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

State of charge estimation method for secondary battery, state of charge estimation system for secondary battery, and abnormality detection method for secondary battery

A secondary battery, state-of-charge technology, applied in anomaly detection systems, electronic equipment using neural networks, vehicles, processes, products or compositions, and machine fields using neural networks, can solve the problem of difficult to improve SOC estimation accuracy, accumulation, Problems such as the reduction of guessing accuracy

Pending Publication Date: 2021-11-12
SEMICON ENERGY LAB CO LTD
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] When using the existing method for a long time and repeatedly charging or discharging, errors will accumulate and the estimation accuracy of the charging rate, that is, SOC (State of Charge: State of Charge) may be significantly reduced
In addition, it is difficult to improve the accuracy of SOC estimation due to the change of initial SOC(0) due to self-discharge even when the battery is not in use.
The coulomb counting method has the disadvantages of not being able to modify the error of the initial SOC (0) or the error of the cumulative current sensor.

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
  • State of charge estimation method for secondary battery, state of charge estimation system for secondary battery, and abnormality detection method for secondary battery
  • State of charge estimation method for secondary battery, state of charge estimation system for secondary battery, and abnormality detection method for secondary battery
  • State of charge estimation method for secondary battery, state of charge estimation system for secondary battery, and abnormality detection method for secondary battery

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0066] In this embodiment, image 3 A procedure of performing a cycle test of a secondary battery as a reference, constructing a learning model based on the data, and estimating the capacity, and a procedure of detecting an abnormality using the learning model are shown.

[0067]First, a charge-discharge cycle test of a secondary battery as a reference was performed. (S1)

[0068] Collect the data obtained through the charge-discharge cycle test. (S2) In this data collection, various data are collected. For example, for CC time, CV time, temperature, discharge voltage, initial FCC (mAh), number of cycles, charge start voltage, voltage 1 second after charge start, voltage 2 seconds after charge start, 60 seconds after charge start The voltage after charging, the voltage after 120 seconds after charging, the voltage immediately after charging, the voltage that sleeps for 1 second after charging, the voltage that sleeps for 2 seconds after charging, the voltage that sleeps for...

Embodiment approach 2

[0104] In this embodiment, a comparison between Embodiment 1 and a comparative example different from Embodiment 1 will be described below with reference to FIG. 2 .

[0105] Figure 2A The result of obtaining the estimation error by changing the input data using the same learning model as in the first embodiment is shown.

[0106] Notice, Figure 2A and Figure 2B Input 3 shown with Figure 1A The inputs 3 shown are identical, which show the results under the same conditions.

[0107] in addition, Figure 2A and Figure 2B Input 5 shown is the result of using CC time and CV time, which is one of the inventions. In input 5, the average value is 5.9, and the minimum value is 3.2 compared to input 3, and the inference accuracy is lower than input 3.

[0108] in addition, Figure 2C The shown input 6, input 7, input 8, and input 9 are comparative examples, and the estimation errors of the comparative examples are all 10 (mAh) or more. The data of input 6 uses the chargin...

Embodiment approach 3

[0112] An example of a coin-type secondary battery will be described. Figure 9A It is an external view of a coin type (single layer flat type) secondary battery, Figure 9B is its cross-sectional view.

[0113] In the coin-type secondary battery 300 , a positive electrode can 301 serving as a positive terminal and a negative electrode can 302 serving as a negative terminal are insulated and sealed by a gasket 303 formed of polypropylene or the like. The positive electrode 304 is formed of a positive electrode current collector 305 and a positive electrode active material layer 306 provided in contact therewith. The negative electrode 307 is formed of a negative electrode current collector 308 and a negative electrode active material layer 309 provided in contact therewith.

[0114] Active material layers included in each of the positive electrode 304 and the negative electrode 307 used in the coin-type secondary battery 300 may be formed on only one surface.

[0115] As th...

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

Provided is a state of charge (SOC) estimation method for a secondary battery, the method realizing highly precise estimation even if the deterioration of the secondary battery is advanced. Also provided is a capacity measurement system for the secondary battery, the system achieving highly precise estimation of SOC that can be performed in a short time and at a low cost. If the capacity of the second battery can be estimated with high precision, abnormal detection can be performed on the basis of that estimated value. Further provided is a novel abnormality detection method for a secondary battery. In a CCCV charging method, the CC time and the CV time are used as learning parameters for constructing a learning model. If the learning model is used, a highly precise estimated-capacity can be obtained by using, as the minimum input data, two parameters which are CC time and CV time, or three parameters which are CC time, CV time and a charge initiation voltage.

Description

technical field [0001] One aspect of the present invention relates to an article, a method, or a manufacturing method. Furthermore, the present invention relates to a process, machine, manufacture or composition of matter. One aspect of the present invention relates to a semiconductor device, a display device, a light emitting device, an electrical storage device, a lighting device, an electronic device, or a method of manufacturing the same. Also, an aspect of the present invention relates to a method of estimating a state of charge of an electric storage device, a system for estimating a state of charge of an electric storage device, and an abnormality detection method. In particular, one aspect of the present invention relates to a charge state estimation system for an electric storage device and an abnormality detection system for the electric storage device. [0002] Note that in this specification, the power storage device refers to all elements and devices that have a...

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 Applications(China)
IPC IPC(8): H01M10/48H02J7/00H02J7/02G01R31/36G01R31/3828G01R31/388
CPCH01M2220/20H01M10/425H01M2010/4271H01M50/107H01M50/109H02J7/0048H02J7/04H01M10/48G01R31/392G01R31/367G01R31/3648G01R31/3842G01R31/3828G01R31/388G06N3/08H02J7/02
Inventor 千田章裕三上真弓
Owner SEMICON ENERGY LAB CO LTD
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