CN116720650A - Battery life cycle management method and device, electronic equipment and storage medium - Google Patents
Battery life cycle management method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116720650A CN116720650A CN202310566149.5A CN202310566149A CN116720650A CN 116720650 A CN116720650 A CN 116720650A CN 202310566149 A CN202310566149 A CN 202310566149A CN 116720650 A CN116720650 A CN 116720650A
- Authority
- CN
- China
- Prior art keywords
- battery
- capacity
- cell
- managed
- life cycle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 68
- 238000003860 storage Methods 0.000 title claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 46
- 238000004146 energy storage Methods 0.000 claims abstract description 33
- 238000003745 diagnosis Methods 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims description 46
- 230000008569 process Effects 0.000 claims description 35
- 239000002994 raw material Substances 0.000 claims description 25
- 238000004519 manufacturing process Methods 0.000 claims description 21
- 230000003068 static effect Effects 0.000 claims description 17
- 238000012360 testing method Methods 0.000 claims description 14
- 230000004913 activation Effects 0.000 claims description 9
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 6
- 229910052799 carbon Inorganic materials 0.000 claims description 6
- 230000003213 activating effect Effects 0.000 claims description 4
- 238000012423 maintenance Methods 0.000 description 23
- 238000004458 analytical method Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 239000000306 component Substances 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000011084 recovery Methods 0.000 description 5
- 238000000638 solvent extraction Methods 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 238000006116 polymerization reaction Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 239000000178 monomer Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 239000007921 spray Substances 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Evolutionary Biology (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Secondary Cells (AREA)
Abstract
The embodiment of the application discloses a battery life cycle management method, a device, electronic equipment and a storage medium. The embodiment of the application comprises the following steps: acquiring original cell data corresponding to a cell to be managed; carrying out standardization processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; clustering the battery clusters to obtain clustered battery stacks; configuring an energy storage system through the clustered battery stack to obtain battery operation parameters; based on the fault diagnosis model, a faulty battery is identified from the battery operating parameters. The application not only reduces bias current conditions among battery clusters, but also reduces cost, shortens project debugging period, improves battery utilization rate and battery operation safety, and reduces battery rejection rate.
Description
Technical Field
The present application relates to the field of battery management technologies, and in particular, to a method and apparatus for managing a battery life cycle, an electronic device, and a storage medium.
Background
As one of the core components of various energy storage systems, batteries have been the main cost source of the system and are also the most valuable components. For example, in recent years, with technical progress and large-scale development, the power battery of the electric vehicle is taken as an important part of the safety of the whole vehicle, the safety problem of the power battery is always focused, and the energy storage characteristic of the battery possibly develops new business modes and other characteristics, so that the whole life cycle management of the power battery is significant.
However, the whole process of the whole life cycle management of the battery at present depends on manual analysis performed by invested personnel to an actual site, the battery cells are matched through analysis to adapt to energy storage systems of different scenes, and along with the increase of the proportion of the energy storage systems, the time and the complexity of the analysis are gradually increased, so that the battery cell assembly is difficult to trace; moreover, as the operation and maintenance of the battery after operation do not have professional guidance comments, the attenuation trend of the battery cannot be accurately known; meanwhile, the replacement and maintenance of the battery are not tracked, and the value of the battery at the end of the life cycle cannot be reflected in time; the data hysteresis data cannot be updated and analyzed in real time, whether the battery product is a quality problem or a use problem cannot be accurately determined, and a decision cannot be quickly made, so that time and economic cost are increased.
Disclosure of Invention
In view of the foregoing, the present specification is directed to providing a battery life cycle management method, apparatus, electronic device, and storage medium that overcome or at least partially solve the foregoing problems.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
In a first aspect, an embodiment of the present application provides a battery life cycle management method, including: acquiring original cell data corresponding to a cell to be managed; carrying out standardization processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; clustering the battery clusters to obtain clustered battery stacks; configuring an energy storage system through the clustered battery stack to obtain battery operation parameters; based on the fault diagnosis model, a fault battery is identified according to the battery operation parameters to complete battery life cycle management.
In some embodiments, the normalizing process is performed on the original cell data to obtain normalized cell data corresponding to the cell to be managed, including: acquiring an activating terminal voltage and a process flow number corresponding to a battery cell to be managed through original battery cell data; according to the voltage of the active end and the process flow number corresponding to the battery cells to be managed, the battery cells to be managed are initially classified to obtain a plurality of battery pack original groups, and each battery pack original group comprises a plurality of battery cells; calculating the standard deviation of the voltage of the active end corresponding to any one of the battery pack original groups and the average value of the voltage of the active end; acquiring the voltage of an active terminal corresponding to any one cell in any one cell pack original group; subtracting the voltage of the activation terminal corresponding to any one of the battery cells from the average value of the voltage of the activation terminal to obtain a first parameter; and taking the ratio between the first parameter and the standard deviation of the voltage of the active terminal as standardized cell data corresponding to any one cell.
In some embodiments, the twice capacity-dividing processing is performed on the to-be-managed battery cell according to the standardized battery cell data to obtain a battery cluster formed after the twice capacity-dividing processing of the to-be-managed battery cell, including: performing charge and discharge test on each cell in the original group of the battery pack under the same environment, and outputting a calibration capacity and an initial capacity; based on the calibrated capacity and the initial capacity, carrying out primary capacity division on each battery cell in the battery pack original group to obtain a battery pack subjected to primary capacity division; and carrying out secondary capacity division on the battery pack subjected to primary capacity division to obtain a battery cluster subjected to secondary capacity division.
In some embodiments, performing secondary capacity division according to the battery pack after primary capacity division to obtain a battery cluster after secondary capacity division, including: performing charge and discharge test on the battery pack subjected to primary capacity division under the same environment, and outputting the extremely poor dynamic voltage and the static terminal voltage; and carrying out secondary capacity division on the battery pack subjected to primary capacity division based on the dynamic voltage range and the static terminal voltage to obtain a plurality of battery clusters subjected to secondary capacity division.
In some embodiments, clustering is performed on the battery clusters to obtain clustered battery stacks, including: obtaining capacity-dividing data corresponding to each of a plurality of battery clusters; calculating correlation coefficients among a plurality of battery clusters based on capacity-dividing data corresponding to the battery clusters; and clustering the plurality of battery clusters according to the correlation coefficient to obtain a clustered battery stack.
In some embodiments, the battery life cycle management method further comprises: creating a raw material warehouse and a traceability warehouse; synchronizing original electric core data corresponding to the electric core to be managed to a raw material warehouse; numbering the battery packs subjected to primary capacity division, and synchronizing the battery pack numbers and the initial capacities of the single battery cells to a raw material warehouse; numbering the battery clusters, and synchronizing the battery cluster numbers and the capacity-dividing data of the battery clusters to a tracing warehouse; and numbering the cell stacks, and synchronizing the cell stack numbers to a traceability warehouse.
In some embodiments, based on the fault diagnosis model, a faulty battery is identified according to the battery operating parameters to complete battery life cycle management, further comprising: when the fault battery needs to be replaced, matching the replaceable battery corresponding to the fault battery according to the raw material warehouse and the traceability warehouse, and replacing the fault battery by using the replaceable battery; when the fault battery needs to be maintained, the fault battery is maintained, and the maintained fault battery is stored in a battery echelon utilization library; when the faulty battery needs to be scrapped, the carbon of the faulty battery is recovered after the faulty battery is disassembled, and battery attenuation factors corresponding to the faulty battery are determined, so that a disassembly report is obtained to optimize the production process.
In a second aspect, an embodiment of the present application provides a battery life cycle management apparatus, including: the data acquisition module is used for acquiring original cell data corresponding to the cell to be managed; the standardized processing module is used for carrying out standardized processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed; the capacity-division processing module is used for carrying out capacity-division processing on the battery cells to be managed for two times according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after the capacity-division processing for two times; the clustering module is used for carrying out clustering treatment on the battery clusters to obtain clustered battery stacks; the battery configuration module is used for configuring the energy storage system through the clustered battery stack to obtain battery operation parameters; and the fault identification module is used for identifying a fault battery according to the battery operation parameters based on the fault diagnosis model so as to complete the life cycle management of the battery.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory storing a plurality of instructions; the processor loads instructions from the memory to perform steps in any of the battery life cycle management methods provided by embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform steps in any of the battery life cycle management methods provided by the embodiments of the present application.
The embodiment of the application can firstly acquire the original cell data corresponding to the cell to be managed; then, carrying out standardization processing on the original electric core data to obtain standardized electric core data corresponding to the electric core to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; then carrying out clustering treatment on the battery clusters to obtain clustered battery stacks; the energy storage system is configured through the clustered battery stack, and battery operation parameters are obtained; and finally, based on the fault diagnosis model, identifying a fault battery according to the battery operation parameters so as to complete the life cycle management of the battery.
The application can not only improve the polymerization degree of the capacity of the battery, so as to improve the consistency of each battery core in the battery cluster; the full life cycle tracing can be carried out on the battery, data support is provided for production technology and integrated design, and the residual value of the battery pack after the battery pack is retired from energy storage is accurately determined. In addition, the application not only can lighten the bias current situation among the battery clusters, but also can reduce personnel and time cost, shorten project debugging period, accurately know the attenuation situation of the battery, thereby judging whether the battery is a quality problem or a use problem, and further providing a professional maintenance means for the operation and maintenance of the battery after operation; the whole life cycle of battery production, battery operation, battery maintenance and battery recovery is in an ordered state. The application not only improves the utilization rate of the battery, but also improves the running safety of the battery and reduces the rejection rate.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a scenario of a battery life cycle management method according to an embodiment of the present application;
FIG. 2a is a flowchart of a battery life cycle management method according to an embodiment of the present application;
FIG. 2b is a schematic flow chart of a process for managing a battery taking different measures according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a battery life cycle management device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to fall within the scope of the application.
The embodiment of the application provides a battery life cycle management method, a device, electronic equipment and a storage medium.
The battery life cycle management device can be integrated in an electronic device, and the electronic device can be a terminal, a server and other devices. The terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer (Personal Computer, PC) or the like; the server may be a single server or a server cluster composed of a plurality of servers.
In some embodiments, the battery life cycle management device may also be integrated in a plurality of electronic devices, for example, the battery life cycle management device may be integrated in a plurality of servers, and the battery life cycle management method of the present application is implemented by the plurality of servers.
In some embodiments, the server may also be implemented in the form of a terminal.
For example, referring to fig. 1, a schematic view of a scenario of a battery life cycle management method according to an embodiment of the present application may include a server 100, a storage terminal 110, an energy storage system 120, a raw material warehouse 130, a traceability warehouse 140, a battery echelon utilization warehouse 150, and the like; the server 100, the memory 110, and the energy storage system 120 may be communicatively connected to each other, which is not described herein.
The server 100 may first obtain the original cell data corresponding to the cell to be managed; then, carrying out standardization processing on the original electric core data to obtain standardized electric core data corresponding to the electric core to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; then carrying out clustering treatment on the battery clusters to obtain clustered battery stacks; the energy storage system is configured through the clustered battery stack, and battery operation parameters are obtained; and finally, based on the fault diagnosis model, identifying a fault battery according to the battery operation parameters so as to complete battery life cycle management and the like. The storage terminal 110 may store original cell data corresponding to the cell to be managed, and the like.
The energy storage system 120 may be a power battery of an electric vehicle, an energy storage battery of various power stations, and the like. The raw material warehouse 130 can be used for synchronizing original cell data corresponding to the cells to be managed, the serial numbers of the battery packs after one-time capacity division and the initial capacity of the single cell, and can also store different types of cells and the like; the traceability warehouse 140 may be used for synchronizing the serial numbers of the battery clusters, the capacity data of the battery clusters, the serial numbers of the battery stacks, and the like, and may also store different types of battery clusters, battery stacks, and the like. The battery cascade utilization library 150 may be used to store repaired faulty batteries, etc.
The following will describe in detail. The numbers of the following examples are not intended to limit the preferred order of the examples.
In this embodiment, a battery life cycle management method is provided, as shown in fig. 2a, and the specific flow of the battery life cycle management method applied to a server may be as follows: 200. and acquiring original cell data corresponding to the cell to be managed.
In the embodiment of the application, the battery cell to be managed may be a battery cell just produced from a production line. The original cell data of the cell to be managed may include production data including, but not limited to, line number, date of production; process data includes, but is not limited to, process number, cell number, and initial terminal voltage.
The embodiment of the application can spray codes (such as two-dimensional codes and the like) on the shell of the battery cell, and automatically input production information and process information: line number, process number, cell code, date of production, initial (active) terminal voltage, etc.
210. And carrying out standardization processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed.
In some embodiments, the normalizing process is performed on the original cell data to obtain normalized cell data corresponding to the cell to be managed, including: acquiring an activating terminal voltage and a process flow number corresponding to a battery cell to be managed through original battery cell data; according to the voltage of the active end and the process flow number corresponding to the battery cells to be managed, the battery cells to be managed are initially classified to obtain a plurality of battery pack original groups, and each battery pack original group comprises a plurality of battery cells; calculating the standard deviation of the voltage of the active end corresponding to any one of the battery pack original groups and the average value of the voltage of the active end; acquiring the voltage of an active terminal corresponding to any one cell in any one cell pack original group; subtracting the voltage of the activation terminal corresponding to any one of the battery cells from the average value of the voltage of the activation terminal to obtain a first parameter; and taking the ratio between the first parameter and the standard deviation of the voltage of the active terminal as standardized cell data corresponding to any one cell.
In the embodiment of the application, the voltage of the activation terminal is the initial terminal voltage of the battery cell, which refers to the voltage of the battery cell when leaving a factory. The initial voltages of the different types of battery cells are also different.
According to the embodiment of the application, sprayed battery cells and battery cell data can be stored in a raw material warehouse, the battery pack original group is formed by preliminary classification according to the active terminal voltage and the process flow number, the active terminal voltage standard deviation and the active terminal voltage average value corresponding to any one battery pack original group are calculated, and the standard battery cells in any one battery pack original group are standardized through the active terminal voltage standard deviation and the active terminal voltage average value corresponding to any one battery pack original group, so that the standardized battery cell data corresponding to any one battery cell is obtained. The embodiment of the application is beneficial to improving the consistency of each electric core in the battery pack by carrying out standardized processing on the original electric core data.
220. And carrying out twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment.
In some embodiments, the twice capacity-dividing processing is performed on the to-be-managed battery cell according to the standardized battery cell data to obtain a battery cluster formed after the twice capacity-dividing processing of the to-be-managed battery cell, including: performing charge and discharge test on each cell in the original group of the battery pack under the same environment, and outputting a calibration capacity and an initial capacity; based on the calibrated capacity and the initial capacity, carrying out primary capacity division on each battery cell in the battery pack original group to obtain a battery pack subjected to primary capacity division; and carrying out secondary capacity division on the battery pack subjected to primary capacity division to obtain a battery cluster subjected to secondary capacity division.
In the embodiment of the application, after a batch of cells are manufactured, the capacity of the cells is different although the cells are the same in size. The primary capacity-dividing processing process of the embodiment of the application is a process of detecting the capacity of each battery cell under the same environment by charging and discharging test, outputting the calibrated capacity and the initial capacity of each battery cell, and screening out qualified battery cells and forming a battery pack based on the calibrated capacity and the initial capacity of each battery cell.
In some embodiments, performing secondary capacity division according to the battery pack after primary capacity division to obtain a battery cluster after secondary capacity division, including: performing charge and discharge test on the battery pack subjected to primary capacity division under the same environment, and outputting the extremely poor dynamic voltage and the static terminal voltage; and carrying out secondary capacity division on the battery pack subjected to primary capacity division based on the dynamic voltage range and the static terminal voltage to obtain a plurality of battery clusters subjected to secondary capacity division.
In the embodiment of the application, a plurality of battery packs are obtained after one-time capacity division, and terminal voltages of different battery packs are different. The secondary capacity-dividing processing process of the embodiment of the application is a process of carrying out charge and discharge test on the battery pack subjected to primary capacity division under the same environment, outputting the dynamic voltage range and the static terminal voltage, and screening out qualified battery packs and forming a battery cluster based on the dynamic voltage range and the static terminal voltage.
The capacity-dividing and pairing process is carried out twice, and the capacity-dividing process is carried out once by firstly carrying out charge and discharge test on each battery cell in the original battery pack group under the same environment, and outputting the calibrated capacity and the initial capacity; and then, based on the calibrated capacity and the initial capacity, carrying out primary capacity division on each battery cell in the battery pack original group to obtain the battery pack after primary capacity division. The secondary capacity-dividing process is to perform charge and discharge test on the battery pack subjected to primary capacity division under the same environment, and output the extremely poor dynamic voltage and the static terminal voltage; and then, based on the dynamic voltage range and the static terminal voltage, carrying out secondary capacity division on the battery pack subjected to primary capacity division to obtain a plurality of battery clusters subjected to secondary capacity division.
According to the embodiment of the application, the battery PACK can be subjected to charge and discharge test in the same environment according to the standardized battery PACK original group, the calibration capacity and the initial capacity are output, the batch is subjected to primary capacity division to form a PACK (battery PACK), the serial numbers are carried out, and the battery PACK serial numbers, the initial capacity of a single battery cell and the like are stored in a raw material warehouse. Then, the battery pack formed by primary capacity division is charged and discharged under the same environment, the dynamic voltage range and the static terminal voltage are output, secondary capacity division is carried out to form a battery cluster, numbering is carried out, and the battery cluster numbering and the like are synchronized to a tracing warehouse.
In the embodiment of the application, the dynamic voltage limit of the battery pack is the difference between the voltage maximum value and the voltage minimum value of the battery pack at a certain moment in the use process. The static terminal voltage of the battery pack is the terminal voltage measured after the terminal voltage is placed for a preset period of time after the two terminals are disconnected after the battery pack is fully charged.
230. And carrying out clustering treatment on the battery clusters to obtain the clustered battery stacks.
In some embodiments, clustering is performed on the battery clusters to obtain clustered battery stacks, including: obtaining capacity-dividing data corresponding to each of a plurality of battery clusters; calculating correlation coefficients among a plurality of battery clusters based on capacity-dividing data corresponding to the battery clusters; and clustering the plurality of battery clusters according to the correlation coefficient to obtain a clustered battery stack.
In the embodiment of the application, the capacity-dividing data corresponding to the battery cluster can comprise dynamic voltage range, static terminal voltage and the like. The dynamic voltage range corresponding to the battery cluster is the difference between the voltage maximum value and the voltage minimum value of the battery cluster at a certain moment in the use process. The static terminal voltage corresponding to the battery cluster is the terminal voltage measured after the terminal voltage is placed for a preset time period after the two terminals are disconnected after the battery cluster is fully charged.
In the embodiment of the application, three layers of battery packs, battery clusters and battery stacks are formed by pairing the battery cells, and then the energy storage systems in different scenes are adapted by random combination of the three layers. When the cell stack layer is formed, a clustering algorithm can be adopted to perform clustering processing on the cell clusters, so that the cell stack after clustering processing is obtained.
According to the embodiment of the application, the capacity-division data obtained through twice capacity division are respectively subjected to clustering analysis to obtain the corresponding correlation coefficients of different data sets, the farther the distance is, the more dissimilar the two sets of data are, the cluster numbers are divided through the correlation coefficients to form a cell stack, and the cell stack numbers are associated to a traceability warehouse.
240. And configuring an energy storage system through the clustered battery stack to obtain battery operation parameters.
In the embodiment of the application, the energy storage system can be configured through the clustered battery stack, namely, the energy storage system in different scenes can be adapted through the random combination of the battery pack, the battery cluster and the battery stack. According to the application, batteries of different levels are configured according to the type of the energy storage system, and then the battery operation parameters are obtained in real time in the operation process of the energy storage system.
In the embodiment of the application, the energy storage system can be composed of a plurality of battery packs, a plurality of battery clusters and a plurality of battery stacks, wherein the battery clusters comprise a plurality of battery packs, and a battery management system can be arranged in the battery packs. The battery management system is used for managing the charge and discharge protection of the battery pack. When the battery pack is fully charged, the voltage difference between the single batteries can be ensured to be smaller than a set value, the single batteries of the battery pack are uniformly charged, and the charging effect in a serial charging mode is effectively improved. Meanwhile, overvoltage, undervoltage, overcurrent, short circuit and overtemperature states of all single batteries in the battery pack are detected, and the service life of the battery is protected and prolonged. The embodiment of the application can adopt a three-level architecture, namely a battery pack, a battery cluster and a battery stack are respectively provided with a control unit.
250. Based on the fault diagnosis model, a fault battery is identified according to the battery operation parameters to complete battery life cycle management.
In the embodiment of the application, the battery operation parameters are input into the fault diagnosis model, the fault diagnosis model can set different threshold parameters, the fault diagnosis model outputs the fault type corresponding to the fault battery by comparing and analyzing the battery operation parameters with the different threshold parameters, and then corresponding management measures are carried out on the fault battery according to the fault type.
In an embodiment of the present application, the fault types may include, but are not limited to: the monomer SOC (State of Charge) has low consistency, high monomer SOC consistency, large self-discharge capacity, insufficient capacity, poor electrical connection and the like. Then in some embodiments, the battery life cycle management method further comprises: creating a raw material warehouse and a traceability warehouse; synchronizing original electric core data corresponding to the electric core to be managed to a raw material warehouse; numbering the battery packs subjected to primary capacity division, and synchronizing the battery pack numbers and the initial capacities of the single battery cells to a raw material warehouse; numbering the battery clusters, and synchronizing the battery cluster numbers and the capacity-dividing data of the battery clusters to a tracing warehouse; and numbering the cell stacks, and synchronizing the cell stack numbers to a traceability warehouse.
In some embodiments, based on the fault diagnosis model, a faulty battery is identified according to the battery operating parameters to complete battery life cycle management, further comprising: when the fault battery needs to be replaced, matching the replaceable battery corresponding to the fault battery according to the raw material warehouse and the traceability warehouse, and replacing the fault battery by using the replaceable battery; when the fault battery needs to be maintained, the fault battery is maintained, and the maintained fault battery is stored in a battery echelon utilization library; when the faulty battery needs to be scrapped, the carbon of the faulty battery is recovered after the faulty battery is disassembled, and battery attenuation factors corresponding to the faulty battery are determined, so that a disassembly report is obtained to optimize the production process.
The embodiment of the application recognizes the fault problem of the battery in real time through the fault diagnosis model and gives a conclusion, and then automatically provides the treatment measures for the battery according to the conclusion: the battery is not replaced (the fault expert database provides maintenance or other professional maintenance means for the battery), and the model judgment is returned after the operation requirement is met. Maintaining: and storing the maintained battery cells and the battery packs in a battery echelon utilization warehouse for secondary utilization or as spare parts. Scrapping: and disassembling the battery reaching the scrapping standard to determine the attenuation reason of the battery, recovering carbon, and disassembling to form a disassembly report and optimizing the production process.
Specifically, as shown in fig. 2b, the battery cell casing of the embodiment of the application sprays codes (two-dimensional codes) and automatically inputs production information and process information: line number, process number, cell code, date of production, initial (active) terminal voltage. Creating a raw material warehouse, storing sprayed electric core data into the raw material warehouse, performing preliminary classification according to the voltage of an active terminal and the number of a process flow to form a battery pack original group, calculating the standard deviation of the battery pack original group, and standardizing the battery pack original group. And then, according to the standardized battery pack original group, carrying out charge and discharge test on the group under the same environment, outputting the calibrated capacity and the initial capacity, carrying out primary capacity division on the batch to form a battery pack, numbering, and storing the battery pack number and the single-cell initial capacity into a raw material warehouse. And creating a tracing warehouse, then charging and discharging under the same environment according to the battery pack formed by primary capacity division, outputting the dynamic voltage range and the static terminal voltage, forming a battery cluster by secondary capacity division, numbering, and synchronizing the number of the battery cluster to the tracing warehouse. And then, carrying out clustering analysis on the data obtained by the twice capacity division respectively to obtain corresponding correlation coefficients of different data groups, wherein the more the distance is, the more dissimilar the two groups of data are, the cell cluster numbers are divided by the correlation coefficients to form a cell stack, and the cell stack numbers are associated to a tracing warehouse. The energy storage system is configured according to the type of the energy storage system (such as the site type of a power station, etc.), and the battery operation parameters are obtained in real time. And inputting the battery operation parameters into a fault diagnosis model, and identifying the battery fault problem in real time and giving a conclusion. And finally, automatically providing treatment measures for the fault battery according to the conclusion: the battery is not replaced (the fault expert database provides maintenance or other professional maintenance means for the battery), and the model judgment is returned after the operation requirement is met. And (3) replacement: the raw materials (i.e., replaceable batteries) corresponding to the failed cells or battery packs are matched from the raw material warehouse. Scrapping: disassembling the battery reaching the scrapping standard to determine the attenuation reason of the battery, and maintaining: and storing the maintained battery cells and battery packs into a battery echelon utilization warehouse for secondary utilization or as spare parts. The scrapped products, the maintained battery cells and the battery packs can be subjected to carbon recovery, and are disassembled to form a disassembly report, so that the battery problems are determined, and the production process is continuously optimized.
In the traditional battery management method at present, the whole battery production-capacity distribution pairing-activation operation-maintenance replacement process is manually analyzed by virtue of investors to the actual site, the battery cells are paired through the manual analysis to form three layers of a battery pack, a battery cluster and a battery stack, an energy storage system adapting to different scenes is finally formed, the time and the complexity required by the increase of the proportion of the energy storage system are gradually increased, the battery cells, the battery packs and the like are difficult to trace, and the battery attenuation trend cannot be accurately known without professional guidance opinion on the operation and maintenance of the battery after operation. The prior art has the defects that the capacity distribution of the battery pack, the battery cluster and the battery stack is huge, the labor and the time are needed to be input for analysis, the fault tolerance rate of the analysis result is low, the distribution result is often not ideal, and the operation level of a power station is directly caused. At present, hysteresis exists in the operation condition of the manual collection battery, and the operation state of the battery cannot be known in real time. The battery replacement and maintenance after operation is not provided with a continuous record tracking means, and the subsequent tracing difficulty is high. The on-site maintenance cannot be quickly decided and the professionality and accuracy of the maintenance means cannot be guaranteed.
Compared with the traditional battery management method, the method can not only improve the aggregation degree of battery capacity division, but also improve the consistency of each battery core in the battery cluster; the full life cycle tracing can be carried out on the battery, data support is provided for production technology and integrated design, and the residual value of the battery pack after the battery pack is retired from energy storage is accurately determined. In addition, the application not only can lighten the bias current situation among the battery clusters, but also can reduce personnel and time cost, shorten project debugging period, accurately know the attenuation situation of the battery, thereby judging whether the battery is a quality problem or a use problem, and further providing a professional maintenance means for the operation and maintenance of the battery after operation; the whole life cycle of battery production, battery operation, battery maintenance and battery recovery is in an ordered state. The application not only improves the utilization rate of the battery, but also improves the running safety of the battery and reduces the rejection rate.
In order to better implement the method, the embodiment of the application also provides a battery life cycle management device, which can be integrated in electronic equipment, wherein the electronic equipment can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet personal computer, an intelligent Bluetooth device, a notebook computer, a personal computer and other devices; the server may be a single server or a server cluster composed of a plurality of servers.
For example, in the present embodiment, a method according to an embodiment of the present application will be described in detail by taking a specific integration of a battery life cycle management device in an electronic device as an example.
For example, as shown in fig. 3, the battery life cycle management device may include: a data acquisition module 300, a normalization processing module 310, a capacity partitioning processing module 320, a clustering module 330, a battery configuration module 340, and a fault identification module 350. The data acquisition module 300 is configured to acquire original cell data corresponding to the cell to be managed; the standardized processing module 310 is configured to perform standardized processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed; the capacity-division processing module 320 is configured to perform capacity-division processing on the to-be-managed battery cell twice according to the standardized battery cell data, so as to obtain a battery cluster formed by the to-be-managed battery cell after the capacity-division processing twice; the clustering module 330 is configured to perform clustering on the battery clusters to obtain clustered battery stacks; the battery configuration module 340 is configured to configure the energy storage system through the clustered battery stack to obtain battery operation parameters; the fault identification module 350 is configured to identify a faulty battery according to the battery operation parameters based on the fault diagnosis model, so as to complete the battery life cycle management.
In some embodiments, the normalization processing module 310 includes a normalization processing sub-module configured to: acquiring an activating terminal voltage and a process flow number corresponding to a battery cell to be managed through original battery cell data; according to the voltage of the active end and the process flow number corresponding to the battery cells to be managed, the battery cells to be managed are initially classified to obtain a plurality of battery pack original groups, and each battery pack original group comprises a plurality of battery cells; calculating the standard deviation of the voltage of the active end corresponding to any one of the battery pack original groups and the average value of the voltage of the active end; acquiring the voltage of an active terminal corresponding to any one cell in any one cell pack original group; subtracting the voltage of the activation terminal corresponding to any one of the battery cells from the average value of the voltage of the activation terminal to obtain a first parameter; and taking the ratio between the first parameter and the standard deviation of the voltage of the active terminal as standardized cell data corresponding to any one cell.
In some embodiments, the capacity-partitioning processing module 320 includes a capacity-partitioning processing sub-module configured to: performing charge and discharge test on each cell in the original group of the battery pack under the same environment, and outputting a calibration capacity and an initial capacity; based on the calibrated capacity and the initial capacity, carrying out primary capacity division on each battery cell in the battery pack original group to obtain a battery pack subjected to primary capacity division; and carrying out secondary capacity division on the battery pack subjected to primary capacity division to obtain a battery cluster subjected to secondary capacity division.
In some embodiments, the capacity-partitioning processing sub-module includes a secondary capacity-partitioning module configured to: performing charge and discharge test on the battery pack subjected to primary capacity division under the same environment, and outputting the extremely poor dynamic voltage and the static terminal voltage; and carrying out secondary capacity division on the battery pack subjected to primary capacity division based on the dynamic voltage range and the static terminal voltage to obtain a plurality of battery clusters subjected to secondary capacity division.
In some embodiments, the clustering module 330 includes a clustering sub-module configured to: obtaining capacity-dividing data corresponding to each of a plurality of battery clusters; calculating correlation coefficients among a plurality of battery clusters based on capacity-dividing data corresponding to the battery clusters; and clustering the plurality of battery clusters according to the correlation coefficient to obtain a clustered battery stack.
In some embodiments, the battery life cycle management apparatus further comprises a warehouse building module configured to: creating a raw material warehouse and a traceability warehouse; synchronizing original electric core data corresponding to the electric core to be managed to a raw material warehouse; numbering the battery packs subjected to primary capacity division, and synchronizing the battery pack numbers and the initial capacities of the single battery cells to a raw material warehouse; numbering the battery clusters, and synchronizing the battery cluster numbers and the capacity-dividing data of the battery clusters to a tracing warehouse; and numbering the cell stacks, and synchronizing the cell stack numbers to a traceability warehouse.
In some embodiments, the fault identification module 350 further includes a fault identification sub-module configured to: when the fault battery needs to be replaced, matching the replaceable battery corresponding to the fault battery according to the raw material warehouse and the traceability warehouse, and replacing the fault battery by using the replaceable battery; when the fault battery needs to be maintained, the fault battery is maintained, and the maintained fault battery is stored in a battery echelon utilization library; when the faulty battery needs to be scrapped, the carbon of the faulty battery is recovered after the faulty battery is disassembled, and battery attenuation factors corresponding to the faulty battery are determined, so that a disassembly report is obtained to optimize the production process.
In the implementation, each unit may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit may be referred to the foregoing method embodiment, which is not described herein again.
As can be seen from the above, the battery life cycle management device of the present embodiment may first obtain the original battery cell data corresponding to the battery cell to be managed; then, carrying out standardization processing on the original electric core data to obtain standardized electric core data corresponding to the electric core to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; then carrying out clustering treatment on the battery clusters to obtain clustered battery stacks; the energy storage system is configured through the clustered battery stack, and battery operation parameters are obtained; and finally, based on the fault diagnosis model, identifying a fault battery according to the battery operation parameters so as to complete the life cycle management of the battery.
The application can not only improve the polymerization degree of the capacity of the battery, so as to improve the consistency of each battery core in the battery cluster; the full life cycle tracing can be carried out on the battery, data support is provided for production technology and integrated design, and the residual value of the battery pack after the battery pack is retired from energy storage is accurately determined. In addition, the application not only can lighten the bias current situation among the battery clusters, but also can reduce personnel and time cost, shorten project debugging period, accurately know the attenuation situation of the battery, thereby judging whether the battery is a quality problem or a use problem, and further providing a professional maintenance means for the operation and maintenance of the battery after operation; the whole life cycle of battery production, battery operation, battery maintenance and battery recovery is in an ordered state. The application not only improves the utilization rate of the battery, but also improves the running safety of the battery and reduces the rejection rate.
The embodiment of the application also provides electronic equipment which can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer and the like; the server may be a single server, a server cluster composed of a plurality of servers, or the like.
In some embodiments, the battery life cycle management device may also be integrated in a plurality of electronic devices, for example, the battery life cycle management device may be integrated in a plurality of servers, and the battery life cycle management method of the present application is implemented by the plurality of servers.
In this embodiment, a detailed description will be given taking an example that the electronic device of this embodiment is a server, for example, as shown in fig. 4, which shows a schematic structural diagram of the server according to the embodiment of the present application, specifically:
the server may include one or more processors 401 of a processing core, memory 402 of one or more computer readable storage media, a power supply 403, an input module 404, and a communication module 405, among other components. Those skilled in the art will appreciate that the server architecture shown in fig. 4 is not limiting of the server and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:
the processor 401 is a control center of the server, connects respective portions of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the server. In some embodiments, processor 401 may include one or more processing cores; in some embodiments, processor 401 may integrate an application processor that primarily processes operating systems, user interfaces, applications, and the like, with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 401.
The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by executing the software programs and modules stored in the memory 402. The memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the server, etc. In addition, memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.
The server also includes a power supply 403 for powering the various components, and in some embodiments, the power supply 403 may be logically connected to the processor 401 by a power management system, such that charge, discharge, and power consumption management functions are performed by the power management system. The power supply 403 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The server may also include an input module 404, which input module 404 may be used to receive entered numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
The server may also include a communication module 405, and in some embodiments the communication module 405 may include a wireless module, through which the server may wirelessly transmit over short distances, thereby providing wireless broadband internet access to the user. For example, the communication module 405 may be used to assist a user in e-mail, browsing web pages, accessing streaming media, and so forth.
Although not shown, the server may further include a display unit or the like, which is not described herein. In this embodiment, the processor 401 in the server loads executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 executes the application programs stored in the memory 402, so as to implement various functions in the battery life cycle management device.
In some embodiments, a computer program product is also presented, comprising a computer program or instructions which, when executed by a processor, implement the steps in any of the battery life cycle management methods described above.
The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.
From the above, the embodiment of the application not only can improve the polymerization degree of the capacity of the battery, but also can improve the consistency of each battery core in the battery cluster; the full life cycle tracing can be carried out on the battery, data support is provided for production technology and integrated design, and the residual value of the battery pack after the battery pack is retired from energy storage is accurately determined. In addition, the application not only can lighten the bias current situation among the battery clusters, but also can reduce personnel and time cost, shorten project debugging period, accurately know the attenuation situation of the battery, thereby judging whether the battery is a quality problem or a use problem, and further providing a professional maintenance means for the operation and maintenance of the battery after operation; the whole life cycle of battery production, battery operation, battery maintenance and battery recovery is in an ordered state. The application not only improves the utilization rate of the battery, but also improves the running safety of the battery and reduces the rejection rate.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer readable storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any of the battery life cycle management methods provided by embodiments of the present application. For example, the instructions may perform the steps of: acquiring original cell data corresponding to a cell to be managed; carrying out standardization processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed; performing twice capacity-division treatment on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after twice capacity-division treatment; clustering the battery clusters to obtain clustered battery stacks; configuring an energy storage system through the clustered battery stack to obtain battery operation parameters; based on the fault diagnosis model, a fault battery is identified according to the battery operation parameters to complete battery life cycle management and the like.
Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions to cause the computer device to perform the methods provided in the various alternative implementations of the battery life cycle management aspects provided in the above embodiments.
The instructions stored in the storage medium can execute the steps in any battery life cycle management method provided by the embodiment of the present application, so that the beneficial effects that any battery life cycle management method provided by the embodiment of the present application can be achieved, and detailed descriptions of the foregoing embodiments are omitted.
The foregoing has described in detail the methods, apparatus, servers and computer readable storage media for battery life cycle management provided by the embodiments of the present application, and specific examples have been applied to illustrate the principles and embodiments of the present application, the above examples are only used to help understand the methods and core ideas of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the present description should not be construed as limiting the present application in summary.
Claims (10)
1. A battery life cycle management method, comprising:
acquiring original cell data corresponding to a cell to be managed;
carrying out standardization processing on the original electric core data to obtain standardized electric core data corresponding to the electric core to be managed;
Performing capacity division processing on the battery cells to be managed for two times according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after the capacity division processing for two times;
clustering the battery clusters to obtain clustered battery stacks;
configuring an energy storage system through the clustered battery stacks to obtain battery operation parameters;
and identifying a fault battery according to the battery operation parameters based on the fault diagnosis model so as to complete battery life cycle management.
2. The battery life cycle management method of claim 1, wherein the normalizing the raw cell data to obtain normalized cell data corresponding to the to-be-managed cell comprises:
acquiring an activating terminal voltage and a process flow number corresponding to the battery cell to be managed through the original battery cell data;
according to the voltage of the active end and the process flow number corresponding to the battery cell to be managed, primarily classifying the battery cell to be managed to obtain a plurality of battery pack original groups, wherein each battery pack original group comprises a plurality of battery cells;
calculating an active end voltage standard deviation corresponding to any one of the battery pack original groups and an active end voltage average value;
Acquiring the voltage of an active terminal corresponding to any one cell in the original group of any one battery pack;
subtracting the voltage of the activation terminal corresponding to any one of the battery cells from the average value of the voltage of the activation terminal to obtain a first parameter; and taking the ratio between the first parameter and the standard deviation of the voltage of the active terminal as standardized cell data corresponding to any one cell.
3. The battery life cycle management method of claim 2, wherein the performing twice capacity division processing on the battery cells to be managed according to the standardized battery cell data to obtain a battery cluster formed by the twice capacity division processing on the battery cells to be managed comprises:
performing charge and discharge tests on each battery cell in the battery pack original group under the same environment, and outputting a calibration capacity and an initial capacity;
based on the calibrated capacity and the initial capacity, carrying out primary capacity division on each battery cell in the battery pack original group to obtain a battery pack subjected to primary capacity division;
and carrying out secondary capacity division on the battery pack subjected to primary capacity division to obtain a battery cluster subjected to secondary capacity division.
4. The battery life cycle management method of claim 3, wherein the performing secondary capacity division according to the battery pack after primary capacity division to obtain the battery cluster after secondary capacity division comprises:
Performing charge and discharge test on the battery pack subjected to primary capacity division in the same environment, and outputting the extremely poor dynamic voltage and the static terminal voltage; and carrying out secondary capacity division on the battery pack subjected to primary capacity division based on the dynamic voltage range and the static terminal voltage to obtain a plurality of battery clusters subjected to secondary capacity division.
5. The method for managing battery life cycle according to claim 4, wherein the clustering the battery clusters to obtain the clustered battery stack comprises:
obtaining capacity-dividing data corresponding to each of the plurality of battery clusters;
calculating correlation coefficients among the battery clusters based on the capacity-dividing data corresponding to the battery clusters;
and clustering the plurality of battery clusters according to the correlation coefficient to obtain a clustered battery stack.
6. The battery life cycle management method of claim 5, wherein the method further comprises:
creating a raw material warehouse and a traceability warehouse;
synchronizing the original electric core data corresponding to the electric core to be managed to a raw material warehouse;
numbering the battery packs subjected to primary capacity division, and synchronizing the battery pack numbers and the initial capacities of the single battery cells to a raw material warehouse;
Numbering the battery clusters, and synchronizing the battery cluster numbers and the capacity-dividing data of the battery clusters to a tracing warehouse;
and numbering the cell stacks, and synchronizing the cell stack numbers to the traceability warehouse.
7. The battery life cycle management method of claim 6, wherein the identifying a faulty battery based on the battery operation parameters based on the fault diagnosis model to complete battery life cycle management further comprises:
when the fault battery needs to be replaced, matching the replaceable battery corresponding to the fault battery according to the raw material warehouse and the traceability warehouse, and replacing the fault battery by using the replaceable battery;
when the fault battery needs to be maintained, the fault battery is maintained, and the maintained fault battery is stored in a battery echelon utilization library;
and when the fault battery needs to be scrapped, recovering carbon after the fault battery is disassembled, determining battery attenuation factors corresponding to the fault battery, and obtaining a disassembly report to optimize the production process.
8. A battery life cycle management device, comprising:
the data acquisition module is used for acquiring original cell data corresponding to the cell to be managed;
The standardized processing module is used for carrying out standardized processing on the original cell data to obtain standardized cell data corresponding to the cell to be managed;
the capacity-division processing module is used for carrying out capacity-division processing on the battery cells to be managed for two times according to the standardized battery cell data to obtain a battery cluster formed by the battery cells to be managed after the capacity-division processing for two times;
the clustering module is used for carrying out clustering treatment on the battery clusters to obtain clustered battery stacks;
the battery configuration module is used for configuring the energy storage system through the clustered battery stacks to obtain battery operation parameters; and the fault identification module is used for identifying a fault battery according to the battery operation parameters based on the fault diagnosis model so as to complete battery life cycle management.
9. An electronic device comprising a processor and a memory, the memory storing a plurality of instructions; a processor loads instructions from a memory to perform the steps in the battery life cycle management method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor to perform the steps of the battery life cycle management method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310566149.5A CN116720650A (en) | 2023-05-16 | 2023-05-16 | Battery life cycle management method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310566149.5A CN116720650A (en) | 2023-05-16 | 2023-05-16 | Battery life cycle management method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116720650A true CN116720650A (en) | 2023-09-08 |
Family
ID=87866928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310566149.5A Pending CN116720650A (en) | 2023-05-16 | 2023-05-16 | Battery life cycle management method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116720650A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786488A (en) * | 2023-12-26 | 2024-03-29 | 国网青海省电力公司清洁能源发展研究院 | Active safety prevention and control method for electrochemical energy storage full life cycle |
-
2023
- 2023-05-16 CN CN202310566149.5A patent/CN116720650A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117786488A (en) * | 2023-12-26 | 2024-03-29 | 国网青海省电力公司清洁能源发展研究院 | Active safety prevention and control method for electrochemical energy storage full life cycle |
| CN117786488B (en) * | 2023-12-26 | 2024-06-07 | 国网青海省电力公司清洁能源发展研究院 | Active safety prevention and control method for electrochemical energy storage full life cycle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xu et al. | Life prediction of lithium-ion batteries based on stacked denoising autoencoders | |
| WO2021208309A1 (en) | Method and system for online evaluation of electrochemical cell of energy storage power station | |
| Xu et al. | A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries | |
| Sun et al. | Detection of voltage fault in the battery system of electric vehicles using statistical analysis | |
| CN114977414B (en) | Battery storage intelligent management system based on multi-cluster parallel energy storage | |
| CN109633448B (en) | Method and device for identifying battery health state and terminal equipment | |
| CN115330275B (en) | Echelon utilization method and device for retired battery | |
| CN116720650A (en) | Battery life cycle management method and device, electronic equipment and storage medium | |
| US20240012065A1 (en) | Battery diagnosis apparatus and method | |
| CN111460656B (en) | Method and system for evaluating operation life of communication power supply of electric power machine room | |
| Bai et al. | Study on distributed lithium-ion power battery grouping scheme for efficiency and consistency improvement | |
| CN116128467A (en) | Power utilization system maintenance method, device, computer equipment and storage medium | |
| CN110927609B (en) | Decline evaluation method and device for battery energy storage system by utilizing battery in echelon | |
| CN112418638A (en) | Early warning system and early warning method for operation and maintenance risks of DC power supply system for station | |
| CN115684792A (en) | Electrical automation equipment detecting system based on artificial intelligence | |
| Wang et al. | Rapid-regroup strategy for retired batteries based on short-time dynamic voltage and electrochemical impedance spectroscopy | |
| CN117668692A (en) | Intelligent electric energy monitoring method, device, equipment and medium based on fault detection | |
| Wang et al. | A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model | |
| Wang et al. | An inconsistency fault diagnosis method for lithium-ion cells in the battery pack based on piecewise dimensionality reduction and outlier identification | |
| CN115549247A (en) | Mixed power management method and system | |
| CN110874663B (en) | Fault optimization method and device for uninterruptible power supply of wind generating set | |
| Ghaeminezhad et al. | A review on fault-tolerant control strategies for lithium-ion battery systems | |
| Xia et al. | Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis | |
| Li et al. | A fault diagnosis method for VRLA battery in data center | |
| CN117728421B (en) | Micro-grid cluster coordinated scheduling method, system, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |