A Hybrid Convolutional Neural Network Driven Lithium Battery Multi-category Fault Diagnosis Modeling Method

Abstract

The invention relates to a lithium battery multi-type fault diagnosis modeling method driven by a hybrid convolutional neural network, which belongs to the technical field of batteries. Using the fractional Fourier transform to obtain the measured and screened lithium battery refined fault spectrum, constitute a mixed big data labeling sample for lithium battery fault diagnosis; design a global convolutional neural network for hybrid lithium battery fault samples, and respectively According to the local convolutional neural network of the measured and screened lithium battery fault data, a hybrid convolutional neural network lithium battery fault diagnosis model is formed; by learning the global and local lithium battery fault characteristics in the convolutional neural network, and using the full Connect classification mapping to realize multi-classification and location of lithium battery faults. This method improves the reliability and safety of the battery management system, reduces the computational complexity caused by numerous parameters, and solves the problem that the multi-physics coupling diagnosis model can only be used for a certain type of lithium battery fault diagnosis task in practical applications.

Description

technical field [0001] The invention belongs to the technical field of batteries, and relates to a multi-type fault diagnosis modeling method for lithium batteries driven by a hybrid convolutional neural network. Background technique [0002] In recent years, lithium batteries, as green and clean secondary batteries, have been widely used in various electronic devices, such as automobiles, ships, airplanes and even some military electronic devices. How to effectively evaluate the reliability of lithium-ion batteries, in order to avoid the failure of lithium-ion batteries and cause serious consequences ranging from operational damage to performance degradation or even catastrophic failure, requires accurate diagnosis and analysis of multiple types of lithium-ion battery failures. The lithium battery fault diagnosis coupling model established by commonly used physical and chemical laws is difficult to reduce the computational complexity caused by the large number of parameters...

AI Technical Summary

Problems solved by technology

The lithium battery fault diagnosis coupling model established by commonly used physical and chemical laws is difficult to reduce the computational complexity caused by the large number of parameters, and the multi-physics coupling diagnosis model can only be used for a certain type of lithium battery fault diagnosis task in practical applications; in the face of multiple types of For fault diagnosis, the existing neural network method can learn the interrelated behavior of various faults from the measured fault data to a certain extent, but due to incomplete high-value fault data and insufficient fine extraction of various fault features , resulting in the deep neural network not playing its due simultaneous and spatial diagnostic energy efficiency in the field of lithium battery fault diagnosis

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