Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Battery fault diagnosis method based on crisscrossing optimizing fuzzy BP neural network

A BP neural network and battery failure technology, applied in the field of power supply, can solve problems such as inaccurate estimation

Inactive Publication Date: 2019-06-14
GUANGDONG UNIV OF TECH
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a battery fault diagnosis method based on vertical and horizontal cross-optimized fuzzy BP neural network in order to overcome the technical defect that it is easy to fall into local optimum in the operation process of the existing battery fault diagnosis method algorithm, resulting in inaccurate estimation

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
  • Battery fault diagnosis method based on crisscrossing optimizing fuzzy BP neural network
  • Battery fault diagnosis method based on crisscrossing optimizing fuzzy BP neural network
  • Battery fault diagnosis method based on crisscrossing optimizing fuzzy BP neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0100] Such as figure 1 , figure 2 As shown, the battery fault diagnosis method based on cross-cut optimization fuzzy BP neural network includes the following steps:

[0101] S1: Obtain sample data and preprocess the sample data;

[0102] S2: According to the preprocessed sample data, artificially analyze the common faults of the battery, and obtain the fault symptoms of each battery;

[0103] S3: Take the symptoms of each battery failure as input, perform fuzzy processing, and obtain training samples of the fuzzy BP neural network;

[0104] S4: Construct a BP neural network model according to each battery failure symptom, and initialize the model algorithm parameters;

[0105] S5: Calculate the output value of the BP neural network and the connection weights and thresholds between each layer;

[0106] S6: Calculate according to the current weight and threshold, and compare and determine the current optimal position;

[0107] S7: Use the vertical and horizontal cross alg...

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 provides a battery fault diagnosis method based on a crisscrossing optimizing fuzzy BP neural network. The battery fault diagnosis method includes the steps that firstly, fuzzification is conducted on a sample, information of inaccuracy or indeterminacy and the like in fault diagnosis are processed, and thus atraining sample of the neural network is more accurate; and next, various weights and threshold valves of the neural network are optimized by using a crisscrossing algorithm, thus a convergence speed of the neural network is accelerated, local optimum cannot be trapped, a fuzzy theory is combined with optimizing of crisscrossing to the neural network, and thus diagnosis to a battery fault is more accurate. The battery fault diagnosis method based on the crisscrossing optimizing fuzzy BP neural network is applied to a series of common batteries of lithium batteries, lead batteries, other fuel batteries and the like, the battery fault can be diagnosed in real time either a standing state or a using state, and compared with other existing battery fault diagnosing methods, the method is higher in accuracy and smaller in error.

Description

technical field [0001] The present invention relates to the technical field of power supply, and more specifically, relates to a battery fault diagnosis method based on fuzzy BP neural network with vertical and horizontal cross optimization. Background technique [0002] With the rapid development of social economy, people are more and more dependent on means of transportation, and the number of cars as the main means of transportation is increasing year by year. However, these convenient means of transportation have brought convenience to people's daily life. , but also make many problems such as environmental pollution increasingly prominent. In order to solve the global energy crisis and serious environmental pollution problems, people are vigorously researching and developing electric vehicles. Electric vehicles are valued by people for their advantages of energy saving and environmental protection. From the perspectives of environmental protection, energy shortage, econ...

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): G01R31/36G01R31/367G06N3/00G06N3/04G06N3/08
Inventor 陈风吴杰康叶辉良
Owner GUANGDONG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com