Power battery pack fault fusion diagnosis method and system based on improved CNN

A technology of a power battery pack and a diagnostic method, which is applied in the field of battery fault diagnosis, can solve the problems of reduced diagnostic accuracy and size reduction, and achieves the effect of high accuracy

Pending Publication Date: 2020-09-11
SHANGHAI UNIVERSITY OF ELECTRIC POWER
View PDF2 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, as the number of CNN layers increases, the size of the final feature vectors continues to decrease. If only the features with the highest

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Power battery pack fault fusion diagnosis method and system based on improved CNN
  • Power battery pack fault fusion diagnosis method and system based on improved CNN
  • Power battery pack fault fusion diagnosis method and system based on improved CNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] refer to figure 1 , which is a schematic diagram of the overall flow of an improved CNN-based power battery pack fault fusion diagnosis method proposed in this embodiment. The method specifically includes the following steps,

[0050] S1: The voltage change signal and SOC change signal of the lithium battery are processed separately by wavelet packet decomposition, and the energy value is obtained to form the input feature vector.

[0051] Specifically, the fault feature extraction can collect the voltage and SOC change data of each single battery in the battery pack under the attenuation state of different performance parameters in the battery pack under the cycle conditions of American cities as the original data signal for fault diagnosis, and use a three-layer The wavelet packet decomposes the power battery voltage and SOC signals respectively. The third layer wavelet packet can divide each signal into 8 signal components from low frequency to high frequency. Recons...

Embodiment 2

[0127] refer to Figure 7 , which is a schematic structural diagram of an improved CNN-based power battery pack fault fusion diagnosis system proposed in this embodiment. The improved CNN-based power battery pack fault fusion diagnosis method proposed in the above embodiment can be implemented based on this system.

[0128] The power battery pack fault fusion diagnosis system based on improved CNN includes a signal processing module 100 , a first diagnosis network module 200 , a judgment module 300 , an auxiliary diagnosis module 400 and a fusion diagnosis module 500 . Each module in the system is software that depends on the operation of the computer. Specifically,

[0129]The signal processing module 100 processes the signal using wavelet packet decomposition, and the obtained energy value is used as an input feature vector;

[0130] The newly diagnosed network module 200 is an improved CNN network, and its input is the feature vector obtained after the signal processing mo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a power battery pack fault fusion diagnosis method and system based on an improved CNN, and the method comprises the steps: carrying out the wavelet packet decomposition of a voltage change signal and an SOC change signal of a lithium battery, and obtaining an energy value to form an input feature vector; enabling the diagnosis network to perform preliminary diagnosis on the power battery pack fault; judging whether the preliminary diagnosis result meets a definite diagnosis condition or not; if yes, obtaining a diagnosis result of the power battery pack; if the diagnosis condition is not met, performing auxiliary diagnosis on the power battery pack fault by adopting a CNN network; performing fusion diagnosis on the preliminary diagnosis result and the auxiliary diagnosis result through a D-S evidence theory method; and judging the fusion diagnosis result to obtain a final diagnosis result. According to the method and system, the structure of the CNN network isimproved, the optimal convolution kernel size in the convolution layer is determined through the BIC criterion, the definite diagnosis condition is judged, and the auxiliary diagnosis network is further adopted for auxiliary and fusion diagnosis, so that the fault diagnosis accuracy of the power battery pack is improved.

Description

technical field [0001] The present invention relates to the technical field of battery fault diagnosis, in particular to an improved CNN-based power battery pack fault fusion diagnosis method and system. Background technique [0002] In recent years, lithium-ion batteries have become an important part of power batteries due to their advantages such as high cell voltage, high cycle times, high energy density, and no pollution. Ensuring the safe and reliable operation of lithium-ion batteries is the core of its development. At present, my country's lithium-ion battery technology is not fully mature, and the initial characteristics of inconsistent failures of battery packs are not easy to be found. The inconsistency of lithium-ion battery packs means that there are large deviations in parameters such as capacity, internal resistance, and voltage of batteries of the same model and specification, which will lead to overcharge and overdischarge of the battery during charging and d...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06N3/04G06N3/08G01R31/367G01R31/387G01R31/396
CPCG06N3/08G01R31/367G01R31/387G01R31/396G06N3/045Y02T10/70
Inventor 夏飞彭运赛张传林龚春阳
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap