Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
2026-06-15 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors present TC-SOH, a new system that predicts how healthy a lithium-ion battery is without needing complicated manual input. Their approach learns important features directly from the battery's raw data using a special technique that compares time-based patterns. They also make their model clearer by showing how the learned features relate to expert knowledge and improve prediction over time. When tested on multiple datasets, their method predicts battery health more accurately than existing methods.
lithium-ion batterystate of health (SOH)feature engineeringtime series datacontrastive learningrepresentation learningprediction modelsmodel interpretabilitybattery management systems
Authors
Junting Wen, Dan Li, Qihao Quan, Xiwen Wang, Hang Yang, Zhaohong Meng, Zigui Jiang, Changlin Yang, Tianle Liu, Diego Muñoz-Carpintero, Jian Lou
Abstract
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.