Supercharge Your Innovation With Domain-Expert AI Agents!

Battery health degree prediction method, system and computer-readable storage medium

A technology of a battery system and a prediction method, applied in the field of big data analysis, can solve the problem of inability to accurately and effectively predict the health degree, inability to intuitively and effectively obtain the change status and trend analysis results of the battery system health degree, and inability to accurately and intuitively predict the battery. System failure and other problems, to achieve the effect of accurate and intuitive prediction of failure, accurate and effective health prediction

Active Publication Date: 2020-07-03
云科(山东)电子科技有限公司
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing methods cannot achieve accurate and effective health prediction, cannot accurately and intuitively predict battery system failures, and cannot intuitively and effectively obtain battery system health changes and trend analysis results

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 health degree prediction method, system and computer-readable storage medium
  • Battery health degree prediction method, system and computer-readable storage medium
  • Battery health degree prediction method, system and computer-readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] see figure 1 As shown, a method for predicting the health of a battery system provided by Embodiment 1 of the present invention mainly includes:

[0045] Step 101, obtaining time-series historical data of battery operating parameters in the battery system, where the battery operating parameters include at least one parameter type.

[0046] Specifically, the battery operating parameters may include at least one of the following parameter types: battery cell voltage, battery pack voltage, battery cell internal resistance, battery cell temperature, battery cell current, and battery pack current.

[0047] That is to say, the operation of step 101 may be performed only for one parameter type among battery cell voltage, battery pack voltage, battery cell internal resistance, battery cell temperature, battery cell current, and battery pack current. Of course, the step 101 can also be performed for two or more parameter types in battery cell voltage, battery pack voltage, batt...

Embodiment 2

[0080] like figure 2 As shown, the method for predicting the health of a storage battery provided in Embodiment 2 of the present invention, after step 103 in Embodiment 1 above, further includes:

[0081] Step 104, determine potential fault source information according to health degree information analysis. Specifically:

[0082] Fault source information corresponding to each order of fault symptoms in the transition probability matrix of preset multi-order fault symptoms;

[0083] According to the transition probability matrix of the multi-order fault symptom corresponding to the health degree, the probability information of the target fault caused by the corresponding fault source is calculated.

[0084] The health curve of the battery system is analyzed, and if the changing trend of the response in the health curve and the value of the health degree meet the preset failure warning conditions, it is determined that there is a potential failure risk. In practical applicat...

Embodiment 3

[0086] Corresponding to the battery health prediction method of the embodiment of the present invention, the embodiment of the present invention also provides a battery health prediction system, such as image 3 As shown, the system mainly includes:

[0087] A historical data obtaining unit 10, configured to obtain time-series historical data of each battery operating parameter in the battery system, where the battery operating parameter includes at least one parameter type;

[0088] The symptom occurrence probability obtaining unit 20 is used to calculate the transition probability of the multi-stage fault symptom corresponding to each battery operating parameter according to the obtained time series historical data of the battery operating parameters in the battery system;

[0089] The health degree information obtaining unit 30 is configured to calculate and obtain the health degree information of the battery system according to the transition probabilities of the multi-sta...

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 method and a system for predicting health degree of a storage battery and a computer readable storage medium. The method comprises the following steps of acquiring time series historical data of battery operation parameters in a storage battery system, wherein the battery operation parameters comprise at least one parameter type; computing a transition probability of a multi-order failure symptom corresponding to each battery operation parameter according to the obtained time series historical data of the battery operation parameters in the storage battery system; andcomputing health degree information of the storage battery system according to the transition probability of the multi-order failure symptom corresponding to each battery operation parameters. Through the implementation of the method, the system and the computer readable storage medium, the health degree of the storage battery system can be accurately and effectively predicted.

Description

technical field [0001] The present invention relates to the technical field of big data analysis, in particular to a method, system and computer-readable storage medium for predicting the health of a storage battery. Background technique [0002] The health detection of the battery system is an important task in daily maintenance. In the prior art, a regular detection method is usually used to judge and predict the failure of the battery system directly based on the results of the measured parameters. Existing methods cannot achieve accurate and effective health prediction, cannot accurately and intuitively predict battery system failures, and cannot intuitively and effectively obtain battery system health changes and trend analysis results. Contents of the invention [0003] In view of this, the present invention provides a battery health prediction method, system and computer-readable storage medium, so as to at least solve the above technical problems existing in the p...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/385G01R31/392
CPCG01R31/385G01R31/392
Inventor 籍宏飞徐鹏李彬姜丛斌侯博伟
Owner 云科(山东)电子科技有限公司
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com