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Related Experiment Video

Updated: Feb 20, 2026

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Adaptive Neural Network-Based Fault Detection for Thermal Process of Battery Cells.

Yun Feng, Xingyu Zhu, Ya-Zhi Zhang

    IEEE Transactions on Cybernetics
    |February 18, 2026
    PubMed
    Summary

    This study introduces an adaptive neural network framework for detecting thermal faults in lithium-ion batteries. The method accurately identifies abnormalities using reduced-order modeling and neural observation, enhancing battery safety.

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    Area of Science:

    • Battery thermal management
    • Fault detection systems
    • Artificial intelligence in engineering

    Background:

    • Lithium-ion batteries are critical energy storage devices.
    • Thermal runaway poses significant safety risks.
    • Accurate thermal monitoring is essential for safe operation.

    Purpose of the Study:

    • To develop an adaptive neural network (AdNN)-based fault detection framework.
    • To address challenges of unknown nonlinear heat generation and limited sensor data.
    • To enable reliable detection of thermal abnormalities in Li-ion batteries.

    Main Methods:

    • A two-stage approach combining reduced-order modeling and adaptive neural observation.
    • Spectral approximation techniques for creating a computationally tractable reduced-order model.
    • An adaptive neural observer to estimate battery states and unknown dynamics from surface temperature data.
    • A hybrid fault detection scheme integrating model-based residual and data-driven threshold generation.

    Main Results:

    • Successfully estimated battery states and unknown nonlinear dynamics.
    • Effectively detected thermal abnormalities using the hybrid fault detection scheme.
    • Experimental validation on a pouch-type battery confirmed the method's reliability.

    Conclusions:

    • The proposed AdNN-based framework reliably detects thermal abnormalities in Li-ion batteries.
    • The combination of reduced-order modeling and adaptive neural observation is effective.
    • This approach enhances the safety and reliability of battery thermal management systems.