Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting

Causal Physics-Infused Hybrid Learning (CPIHL) Framework for Next-Gen Battery Health Forecasting

A novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage...

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Bibliographic Details
Main Authors: Sahar Qaadan, Aiman Alshare, Rami Alazrai, Alexander Popp, Benedikt Schmuelling
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Causality analysis
deep learning
hybrid learning
lithium-ion battery degradation
physics-infused model
state of health
Online Access:https://ieeexplore.ieee.org/document/10945873/
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