A lithium-ion battery degrades gradually until it doesn't. The capacity fade is nearly linear for hundreds of cycles, then accelerates sharply — the degradation knee. After the knee, the cell fails fast. Predicting when the knee arrives is the difference between a battery that lasts and one that doesn't.
A world model for battery degradation (arXiv:2603.10527) encodes raw voltage, current, and temperature time-series from each charge cycle into a latent state, then propagates that state forward through learned dynamics. The critical finding: iterative rollout — stepping the dynamics model forward cycle by cycle — halves the trajectory forecast error compared to direct regression from the same encoder. The model that simulates the future outperforms the model that jumps to it.
The physics enters through a Single Particle Model constraint in the training loss. The SPM connects internal resistance changes to capacity fade — a relationship that's most predictive exactly at the degradation knee, where resistance-driven failure accelerates. Without the constraint, the learned dynamics fit a smooth trajectory that misses the nonlinearity. With it, the model captures mechanism-specific behavior precisely when generic fitting fails.
The structural insight: the knee isn't an anomaly in the degradation trajectory. It's where one degradation mechanism (e.g., SEI growth) hands off to another (e.g., lithium plating). A purely data-driven model sees a curve. A physics-constrained model sees a regime change. The constraint doesn't just improve accuracy — it changes what the model represents. Fitting a curve is interpolation. Modeling a regime change is understanding.