A lithium battery fault intelligent diagnosis method based on deep learning

By constructing an intelligent fault diagnosis method for lithium batteries using deep learning technology, the problem of diagnostic stability of lithium batteries under multiple operating conditions is solved, high-precision and traceable fault diagnosis results are achieved, and an adaptive closed-loop diagnosis system is formed.

CN121659028BActive Publication Date: 2026-06-26JIANGXI YUNDING NEW ENERGY TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511855008.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-10
Publication Date
2026-06-26
Estimated Expiration
2045-12-10

AI Technical Summary

Technical Problem

Existing lithium battery fault diagnosis methods are susceptible to noise interference, data loss, and nonlinear characteristics under various operating conditions, resulting in insufficient stability of diagnostic results. Furthermore, they lack a secondary verification mechanism for low-confidence diagnostic results, making it difficult to form an adaptive and evolving closed-loop diagnostic system.

Method used

A deep learning-based intelligent fault diagnosis method for lithium batteries is adopted. Through working condition perception modeling, multi-scale feature extraction, confidence assessment and bidirectional backtracking correlation analysis, a mechanism for multi-source data synchronous processing, feature noise suppression and range normalization is constructed. Multi-scale feature vectors are extracted by combining convolutional network and recurrent network, and the accuracy and stability of the diagnosis results are achieved through consistency comparison and pattern matching correction.

Benefits of technology

It achieves high-precision lithium battery fault diagnosis under complex working conditions, ensuring the accuracy, stability and traceability of the diagnostic results, forming a complete closed-loop diagnostic system, and improving the reliability of lithium battery fault diagnosis.

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Abstract

The application discloses a kind of lithium battery fault intelligent diagnosis method based on deep learning, comprising the following steps: collecting lithium battery operating data and carrying out quality correction, obtain operating data set;Input to operating condition perception model generates multi-scale feature vector representing different operating states;Initial fault diagnosis model is obtained by initial fault class label, initial fault probability value and diagnostic feature vector;Form uncertainty feature vector set by confidence evaluation;Perform bidirectional backtracking correlation analysis, extract associated time period operating data subset;Diagnosis is carried out again, generates review result and carries out consistency comparison with initial result;Conflict result generates conflict mode information and is stored to fault mode library, without conflict when directly output final diagnosis result;The diagnosis result of new data is corrected using fault mode library Pattern matching, output corrected final diagnosis result.The application can significantly improve fault detection rate, reduce misjudgment rate and shorten diagnosis duration.
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Citation Information

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