aiqbench analysis
This research presents TC-SOH, a novel machine learning framework that automates battery state-of-health prediction by directly extracting degradation features from raw data using temporal-contrastive representation learning. By eliminating the need for manual feature engineering and offering enhanced model transparency through rigorous diagnostic analysis, this approach addresses key barriers to scalable industrial deployment of AI in energy storage systems. The method demonstrates significant performance gains over existing physics-informed and data-driven baselines across multiple public datasets.
Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
arXiv:2606.16434v1 Announce Type: new 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.16434 [cs.LG]
(or arXiv:2606.16434v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.16434 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 153 words