World Model for Battery Degradation Prediction Under Non-Stationary Aging

arXiv cs.LG / 3/12/2026

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Key Points

Introduces battery degradation prognosis as a world-model problem that encodes per-cycle voltage, current, and temperature time-series into a latent state and propagates it forward to forecast an 80-cycle future trajectory.
The training objective incorporates a Single Particle Model (SPM) constraint to inject electrochemical knowledge into the learned dynamics, improving predictions at key degradation phases.
On the Severson LiFePO4 dataset (138 cells), iterative rollouts halve the trajectory forecast error compared with direct regression using the same encoder.
The work highlights potential for more accurate remaining-life forecasts under non-stationary aging, with implications for battery management and lifecycle planning.

Abstract

Degradation prognosis for lithium-ion cells requires forecasting the state-of-health (SOH) trajectory over future cycles. Existing data-driven approaches can produce trajectory outputs through direct regression, but lack a mechanism to propagate degradation dynamics forward in time. This paper formulates battery degradation prognosis as a world model problem, encoding raw voltage, current, and temperature time-series from each cycle into a latent state and propagating it forward via a learned dynamics transition to produce a future trajectory spanning 80 cycles. To investigate whether electrochemical knowledge improves the learned dynamics, a Single Particle Model (SPM) constraint is incorporated into the training loss. Three configurations are evaluated on the Severson LiFePO4 (LFP) dataset of 138 cells. Iterative rollout halves the trajectory forecast error compared to direct regression from the same encoder. The SPM constraint improves prediction at the degradation knee where the resistance to SOH relationship is most applicable, without changing aggregate accuracy.