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Related Experiment Video

Updated: Jan 14, 2026

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling.

Shanling Ji^1,2,3,4,5,6, Jun Yuan^2,3,4,5,6, Bojing Zhang^2,3,4,5,6

¹School of Mechanical Engineering, Southeast University, Nanjing, 211189, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|October 27, 2025

Summary

This summary is machine-generated.

A new physics-informed battery modeling network (PIBMN) accurately predicts battery performance across various conditions. This versatile framework enhances battery management and manufacturing processes.

Keywords:

aging prediction battery model mechanism diagnostics physics‐informed machine learning

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Published on: August 22, 2025

Area of Science:

Materials Science
Electrochemistry
Computational Science

Background:

Accurate battery behavior prediction is crucial but challenged by diverse materials and architectures.
Conventional prognostic methods lack generalizability for new battery technologies.

Purpose of the Study:

To develop a novel physics-informed battery modeling network (PIBMN) for broad applicability.
To enable continuous parameter adaptation and high-fidelity prediction across different battery formats and chemistries.

Main Methods:

Integration of data-driven learning with physical priors into a neural network framework.
Development of a model ensuring nonlinear expressivity and numerical stability for dynamic responses.
Implementation for capturing fast/slow dynamics under diverse load profiles.

Main Results:

PIBMN demonstrated high-fidelity, interpretable representations of internal electrochemical states.
The model effectively captured dynamic responses for commercial and laboratory cells.
PIBMN enabled decoupling of complex kinetics and real-time terminal voltage tracking.

Conclusions:

PIBMN offers a versatile and scalable framework for battery prognostics and diagnostics.
The model supports in-line quality control and adaptive battery management.
PIBMN facilitates data-informed optimization of next-generation battery manufacturing.