CN116224093A - Retired battery module sorting method and device, electronic equipment and storage medium - Google Patents
Retired battery module sorting method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a retired power battery module sorting method, a retired power battery module sorting device, electronic equipment and a storage medium, wherein the retired power battery module sorting method comprises the following steps: obtaining the residual capacity of a plurality of retired power battery modules under a first charge and discharge test; acquiring pulse charge and discharge data of the battery module in the last pulse charge and discharge after three continuous pulse charge and discharge, calculating first characteristic parameters, second characteristic parameters, terminal voltage and internal resistance of a plurality of retired power battery modules based on the pulse charge and discharge data of the last pulse charge and discharge, and performing dimension reduction treatment to obtain a m-dimension characteristic matrix; and finally, constructing a neural network model, training the neural network model to obtain a capacity prediction model, and sorting the battery modules after capacity prediction is carried out on the retired power battery modules based on the capacity prediction model. The invention solves the technical problem that the sorting of the retired battery modules cannot be effectively realized due to the fact that the capacity of the battery modules cannot be accurately estimated at present.
Description
Technical Field
The invention relates to the technical field of battery sorting, in particular to a retired battery module sorting method, a retired battery module sorting device, electronic equipment and a storage medium.
Background
With the rising of the electric automobile industry, the sales of electric automobiles are increased, and meanwhile, the sales of lithium ion batteries are also driven to rise sharply. For electric vehicles, in order to ensure the cruising ability of the vehicles, after the capacity of the battery is reduced to a certain degree (for example 80%), the battery needs to be eliminated from the vehicles, and if the battery is directly eliminated, the waste of the residual value of the battery is caused, so that the retired battery module needs to be utilized in a gradient manner, and further, the waste of energy is avoided.
However, since the retired battery module has large performance difference after undergoing different charge and discharge processes, if the retired battery module is directly used without sorting, capacity performance, power performance and residual life of the battery pack for secondary use can be affected, and meanwhile, the retired battery module is more likely to generate faults.
The existing sorting method generally sorts the battery modules according to the capacity of the battery modules after estimating the capacity of the battery modules, but the capacity of the battery modules is predicted completely through a charging and discharging process, and the estimation of the capacity of the battery modules is not accurate enough, so that the sorting of the retired battery modules cannot be effectively realized.
Disclosure of Invention
The invention aims to overcome the technical defects, and provides a retired battery module sorting method, a retired battery module sorting device, electronic equipment and a storage medium, which solve the technical problem that the retired battery module cannot be effectively sorted due to the fact that the capacity of the battery module cannot be accurately estimated in the prior art.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for sorting retired battery modules, comprising the steps of:
obtaining the residual capacity of a plurality of retired power battery modules under a first charge and discharge test;
acquiring pulse charge and discharge data of a plurality of retired power battery modules in the last pulse charge and discharge after three continuous pulse charge and discharge, and calculating first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data in the last pulse charge and discharge;
obtaining the difference between the sum of the first characteristic parameters of each single battery of a plurality of retired power battery modules in the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and taking the difference as a second characteristic parameter;
obtaining terminal voltage and internal resistance of a plurality of retired power battery modules;
performing dimension reduction treatment on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the power battery modules to obtain a dimension-reduced m-dimension characteristic matrix;
and constructing a neural network model, taking the m-dimensional feature matrix as input, taking the discharge capacity of the retired power battery module as output, training the neural network model to obtain a capacity prediction model, and sorting the retired power battery module according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery module after carrying out capacity prediction on the retired power battery module based on the capacity prediction model.
In some embodiments, the obtaining the remaining capacities of the plurality of retired power battery modules under the first charge and discharge test includes:
and obtaining residual capacity of the plurality of retired power battery modules after standing at the first temperature and performing charge and discharge testing at the first charge and discharge multiplying power.
In some embodiments, the first characteristic parameter includes at least ohmic internal resistance, polarized internal resistance, and charge-discharge power.
In some embodiments, the acquiring pulse charge and discharge data of the last pulse charge and discharge after three continuous pulse charge and discharge of the plurality of retired power battery modules includes:
and obtaining pulse charge and discharge data of the last time after a plurality of retired power battery modules are subjected to three continuous pulse charge and discharge tests under a second charge and discharge multiplying power after being stood at a second temperature.
In some embodiments, the first temperature is 15 ℃ to 35 ℃, the second temperature is 15 ℃ to 35 ℃, the first charge-discharge rate is 0.3C to 1C, and the second charge-discharge rate is 1C to 3C.
In some embodiments, the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the plurality of power battery modules are subjected to dimension reduction processing by adopting a principal component analysis method.
In some embodiments, the neural network model is a support vector machine.
In a second aspect, the present invention also provides a retired battery module sorting apparatus, including:
the residual capacity acquisition module is used for acquiring the residual capacities of the decommissioning power battery modules under the first charge and discharge test;
the device comprises a first characteristic parameter acquisition module, a second characteristic parameter acquisition module and a third characteristic parameter acquisition module, wherein the first characteristic parameter acquisition module is used for acquiring pulse charge and discharge data of a plurality of retired power battery modules in the last pulse charge and discharge after three continuous pulse charge and discharge, and calculating the first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data in the last pulse charge and discharge;
the second characteristic parameter acquisition module is used for acquiring the difference value between the sum of the first characteristic parameters of each single battery of the plurality of retired power battery modules in the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and taking the difference value as a second characteristic parameter;
the third characteristic parameter acquisition module is used for acquiring terminal voltage and internal resistance of the plurality of retired power battery modules;
the dimension reduction processing module is used for carrying out dimension reduction processing on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the power battery modules to obtain a dimension-reduced m-dimension characteristic matrix;
the prediction module is used for constructing a neural network model, taking the m-dimensional feature matrix as input, taking the discharge capacity of the retired power battery module as output, training the neural network model to obtain a capacity prediction model, and sorting the retired power battery module according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery module after carrying out capacity prediction on the retired power battery module based on the capacity prediction model.
In a third aspect, the present invention also provides an electronic device, including: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, implements the steps in the retired battery module sorting method as described above.
In a fourth aspect, the present invention also provides a computer readable storage medium storing one or more programs executable by one or more processors to implement the steps in the retired battery module sorting method as described above.
Compared with the prior art, the retired power battery module sorting method, the retired power battery module sorting device, the electronic equipment and the storage medium provided by the invention are used for carrying out uniform processing on the initial state of the retired battery module, so that the capacity of the retired power battery module can be predicted better by utilizing the parameter difference in the test process, the internal structural characteristics of the retired power battery module during charging and discharging can be better predicted by utilizing the characteristic parameter values between a plurality of reaction modules and monomers obtained by pulse test, the capacity of the module can be predicted more accurately, the information quantity can be reduced by carrying out dimension reduction processing on the characteristic parameters, the mutual influence between original data is eliminated, the calculated quantity of a model is reduced, and in addition, the more the number of training set samples is, the more accurate the built prediction model is, and the rapid and accurate prediction of the retired power battery module can be realized on the premise of not needing complete charging and discharging by using the technical valve rod.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for sorting retired power battery modules according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of a retired power battery module sorting device according to the present invention;
fig. 3 is a schematic view of an operating environment of an embodiment of a retired power battery module sorting procedure according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, the method for sorting retired power battery modules according to the embodiment of the invention includes the following steps:
s100, obtaining residual capacities of a plurality of retired power battery modules under a first charge and discharge test;
s200, acquiring pulse charge and discharge data of a plurality of retired power battery modules in the last pulse charge and discharge after three continuous pulse charge and discharge, and calculating first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data in the last pulse charge and discharge;
s300, obtaining the difference value between the sum of the first characteristic parameters of each single battery of a plurality of retired power battery modules in the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and taking the difference value as a second characteristic parameter;
s400, obtaining terminal voltage and internal resistance of a plurality of retired power battery modules;
s500, performing dimension reduction treatment on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the power battery modules to obtain a dimension-reduced m-dimension characteristic matrix;
s600, constructing a neural network model, taking the m-dimensional feature matrix as input, taking the discharge capacity of the retired power battery module as output, training the neural network model to obtain a capacity prediction model, performing capacity prediction on the retired power battery module based on the capacity prediction model,
and sorting the retired power battery 5 modules according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery modules.
In this embodiment, the initial state of the retired battery module is subjected to the uniform processing, so that the parameter difference in the testing process can be better utilized to predict the capacity of the retired power battery module, and the characteristic parameter values between the plurality of reaction modules and the single body obtained by the pulse test can be utilized to better react the module
The internal structure characteristics during charging and discharging of the group can more accurately predict the capacity of the module, the information quantity can be reduced by carrying out dimension reduction processing on the characteristic parameter 0, the mutual influence between the original data is eliminated,
in addition, the more the number of samples of the training set is, the more accurate the built prediction model is, and the rapid and accurate prediction of the retired power battery module can be realized on the premise of not needing complete charge and discharge through the technical valve rod.
In some embodiments, the step S100 specifically includes: 5 obtaining a plurality of retired power battery modules at a first charge-discharge multiplying power after standing at a first temperature
Residual capacity after charge and discharge test.
In this embodiment, after a plurality of retired power battery modules to be sorted are kept stand at normal temperature for a sufficient time, a charge and discharge test is performed at a first charge and discharge rate to obtain retired power battery modules
The remaining capacity of the group. Wherein the first temperature is 15-35 ℃, and the first charge-discharge multiplying power is 0.3-1C. Preferably, the first temperature is 25 ℃, and the first charge-discharge rate is 1C.
In some embodiments, the first characteristic parameter includes at least ohmic internal resistance, polarized internal resistance, and charge-discharge power.
In some embodiments, in step S200, the obtaining pulse charge and discharge data of the last pulse charge and discharge after three continuous pulse charge and discharge of the plurality of retired power battery modules includes:
and obtaining pulse charge and discharge data of the last time after a plurality of retired power battery modules are subjected to continuous pulse charge and discharge tests for three times under the second charge and discharge multiplying power after being stood at the second temperature.
In this embodiment, after a plurality of retired power battery modules are adjusted to the same state at 1C (0.3C-1C), three continuous pulse discharge and pulse charge tests are performed at a second charge/discharge rate at a second temperature, and the last pulse charge/discharge data of the modules is selected to calculate the ohmic internal resistance (R) n1 、R n2 ) Internal resistance to polarization (R) f1 、R f2 ) Charge and discharge power (P) 1 、P 2 ) As a first characteristic parameter. In 0, the second temperature is 15-35 ℃, and the second charge-discharge multiplying power is 1-3 ℃. Preferably, the method comprises the steps of,
the second temperature is 25 ℃, and the second charge-discharge multiplying power is 1C.
In some embodiments, after three continuous pulse discharge and pulse charge tests, ohmic internal resistance, polarized internal resistance and charge-discharge power of the last pulse charge-discharge of each single battery are calculated
The difference between the sum and the retired power battery module is respectively designated as DeltaR n1 、ΔR n2 、ΔR f1 、ΔR f2 、5ΔP 1 、ΔP 2 As a second characteristic parameter.
Further, after the first characteristic parameter and the second characteristic parameter are obtained, standing the retired power battery module subjected to pulse for a long time, and then using a voltage internal resistance tester to test terminal voltage and internal resistance of the retired power battery module to obtain terminal voltage U and internal resistance R as characteristic parameters.
0 in some embodiments, the principal component analysis method is used for the first characteristics of a plurality of power battery modules
And performing dimension reduction treatment on the characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance.
In this embodiment, the Principal Component Analysis (PCA) is used to perform dimension reduction processing on the 14 sets of feature parameters, so as to project the original features onto the dimension with the largest projection information amount as far as possible, and project the original features onto these dimensions, so that the information amount loss after dimension reduction is minimized, and the interaction between the original data components can be eliminated.
Specifically, assume that there are X sample sizes { X 1 ，x 2 ，…x n Each sample has a k-dimensional feature x 1 ＝{x 11 ，x 12 ，…x 1k The method comprises the steps of firstly carrying out de-averaging treatment on a data set, subtracting respective average values from each feature quantity, then calculating a covariance matrix of the feature quantity to obtain feature values and feature vectors of the covariance matrix, arranging the feature vectors into a matrix according to the corresponding feature values from top to bottom in rows, and taking the first m rows to form a matrix P to obtain the feature matrix with the dimension reduced into m dimensions.
In some embodiments, the neural network model is a support vector machine.
In this embodiment, a Support Vector Machine (SVM) is a supervised learning model with a relevant learning algorithm, and a battery module capacity prediction model is established by using a mapping relationship between an SVM-dimensional feature matrix and a module discharge capacity, wherein the feature matrix is used as an input value of the capacity prediction model, and the module discharge capacity is used as an output value. Specifically, training sets and prediction sets are set, the number of training set modules is at least 20, the number of prediction set modules is at least 10, the KS-test is used for carrying out same distribution verification on the divided training sets and test sets, the rationality of the division of the training sets and the test sets can be verified in this way, and finally, the battery module capacity prediction model is obtained through training.
And finally, sorting the battery modules according to the predicted capacity, the module terminal voltage and the internal resistance, thereby achieving the purpose of quick sorting.
According to the technical scheme provided by the invention, the initial state of the retired battery module is subjected to uniform treatment, the capacity of the retired power battery module can be predicted by utilizing the parameter difference in the test process, the internal structural characteristics of the reaction module during charging and discharging can be better obtained by utilizing the characteristic parameter values between a plurality of reaction modules and single bodies, the capacity of the module can be predicted more accurately, the information quantity can be reduced, the mutual influence between original data is eliminated, the calculated quantity of a model is reduced, in addition, the more the number of training set samples is, the more accurate the built prediction model is, and the rapid and accurate prediction of the retired power battery module can be realized on the premise of not needing complete charging and discharging by utilizing the technical valve rod.
Based on the above-mentioned method for monitoring bird damage of a power transmission line, the embodiment of the invention further provides a device 700 for monitoring bird damage of a power transmission line, referring to fig. 2, the device 700 for sorting retired battery modules includes a remaining capacity obtaining module 710, a first characteristic parameter obtaining module 720, a second characteristic parameter obtaining module 730, a third characteristic parameter obtaining module 740, a dimension reduction processing module 750, and a prediction module 760.
The remaining capacity obtaining module 710 is configured to obtain remaining capacities of the plurality of retired power battery modules under the first charge and discharge test.
The first characteristic parameter obtaining module 720 is configured to obtain pulse charge and discharge data of the last pulse charge and discharge after three continuous pulse charges and discharges of the plurality of retired power battery modules, and calculate first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data of the last pulse charge and discharge.
The second characteristic parameter obtaining module 730 is configured to obtain a difference value between a sum of first characteristic parameters of each single battery of the plurality of retired power battery modules during the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and take the difference value as a second characteristic parameter.
The third characteristic parameter obtaining module 740 is configured to obtain terminal voltages and internal resistances of the plurality of retired power battery modules.
The dimension reduction processing module 750 is configured to perform dimension reduction processing on the first feature parameters, the second feature parameters, the terminal voltage and the internal resistance of the plurality of power battery modules, so as to obtain a feature matrix with m dimensions after dimension reduction.
The prediction module 760 is configured to construct a neural network model, take the m-dimensional feature matrix as input, take the discharge capacity of the retired power battery module as output, train the neural network model to obtain a capacity prediction model, and sort the retired power battery module according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery module after performing capacity prediction on the retired power battery module based on the capacity prediction model.
In this embodiment, the initial state of the retired battery module is subjected to uniform processing, the capacity of the retired power battery module can be predicted better by utilizing the parameter difference in the test process, the internal structural characteristics of the retired power battery module during charging and discharging can be predicted more accurately by utilizing the characteristic parameter values between a plurality of reaction modules and monomers obtained by pulse test, the capacity of the modules can be predicted more accurately by performing dimension reduction processing on the characteristic parameters, the information quantity can be reduced, the mutual influence between original data is eliminated, the calculated quantity of models is reduced, in addition, the more training set samples are, the more accurate the prediction model is established, and the rapid and accurate prediction of the retired power battery module can be realized on the premise of not completely charging and discharging through the technical valve rod.
In some embodiments, the remaining capacity obtaining module 710 is specifically configured to:
and obtaining residual capacity of the plurality of retired power battery modules after standing at the first temperature and performing charge and discharge testing at the first charge and discharge multiplying power.
In some embodiments, the first characteristic parameter includes at least ohmic internal resistance, polarized internal resistance, and charge-discharge power.
In some embodiments, the acquiring pulse charge and discharge data of the last pulse charge and discharge after three continuous pulse charge and discharge of the plurality of retired power battery modules includes:
and obtaining pulse charge and discharge data of the last time after a plurality of retired power battery modules are subjected to three continuous pulse charge and discharge tests under a second charge and discharge multiplying power after being stood at a second temperature.
In some embodiments, the first temperature is 15 ℃ to 35 ℃, the second temperature is 15 ℃ to 35 ℃, the first charge-discharge rate is 0.3C to 1C, and the second charge-discharge rate is 1C to 3C.
In some embodiments, the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the plurality of power battery modules are subjected to dimension reduction processing by adopting a principal component analysis method.
In some embodiments, the neural network model is a support vector machine.
As shown in fig. 3, based on the retired battery module sorting method, the invention further provides an electronic device, which can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and other computing devices. The electronic device includes a processor 10, a memory 20, and a display 30. Fig. 3 shows only some of the components of the electronic device, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead.
The memory 20 may in some embodiments be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 20 may also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like. Further, the memory 20 may also include both internal storage units and external storage devices of the electronic device. The memory 20 is used for storing application software installed in the electronic device and various data, such as program codes for installing the electronic device. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores a retired battery module sorting program 40, and the retired battery module sorting program 40 may be executed by the processor 10 to implement the retired battery module sorting method according to the embodiments of the present application.
The processor 10 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 20, such as performing retired battery module sorting methods, etc.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 30 is used for displaying information of the retired battery module sorting equipment and a visual user interface. The components 10-30 of the electronic device communicate with each other via a system bus.
In an embodiment, the steps in the retired battery module sorting method according to the above embodiments are implemented when the processor 10 executes the retired battery module sorting program 40 in the memory 20, and the retired battery module sorting method is not described in detail herein.
In summary, the method, the device, the electronic equipment and the storage medium for sorting the retired battery modules provided by the invention are used for carrying out uniform processing on the initial state of the retired battery modules, so that the capacity of the retired power battery modules can be predicted better by utilizing the parameter difference in the test process, the internal structural characteristics of the retired power battery modules during charging and discharging can be predicted better by utilizing the characteristic parameter values between a plurality of reaction modules and single bodies obtained by pulse test, the capacity of the modules can be predicted more accurately, the information quantity can be reduced by carrying out dimension reduction processing on the characteristic parameters, the mutual influence between original data is eliminated, the calculated quantity of models is reduced, and in addition, the more the number of training set samples is, the more accurate the built prediction model is, and the rapid and accurate prediction of the retired power battery modules can be realized on the premise of not needing complete charging and discharging by using the technical valve rod.
Of course, those skilled in the art will appreciate that implementing all or part of the above-described methods may be implemented by a computer program for instructing relevant hardware (e.g., a processor, a controller, etc.), where the program may be stored in a computer-readable storage medium, and where the program may include the steps of the above-described method embodiments when executed. The storage medium may be a memory, a magnetic disk, an optical disk, or the like.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any other corresponding changes and modifications made in accordance with the technical idea of the present invention shall be included in the scope of the claims of the present invention.
Claims (10)
1. The retired battery module sorting method is characterized by comprising the following steps:
obtaining the residual capacity of a plurality of retired power battery modules under a first charge and discharge test;
acquiring pulse charge and discharge data of a plurality of retired power battery modules in the last pulse charge and discharge after three continuous pulse charge and discharge, and calculating first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data in the last pulse charge and discharge;
obtaining the difference between the sum of the first characteristic parameters of each single battery of a plurality of retired power battery modules in the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and taking the difference as a second characteristic parameter;
obtaining terminal voltage and internal resistance of a plurality of retired power battery modules;
performing dimension reduction treatment on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the power battery modules to obtain a dimension-reduced m-dimension characteristic matrix;
and constructing a neural network model, taking the m-dimensional feature matrix as input, taking the discharge capacity of the retired power battery module as output, training the neural network model to obtain a capacity prediction model, and sorting the retired power battery module according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery module after carrying out capacity prediction on the retired power battery module based on the capacity prediction model.
2. The method for sorting out of service battery modules according to claim 1, wherein the obtaining the remaining capacities of the plurality of out of service power battery modules under the first charge and discharge test comprises:
and obtaining residual capacity of the plurality of retired power battery modules after standing at the first temperature and performing charge and discharge testing at the first charge and discharge multiplying power.
3. The retired battery module sorting method according to claim 2, wherein the first characteristic parameters include at least ohmic internal resistance, polarized internal resistance and charge-discharge power.
4. The method for sorting retired battery modules according to claim 3, wherein the step of obtaining pulse charge and discharge data of a last pulse charge and discharge after three continuous pulse charge and discharge of a plurality of retired power battery modules comprises:
and obtaining pulse charge and discharge data of the last time after a plurality of retired power battery modules are subjected to three continuous pulse charge and discharge tests under a second charge and discharge multiplying power after being stood at a second temperature.
5. The method according to claim 4, wherein the first temperature is 15-35 ℃, the second temperature is 15-35 ℃, the first charge-discharge rate is 0.3-1C, and the second charge-discharge rate is 1-3C.
6. The method according to claim 5, wherein the main component analysis method is used to perform dimension reduction processing on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the plurality of power battery modules.
7. The retired battery module sorting method of claim 6, wherein the neural network model is a support vector machine.
8. The retired battery module sorting device is characterized by comprising:
the residual capacity acquisition module is used for acquiring the residual capacities of the decommissioning power battery modules under the first charge and discharge test;
the device comprises a first characteristic parameter acquisition module, a second characteristic parameter acquisition module and a third characteristic parameter acquisition module, wherein the first characteristic parameter acquisition module is used for acquiring pulse charge and discharge data of a plurality of retired power battery modules in the last pulse charge and discharge after three continuous pulse charge and discharge, and calculating the first characteristic parameters of the plurality of retired power battery modules based on the pulse charge and discharge data in the last pulse charge and discharge;
the second characteristic parameter acquisition module is used for acquiring the difference value between the sum of the first characteristic parameters of each single battery of the plurality of retired power battery modules in the last pulse charge and discharge and the first characteristic parameters of the plurality of retired power battery modules, and taking the difference value as a second characteristic parameter;
the third characteristic parameter acquisition module is used for acquiring terminal voltage and internal resistance of the plurality of retired power battery modules;
the dimension reduction processing module is used for carrying out dimension reduction processing on the first characteristic parameters, the second characteristic parameters, the terminal voltage and the internal resistance of the power battery modules to obtain a dimension-reduced m-dimension characteristic matrix;
the prediction module is used for constructing a neural network model, taking the m-dimensional feature matrix as input, taking the discharge capacity of the retired power battery module as output, training the neural network model to obtain a capacity prediction model, and sorting the retired power battery module according to the predicted capacity, the terminal voltage and the internal resistance of the retired power battery module after carrying out capacity prediction on the retired power battery module based on the capacity prediction model.
9. An electronic device, comprising: a processor and a memory;
the memory has stored thereon a computer readable program executable by the processor;
the processor, when executing the computer readable program, performs the steps of the retired battery module sorting method according to any one of claims 1-7.
10. A computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps in the retired battery module sorting method of any one of claims 1-7.
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