无需人工特征工程，直接从原始数据端到端预测电池健康度
Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
- 用时间对比学习自动提取电池退化特征
- 在4个数据集上误差比基线降低2倍以上
- 模型可解释性强，适合工业部署
准确的电池健康状态（SOH）估计对锂离子电池管理至关重要。然而，依赖人工特征工程和黑箱模型限制了其在工业中的规模化应用。为此，我们提出TC-SOH：一种模块化、即插即用的自主端到端SOH预测服务架构。TC-SOH采用时间对比机制和跨窗口预测预训练任务，直接从原始运行数据中提取与退化相关的表征。为提升透明性，我们将模型性能与表征诊断相结合：通过可视化、敏感性分析、冗余分析、双向探测、未来SOH探测和时间打乱实验发现，学习到的特征与专家选定描述符重叠，同时保留额外的SOH相关变化，且有序的时间上下文有助于后续SOH预测。在四个公开数据集上，TC-SOH优于所考虑的物理驱动和数据驱动基线，平均绝对百分比误差（MAPE）降低1.91倍，均方根误差（RMSE）降低2.13倍。
原文摘要 · Abstract (English)
Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management. However, reliance on labor-intensive manual feature engineering and opaque black-box models hinders scalable industrial deployment. To address this, we introduce TC-SOH: a modular, plug-and-play service architecture for autonomous, end-to-end SOH prediction. TC-SOH employs a temporal-contrastive mechanism and a cross-window prediction pretext task to extract degradation-relevant representations directly from raw operational data. To improve transparency, we connect model efficacy with representation diagnostics: visualization, sensitivity analysis, redundancy analysis, bidirectional probing, future-SOH probing, and temporal shuffling show that learned features overlap with selected expert descriptors while retaining additional SOH-relevant variation, and that ordered temporal context improves subsequent-SOH prediction. Across four public datasets, TC-SOH outperforms the considered physics-informed and data-driven baselines, reducing MAPE by 1.91 times and RMSE by 2.13 times.
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