A method and system for recycling a power battery pack

Abstract

The application provides a power battery pack recycling method and system, the method comprising: when a power battery pack is detected, inputting the power battery pack into a preset disassembly system, and disassembling the power battery pack into a plurality of corresponding battery components through the preset disassembly system; obtaining factory battery parameters corresponding to the power battery pack in a preset battery database, and constructing a corresponding battery detection model based on the factory parameters according to a preset neural network; detecting actual battery parameters corresponding to the plurality of battery components respectively, and calculating the attenuation rate of each battery component according to the actual battery parameters through the battery detection model, so as to classify and recycle the plurality of battery components according to the attenuation rate. The application can effectively recycle each battery component, and greatly improves the recycling efficiency of the power battery pack.

Description

Technical Field

[0001] This invention relates to the field of new energy electric vehicle technology, and in particular to a method and system for recycling power battery packs. Background Technology

[0002] With the advancement of science and technology and the rapid development of productivity, new energy electric vehicle technology has become increasingly mature and has been widely adopted in people's daily lives, reducing travel costs and greatly facilitating people's lives.

[0003] Among them, the power battery pack is one of the core components of new energy electric vehicles, which is used to provide electrical energy to the drive motor inside the new energy electric vehicle so that the vehicle can move.

[0004] Specifically, the power battery packs inside existing new energy electric vehicles are all made of metallic lithium, and their internal structure is complex, including a variety of materials such as positive electrode, negative electrode, separator, electrolyte and shell, and these materials contain rare metal elements such as cobalt, nickel and lithium, which have high economic value.

[0005] Therefore, it is necessary to provide a method that can quickly and effectively recycle the various components inside a power battery pack. Summary of the Invention

[0006] Therefore, the purpose of this invention is to provide a method and system for recycling power battery packs, so as to provide a method for recycling power battery packs quickly and effectively.

[0007] The first aspect of the present invention proposes:

[0008] A method for recycling a power battery pack, wherein the method includes:

[0009] When a power battery pack is detected, the power battery pack is input into a preset disassembly system, and the power battery pack is disassembled into several corresponding battery components by the preset disassembly system.

[0010] Obtain the factory battery parameters corresponding to the power battery pack from the preset battery database, and construct the corresponding battery detection model based on the factory parameters using the preset neural network;

[0011] The actual battery parameters corresponding to several battery components are detected, and the degradation rate of each battery component is calculated based on the actual battery parameters using the battery detection model, so as to classify and recycle several battery components according to the degradation rate.

[0012] The beneficial effects of this invention are as follows: By splitting the input power battery pack into several corresponding battery components in real time, and further constructing a battery detection model adapted to the current type of power battery pack based on the obtained factory battery parameters, the actual battery parameters corresponding to the above-mentioned battery components are simultaneously acquired. Based on this, the degradation rate of each battery component can be calculated through the current battery detection model, thereby enabling specialized recycling based on the magnitude of the degradation rate. That is, battery components with the same or similar degradation rates are classified and recycled together, facilitating subsequent reuse, thus significantly improving the recycling efficiency of the power battery pack and enhancing the user experience.

[0013] Furthermore, the step of constructing a corresponding battery detection model based on a preset neural network according to the factory parameters includes:

[0014] When the factory battery parameters are obtained, the factory battery parameters are preprocessed, and the preprocessed factory battery parameters are used to construct the corresponding target dataset.

[0015] The target dataset is split into a corresponding validation set and a training set based on preset rules, so as to construct the battery detection model through the validation set, the training set and the preset neural network.

[0016] Furthermore, the step of constructing the battery detection model using the validation set, the training set, and the preset neural network includes:

[0017] The training set is input into the parsing layer of a preset CNN network so that the parsing layer can parse the training set into the corresponding training code.

[0018] The training code is input into the encoding layer of the CNN network so that the encoding layer converts the training code into corresponding training feature values.

[0019] The training feature values ​​are input into the learning layer of the CNN network to construct the battery detection model accordingly.

[0020] Furthermore, the step of inputting the training feature values ​​into the learning layer of the CNN network to construct the battery detection model includes:

[0021] The training feature values ​​are serialized using a preset DTW algorithm to generate several corresponding feature sequences, and feature factors contained in several of the feature sequences are extracted.

[0022] Several feature factors are embedded into the learning network of the learning layer, and the original network parameters in the learning layer are adjusted to target network parameters through the several feature factors, so as to construct the battery detection model based on the target network parameters.

[0023] Furthermore, the step of calculating the degradation rate of each battery component based on the actual battery parameters using the battery detection model includes:

[0024] When a number of battery components are obtained, the natural degradation coefficient corresponding to each battery component is detected one by one, and the target difference between the actual battery parameters of each battery component and the factory battery parameters is calculated.

[0025] The attenuation rate corresponding to each of the battery components is calculated one by one based on the natural attenuation coefficient and the target difference, and the natural attenuation coefficient is unique.

[0026] Furthermore, the method also includes:

[0027] Extract the target attenuation value contained in each attenuation rate, and compare each target attenuation value with a preset classification table. The preset classification table contains several attenuation levels, and each attenuation level corresponds to an attenuation threshold.

[0028] Each battery component is assigned a target attenuation level based on the target attenuation value and the attenuation threshold, and the battery components are then classified and recycled according to the target attenuation level.

[0029] Furthermore, the step of classifying and recycling the battery components according to the target degradation level includes:

[0030] Several different recycling strategies are constructed, and a mapping relationship is established between each recycling strategy and each target degradation level, so as to recycle each battery component accordingly through each recycling strategy.

[0031] The second aspect of the present invention proposes:

[0032] A power battery pack recycling system, wherein the system comprises:

[0033] The detection module is used to input the power battery pack into a preset disassembly system when the power battery pack is detected, and to disassemble the power battery pack into several corresponding battery components through the preset disassembly system.

[0034] The acquisition module is used to acquire the factory battery parameters corresponding to the power battery pack from the preset battery database, and construct the corresponding battery detection model based on the factory parameters using the preset neural network.

[0035] The calculation module is used to detect the actual battery parameters corresponding to the several battery components respectively, and calculate the degradation rate of each battery component based on the actual battery parameters through the battery detection model, so as to classify and recycle the several battery components according to the degradation rate.

[0036] Furthermore, the acquisition module is specifically used for:

[0037] When the factory battery parameters are obtained, the factory battery parameters are preprocessed, and the preprocessed factory battery parameters are used to construct the corresponding target dataset.

[0038] The target dataset is split into a corresponding validation set and a training set based on preset rules, so as to construct the battery detection model through the validation set, the training set and the preset neural network.

[0039] Furthermore, the acquisition module is specifically used for:

[0040] The training set is input into the parsing layer of a preset CNN network so that the parsing layer can parse the training set into the corresponding training code.

[0041] The training code is input into the encoding layer of the CNN network so that the encoding layer converts the training code into corresponding training feature values.

[0042] The training feature values ​​are input into the learning layer of the CNN network to construct the battery detection model accordingly.

[0043] Furthermore, the acquisition module is specifically used for:

[0044] The training feature values ​​are serialized using a preset DTW algorithm to generate several corresponding feature sequences, and feature factors contained in several of the feature sequences are extracted.

[0045] Several feature factors are embedded into the learning network of the learning layer, and the original network parameters in the learning layer are adjusted to target network parameters through the several feature factors, so as to construct the battery detection model based on the target network parameters.

[0046] Furthermore, the calculation module is specifically used for:

[0047] When a number of battery components are obtained, the natural degradation coefficient corresponding to each battery component is detected one by one, and the target difference between the actual battery parameters of each battery component and the factory battery parameters is calculated.

[0048] The attenuation rate corresponding to each of the battery components is calculated one by one based on the natural attenuation coefficient and the target difference, and the natural attenuation coefficient is unique.

[0049] Furthermore, the power battery pack recycling system also includes a processing module, which is specifically used for:

[0050] Extract the target attenuation value contained in each attenuation rate, and compare each target attenuation value with a preset classification table. The preset classification table contains several attenuation levels, and each attenuation level corresponds to an attenuation threshold.

[0051] Each battery component is assigned a target attenuation level based on the target attenuation value and the attenuation threshold, and the battery components are then classified and recycled according to the target attenuation level.

[0052] Furthermore, the processing module is specifically used for:

[0053] Several different recycling strategies are constructed, and a mapping relationship is established between each recycling strategy and each target degradation level, so as to recycle each battery component accordingly through each recycling strategy.

[0054] The third aspect of the present invention proposes:

[0055] A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power battery pack recycling method described above.

[0056] The fourth aspect of the present invention proposes:

[0057] A readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the power battery pack recycling method as described above.

[0058] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0059] Figure 1 A flowchart of a power battery pack recycling method provided in the first embodiment of the present invention;

[0060] Figure 2 This is a structural block diagram of a power battery pack recycling system provided in the sixth embodiment of the present invention.

[0061] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0062] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0063] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0065] Please see Figure 1 The image shows a power battery pack recycling method provided in the first embodiment of the present invention. The power battery pack recycling method provided in this embodiment can significantly improve the recycling efficiency of the power battery pack and at the same time improve the user experience.

[0066] Specifically, the power battery pack recycling method provided in this embodiment includes the following steps:

[0067] Step S10: When the power battery pack is detected, the power battery pack is input into the preset disassembly system, and the power battery pack is disassembled into several corresponding battery components through the preset disassembly system.

[0068] Step S20: Obtain the factory battery parameters corresponding to the power battery pack from the preset battery database, and construct the corresponding battery detection model based on the factory parameters using the preset neural network.

[0069] Step S30: Detect the actual battery parameters corresponding to the various battery components, and calculate the degradation rate of each battery component based on the actual battery parameters using the battery detection model, so as to classify and recycle the various battery components according to the degradation rate.

[0070] Specifically, in this embodiment, it should first be noted that the pre-set disassembly system can divide the power battery pack received in real time into several battery components. Specifically, these several battery components include battery modules, cells, electronic control systems, and temperature control systems. Furthermore, based on the factory battery parameters of the current power battery pack and the pre-set CNN network, the required battery detection model can be constructed in real time.

[0071] Furthermore, the actual battery parameters corresponding to the aforementioned battery components are input into the current battery detection model, thereby enabling the output of the degradation rate corresponding to each battery component. Based on this degradation rate, the classification and recycling of the current battery components can be completed.

[0072] Second Embodiment

[0073] Specifically, in this embodiment, it should be noted that the steps of constructing the corresponding battery detection model based on the factory parameters using a preset neural network include:

[0074] When the factory battery parameters are obtained, the factory battery parameters are preprocessed, and the preprocessed factory battery parameters are used to construct the corresponding target dataset.

[0075] The target dataset is split into a corresponding validation set and a training set based on preset rules, so as to construct the battery detection model through the validation set, the training set and the preset neural network.

[0076] Specifically, in this embodiment, it should be noted that the above method can perform preprocessing such as filtering and noise reduction on the real-time acquired factory battery parameters, and at the same time, can generate the required target dataset.

[0077] Furthermore, the current target dataset is split into a corresponding training set and a validation set in a 7:3 ratio. Based on this, the aforementioned preset neural network is trained using the current training set and validation set.

[0078] Specifically, in this embodiment, it should also be noted that the steps of constructing the battery detection model using the validation set, the training set, and the preset neural network include:

[0079] The training set is input into the parsing layer of a preset CNN network so that the parsing layer can parse the training set into the corresponding training code.

[0080] The training code is input into the encoding layer of the CNN network so that the encoding layer converts the training code into corresponding training feature values.

[0081] The training feature values ​​are input into the learning layer of the CNN network to construct the battery detection model accordingly.

[0082] Specifically, in this embodiment, it should also be noted that after obtaining the required training set through the above method, the current training set is further input into the pre-set CNN network, specifically into the encoding layer of the current CNN network, to encode the corresponding training feature values.

[0083] Furthermore, several training feature values ​​are input into the learning layer of the current CNN network to learn the aforementioned battery detection model.

[0084] Additionally, in this embodiment, it should be noted that the step of inputting the training feature values ​​into the learning layer of the CNN network to construct the battery detection model includes:

[0085] The training feature values ​​are serialized using a preset DTW algorithm to generate several corresponding feature sequences, and feature factors contained in several of the feature sequences are extracted.

[0086] Several feature factors are embedded into the learning network of the learning layer, and the original network parameters in the learning layer are adjusted to target network parameters through the several feature factors, so as to construct the battery detection model based on the target network parameters.

[0087] In addition, in this embodiment, it should be noted that the above-mentioned training feature values ​​can be converted into corresponding learning factors in the above manner. Based on this, the network parameters in the learning network are adaptively adjusted by the above-mentioned learning factors, so as to adaptively learn the above-mentioned battery detection model.

[0088] Third Embodiment

[0089] In addition, in this embodiment, it should be noted that the step of calculating the degradation rate of each battery component based on the actual battery parameters using the battery detection model includes:

[0090] When a number of battery components are obtained, the natural degradation coefficient corresponding to each battery component is detected one by one, and the target difference between the actual battery parameters of each battery component and the factory battery parameters is calculated.

[0091] The attenuation rate corresponding to each of the battery components is calculated one by one based on the natural attenuation coefficient and the target difference, and the natural attenuation coefficient is unique.

[0092] In addition, it should be noted in this embodiment that the natural decay coefficient corresponding to each battery component can be extracted in real time through the above method. Furthermore, by simply multiplying the current natural decay coefficient with the target difference calculated in real time, the decay rate can be calculated simply and quickly for subsequent processing.

[0093] Fourth embodiment

[0094] In this embodiment, it should be noted that the method further includes:

[0095] Extract the target attenuation value contained in each attenuation rate, and compare each target attenuation value with a preset classification table. The preset classification table contains several attenuation levels, and each attenuation level corresponds to an attenuation threshold.

[0096] Each battery component is assigned a target attenuation level based on the target attenuation value and the attenuation threshold, and the battery components are then classified and recycled according to the target attenuation level.

[0097] In this embodiment, it should be noted that the degradation value corresponding to each battery component can be obtained through the above method, and at the same time, the corresponding target degradation level can be determined in the preset classification table.

[0098] Furthermore, several battery components are recycled based on the magnitude of each target degradation level.

[0099] In this embodiment, it should be noted that the step of classifying and recycling the battery components according to the target degradation level includes:

[0100] Several different recycling strategies are constructed, and a mapping relationship is established between each recycling strategy and each target degradation level, so as to recycle each battery component accordingly through each recycling strategy.

[0101] In this embodiment, it should be noted that several recovery strategies are pre-set, and a one-to-one correspondence is established between each recovery strategy and each target decay level.

[0102] Furthermore, the corresponding recycling strategy can be determined simply by considering the target degradation level of each battery component.

[0103] Please see Figure 2 The sixth embodiment of the present invention provides:

[0104] A power battery pack recycling system, wherein the system comprises:

[0105] The detection module is used to input the power battery pack into a preset disassembly system when the power battery pack is detected, and to disassemble the power battery pack into several corresponding battery components through the preset disassembly system.

[0106] The acquisition module is used to acquire the factory battery parameters corresponding to the power battery pack from the preset battery database, and construct the corresponding battery detection model based on the factory parameters using the preset neural network.

[0107] The calculation module is used to detect the actual battery parameters corresponding to the several battery components respectively, and calculate the degradation rate of each battery component based on the actual battery parameters through the battery detection model, so as to classify and recycle the several battery components according to the degradation rate.

[0108] In the aforementioned power battery pack recycling system, the acquisition module is specifically used for:

[0109] When the factory battery parameters are obtained, the factory battery parameters are preprocessed, and the preprocessed factory battery parameters are used to construct the corresponding target dataset.

[0110] The target dataset is split into a corresponding validation set and a training set based on preset rules, so as to construct the battery detection model through the validation set, the training set and the preset neural network.

[0111] In the aforementioned power battery pack recycling system, the acquisition module is specifically used for:

[0112] The training set is input into the parsing layer of a preset CNN network so that the parsing layer can parse the training set into the corresponding training code.

[0113] The training code is input into the encoding layer of the CNN network so that the encoding layer converts the training code into corresponding training feature values.

[0114] The training feature values ​​are input into the learning layer of the CNN network to construct the battery detection model accordingly.

[0115] In the aforementioned power battery pack recycling system, the acquisition module is specifically used for:

[0116] The training feature values ​​are serialized using a preset DTW algorithm to generate several corresponding feature sequences, and feature factors contained in several of the feature sequences are extracted.

[0117] Several feature factors are embedded into the learning network of the learning layer, and the original network parameters in the learning layer are adjusted to target network parameters through the several feature factors, so as to construct the battery detection model based on the target network parameters.

[0118] In the aforementioned power battery pack recycling system, the calculation module is specifically used for:

[0119] When a number of battery components are obtained, the natural degradation coefficient corresponding to each battery component is detected one by one, and the target difference between the actual battery parameters of each battery component and the factory battery parameters is calculated.

[0120] The attenuation rate corresponding to each of the battery components is calculated one by one based on the natural attenuation coefficient and the target difference, and the natural attenuation coefficient is unique.

[0121] The aforementioned power battery pack recycling system further includes a processing module, which is specifically used for:

[0122] Extract the target attenuation value contained in each attenuation rate, and compare each target attenuation value with a preset classification table. The preset classification table contains several attenuation levels, and each attenuation level corresponds to an attenuation threshold.

[0123] Each battery component is assigned a target attenuation level based on the target attenuation value and the attenuation threshold, and the battery components are then classified and recycled according to the target attenuation level.

[0124] In the aforementioned power battery pack recycling system, the processing module is specifically used for:

[0125] Several different recycling strategies are constructed, and a mapping relationship is established between each recycling strategy and each target degradation level, so as to recycle each battery component accordingly through each recycling strategy.

[0126] The seventh embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the power battery pack recycling method provided in the above embodiments.

[0127] The eighth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the power battery pack recycling method provided in the above embodiments.

[0128] In summary, the power battery pack recycling method and system provided by the above embodiments of the present invention can significantly improve the recycling efficiency of power battery packs and enhance the user experience.

[0129] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0130] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0131] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0132] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0133] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0134] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for recycling a power battery pack, characterized in that, The method includes: When a power battery pack is detected, the power battery pack is input into a preset disassembly system, and the power battery pack is disassembled into several corresponding battery components by the preset disassembly system. Obtain the factory battery parameters corresponding to the power battery pack from the preset battery database, and construct the corresponding battery detection model based on the factory battery parameters using the preset neural network; The actual battery parameters corresponding to several battery components are detected, and the degradation rate of each battery component is calculated based on the actual battery parameters using the battery detection model, so as to classify and recycle several battery components according to the degradation rate. The method further includes: Extract the target attenuation value contained in each attenuation rate, and compare each target attenuation value with a preset classification table. The preset classification table contains several attenuation levels, and each attenuation level corresponds to an attenuation threshold. Each battery component is assigned a target attenuation level based on the target attenuation value and the attenuation threshold, and the battery components are then classified and recycled according to the target attenuation level. The step of classifying and recycling the battery components according to the target degradation level includes: Several different recycling strategies are constructed, and a mapping relationship is established between each recycling strategy and each target degradation level, so as to recycle each battery component accordingly through each recycling strategy.

2. The method for recycling power battery packs according to claim 1, characterized in that: The step of constructing a corresponding battery detection model based on a preset neural network according to the factory battery parameters includes: When the factory battery parameters are obtained, the factory battery parameters are preprocessed, and a corresponding target dataset is constructed based on the preprocessed factory battery parameters. The target dataset is split into a corresponding validation set and a training set based on preset rules, so as to construct the battery detection model through the validation set, the training set and the preset neural network.

3. The method for recycling power battery packs according to claim 2, characterized in that: The step of constructing the battery detection model using the validation set, the training set, and the preset neural network includes: The training set is input into the parsing layer of a preset CNN network so that the parsing layer can parse the training set into the corresponding training code. The training code is input into the encoding layer of the CNN network so that the encoding layer converts the training code into corresponding training feature values. The training feature values ​​are input into the learning layer of the CNN network to construct the battery detection model accordingly.

4. The method for recycling a power battery pack according to claim 3, characterized in that: The step of inputting the training feature values ​​into the learning layer of the CNN network to construct the battery detection model includes: The training feature values ​​are serialized using a preset DTW algorithm to generate several corresponding feature sequences, and feature factors contained in several of the feature sequences are extracted. Several feature factors are embedded into the learning network of the learning layer, and the original network parameters in the learning layer are adjusted to target network parameters through the several feature factors, so as to construct the battery detection model based on the target network parameters.

5. The method for recycling a power battery pack according to claim 1, characterized in that: The step of calculating the degradation rate of each battery component based on the actual battery parameters using the battery detection model includes: When a number of battery components are obtained, the natural degradation coefficient corresponding to each battery component is detected one by one, and the target difference between the actual battery parameters of each battery component and the factory battery parameters is calculated. The attenuation rate corresponding to each of the battery components is calculated one by one based on the natural attenuation coefficient and the target difference, and the natural attenuation coefficient is unique.

6. A power battery pack recycling system, characterized in that, The system, applicable to the method of any one of claims 1 to 5, comprises: The detection module is used to input the power battery pack into a preset disassembly system when the power battery pack is detected, and to disassemble the power battery pack into several corresponding battery components through the preset disassembly system. The acquisition module is used to acquire the factory battery parameters corresponding to the power battery pack from the preset battery database, and to construct the corresponding battery detection model based on the factory battery parameters using the preset neural network. The calculation module is used to detect the actual battery parameters corresponding to the several battery components respectively, and calculate the degradation rate of each battery component based on the actual battery parameters through the battery detection model, so as to classify and recycle the several battery components according to the degradation rate.

7. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the power battery pack recycling method as described in any one of claims 1 to 5.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the power battery pack recycling method as described in any one of claims 1 to 5.

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