CN114019393A - Data-based battery capacity prediction method and system - Google Patents
Data-based battery capacity prediction method and system Download PDFInfo
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- CN114019393A CN114019393A CN202111274228.6A CN202111274228A CN114019393A CN 114019393 A CN114019393 A CN 114019393A CN 202111274228 A CN202111274228 A CN 202111274228A CN 114019393 A CN114019393 A CN 114019393A
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- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000004519 manufacturing process Methods 0.000 claims abstract description 104
- 238000012549 training Methods 0.000 claims abstract description 12
- 238000003860 storage Methods 0.000 claims abstract description 8
- 238000010801 machine learning Methods 0.000 claims abstract description 4
- 239000011248 coating agent Substances 0.000 claims description 10
- 238000000576 coating method Methods 0.000 claims description 10
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 238000002347 injection Methods 0.000 claims description 4
- 239000007924 injection Substances 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 238000004804 winding Methods 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000003756 stirring Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 8
- 229910001416 lithium ion Inorganic materials 0.000 description 8
- 238000007599 discharging Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 2
- 238000007639 printing Methods 0.000 description 2
- 238000005507 spraying Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010030 laminating Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000002002 slurry Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- 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
- G01R31/387—Determining ampere-hour charge capacity or SoC
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Abstract
The invention belongs to the field of rechargeable batteries, and relates to a battery capacity prediction method and system based on data, which comprises the following steps: the method comprises the steps that a data acquisition unit arranged on production equipment is used for acquiring various running state data of the production equipment at regular time during battery production; the collected operation state data are periodically sent to a storage unit, and the collected operation state data are stored; analyzing the relation between the collected running state data and the battery capacity grade by a machine learning method, and constructing a prediction model of the battery capacity grade by taking the collected running state data and the correspondingly produced battery capacity as training samples; and inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade to obtain the grade of the produced battery capacity.
Description
Technical Field
The invention belongs to the technical field of rechargeable batteries, and relates to a battery capacity prediction method and system based on data.
Background
The lithium ion battery has the advantages of high voltage, large specific energy, long charging and discharging service life and the like, so the lithium ion battery is widely applied to products in multiple fields such as electronic products, portable small-sized electrical appliances, energy storage systems and the like; the production process of the lithium ion battery comprises the working procedures of slurry mixing, coating, rolling, slicing, laminating (winding), assembling, liquid injection, formation and the like. When the lithium battery is produced, under the same process conditions, the capacities of the single lithium ion batteries produced in batch are different, and if the single lithium ion batteries with different capacities are directly combined into the battery pack in a series-parallel connection mode, the overcharge of part of the single lithium ion batteries and the unsaturated charge of the other part of the single lithium ion batteries are easy to occur in the charging and discharging processes of the battery pack, so that the service life of the battery pack is influenced, and the capacity grading of the batteries with different capacities is needed.
At present, the capacity grading method for the battery is to age and polish the cleaned battery core, and then put the battery core into a capacity grading cabinet to complete charging and discharging of the battery so as to obtain the capacity of the battery, thereby grading the battery. Although the capacity grading method can accurately and effectively complete the capacity grading of the battery, the capacity grading method takes long time, so that the total time consumed in the manufacturing process of the lithium ion battery is long, the capacity grading needs to be carried out by using a capacity grading cabinet, and the capacity grading cost is high.
Disclosure of Invention
The invention aims to provide a data-based battery capacity prediction method aiming at the defects of the prior art, which can complete the prediction of the battery capacity during the production of the battery and solve the problem that the capacity of the battery can be classified only by charging and discharging when the battery capacity is classified at present, thereby reducing the cost of the battery during the production.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for data-based battery capacity prediction, comprising the steps of:
the method comprises the steps that a data acquisition unit arranged on production equipment is used for acquiring various running state data of the production equipment at regular time during battery production;
the collected operation state data are periodically sent to a storage unit, and the collected operation state data are stored;
analyzing the relation between the collected running state data and the battery capacity grade by a machine learning method, and constructing a prediction model of the battery capacity grade by taking the collected running state data and the correspondingly produced battery capacity as training samples;
and inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade to obtain the grade of the produced battery capacity.
Further, the batteries and the production equipment are associated to track operational status data corresponding to the batteries on the production equipment as the batteries pass each production equipment.
Further, the acquiring the operation state data of the production equipment includes: stirring, coating, roll aligning, strip dividing, winding, shelling, liquid injection, formation and volume grading.
Further, a plurality of product batches are simultaneously performed during the training.
Further, the analyzing the relationship between the collected operation state data and the battery capacity grade includes: the operation states of a single device and a single device in a plurality of devices are analyzed respectively.
Further, the analyzing the operation state of the single device comprises the following steps: collecting the running state data of a single device in a period of time, setting the running state data threshold value of the abnormal condition of the device, and if the running state data in the running process of the device reaches the marginal state of the threshold value, determining that the device runs abnormally.
Further, when a prediction model of the battery capacity grade is constructed, the division of the battery capacity grade is completed through manual testing.
Further, when a prediction model of the battery capacity grade is constructed, the grade of the battery capacity is defined as a grade a, a grade B and a grade C, if the grade of the battery is tested to be the grade a, the operation state data of the corresponding production equipment is labeled as the grade a, if the grade of the battery is tested to be the grade B, the operation state data of the corresponding production equipment is labeled as the grade B, and if the grade of the battery is tested to be the grade C, the operation state data of the corresponding production equipment is labeled as the grade C.
Further, the plurality of operation state data includes: the instruction sequence sent or received by the production equipment, the operation parameters set by the production equipment and the detection quantity of the production equipment during operation.
The invention also provides a battery capacity prediction system based on data, comprising:
the data acquisition unit is used for acquiring various operating state data of the production equipment during battery production;
the storage unit is used for receiving the collected running state data of the production equipment and storing the running state data;
the modeling generation unit is used for establishing a prediction model of the battery capacity grade by taking the collected operation state data and the correspondingly produced battery capacity as training samples according to the analyzed relation between the operation state data and the battery capacity grade;
and the battery capacity prediction unit is used for inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade so as to obtain the grade of the produced battery capacity.
The invention has the beneficial effects that:
the method comprises the steps of establishing a relation between running state data of production equipment and battery capacity grades by obtaining the running state data of the production equipment, and taking the collected running state data and correspondingly produced battery capacity as training samples to construct a prediction model of the battery capacity grades, so that in the subsequent production process of the battery, the produced battery capacity grades can be obtained only by inputting the running state data of the production equipment corresponding to the battery and the battery to be predicted into the prediction model of the battery capacity grades, the capacity of the battery can be predicted directly through the running state data of the production equipment, the link of reclassifying the battery after the production of the battery is finished is omitted, and the purpose of reducing the cost of the battery in production is achieved.
Drawings
Fig. 1 is a flow chart of a battery capacity prediction method according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, a method for predicting battery capacity based on data includes the steps of: the method comprises the steps that a data acquisition unit arranged on production equipment is used for acquiring various running state data of the production equipment at regular time during battery production; the collected operation state data are periodically sent to a storage unit and a data processing unit, and the collected operation state data are stored; analyzing the relation between the collected running state data and the battery capacity grade by a machine learning method, and constructing a prediction model of the battery capacity grade by taking the collected running state data and the correspondingly produced battery capacity as training samples; and inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade to obtain the grade of the produced battery capacity.
In the above embodiment, the battery and the production equipment are associated to track the running state data corresponding to the battery on the production equipment when the battery passes through each production equipment, specifically, the label printing, spraying and scanning systems are arranged at the head end and the tail end of each production equipment, the battery can have an identification mark in the production process, and the label scanning system on the production equipment can scan the label on the battery to track the running state data corresponding to the battery on the production equipment when the battery passes through each production equipment, so that the battery identification mark can correspond to the running state data of the production equipment when the battery passes through the production equipment.
In the above embodiment, the acquiring the operation state data of the production equipment includes: stirring, coating, roll aligning, strip dividing, winding, shelling, liquid injection, formation and volume grading.
In the above embodiment, analyzing the relationship between the collected operation state data and the battery capacity level includes: analyzing the operation state of the single device and the operation states of the single devices in the multiple devices respectively, further, analyzing the operation states of the single devices comprises the following steps: collecting the running state data of a single device in a period of time, setting the running state data threshold value of the abnormal condition of the device, if the running state data in the running process of the device reaches the marginal state of the threshold value, determining that the device runs abnormally, and analyzing the running states of a plurality of devices comprises the following steps: collecting the equipment information of each battery; analyzing the quality of all batteries passing through the equipment A, comparing the quality of the batteries passing through the equipment A with the quality of all batteries passing through the equipment B, in the embodiment, six batteries are selected, wherein the batteries 1, 2 and 3 pass through the coating machine A in the coating link, and the batteries 4, 5 and 6 pass through the coating machine B in the coating link, the average capacity of the batteries passing through the coating machine A and the average capacity of the batteries passing through the coating machine B are detected, and the running state conditions of the equipment A and the equipment B can be determined by comparing the average capacities detected twice.
In the above embodiment, during the training, a plurality of products are batched at the same time, so that a predetermined number of batteries are required during the training, during the construction of the prediction model of the battery capacity class, the battery capacity class division is completed through manual testing, specifically, during the construction of the prediction model of the battery capacity class, the capacity of the battery is tested manually, and the capacity class is determined according to the tested capacity, in this embodiment, the battery capacity is divided into a plurality of continuous range intervals, when the tested capacity is located in a first range interval, the tested battery class is identified as a class a, correspondingly, the operation state data of the corresponding production equipment is identified as a class a, when the tested capacity is located in a second range interval, the tested battery class is identified as a class B, correspondingly, the operation state data of the corresponding production equipment is identified as a class B, when the tested capacity is in the third range interval, if the tested battery is in the C level, the operation state data of the corresponding production equipment is designated as the C level, and here, when it needs to be understood, the higher the battery capacity is, in this embodiment, the level of the battery capacity is a > B > C, and correspondingly, the battery capacity value in the first range interval > the battery capacity value in the second range interval > the battery capacity value in the third range interval.
In the above embodiment, the plurality of operation state data includes: the instruction sequence sent or received by the production equipment, the operation parameters set by the production equipment and the detection quantity of the production equipment during operation include, but are not limited to, the following instructions: in order to correspond the operation state data of the production equipment to the production equipment during the production of the battery, the ID of each production equipment, the ID of the corresponding controller on each production equipment, and all the operation state data on each production equipment within a preset time period are collected.
The present invention also provides a data-based battery capacity prediction system connected to a production facility for a full battery production process to predict the capacity of a battery produced during the production of the battery, the battery capacity prediction system comprising: the data acquisition unit is used for acquiring various operating state data of production equipment during battery production, and the acquired data comprises the following data: the instruction sequence sent or received by the production equipment, the operation parameters set by the production equipment and the detection quantity of the production equipment during operation include, but are not limited to, the following instructions: starting/stopping of the production equipment and opening/closing of the valve, wherein when the battery is produced, in order to enable the running state data of the production equipment to correspond to the production equipment, the ID of each production equipment, the ID of the corresponding controller on each production equipment and all the running state data on each production equipment in a preset time period are collected, wherein the collection unit is a data collection gateway; the storage unit receives the collected operation state data of the production equipment and stores the operation state data, and the number stored by the storage unit is not limited to the operation state data and also comprises the following steps: the ID of each production device and the ID of the corresponding controller on each production device; the modeling generation unit is used for establishing a prediction model of the battery capacity grade by taking the collected operation state data and the correspondingly produced battery capacity as training samples according to the analyzed relation between the operation state data and the battery capacity grade; and the battery capacity prediction unit is used for inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into the prediction model of the battery capacity grade so as to obtain the grade of the produced battery capacity.
In the above embodiment, the battery capacity prediction system further includes: and the display device is connected with the battery capacity prediction unit, so that the battery capacity prediction unit can transmit the predicted battery capacity information to the display device, and the display device can display various reminding messages generated in the production process besides displaying the battery capacity information.
In the above embodiment, the label printing, spraying and scanning systems are arranged at the head end and the tail end of each production device, so that the battery can have an identity in the production process, and the label scanning system on the production device can scan the label on the battery, so as to track the running state data of the battery corresponding to the battery on the production device when the battery passes through each production device, and ensure that the running state data of the production device corresponds to the running state data of the battery when the battery passes through the production device.
The above-described embodiments are only one of the preferred embodiments of the present invention, and general changes and substitutions by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention.
Claims (10)
1. A method for predicting battery capacity based on data, comprising the steps of:
the method comprises the steps that a data acquisition unit arranged on production equipment is used for acquiring various running state data of the production equipment at regular time during battery production;
the collected operation state data are periodically sent to a storage unit, and the collected operation state data are stored;
analyzing the relation between the collected running state data and the battery capacity grade by a machine learning method, and constructing a prediction model of the battery capacity grade by taking the collected running state data and the correspondingly produced battery capacity as training samples;
and inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade to obtain the grade of the produced battery capacity.
2. The data-based battery capacity prediction method of claim 1, wherein the battery and the production equipment are associated to track operational status data corresponding to the battery on the production equipment as the battery passes each production equipment.
3. The method of claim 1, wherein collecting operational status data at a production facility comprises: stirring, coating, roll aligning, strip dividing, winding, shelling, liquid injection, formation and volume grading.
4. The method of claim 1, wherein the training is performed simultaneously for a plurality of product lots.
5. A data-based battery capacity prediction method as claimed in claim 1 wherein analyzing the relationship between the collected operating state data and the battery capacity rating comprises: the operation states of a single device and a single device in a plurality of devices are analyzed respectively.
6. The data-based battery capacity prediction method of claim 5, wherein the analyzing the operating status of the individual device comprises the steps of: collecting the running state data of a single device in a period of time, setting the running state data threshold value of the abnormal condition of the device, and if the running state data in the running process of the device reaches the marginal state of the threshold value, determining that the device runs abnormally.
7. The data-based battery capacity prediction method of claim 1, wherein the classification of the battery capacity classes is performed by manual testing when constructing the prediction model of the battery capacity classes.
8. The method according to claim 7, wherein when the prediction model of the battery capacity class is constructed, the classes of the battery capacity are defined as class a, class B and class C, if the class of the battery is tested as class a, the operation state data of the corresponding production equipment is defined as class a, if the class of the battery is tested as class B, the operation state data of the corresponding production equipment is defined as class B, and if the class of the battery is tested as class C, the operation state data of the corresponding production equipment is defined as class C.
9. A data-based battery capacity prediction method as claimed in claim 2, wherein the plurality of operating state data comprises: the instruction sequence sent or received by the production equipment, the operation parameters set by the production equipment and the detection quantity of the production equipment during operation.
10. A data-based battery capacity prediction system, comprising:
the data acquisition unit is used for acquiring various operating state data of the production equipment during battery production;
the storage unit is used for receiving the acquired running state data of the production equipment and storing the running state data;
the modeling generation unit is used for establishing a prediction model of the battery capacity grade by taking the collected operation state data and the correspondingly produced battery capacity as training samples according to the analyzed relation between the operation state data and the battery capacity grade;
and the battery capacity prediction unit is used for inputting the running state data of the battery to be predicted and the production equipment corresponding to the battery into a prediction model of the battery capacity grade so as to obtain the grade of the produced battery capacity.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116794533A (en) * | 2023-08-21 | 2023-09-22 | 深圳海辰储能控制技术有限公司 | Battery cell capacity grading method and related products |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014054638A1 (en) * | 2012-10-02 | 2014-04-10 | 株式会社日立製作所 | Discharge capacity estimating device, program, and method for manufacturing batteries |
| CN109613440A (en) * | 2019-01-17 | 2019-04-12 | 清华-伯克利深圳学院筹备办公室 | Stage division, device, equipment and the storage medium of battery |
| CN109604192A (en) * | 2018-11-21 | 2019-04-12 | 中国科学院自动化研究所 | Battery sorting method and system based on big data analysis |
| CN109683094A (en) * | 2018-12-19 | 2019-04-26 | 武汉新能源研究院有限公司 | A kind of quick method for separating and its sorting unit of lithium ion battery |
| CN109738824A (en) * | 2018-12-29 | 2019-05-10 | 东莞市金源电池科技有限公司 | A kind of chemical conversion survey appearance method of lithium ion battery |
| CN112379292A (en) * | 2020-11-06 | 2021-02-19 | 桑顿新能源科技有限公司 | Lithium battery capacity prediction method and prediction device |
-
2021
- 2021-10-29 CN CN202111274228.6A patent/CN114019393A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014054638A1 (en) * | 2012-10-02 | 2014-04-10 | 株式会社日立製作所 | Discharge capacity estimating device, program, and method for manufacturing batteries |
| CN109604192A (en) * | 2018-11-21 | 2019-04-12 | 中国科学院自动化研究所 | Battery sorting method and system based on big data analysis |
| CN109683094A (en) * | 2018-12-19 | 2019-04-26 | 武汉新能源研究院有限公司 | A kind of quick method for separating and its sorting unit of lithium ion battery |
| CN109738824A (en) * | 2018-12-29 | 2019-05-10 | 东莞市金源电池科技有限公司 | A kind of chemical conversion survey appearance method of lithium ion battery |
| CN109613440A (en) * | 2019-01-17 | 2019-04-12 | 清华-伯克利深圳学院筹备办公室 | Stage division, device, equipment and the storage medium of battery |
| CN112379292A (en) * | 2020-11-06 | 2021-02-19 | 桑顿新能源科技有限公司 | Lithium battery capacity prediction method and prediction device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116794533A (en) * | 2023-08-21 | 2023-09-22 | 深圳海辰储能控制技术有限公司 | Battery cell capacity grading method and related products |
| CN116794533B (en) * | 2023-08-21 | 2023-12-29 | 深圳海辰储能科技有限公司 | Battery cell capacity grading method and related products |
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