用熵引导的液态网络在线优化电池容量衰减轨迹，提升预测精度与适应性。
EntroLnn: Entropy-Guided Liquid Neural Networks for Operando Refinement of Battery Capacity Fade Trajectories
- 基于熵特征与可变液态神经网络，联合建模容量衰减全过程。
- 容量轨迹预测误差仅0.004577，寿命终点预测误差18周期。
- 轻量计算、自适应强，适合实际电池管理系统部署。
电池容量退化预测是电池健康分析的核心问题，传统研究多聚焦于健康状态（SoH）估计与寿命终点（EoL）预测。本研究将视角扩展至在线优化整个容量衰减轨迹（CFT），提出基于熵引导可变液态神经网络（EntroLnn）的框架。该框架将CFT优化视为统一过程，而非独立的点状SoH与EoL任务。首次引入源自在线温度场的熵特征，结合定制化液态神经网络（LNNs），有效建模电池时序动态。该方法增强LNNs的静态与动态适应性，在不同电池与工况下均实现鲁棒且泛化的CFT优化。所提方法具备高保真度与轻量化计算特性，容量轨迹预测平均绝对误差仅为0.004577，EoL预测误差为18个循环。本工作建立了电池分析中熵感知学习的基础，支持自适应、轻量、可解释的电池健康预测，适用于实际电池管理系统的部署。
原文摘要 · Abstract (English)
Battery capacity degradation prediction has long been a central topic in battery health analytics, and most studies focus on state of health (SoH) estimation and end of life (EoL) prediction. This study extends the scope to online refinement of the entire capacity fade trajectory (CFT) through EntroLnn, a framework based on entropy-guided transformable liquid neural networks (LNNs). EntroLnn treats CFT refinement as an integrated process rather than two independent tasks for pointwise SoH and EoL. We introduce entropy-based features derived from online temperature fields, applied for the first time in battery analytics, and combine them with customized LNNs that model temporal battery dynamics effectively. The framework enhances both static and dynamic adaptability of LNNs and achieves robust and generalizable CFT refinement across different batteries and operating conditions. The approach provides a high fidelity battery health model with lightweight computation, achieving mean absolute errors of only 0.004577 for CFT and 18 cycles for EoL prediction. This work establishes a foundation for entropy-informed learning in battery analytics and enables self-adaptive, lightweight, and interpretable battery health prediction in practical battery management systems.
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