3 papers
cs.AI2025
BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis
Songqi Zhou, Ruixue Liu, Boman Su +3
Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders i…
cs.LG2025
FairNet: Dynamic Fairness Correction without Performance Loss via Contrastive Conditional LoRA
Songqi Zhou, Zeyuan Liu, Benben Jiang
Ensuring fairness in machine learning models is a critical challenge. Existing debiasing methods often compromise performance, rely on static correction strategies, and struggle wi…
cs.LG2025
BatteryBERT for Realistic Battery Fault Detection Using Point-Masked Signal Modeling
Songqi Zhou, Ruixue Liu, Yixing Wang +2
Accurate fault detection in lithium-ion batteries is essential for the safe and reliable operation of electric vehicles and energy storage systems. However, existing methods often…