CN116908720B - Battery pack consistency state diagnosis method, device and storage medium - Google Patents
Battery pack consistency state diagnosis method, device and storage medium Download PDFInfo
- Publication number
- CN116908720B CN116908720B CN202311147800.1A CN202311147800A CN116908720B CN 116908720 B CN116908720 B CN 116908720B CN 202311147800 A CN202311147800 A CN 202311147800A CN 116908720 B CN116908720 B CN 116908720B
- Authority
- CN
- China
- Prior art keywords
- consistency
- battery pack
- state
- index
- battery
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000003745 diagnosis Methods 0.000 title claims abstract description 17
- 238000003860 storage Methods 0.000 title claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 43
- 238000010606 normalization Methods 0.000 claims abstract description 40
- 238000004364 calculation method Methods 0.000 claims abstract description 21
- 238000004590 computer program Methods 0.000 claims description 17
- 238000007599 discharging Methods 0.000 claims description 13
- 230000008859 change Effects 0.000 claims description 8
- 230000000875 corresponding effect Effects 0.000 claims description 8
- 238000002405 diagnostic procedure Methods 0.000 claims description 6
- 230000003068 static effect Effects 0.000 claims description 6
- 230000002596 correlated effect Effects 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 241000764238 Isis Species 0.000 claims 2
- 238000012423 maintenance Methods 0.000 claims 2
- 238000011156 evaluation Methods 0.000 abstract description 7
- 238000004146 energy storage Methods 0.000 abstract description 5
- 238000005457 optimization Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000006866 deterioration Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Secondary Cells (AREA)
Abstract
The invention relates to the technical field of intelligent diagnosis of battery energy storage systems, in particular to a method, a device, equipment and a computer storage medium for diagnosing consistency states of battery packs. Firstly, determining a plurality of indexes for evaluating consistency of a battery pack, and carrying out normalization processing according to an optimal direction; secondly, based on the pareto optimal method, the consistency states of the batteries are ordered in the prior multiple groups of battery packs, and the relative consistency states of the battery packs among the different battery packs are determined; finally, respectively calculating the consistency quality states of the battery packs in a certain time sequence interval, thereby determining the battery packs with obviously deteriorated consistency; according to the invention, the objective ordering of the consistency quality among different battery packs is carried out on the basis of the pareto optimization method without determining the threshold intervals of different evaluation indexes and the importance weights of different indexes, and the calculation of the consistency relative quality of the battery packs can be carried out quickly and accurately.
Description
Technical Field
The invention relates to the technical field of intelligent diagnosis of battery energy storage systems, in particular to a method, a device, equipment and a computer storage medium for diagnosing consistency states of battery packs.
Background
Along with the rapid development of renewable energy sources, a battery energy storage power station is becoming an important means for solving the problems of intermittent power generation and power scheduling of renewable energy sources, and plays an important role in the aspects of recovering power grid fluctuation, absorbing renewable energy sources for power generation and the like.
With the increasing scale of power stations, the number of single batteries in a single power station is increasing, and the number of batteries in a single energy storage power station is even hundreds of thousands to millions. A certain number of battery packs are often integrated into a certain scale of battery packs, such as battery packs, battery clusters, etc., through a certain serial-parallel topology. Ideally, all batteries should have the same or similar operating conditions and be capable of being charged and discharged synchronously. However, the manufacturing process of the battery cannot be completely consistent, and the service environment of the battery cannot be guaranteed to be completely the same. The battery cells in the battery pack will exhibit certain differences, i.e., certain inconsistencies, during use. And as the scale of the energy storage power station is continuously increased, the number of integrated battery monomers is rapidly increased, and the consistency difference among batteries is more prominent. The uniformity difference of the battery packs can lead to the overall service life and performance of the battery packs to be obviously reduced, and is a key problem affecting the performance of the battery packs and even power stations. Therefore, the consistency state of the battery pack is required to be accurately mastered, and the safe and stable operation of the power station is ensured.
Battery pack consistency assessment typically requires extracting multiple battery parameters and comprehensively assessing battery pack consistency from multiple dimensions. However, in the implementation process, the consistency state threshold intervals of different parameters are often difficult to accurately and objectively divide, and the weight of each index is difficult to objectively determine, so that under a plurality of groups of indexes, the consistency of the battery pack is often difficult to comprehensively evaluate.
Disclosure of Invention
Therefore, the invention aims to solve the technical problem of inaccurate evaluation of the consistency state of the battery pack in the prior art.
In order to solve the above technical problems, the present invention provides a method for diagnosing a consistency state of a battery pack, including:
step 1: extracting a plurality of indexes for consistency assessment of the battery pack;
step 2: carrying out data normalization processing on each index according to the optimal direction;
step 3: calculating the consistency relative state of each battery pack according to the index after the data normalization processing, and sorting the quality;
step 4: repeating the contents of the step 2-3 in time sequence within a preset time sequence interval to obtain a consistency variation trend of the battery pack;
step 5: and identifying the battery pack with poor consistency state and poor consistency state according to the battery pack consistency change trend.
Preferably, the plurality of indicators for battery pack consistency assessment include a voltage range and a voltage standard deviation.
Preferably, the calculation formula of the voltage range is:
wherein,the maximum and minimum values of cell voltages in a set of cells, respectively.
Preferably, the calculation formula of the standard deviation of the voltage is:
wherein,is the total number of battery cells in a group of batteries, < >>Is a group of cells +.>Cell voltage, ">Is the average of all cell voltages in a group of cells.
Preferably, the calculation formula for performing data normalization processing on each index is as follows:
wherein,maximum and minimum of the extracted index respectively, < ->The indexes before and after the data normalization processing are respectively indicated, and a and b are respectively the row and the column where the data are located.
Preferably, the calculating the consistency relative state of each battery pack according to the index after the data normalization processing includes:
and calculating the number of times each battery pack is subjected to the control of the rest battery packs according to the index after the data normalization processing, wherein the number of times is positively correlated with the consistency relative state.
Preferably, the calculating the number of times each battery pack is dominated by the remaining battery packs according to the index after the data normalization process includes:
if a first battery packIs>Are not more than +.>Index->And battery pack->At least one index is present which is smaller than the battery +.>The first battery pack dominates the second battery pack, whereinN is the total index number;
and calculating the dominant relationship between any two battery packs according to the steps, and counting the dominant times of each battery pack.
The invention also provides a battery pack consistency state diagnosis device, which comprises:
an index extraction module for extracting a plurality of indexes for consistency assessment of the battery pack;
the normalization processing module is used for carrying out data normalization processing on each index according to the optimal direction;
the consistency relative state calculation module is used for calculating the consistency relative state of each battery pack according to the index after the data normalization processing and sequencing the quality;
the consistency variation trend calculation module is used for repeating the contents of the normalization processing module and the consistency relative state calculation module in time sequence in a preset time sequence interval to obtain a consistency variation trend of the battery pack;
and the consistency state diagnosis module is used for identifying the battery pack with poor and deteriorated consistency state according to the battery pack consistency change trend.
The invention also provides a battery pack consistency state diagnosis device, which comprises:
a memory for storing a computer program;
and the processor is used for realizing the steps of the battery pack consistency state diagnosis method when executing the computer program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a battery pack consistency status diagnostic method described above.
Compared with the prior art, the technical scheme of the invention has the following advantages:
the invention relates to a battery pack consistency state diagnosis method, which comprises the steps of firstly determining a plurality of indexes for evaluating the consistency of a battery pack, and carrying out normalization processing according to an optimal direction; secondly, based on the pareto optimal method, the consistency states of the batteries are ordered in the prior multiple groups of battery packs, and the relative consistency states of the battery packs among the different battery packs are determined; finally, respectively calculating the consistency quality states of the battery packs in a certain time sequence interval, thereby determining the battery packs with obviously deteriorated consistency; according to the invention, the objective ordering of the consistency quality among different battery packs is carried out on the basis of the pareto optimization method without determining the threshold intervals of different evaluation indexes and the importance weights of different indexes, and the calculation of the consistency relative quality of the battery packs can be carried out quickly and accurately.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings, in which:
FIG. 1 is a flowchart of a method for diagnosing consistency of battery pack according to the present invention;
FIG. 2 is a diagram of a consistency index value of different battery packs;
FIG. 3 is a schematic diagram of the evaluation of the consistency of different battery packs;
fig. 4 is a schematic diagram of a change trend in battery pack consistency (poor consistency);
fig. 5 is a schematic diagram of a change trend (uniformity deterioration) of the uniformity of the battery pack.
Detailed Description
The invention provides a method, a device, equipment and a computer storage medium for diagnosing the consistency state of a battery pack, which effectively improve the accuracy of the consistency state evaluation of the battery pack.
In order to better understand the aspects of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a method for diagnosing a consistency state of a battery pack according to the present invention; the specific operation steps are as follows:
step S101: extracting a plurality of indexes for consistency assessment of the battery pack;
step S102: carrying out data normalization processing on each index according to the optimal direction;
step S103: calculating the consistency relative state of each battery pack according to the index after the data normalization processing, and sorting the quality;
step S104: repeating the contents of the steps S102-S103 in time sequence within a preset time sequence interval to obtain a consistency variation trend of the battery pack;
step S105: and identifying the battery pack with poor consistency state and poor consistency state according to the battery pack consistency change trend.
Based on the above embodiments, the present embodiment describes step S101 in detail:
the plurality of indicators for battery pack consistency assessment include a voltage range and a voltage standard deviation.
The calculation formula of the voltage range is as follows:
wherein,the maximum and minimum values of cell voltages in a set of cells, respectively.
The calculation formula of the voltage standard deviation is as follows:
wherein,is the total number of battery cells in a group of batteries, < >>Is a group of cells +.>Cell voltage, ">Is the average of all cell voltages in a group of cells. The battery pack consistency index calculation result is shown in fig. 2.
Based on the above embodiments, the present embodiment describes step S102 in detail:
the calculation formula for carrying out data normalization processing on each index is as follows:
wherein,maximum and minimum of the extracted index respectively, < ->The indexes before and after the data normalization processing are respectively indicated, and a and b are respectively the row and the column where the data are located.
Based on the above embodiments, the present embodiment describes in detail step S103:
the calculating the consistency relative state of each battery pack according to the index after the data normalization processing comprises the following steps:
and calculating the number of times each battery pack is subjected to the control of the rest battery packs according to the index after the data normalization processing, wherein the number of times is positively correlated with the consistency relative state.
The calculating the number of times each battery pack is dominated by the rest battery packs according to the index after the data normalization processing comprises the following steps:
if a first battery packIs>Are not more than +.>Index->And battery pack->At least one index is present which is smaller than the battery +.>The first battery pack dominates the second battery pack according to the corresponding index, wherein n is the total index number;
calculating the dominant relationship between any two battery packs according to the steps, and counting the dominant times of each battery pack, wherein the dominant times are as follows:
and the consistency index sequences of all the batteries form a battery pack consistency sample set X. The dominance of each sample, i.e. the number of samples that dominate the current sample, is calculated. Wherein, if samples p and q satisfy the following two conditions simultaneously:
(1) All indices of sample px p,i No greater than the corresponding index of sample qx q,i 。
(2) Sample p has at least one indexx p,j J is less than the corresponding index of sample qx q,j 。
Namely:
for a sample vector x consisting of n components (i=1, 2 … n), two decision vectors are arbitrarily given，/>：
If and only if, forThere is->And at least one is presentMake->Then->Innervating->Or->Quilt (S)>Dominating.
The number of times that any battery pack is subjected to dominance can be obtained by performing dominance solving one by one between different battery packs. The number of times the battery pack is subjected to the control is small, so that the consistency of the battery pack is good.
The results of evaluating the consistency of the different battery packs are shown in fig. 3.
Based on the above embodiments, the present embodiment describes in detail step S104:
and (5) visualizing the consistency, i.e. the dominance, of the battery pack in the time sequence.
Based on the above embodiments, the present embodiment describes in detail step S105:
dividing the working states of the battery pack into two types: standing or charging and discharging; the battery consistency status is divided into four categories: poor in a static state and poor in a charge-discharge state; the device is good in a standing state and poor in a charging and discharging state; poor in a standing state and good in a charging and discharging state; the battery is better in a static state and better in a charge and discharge state. For the first two states, namely: poor in the rest state and poor in the charge-discharge state (shown in fig. 4); better in the rest state and worse in the charge-discharge state (shown in fig. 5); the battery pack of the battery pack is subjected to important attention and overhauling in time.
The invention provides a battery pack consistency diagnosis method under multiple indexes. Firstly, determining a plurality of indexes for consistency evaluation of a battery pack, and carrying out normalization processing according to an optimal direction; secondly, based on the pareto optimal method, the consistency states of the batteries are ordered in the prior multiple groups of battery packs, and the relative consistency states of the battery packs among the different battery packs are determined; and finally, respectively calculating the consistency quality states of the battery packs in a certain time sequence interval, such as one day or one complete charge and discharge period, so as to determine the battery packs with obviously deteriorated consistency. According to the battery pack consistency diagnosis method provided by the invention, the consistency quality among different battery packs is objectively ordered based on the pareto optimization method without determining the threshold intervals of different evaluation indexes and the importance weights of different indexes, and the consistency relative quality of the battery packs can be rapidly calculated.
The embodiment of the invention also provides a battery pack consistency state diagnosis device; the specific apparatus may include:
an index extraction module for extracting a plurality of indexes for consistency assessment of the battery pack;
the normalization processing module is used for carrying out data normalization processing on each index according to the optimal direction;
the consistency relative state calculation module is used for calculating the consistency relative state of each battery pack according to the index after the data normalization processing and sequencing the quality;
the consistency variation trend calculation module is used for repeating the contents of the normalization processing module and the consistency relative state calculation module in time sequence in a preset time sequence interval to obtain a consistency variation trend of the battery pack;
and the consistency state diagnosis module is used for identifying the battery pack with poor and deteriorated consistency state according to the battery pack consistency change trend.
The battery pack consistency state diagnosing apparatus of the present embodiment is used to implement the foregoing battery pack consistency state diagnosing method, and therefore, the specific embodiments of the battery pack consistency state diagnosing apparatus may refer to the embodiment parts of the foregoing battery pack consistency state diagnosing method, for example, the index extracting module, the normalization processing module, the consistency relative state calculating module, the consistency variation trend calculating module, and the consistency state diagnosing module, which are respectively used to implement steps S101, S102, S103, S104, and S105 in the foregoing battery pack consistency state diagnosing method, so that the specific embodiments thereof may refer to the description of the corresponding respective part embodiments, and are not repeated herein.
The specific embodiment of the invention also provides a battery pack consistency state diagnosis device, which comprises: a memory for storing a computer program; and the processor is used for realizing the steps of the battery pack consistency state diagnosis method when executing the computer program.
The specific embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the battery pack consistency state diagnosis method when being executed by a processor.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (7)
1. A battery pack consistency status diagnostic method comprising:
step 1: extracting a plurality of indexes for consistency assessment of the battery pack;
step 2: according to the pareto optimal direction, carrying out data normalization processing on each index, wherein the calculation formula for carrying out data normalization processing on each index is as follows:
wherein,maximum and minimum of the extracted index respectively, < ->Respectively indicating indexes before and after data normalization processing, wherein a and b are respectively the row and the column of data;
step 3: calculating the consistency relative state of each battery pack according to the index after the data normalization processing, and performing the quality sorting, wherein the calculating the consistency relative state of each battery pack according to the index after the data normalization processing comprises the following steps:
calculating the number of times each battery pack is dominated by the rest battery packs according to the index after the data normalization processing, wherein if the first battery pack isIs>Are not more than +.>Corresponding indexAnd battery pack->At least one index is present which is smaller than the battery +.>The corresponding index is the first electricityA pool group dominates the second battery group, wherein n is the index total number;
calculating the dominant relation between any two battery packs according to the steps, and counting the dominant times of each battery pack, wherein the times are positively correlated with the consistency relative state;
step 4: repeating the contents of the step 2-3 in time sequence within a preset time sequence interval to obtain a consistency variation trend of the battery pack;
step 5: identifying a battery pack with poor and deteriorated consistency state according to the battery pack consistency change trend;
wherein, divide group battery operating condition into two types: standing or charging and discharging; the battery consistency status is divided into four categories: poor in a static state and poor in a charge-discharge state; the device is good in a standing state and poor in a charging and discharging state; poor in a standing state and good in a charging and discharging state; better in the static state, better in the charge and discharge state, worse in the static state and worse in the charge and discharge state; the battery pack is good in a standing state, and poor in a charging and discharging state is subjected to important attention, so that maintenance is timely carried out.
2. The battery pack consistency status diagnostic method of claim 1, wherein the plurality of indicators for battery pack consistency assessment comprises a voltage step and a voltage standard deviation.
3. The battery pack consistency status diagnosing method according to claim 2, wherein the calculation formula of the voltage range is:
wherein,the maximum and minimum values of cell voltages in a set of cells, respectively.
4. The battery pack consistency status diagnostic method of claim 2, wherein the calculation formula of the voltage standard deviation is:
wherein,is the total number of battery cells in a group of batteries, < >>Is a group of cells +.>Cell voltage, ">Is the average of all cell voltages in a group of cells.
5. A battery pack consistency status diagnostic apparatus comprising:
an index extraction module for extracting a plurality of indexes for consistency assessment of the battery pack;
the normalization processing module is used for carrying out data normalization processing on each index according to the pareto optimal direction, and the calculation formula for carrying out data normalization processing on each index is as follows:
wherein,maximum and minimum of the extracted index respectively, < ->Respectively indicating indexes before and after data normalization processing, wherein a and b are respectively the row and the column of data;
the consistency relative state calculating module is used for calculating the consistency relative state of each battery pack according to the index after the data normalization processing and sequencing the quality, wherein the calculating of the consistency relative state of each battery pack according to the index after the data normalization processing comprises the following steps:
calculating the number of times each battery pack is dominated by the rest battery packs according to the index after the data normalization processing, wherein if the first battery pack isIs>Are not more than +.>Corresponding indexAnd battery pack->At least one index is present which is smaller than the battery +.>The first battery pack dominates the second battery pack according to the corresponding index, wherein n is the total index number;
calculating the dominant relation between any two battery packs according to the steps, and counting the dominant times of each battery pack, wherein the times are positively correlated with the consistency relative state;
the consistency variation trend calculation module is used for repeating the contents of the normalization processing module and the consistency relative state calculation module in time sequence in a preset time sequence interval to obtain a consistency variation trend of the battery pack;
the consistency state diagnosis module is used for identifying a battery pack with poor and deteriorated consistency state according to the consistency change trend of the battery pack, wherein the working states of the battery pack are divided into two types: standing or charging and discharging; the battery consistency status is divided into four categories: poor in a static state and poor in a charge-discharge state; the device is good in a standing state and poor in a charging and discharging state; poor in a standing state and good in a charging and discharging state; the battery is good in a standing state, good in a charging and discharging state, and poor in the standing state and the charging and discharging state; the battery pack is good in a standing state, and poor in a charging and discharging state is subjected to important attention, so that maintenance is timely carried out.
6. A battery pack consistency status diagnostic apparatus comprising:
a memory for storing a computer program;
a processor for implementing the steps of a battery pack consistency status diagnostic method as recited in any one of claims 1-4 when executing the computer program.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a battery pack consistency status diagnostic method according to any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311147800.1A CN116908720B (en) | 2023-09-07 | 2023-09-07 | Battery pack consistency state diagnosis method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311147800.1A CN116908720B (en) | 2023-09-07 | 2023-09-07 | Battery pack consistency state diagnosis method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116908720A CN116908720A (en) | 2023-10-20 |
| CN116908720B true CN116908720B (en) | 2023-12-26 |
Family
ID=88367103
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311147800.1A Active CN116908720B (en) | 2023-09-07 | 2023-09-07 | Battery pack consistency state diagnosis method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116908720B (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104617621A (en) * | 2015-01-28 | 2015-05-13 | 杭州高特电子设备有限公司 | Improved battery pack maintaining method |
| CN107192954A (en) * | 2017-05-02 | 2017-09-22 | 杭州科工电子科技有限公司 | A kind of performance of lithium ion battery inline diagnosis method |
| CN109324297A (en) * | 2018-10-19 | 2019-02-12 | 张瑞锋 | A kind of method of consistency of battery pack analysis |
| CN109768340A (en) * | 2018-12-05 | 2019-05-17 | 国联汽车动力电池研究院有限责任公司 | A kind of battery discharge procedure voltage inconsistency estimation method and device |
| CN109828548A (en) * | 2019-01-17 | 2019-05-31 | 西安交通大学 | Performance degradation feature evaluation method based on time series variation Singularity detection |
| CN111007401A (en) * | 2019-12-16 | 2020-04-14 | 国网江苏省电力有限公司电力科学研究院 | Electric vehicle battery fault diagnosis method and device based on artificial intelligence |
| CN111007417A (en) * | 2019-12-06 | 2020-04-14 | 重庆大学 | Battery pack SOH and RUL prediction method and system based on inconsistency evaluation |
| CN111693876A (en) * | 2020-05-09 | 2020-09-22 | 清华大学 | Battery pack evaluation method and system |
| CN113484764A (en) * | 2021-07-30 | 2021-10-08 | 合肥工业大学 | Multi-dimensional impedance spectrum-based retired battery SOH and consistency evaluation method |
| CN113554200A (en) * | 2020-04-23 | 2021-10-26 | 广州汽车集团股份有限公司 | Power battery voltage inconsistency prediction method, system and equipment |
| CN113765179A (en) * | 2021-08-17 | 2021-12-07 | 江苏大学 | Cloud battery pack consistency analysis method based on hierarchical clustering and class center distance |
| CN115015775A (en) * | 2022-06-02 | 2022-09-06 | 江汉大学 | Same-batch lithium battery consistency evaluation method and device and processing equipment |
| CN115248393A (en) * | 2022-05-20 | 2022-10-28 | 湖北文理学院 | Battery consistency sorting method, device, equipment and storage medium |
| CN115327422A (en) * | 2022-09-05 | 2022-11-11 | 大连理工大学 | Electric bus power battery health degree evaluation method based on charging and discharging behaviors |
| CN115800433A (en) * | 2022-11-09 | 2023-03-14 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency evaluation and grade evaluation method and device |
| CN115808632A (en) * | 2022-11-03 | 2023-03-17 | 宁德时代新能源科技股份有限公司 | Battery consistency detection method, detection device, computer equipment and storage medium |
| CN115993555A (en) * | 2022-11-17 | 2023-04-21 | 中国华能集团清洁能源技术研究院有限公司 | Method and device for determining consistency grade of energy storage battery pack |
| CN116258417A (en) * | 2023-03-24 | 2023-06-13 | 四川大学 | NSGA-2 genetic algorithm-based lithium battery equalization index optimization method |
| CN116298929A (en) * | 2023-05-08 | 2023-06-23 | 合肥工业大学 | Battery pack cell consistency assessment method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109103953B (en) * | 2018-08-23 | 2021-07-20 | 广州市香港科大霍英东研究院 | Active equalization control method, system and device for battery pack |
-
2023
- 2023-09-07 CN CN202311147800.1A patent/CN116908720B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104617621A (en) * | 2015-01-28 | 2015-05-13 | 杭州高特电子设备有限公司 | Improved battery pack maintaining method |
| CN107192954A (en) * | 2017-05-02 | 2017-09-22 | 杭州科工电子科技有限公司 | A kind of performance of lithium ion battery inline diagnosis method |
| CN109324297A (en) * | 2018-10-19 | 2019-02-12 | 张瑞锋 | A kind of method of consistency of battery pack analysis |
| CN109768340A (en) * | 2018-12-05 | 2019-05-17 | 国联汽车动力电池研究院有限责任公司 | A kind of battery discharge procedure voltage inconsistency estimation method and device |
| CN109828548A (en) * | 2019-01-17 | 2019-05-31 | 西安交通大学 | Performance degradation feature evaluation method based on time series variation Singularity detection |
| CN111007417A (en) * | 2019-12-06 | 2020-04-14 | 重庆大学 | Battery pack SOH and RUL prediction method and system based on inconsistency evaluation |
| CN111007401A (en) * | 2019-12-16 | 2020-04-14 | 国网江苏省电力有限公司电力科学研究院 | Electric vehicle battery fault diagnosis method and device based on artificial intelligence |
| CN113554200A (en) * | 2020-04-23 | 2021-10-26 | 广州汽车集团股份有限公司 | Power battery voltage inconsistency prediction method, system and equipment |
| CN111693876A (en) * | 2020-05-09 | 2020-09-22 | 清华大学 | Battery pack evaluation method and system |
| CN113484764A (en) * | 2021-07-30 | 2021-10-08 | 合肥工业大学 | Multi-dimensional impedance spectrum-based retired battery SOH and consistency evaluation method |
| CN113765179A (en) * | 2021-08-17 | 2021-12-07 | 江苏大学 | Cloud battery pack consistency analysis method based on hierarchical clustering and class center distance |
| CN115248393A (en) * | 2022-05-20 | 2022-10-28 | 湖北文理学院 | Battery consistency sorting method, device, equipment and storage medium |
| CN115015775A (en) * | 2022-06-02 | 2022-09-06 | 江汉大学 | Same-batch lithium battery consistency evaluation method and device and processing equipment |
| CN115327422A (en) * | 2022-09-05 | 2022-11-11 | 大连理工大学 | Electric bus power battery health degree evaluation method based on charging and discharging behaviors |
| CN115808632A (en) * | 2022-11-03 | 2023-03-17 | 宁德时代新能源科技股份有限公司 | Battery consistency detection method, detection device, computer equipment and storage medium |
| CN115800433A (en) * | 2022-11-09 | 2023-03-14 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency evaluation and grade evaluation method and device |
| CN115993555A (en) * | 2022-11-17 | 2023-04-21 | 中国华能集团清洁能源技术研究院有限公司 | Method and device for determining consistency grade of energy storage battery pack |
| CN116258417A (en) * | 2023-03-24 | 2023-06-13 | 四川大学 | NSGA-2 genetic algorithm-based lithium battery equalization index optimization method |
| CN116298929A (en) * | 2023-05-08 | 2023-06-23 | 合肥工业大学 | Battery pack cell consistency assessment method |
Non-Patent Citations (1)
| Title |
|---|
| 基于剩余容量在线估算的电池组主动均衡研究;张利 等;电子测量与仪器学报;第29卷(第10期);1557-1563 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116908720A (en) | 2023-10-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dubarry et al. | Battery energy storage system modeling: Investigation of intrinsic cell-to-cell variations | |
| CN109856559B (en) | Lithium battery cycle life prediction method | |
| CN110161425B (en) | Method for predicting remaining service life based on lithium battery degradation stage division | |
| Sauer et al. | Comparison of different approaches for lifetime prediction of electrochemical systems—Using lead-acid batteries as example | |
| CN108490366B (en) | Rapid assessment method for state of health of electric automobile retired battery module | |
| CN112363075B (en) | Evaluation method for aging of lithium ion battery | |
| CN104584376A (en) | Method and apparatus for charging a battery with globally minimized integral degradation for predefined charging duration | |
| CN109100655B (en) | Data processing method and device for power battery | |
| CN110045291B (en) | Lithium battery capacity estimation method | |
| CN108132441B (en) | Method and device for determining running range of charge state of energy storage battery module | |
| CN111856283A (en) | Battery evaluation system, battery evaluation method, and program | |
| CN112180279A (en) | Retired battery health state diagnostic expert system | |
| CN113554200B (en) | Method, system and equipment for predicting voltage inconsistency of power battery | |
| US20230031420A1 (en) | Method, Apparatus, And Computer Program Product For Determining A Residual Value Of Battery Storage Devices | |
| Zhou et al. | Online state of health estimation for seriesconnected LiFePO 4 battery pack based on differential voltage and inconsistency analysis | |
| CN114035087B (en) | Method, device, equipment and medium for evaluating residual life of energy storage battery | |
| CN110927609A (en) | Decline evaluation method and device for battery energy storage system by utilizing battery in echelon | |
| CN114325446A (en) | Method and device for testing cycle life of battery pack, electronic equipment and storage medium | |
| CN113900035A (en) | Battery detection method, device, equipment and storage medium | |
| Eleftheriadis et al. | Comparative study of machine learning techniques for the state of health estimation of li-ion batteries | |
| CN117330987A (en) | Method, system, medium and apparatus for time-based battery state of health assessment | |
| CN116908720B (en) | Battery pack consistency state diagnosis method, device and storage medium | |
| CN116879763A (en) | Battery fault early warning method based on Kalman filtering algorithm | |
| CN111426966A (en) | Electric vehicle battery recombination method and device and electronic equipment | |
| CN116388347A (en) | Energy comprehensive management platform for household battery energy storage |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |