Autonomous End-to-End SOH Prediction Services for Battery Systems via Temporal-Contrastive Representation Learning
arXiv:2606.16434v1 Announce Type: cross 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 im
The increasing demand for reliable energy storage and the ongoing advancements in AI and representation learning make autonomous battery management a critical area of focus.
Accurate and autonomous battery state-of-health prediction is crucial for extending battery lifespan, improving safety, and enabling more efficient energy systems, impacting various industries.
This research introduces a method for eliminating manual feature engineering in battery SOH prediction, potentially accelerating the development and deployment of advanced battery management systems.
- · Battery manufacturers
- · Electric vehicle industry
- · Renewable energy storage providers
- · AI/ML solution providers
- · Traditional battery diagnostic service providers
More reliable and longer-lasting battery systems will become standard across various applications.
Reduced battery replacement cycles will lower operational costs and contribute to sustainability efforts.
Accelerated adoption of battery-dependent technologies, from EVs to grid storage, due to enhanced trust and performance.
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Read at arXiv cs.AI