用大模型精准预测锂电池健康度，实时监控寿命衰减。
Battery State of Health Estimation Using LLM Framework
- 基于Transformer架构融合循环与瞬时放电数据建模
- MAE低至0.87%，500次循环内准确追踪容量变化
- 适合电动汽车电池健康监测与故障预警场景
电池健康监控对电动汽车的高效可靠运行至关重要。本研究提出一种基于Transformer的框架，利用循环数据和瞬时放电数据，估计锂钛氧化物（LTO）电池单体的健康状态（SoH）并预测剩余使用寿命（RUL）。在8个LTO电池上进行500次循环测试，分析充电时长对储能趋势的影响，并采用微分电压分析（DVA）监测电压范围内容量变化（dQ/dV）。所提出的大型语言模型（LLM）表现优异，平均绝对误差（MAE）低至0.87%，具备多样化的延迟指标，支持高效处理，展现出良好的实时集成潜力。该框架通过高分辨率数据中的异常检测，有效识别早期退化迹象，有助于实现预测性维护，防止突发电池失效，提升能量效率。
原文摘要 · Abstract (English)
Battery health monitoring is critical for the efficient and reliable operation of electric vehicles (EVs). This study introduces a transformer-based framework for estimating the State of Health (SoH) and predicting the Remaining Useful Life (RUL) of lithium titanate (LTO) battery cells by utilizing both cycle-based and instantaneous discharge data. Testing on eight LTO cells under various cycling conditions over 500 cycles, we demonstrate the impact of charge durations on energy storage trends and apply Differential Voltage Analysis (DVA) to monitor capacity changes (dQ/dV) across voltage ranges. Our LLM model achieves superior performance, with a Mean Absolute Error (MAE) as low as 0.87\% and varied latency metrics that support efficient processing, demonstrating its strong potential for real-time integration into EVs. The framework effectively identifies early signs of degradation through anomaly detection in high-resolution data, facilitating predictive maintenance to prevent sudden battery failures and enhance energy efficiency.
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