Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
Enables scalable, transparent SOH estimation for battery management systems without manual feature engineering.
TC-SOH introduces a plug-and-play service for end-to-end battery SOH prediction using temporal-contrastive learning, outperforming baselines with 1.91x lower MAPE and 2.13x lower RMSE across four datasets.
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.