Battery cell expansibility data prediction method and device

Abstract

The invention discloses a cell expansibility data prediction method and device, and relates to the technical field of cell testing. The method comprises the following steps: acquiring first expansive force data of a specific type of battery cell in a preset health range; according to the first expansive force data and health state data included in the preset health range, constructing a first linear regression equation for the battery cell of the specific model; and on the basis of the first linear regression equation corresponding to the battery cell of the specific model, predicting full life cycle expansion force data corresponding to the battery cell of the specific model so as to provide design reference data for a battery module using the battery cell of the specific model. According to the embodiment, the acquisition process of the full-life-cycle expansion force data can be simplified, and the acquisition time is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of battery cell testing, and in particular to a method and device for predicting battery cell expansion force data. Background Art

[0002] During the working process of the battery cell, due to the long-term charge and discharge cycle, chemical reactions occur inside the battery cell, repeated lithium removal and insertion, gas generation, and lithium precipitation, which will cause the battery cell to expand to a certain extent. The expansion of the battery cell will not only affect its own performance and shorten its life, but also affect the structural strength of the battery module, causing greater safety hazards.

[0003] Therefore, when developing battery modules, it is necessary to obtain the expansion force data of the battery cell throughout its life cycle to provide a basis for battery module size design, material performance selection, etc.

[0004] At present, the expansion force data of battery cells throughout their life cycle is mostly obtained through repeated charge and discharge cycle tests, which is a long process and takes a lot of time. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for predicting expansion force data of a battery cell, which can simplify the process of acquiring expansion force data throughout the life cycle and shorten the acquisition time.

[0006] To achieve the above object, according to one aspect of an embodiment of the present invention, a method for predicting cell expansion force data is provided, comprising:

[0007] Collect the first expansion force data of a specific model of battery cells within a preset healthy range;

[0008] Constructing a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range;

[0009] Based on the first linear regression equation corresponding to the specific model of battery cell, the full life cycle expansion force data corresponding to the specific model of battery cell is predicted to provide design reference data for a battery module using the specific model of battery cell.

[0010] Optionally, the above method further includes:

[0011] Verify the accuracy of the first linear regression equation;

[0012] In the event that the accuracy verification result fails, the preset healthy range is re-determined.

[0013] Optionally, the accuracy verification of the first linear regression equation includes:

[0014] Collecting the second expansion force data of a specific model of battery cell in a verified healthy range; wherein the verified healthy range does not overlap with the preset healthy range;

[0015] Constructing a second linear regression equation according to the second expansion force data and the health status data included in the verified health range;

[0016] Calculating the similarity between the first linear regression equation and the second linear regression equation;

[0017] In response to the similarity being greater than the preset similarity threshold, the accuracy verification is passed; in response to the similarity being less than the preset similarity threshold, the accuracy verification is failed.

[0018] Optionally, the calculating the similarity between the first linear regression equation and the second linear regression equation includes:

[0019] The Pearson correlation coefficient between the first linear regression equation and the second linear regression equation is calculated and used as the similarity between the first linear regression equation and the second linear regression equation.

[0020] Optionally, the preset health range is smaller than the health range for the entire life cycle.

[0021] Optionally, the above preset healthy range is 90% SOH to 100% SOH.

[0022] Optionally, the collecting of the first expansion force data of the battery cell of a specific model within a preset healthy range includes:

[0023] Carry out charge and discharge cycle test on the above specific battery models within the preset healthy range;

[0024] During the charge and discharge cycle test, the first expansion force data of the above-mentioned specific model of battery cells within the above-mentioned preset healthy range is collected.

[0025] To achieve the above object, according to another aspect of an embodiment of the present invention, a device for predicting battery cell expansion force data is provided, comprising:

[0026] A collection module, used to collect first expansion force data of a specific type of battery cell within a preset healthy range;

[0027] A construction module, configured to construct a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range;

[0028] A prediction module is used to predict the full life cycle expansion force data corresponding to the above-mentioned specific model of battery cells based on the above-mentioned first linear regression equation corresponding to the above-mentioned specific model of battery cells, so as to provide design reference data for a battery module using the above-mentioned specific model of battery cells.

[0029] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device for predicting battery cell expansion force data is provided.

[0030] An electronic device for predicting battery cell expansion force data according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement a method for predicting battery cell expansion force data according to an embodiment of the present invention.

[0031] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided.

[0032] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements a method for predicting battery cell expansion force data according to an embodiment of the present invention.

[0033] An embodiment of the above invention has the following advantages or beneficial effects: by collecting the first expansion force data of a specific model of battery cells within a preset health range; constructing a first linear regression equation for the above specific model of battery cells according to the above first expansion force data and the health status data included in the above preset health range; based on the above first linear regression equation corresponding to the above specific model of battery cells, predicting the full life cycle expansion force data corresponding to the above specific model of battery cells, so as to provide a technical solution for providing design reference data for a battery module using the above specific model of battery cells, the first expansion force data within a preset health range can be obtained through a charge and discharge cycle test with a small number of cycles, and then the full life cycle expansion force data can be predicted through the limited first expansion force data, without performing a full life cycle charge and discharge cycle test on the specific model of battery cells, thereby simplifying the process of obtaining the full life cycle expansion force data and shortening the acquisition time.

[0034] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0036] Figure 1 is a schematic flow chart of a method for predicting battery cell expansion force data according to an embodiment of the present invention;

[0037] Figure 2 is a schematic diagram of a curve of health status data and expansion force data for the entire life cycle according to an embodiment of the present invention;

[0038] Figure 3 is a schematic flow chart of a method for predicting battery cell expansion force data according to another embodiment of the present invention;

[0039] Figure 4 is a schematic diagram of a first linear regression curve and a second linear regression curve according to an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of main modules of a battery cell expansion force data prediction device according to an embodiment of the present invention;

[0041] Figure 6 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0042] Figure 7 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0044] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0045] Figure 1 FIG. 1 is a schematic diagram of the main steps of a method for predicting battery cell expansion force data according to an embodiment of the present invention. Figure 1 As shown, the method for predicting the cell expansion force data of the embodiment of the present invention mainly includes the following steps S101 to S103:

[0046] Step S101, collecting first expansion force data of a specific type of battery cell within a preset healthy range;

[0047] First, the relationship between the state of health (SOH) data and the expansion force data should be determined. During the charge and discharge cycle test, it was found that the state of health data and the expansion force data were basically linearly negatively correlated.

[0048] Therefore, when predicting the full life cycle expansion force data of a specific model of battery cell, the first expansion force data within a preset health range is collected, that is, each health status data within the preset health range and its corresponding first expansion force data.

[0049] Among them, in order to avoid the problems of large amount of data required to be obtained, long process and time, etc. when obtaining the health status data and corresponding expansion force data of the whole life cycle through charge and discharge cycle test, when the relationship between the health status data and the expansion force data has been determined, the first expansion force data smaller than the preset health range can be obtained only through charge and discharge cycle test with fewer cycles.

[0050] It should be noted that the above-mentioned preset health range is smaller than the health range for the entire life cycle.

[0051] Preferably, the above preset healthy range is 90% SOH to 100% SOH.

[0052] Step S102: constructing a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range;

[0053] The preset health range includes several health status data, and each health status data corresponds to a first expansion force data. As an example, when the preset health range is 90% SOH to 100% SOH, the health status data can be 90% SOH, 92% SOH, 94% SOH, 96% SOH, 98% SOH and 100% SOH, wherein the first expansion force data corresponding to 90% SOH is 13.68 kN, the first expansion force data corresponding to 92% SOH is 11.92 kN, the first expansion force data corresponding to 94% SOH is 10.16 kN, the first expansion force data corresponding to 96% SOH is 8.39 kN, the first expansion force data corresponding to 98% SOH is 6.63 kN, and the first expansion force data corresponding to 100% SOH is 4.87 kN. A first linear regression equation is constructed based on the above health status data and the corresponding first expansion force data.

[0054] The first linear regression equation was constructed using the health status data as the independent variable and the first expansion force data as the dependent variable.

[0055] Step S103: Based on the first linear regression equation corresponding to the specific type of battery cell, predict the full life cycle expansion force data corresponding to the specific type of battery cell, so as to provide design reference data for a battery module using the specific type of battery cell.

[0056] The health status data of the entire life cycle is substituted into the first linear regression equation to calculate the expansion force data of the entire life cycle corresponding to the specific model of battery cell.

[0057] Furthermore, the above-mentioned collecting of the first expansion force data of the specific model of the battery cell within the preset health range includes: performing a charge and discharge cycle test on the above-mentioned specific model of the battery cell within the preset health range; during the charge and discharge cycle test, collecting the first expansion force data of the above-mentioned specific model of the battery cell within the above-mentioned preset health range.

[0058] The embodiment of the present invention only needs to predict the first expansion force data within the healthy range to predict the full life cycle expansion force data. Therefore, there is no need to perform a full life cycle charge and discharge cycle test on a specific model of battery cell. It is only necessary to perform a charge and discharge cycle test within the preset healthy range to collect the first expansion force data within the preset healthy range.

[0059] As an example, when the preset health range is 90% SOH to 100% SOH, there is no need to conduct full life cycle charge and discharge cycle testing for a specific model of battery cell. Instead, it is necessary to conduct charge and discharge cycle testing within the preset health range of 100% SOH to 90% SOH, and collect the first expansion force data corresponding to 90% SOH to 100% SOH, and then predict the full life cycle expansion force data, thereby greatly shortening the process of obtaining the full life cycle expansion force data.

[0060] In an optional embodiment, the above method further includes:

[0061] Verify the accuracy of the first linear regression equation;

[0062] In the event that the accuracy verification result fails, the preset healthy range is re-determined.

[0063] In order to avoid the problem of inaccurate prediction caused by a large deviation between the full life cycle expansion force data predicted by the first linear regression equation and the actual full life cycle expansion force data, the first linear regression equation can be verified for accuracy. If the accuracy verification passes, it proves that the full life cycle expansion force data with a small deviation can be predicted by the first linear regression equation, and the first linear regression equation can continue to be used to predict the full life cycle expansion force data; if the accuracy verification fails, it proves that the full life cycle expansion force data predicted by the first linear regression equation has a large deviation from the actual full life cycle expansion force data, and it is necessary to redefine the preset healthy range, repeat steps S101 to S102 and the step of performing accuracy verification until the accuracy verification result passes.

[0064] It is understandable that the re-determined preset health range should be larger than the preset health range determined last time to reduce data deviation caused by a smaller preset health range.

[0065] In an optional embodiment, the accuracy verification of the first linear regression equation includes:

[0066] Collecting the second expansion force data of a specific model of battery cell in a verified healthy range; wherein the verified healthy range does not overlap with the preset healthy range;

[0067] Constructing a second linear regression equation according to the second expansion force data and the health status data included in the verified health range;

[0068] Calculating the similarity between the first linear regression equation and the second linear regression equation;

[0069] In response to the similarity being greater than the preset similarity threshold, the accuracy verification is passed; in response to the similarity being less than the preset similarity threshold, the accuracy verification is failed.

[0070] In the process of accuracy verification, it is necessary to collect the second expansion force data of the verification health range during the charge and discharge cycle test of a specific model of battery cells. In order to verify the accuracy, verify that the health range does not overlap with the preset health range. As an example, when the preset health range is 90% SOH to 100% SOH, the verification health range can be 80% SOH to 89% SOH, so that the charge and discharge cycle test can be carried out within the health range of 100% SOH to 80% SOH, and the second expansion force data corresponding to 80% SOH to 89% SOH is collected to construct a second linear regression equation, and then the accuracy of the first linear regression equation is verified, and there is no need to conduct a full life cycle charge and discharge cycle test.

[0071] There are many methods for comparing the similarity between the first linear regression equation and the second linear regression equation, such as comparing the absolute value of the difference between the slopes and the absolute value of the difference between the intercepts of the two linear regression equations. The smaller the absolute value, the higher the similarity of the two linear regression equations; calculating the mean square error of the dependent variable of the two linear regression equations when the two linear regression equations have the same independent variable. The smaller the mean square error of the dependent variable, the higher the similarity of the two linear regression equations; comparing the similarity of the corresponding linear regression curves of the two, etc., which are not limited here one by one.

[0072] It is understandable that there are different preset similarity thresholds for different similarity comparison methods, and the preset similarity threshold can be set according to specific circumstances.

[0073] In an optional embodiment, the calculating the similarity between the first linear regression equation and the second linear regression equation includes:

[0074] The Pearson correlation coefficient between the first linear regression equation and the second linear regression equation is calculated and used as the similarity between the first linear regression equation and the second linear regression equation.

[0075] Specifically, for the first linear regression equation and the second linear regression equation, the values ​​of the expansion forces corresponding to the two when the SOH values ​​are the same are calculated, and the Pearson correlation coefficient r is calculated by the following formula:

[0076]

[0077] Among them, x i is the i-th SOH, is the average value of SOH, y 1i is the first linear regression equation i The corresponding expansion force; y 2i is the x in the second linear regression equation i The corresponding expansion force.

[0078] When r is closer to 1, it means that the linear relationship between the first linear regression equation and the second linear regression equation is closer and the similarity is higher.

[0079] The following is a further description of the method for predicting battery cell expansion force data according to the present invention through a specific embodiment.

[0080] Before predicting the cell expansion force data, it is first found that the expansion force data is linearly correlated with SOH. The process can include:

[0081] Two 1A cells were charged and discharged within the healthy range of 100% SOH to 60% SOH, and the expansion force data were collected and plotted. Figure 2 The SOH-expansion force curve with SOH as the horizontal axis and the expansion force data as the vertical axis shows that during the entire life cycle of the two 1A battery cells, the expansion force basically shows a linear growth trend. Therefore, it is concluded that the expansion force data is basically linearly correlated with SOH.

[0082] It is understandable that during the use of the battery cell, once the SOH of the battery cell drops below 60%, it indicates that the performance of the battery cell has significantly declined, which may affect the user experience of the battery cell. Therefore, when the SOH of the battery cell drops to 60%, the battery cell needs to be replaced or maintained; therefore, in the embodiment of the present invention, 60% SOH~100% SOH is used as the full life cycle of the battery cell.

[0083] Based on the above conclusions, if Figure 3 As shown, the method for predicting the battery cell expansion force data of the present invention may include the following steps S301 to S306:

[0084] Step S301, presetting a preset healthy range to 90% SOH to 100% SOH; verifying that the healthy range is 80% SOH to 89% SOH, and performing a charge-discharge cycle test on a battery cell of model 1A within a healthy range of 100% SOH to 80% SOH;

[0085] Step S302, respectively collecting first expansion force data corresponding to 90% SOH to 100% SOH and second expansion force data corresponding to 80% SOH to 89% SOH;

[0086] Step S303, constructing a first linear regression equation with SOH as the independent variable and expansion force as the dependent variable according to the first expansion force data corresponding to 90% SOH to 100% SOH; constructing a second linear regression equation with SOH as the independent variable and expansion force as the dependent variable according to the second expansion force data corresponding to 80% SOH to 89% SOH;

[0087] The first linear regression curve corresponding to the first linear regression equation and the second linear regression curve corresponding to the second linear regression equation are as follows: Figure 4 shown.

[0088] Step S304, calculating the Pearson correlation coefficient between the first linear regression equation and the second linear regression equation at SOH as the similarity between the first linear regression equation and the second linear regression equation;

[0089] Step S305, when the similarity is greater than the preset similarity threshold of 0.98, the accuracy verification is passed, and the expansion force data of the 1A battery cell at 60% SOH to 100% SOH is predicted based on the first linear regression equation;

[0090] Step S306, when the similarity is less than the preset similarity threshold of 0.98, the accuracy verification fails, and the preset health range is re-determined as 85% SOH~100% SOH, and steps SS01~step S306 are repeated until the accuracy verification of the first linear regression equation passes.

[0091] According to the method for predicting the expansion force data of a battery cell of an embodiment of the present invention, first expansion force data of a battery cell of a specific model within a preset health range is collected; a first linear regression equation is constructed for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range; based on the first linear regression equation corresponding to the battery cell of the specific model, the full life cycle expansion force data corresponding to the battery cell of the specific model is predicted, so as to provide a technical solution for providing design reference data for a battery module using the battery cell of the specific model. The first expansion force data within a preset health range can be obtained through a charge and discharge cycle test with a small number of cycles, and then the full life cycle expansion force data can be predicted through the limited first expansion force data. There is no need to perform a full life cycle charge and discharge cycle test on the battery cell of the specific model, which simplifies the process of obtaining the full life cycle expansion force data and shortens the acquisition time.

[0092] Figure 5 Schematic diagram of the main modules of the battery cell expansion force data prediction device according to an embodiment of the present invention. Figure 5 As shown, the battery cell expansion force data prediction device 500 according to the embodiment of the present invention includes:

[0093] The collection module 501 is used to collect the first expansion force data of a specific type of battery cell within a preset healthy range;

[0094] A construction module 502 is used to construct a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range;

[0095] The prediction module 503 is used to predict the full life cycle expansion force data corresponding to the above-mentioned specific model of battery cells based on the above-mentioned first linear regression equation corresponding to the above-mentioned specific model of battery cells, so as to provide design reference data for the battery module using the above-mentioned specific model of battery cells.

[0096] In an optional embodiment of the present invention, the battery cell expansion force data prediction device 500 further includes: a verification module, which is used to verify the accuracy of the above-mentioned first linear regression equation; if the accuracy verification result fails, redetermine the preset healthy range.

[0097] In an optional embodiment of the present invention, the verification module is further used to: collect second expansion force data of a specific model of battery cells within a verified health range; wherein the verified health range does not overlap with the preset health range; construct a second linear regression equation based on the second expansion force data and the health status data included in the verified health range; calculate the similarity between the first linear regression equation and the second linear regression equation; in response to the similarity being greater than a preset similarity threshold, the accuracy verification is passed; in response to the similarity being less than the preset similarity threshold, the accuracy verification is failed.

[0098] In an optional embodiment of the present invention, the verification module is further used to: calculate the Pearson correlation coefficient between the first linear regression equation and the second linear regression equation, and use it as the similarity between the first linear regression equation and the second linear regression equation.

[0099] In an optional embodiment of the present invention, the preset health range is smaller than the health range of the entire life cycle.

[0100] In an optional embodiment of the present invention, the above-mentioned preset healthy range is 90% to 100%.

[0101] In an optional embodiment of the present invention, the acquisition module 501 is also used to: perform a charge and discharge cycle test on the specific model of battery cells within a preset health range; and during the charge and discharge cycle test, collect the first expansion force data of the specific model of battery cells within the preset health range.

[0102] According to the battery cell expansion force data prediction device of the embodiment of the present invention, by collecting the first expansion force data of the battery cell of a specific model within a preset health range; constructing a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range; based on the first linear regression equation corresponding to the battery cell of the specific model, predicting the full life cycle expansion force data corresponding to the battery cell of the specific model, so as to provide a technical solution for design reference data for a battery module using the battery cell of the specific model, the first expansion force data within the preset health range can be obtained through a charge and discharge cycle test with a small number of cycles, and then the full life cycle expansion force data can be predicted through the limited first expansion force data, without performing a full life cycle charge and discharge cycle test on the battery cell of the specific model, thereby simplifying the process of obtaining the full life cycle expansion force data and shortening the acquisition time.

[0103] Figure 6 An exemplary system architecture 600 is shown to which a method or device for predicting battery cell expansion force data according to an embodiment of the present invention may be applied.

[0104] like Figure 6As shown, system architecture 600 may include test equipment 601, network 602, and server 603. Network 602 is used to provide a medium for a communication link between test equipment 601 and server 603. Network 602 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0105] The test device 601 may be a device for testing the expansion force of the battery cell, such as including an expansion force sensor, a telescopic component, and a pressure component. During operation, the test device 601 may apply pressure to the battery cell through the cooperation of the telescopic component and the pressure component, and collect the expansion force data of the battery cell through the expansion force sensor.

[0106] The server 603 may be a server for predicting expansion force data, for example, a background management server for predicting the full life cycle expansion force data corresponding to the above-mentioned specific model of battery cells according to the first expansion force data sent by the test device 601. The background management server may construct a first linear regression equation according to the collected first expansion force data of the specific model of battery cells within a preset healthy range, and then predict the full life cycle expansion force data corresponding to the above-mentioned specific model of battery cells, etc.

[0107] It should be noted that the battery cell expansion force data prediction method provided in the embodiment of the present invention is generally executed by the server 603 , and accordingly, the battery cell expansion force data prediction device is generally disposed in the server 603 .

[0108] It should be understood that Figure 6 The number of test devices, networks and servers in the example is only for illustration purposes. Any number of terminal devices, networks and servers may be provided as required.

[0109] Reference below Figure 7 , which shows a schematic diagram of the structure of a computer system 700 of a terminal device suitable for implementing an embodiment of the present invention. Figure 7 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0110] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the computer system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0111] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0112] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0113] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0114] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, they may be described as: a processor includes an acquisition module, a construction module, and a prediction module. The names of these modules do not, in some cases, constitute a limitation on the modules themselves. For example, the acquisition module may also be described as a "module for collecting the first expansion force data of a specific model of battery cells within a preset health range."

[0116] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: collecting first expansion force data of a specific model of battery cells in a preset health range; constructing a first linear regression equation for the above specific model of battery cells based on the above first expansion force data and the health status data included in the above preset health range; based on the above first linear regression equation corresponding to the above specific model of battery cells, predicting the full life cycle expansion force data corresponding to the above specific model of battery cells, so as to provide design reference data for battery modules using the above specific model of battery cells.

[0117] According to the technical solution of the embodiment of the present invention, by collecting the first expansion force data of a specific model of battery cells within a preset health range; constructing a first linear regression equation for the above-mentioned specific model of battery cells according to the above-mentioned first expansion force data and the health status data included in the above-mentioned preset health range; based on the above-mentioned first linear regression equation corresponding to the above-mentioned specific model of battery cells, predicting the full life cycle expansion force data corresponding to the above-mentioned specific model of battery cells, so as to provide a technical solution for design reference data for a battery module using the above-mentioned specific model of battery cells, the first expansion force data within a preset health range can be obtained through a charge and discharge cycle test with a smaller number of cycles, and then the full life cycle expansion force data can be predicted through the limited first expansion force data, without performing a full life cycle charge and discharge cycle test on the specific model of battery cells, thereby simplifying the process of obtaining the full life cycle expansion force data and shortening the acquisition time.

[0118] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting battery cell expansion force data, characterized in that: include: Collect the first expansion force data of a specific model of battery cells within a preset healthy range; constructing a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range; Based on the first linear regression equation corresponding to the specific model of battery cell, the full life cycle expansion force data corresponding to the specific model of battery cell is predicted to provide design reference data for a battery module using the specific model of battery cell.

2. The method for predicting battery cell expansion force data according to claim 1, characterized in that: The method further comprises: Verifying the accuracy of the first linear regression equation; In the event that the accuracy verification result fails, the preset healthy range is re-determined.

3. The method for predicting battery cell expansion force data according to claim 2, characterized in that: The verifying the accuracy of the first linear regression equation includes: Collecting second expansion force data of a specific model of battery cell within a verified healthy range; wherein the verified healthy range does not overlap with the preset healthy range; constructing a second linear regression equation according to the second expansion force data and the health status data included in the verified health range; Calculating the similarity between the first linear regression equation and the second linear regression equation; In response to the similarity being greater than a preset similarity threshold, the accuracy verification is passed; in response to the similarity being less than the preset similarity threshold, the accuracy verification is failed.

4. The method for predicting battery cell expansion force data according to claim 3, characterized in that: The calculating the similarity between the first linear regression equation and the second linear regression equation includes: The Pearson correlation coefficient between the first linear regression equation and the second linear regression equation is calculated and used as the similarity between the first linear regression equation and the second linear regression equation.

5. The method for predicting battery cell expansion force data according to claim 1, characterized in that: The preset health range is smaller than the health range for the entire life cycle.

6. The method for predicting battery cell expansion force data according to any one of claims 1 to 5, characterized in that: The preset healthy range is 90% SOH to 100% SOH.

7. The method for predicting battery cell expansion force data according to claim 1, characterized in that: The collecting of the first expansion force data of the battery cell of a specific model within a preset healthy range includes: Performing charge and discharge cycle tests on the specific model of battery cells within a preset healthy range; During the charge and discharge cycle test, the first expansion force data of the specific model of the battery cell within the preset healthy range is collected.

8. A battery cell expansion force data prediction device, characterized in that: include: A collection module, used to collect first expansion force data of a specific type of battery cell within a preset healthy range; A construction module, configured to construct a first linear regression equation for the battery cell of the specific model according to the first expansion force data and the health status data included in the preset health range; A prediction module is used to predict the full life cycle expansion force data corresponding to the specific model of battery cell based on the first linear regression equation corresponding to the specific model of battery cell, so as to provide design reference data for a battery module using the specific model of battery cell.

9. An electronic device for predicting battery cell expansion force data, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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