity degradation remain largely unexplored. To enable zero-shot generalization in battery degradation prediction using large model technology, this study proposes a degradation-aware fine-tuning strategy for time-series foundation models. We apply this strategy to fine-tune the Timer model on approximately 10 GB of open-source battery charge discharge data. Validation on our released CycleLife-SJTUIE dataset demonstrates that the fine-tuned Battery-Timer possesses strong zero-shot generalization capability in capacity degradation forecasting. To address the computational challenges of deploying large models, we further propose a knowledge distillation framework that transfers the knowledge of pre-trained foundation models into compact expert models. Distillation results across several state-of-the-art time-series expert models confirm that foundation model knowledge significantly improves the multi-condition generalization of expert models.