Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning

[Submitted on 15 Jun 2026]

Authors:Junting Wen, Dan Li, Qihao Quan, Xiwen Wang, Hang Yang, Zhaohong Meng, Zigui Jiang, Changlin Yang, Tianle Liu, Diego Muñoz-Carpintero, Jian Lou

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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.

Submission history

From: JunTing Wen [view email]
[v1] Mon, 15 Jun 2026 09:06:27 UTC (4,068 KB)