用世界模型预测电池衰减轨迹，提升长期预测精度。
World Model for Battery Degradation Prediction Under Non-Stationary Aging
- 将电池电压电流温度序列编码为隐状态，通过学习动态迁移预测未来80循环
- 迭代滚动预测误差比直接回归降低一半，关键衰减拐点预测更准
- 引入电化学先验知识，在衰减拐点处表现更优，适合电池寿命预测研究者
锂离子电池的退化预测需要对未来循环中的健康状态（SOH）轨迹进行预报。现有数据驱动方法虽能直接回归输出轨迹，但缺乏向前传播退化动态的机制。本文将电池退化预测建模为世界模型问题，将每循环的原始电压、电流和温度时序数据编码为隐状态，并通过学习到的动力学转移过程向前推进，生成跨越80个循环的未来轨迹。为探究电化学知识是否有助于改进学习动力学，训练损失中引入了单粒子模型（SPM）约束。在包含138个电池的Severson LiFePO4（LFP）数据集上评估了三种配置。结果表明，迭代滚动预测使轨迹预测误差相比同编码器的直接回归减少50%；而SPM约束在退化拐点处提升了预测性能，该阶段电阻与SOH关系最为显著，且不改变整体准确率。
原文摘要 · Abstract (English)
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.
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