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.

翻译：准确的健康状态（SOH）估计是锂离子电池管理的关键诊断服务。然而，依赖于劳动密集型的人工特征工程和不透明的黑箱模型阻碍了其规模化工业部署。为此，我们提出TC-SOH：一种模块化、即插即用的服务架构，用于自主、端到端的SOH预测。TC-SOH采用时间对比机制和跨窗口预测前置任务，直接从原始运行数据中提取与退化相关的表示。为提高透明度，我们将模型效能与表示诊断相连接：可视化、敏感性分析、冗余分析、双向探针、未来SOH探针以及时间混洗实验表明，学习到的特征与选定的专家描述符重叠，同时保留了额外的SOH相关变化，且有序的时间上下文有助于改进后续SOH预测。在四个公开数据集上，TC-SOH优于所考虑的基于物理信息和数据驱动的基线方法，平均绝对百分比误差（MAPE）降低了1.91倍，均方根误差（RMSE）降低了2.13倍。