Artificial Intelligence #battery#soh
Autonomous End-to-End SOH Prediction Service Uses Temporal-Contrastive Learning to Cut Error by Half
A new plug-and-play service architecture called TC-SOH uses temporal-contrastive representation learning to predict lithium-ion battery state of health directly from raw operational data, eliminating manual feature engineering. Across four public datasets, it reduces mean absolute percentage error by 1.91 times and root mean squared error by 2.13 times compared to physics-informed and data-driven baselines. The approach also improves model transparency through a suite of representation diagnostics, including visualization and sensitivity analysis.
Jun 16, 2026 1 source