Fig. 2
From: Diagnostic forecasting of battery degradation through contrastive learning
Overview of the contrastive pre-training approach. The two encoders are trained jointly using a contrastive loss. Operational data—including current, temperature, voltage, and static metadata (e.g., cell chemistry)—is mapped into a shared latent space with simulated degradation curves. The model learns to align operational data with its corresponding simulated behaviour by maximising similarity for matched pairs and minimising it for mismatched ones.
