EntroLnn: Entropy-Guided Liquid Neural Networks for Operando Refinement of Battery Capacity Fade Trajectories

TL;DR

EntroLnn introduces an entropy-guided liquid neural network framework for online refinement of battery capacity fade trajectories, improving prediction accuracy and adaptability in battery health management.

Contribution

The paper presents a novel entropy-based feature integration with transformable liquid neural networks for real-time battery capacity trajectory refinement.

Findings

Achieves mean absolute error of 0.004577 in capacity fade prediction

Provides 18-cycle accuracy in end-of-life prediction

Demonstrates robustness across different batteries and conditions

Abstract

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.