A method and system for graded early warning of thermal runaway faults in energy storage batteries

By using multi-scale time windows and Bayesian-optimized wavelet transform parameters, combined with a neural network model, the problem of capturing multi-time-scale features of thermal runaway faults in energy storage batteries was solved, achieving high-precision hierarchical early warning and differentiated control, thereby improving the safety and reliability of energy storage systems.

CN122085129APending Publication Date: 2026-05-26HEFEI GUOXUAN HIGH TECH POWER ENERGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEFEI GUOXUAN HIGH TECH POWER ENERGY
Filing Date
2026-04-02
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively capture the multi-timescale characteristics of thermal runaway faults in energy storage batteries, leading to the loss or dilution of key precursor information. Furthermore, the lack of a collaborative analysis framework limits the accuracy and generalization capabilities of early warning systems.

Method used

By employing a multi-scale time window strategy and overlapping sampling, combined with Bayesian optimization algorithm to select wavelet transform parameters, a neural network model integrating multi-scale features is constructed. Through probabilistic model fusion and multi-task learning, a graded early warning system for thermal runaway is achieved.

Benefits of technology

It significantly increased the effective sample size, comprehensively captured early signs of failure, improved feature quality and prediction accuracy, realized differentiated control with graded early warning, and enhanced the safety and reliability of energy storage systems.

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Abstract

This application discloses a hierarchical early warning method and system for thermal runaway faults in energy storage batteries, belonging to the field of early warning and monitoring technology for energy storage batteries. The method includes: acquiring historical multi-dimensional operating data of the energy storage battery system and preprocessing it; the preprocessing includes cleaning and normalization; applying a multi-scale time window strategy to the preprocessed data to obtain time series at each scale and constructing a time series sample set; using a parameter optimization method to adaptively select wavelet transform parameters for each scale time series and performing wavelet transform to extract time-frequency features; fusing the time-frequency features with statistical features using a probabilistic model to obtain the fused feature vector and uncertainty measure. This application significantly increases the effective sample size available for model training by employing parallel sampling and overlapping sampling strategies for short-term, medium-term, and long-term multi-scale time windows, overcoming the problem of scarce positive samples for thermal runaway faults in energy storage batteries.
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