Expansion force prediction method, expansion force prediction system, electronic device and storage medium

Abstract

The present application discloses an expansion force prediction method, an expansion force prediction system, an electronic device, and a storage medium. The expansion force prediction method is applied to batteries, and the expansion force prediction method includes the following steps: obtaining the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating conditions include any one of a preset temperature and a preset discharge depth; calculating model parameters based on the sample expansion force growth, the sample cumulative discharge energy, and a preset prediction model; wherein the model parameters include sample initial parameters, sample expansion force growth rate parameters, and sample cumulative discharge parameters; obtaining the actual operating conditions of the battery, and predicting the expansion force of the battery based on the actual operating conditions, the model parameters, and the preset prediction model. The present application can predict the battery expansion force growth under different preset operating conditions, thereby improving the universality of expansion force prediction.

Description

Technical Field

[0001] The present application relates to the technical field of expansion force prediction, and in particular to an expansion force prediction method, an expansion force prediction system, an electronic device, and a storage medium. Background Art

[0002] At present, during the charging and discharging process of lithium-ion batteries, Li + The constant insertion and removal of particles between the positive and negative electrodes of a battery changes the lattice parameters and structure of the active materials, generating irreversible stress. This can lead to the crushing or cracking of active particles, causing delamination of the battery electrodes and, in turn, affecting the contact performance of the various components within the battery. During this process, the battery's internal resistance continues to increase, ultimately leading to battery failure due to capacity decay and even explosion hazards. Therefore, it can be seen that the battery's expansion force is closely related to its service life and reliability.

[0003] Related technologies establish a linear relationship between the battery's state of health (SOH) and stress. However, in practice, the SOH varies depending on factors such as the battery's discharge rate and temperature, making this approach less universal. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an expansion force prediction method, expansion force prediction system, electronic device, and storage medium that can predict the growth of battery expansion force under different preset operating conditions, thereby improving the universality of expansion force prediction.

[0005] According to the expansion force prediction method of the first aspect embodiment of the present application, applied to a battery, the expansion force prediction method includes: obtaining the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating conditions include any one of a preset temperature and a preset discharge depth; calculating model parameters based on the sample expansion force growth, the sample cumulative discharge energy and a preset prediction model; wherein the model parameters include sample initial parameters, sample expansion force growth rate parameters and sample cumulative discharge parameters; obtaining the actual operating condition of the battery, and predicting the expansion force of the battery based on the actual operating condition, the model parameters and the preset prediction model; wherein the actual operating condition includes any one of an actual temperature and an actual discharge depth.

[0006] According to some embodiments of the present application, the model parameters are calculated based on the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model, including: obtaining multiple candidate cumulative discharge parameters based on the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model; performing mean processing on the multiple candidate cumulative discharge parameters to obtain the sample cumulative discharge parameters; obtaining the sample initial parameters and the sample expansion force growth rate parameters based on the sample cumulative discharge parameters, the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model.

[0007] According to some embodiments of the present application, the sample initial parameters and the sample expansion force growth rate parameters are obtained according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model, including: obtaining candidate initial parameters and candidate expansion force growth rate parameters according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model; performing mean processing on the candidate expansion force growth rate parameters of the battery obtained by testing at the same preset temperature; or performing mean processing on the candidate expansion force growth rate parameters of the battery obtained by testing at the same preset discharge depth; by measuring Testing the battery at different preset temperatures to obtain calibrated expansion force growth rate parameters of the battery at different preset temperatures; or, testing the battery at different preset depths of discharge to obtain calibrated expansion force growth rate parameters of the battery at different preset depths of discharge; performing linear fitting on the calibrated expansion force growth rate parameters and the parameters of the corresponding preset operating conditions to obtain the sample expansion force growth rate parameters; performing mean processing on the candidate initial parameters of the battery under the same preset operating condition to obtain calibrated initial parameters of the battery under different preset operating conditions; performing linear fitting on the calibrated initial parameters and the corresponding preset operating conditions to obtain the sample initial parameters.

[0008] According to some embodiments of the present application, before obtaining multiple candidate cumulative discharge parameters based on the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model, the model parameters calculated based on the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model also include: deleting the sample expansion force growth that does not meet the preset conditions; wherein the preset conditions include any one of the deviation value of the sample expansion force growth being less than a preset threshold and the sample expansion force growth showing an upward trend.

[0009] According to some embodiments of the present application, obtaining the sample expansion force growth and sample cumulative discharge energy of the battery under different preset operating conditions includes: performing cyclic charge and discharge processing on the battery under the preset operating conditions; collecting candidate expansion values for each cycle according to a preset period, and collecting candidate discharge energies for each cycle; obtaining the sample expansion force growth based on the maximum value of the candidate expansion values of the sample cycle and the maximum value of the candidate expansion values of the first cycle; and accumulating the candidate discharge energies of all cycles before the sample cycle to obtain the sample cumulative discharge energy.

[0010] According to some embodiments of the present application, the preset operating conditions include a preset temperature, and the linear fitting of the calibrated expansion force growth rate parameter and the corresponding parameters of the preset operating conditions to obtain the sample expansion force growth rate parameter includes: logarithmically processing the calibrated expansion force growth rate parameter, and linearly fitting the calibrated expansion force growth rate parameter after logarithmic processing and the preset temperature to obtain the sample expansion force growth rate parameter.

[0011] According to some embodiments of the present application, the preset operating conditions include a preset discharge depth, and the linear fitting of the calibrated expansion force growth rate parameter and the corresponding parameter of the preset operating condition to obtain the sample expansion force growth rate parameter includes: performing logarithmic processing on the preset discharge depth and the calibrated expansion force growth rate parameter, respectively, and performing linear fitting on the logarithmically processed calibrated expansion force growth rate parameter and the logarithmically processed preset discharge depth to obtain the sample expansion force growth rate parameter.

[0012] According to the second aspect of the present application, an expansion force prediction system is applied to a battery, and the expansion force prediction system includes: a first module for obtaining the sample expansion force growth and sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating conditions include any one of a preset temperature and a preset discharge depth; a second module for calculating model parameters based on the sample expansion force growth, the sample cumulative discharge energy and a preset prediction model; wherein the model parameters include sample initial parameters, sample expansion force growth rate parameters and sample cumulative discharge parameters; a third module for obtaining the actual operating conditions of the battery, and predicting the expansion force of the battery based on the actual operating conditions, the model parameters and the preset prediction model; wherein the actual operating conditions include any one of an actual temperature and an actual discharge depth.

[0013] According to the third aspect of the present application, an electronic device includes at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the expansion force prediction method as described in the first aspect.

[0014] According to the computer-readable storage medium of the fourth aspect embodiment of the present application, processor-executable instructions are stored therein, and it is characterized in that the processor-executable instructions are used to implement the expansion force prediction method as described in the first aspect when executed by the processor.

[0015] The expansion force prediction method, expansion force prediction system, electronic device, and storage medium provided in the embodiments of the present application establish a preset prediction model based on the sample expansion force growth and the sample cumulative discharge energy during the battery cyclic charge and discharge process. This allows the preset prediction model to predict the battery expansion force growth under different preset operating conditions (including any one of a preset temperature and a preset discharge depth), and the battery expansion force is predicted based on the expansion force growth, thereby improving the universality of the preset prediction model and the expansion force prediction accuracy.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present application is further described below with reference to the accompanying drawings and embodiments, wherein:

[0018] Figure 1 A schematic diagram of a flow chart of the expansion force prediction method according to an embodiment of the present application;

[0019] Figure 2 This is another schematic flow chart of the expansion force prediction method according to an embodiment of the present application;

[0020] Figure 3 This is another schematic flow chart of the expansion force prediction method according to an embodiment of the present application;

[0021] Figure 4 A schematic diagram of a curve showing the cumulative discharge energy of a sample versus the increase in expansion force of the sample when the preset working conditions of the embodiment of the present application include a preset temperature;

[0022] Figure 5 This is another schematic flow chart of the expansion force prediction method according to an embodiment of the present application;

[0023] Figure 6 A schematic diagram of calibrating the expansion force growth rate parameter and the preset temperature for linear fitting in an embodiment of the present application;

[0024] Figure 7A schematic diagram of calibrating initial parameters and preset temperatures for linear fitting in an embodiment of the present application;

[0025] Figure 8 A schematic diagram showing a linear fit between the expansion force growth rate parameter and the preset discharge depth in accordance with an embodiment of the present application;

[0026] Figure 9A This is a schematic diagram of a curve showing the cumulative discharge energy of a sample at a temperature of 25° C. versus the increase in sample expansion force after linear fitting in an embodiment of the present application;

[0027] Figure 9B This is a schematic diagram of a curve showing the cumulative discharge energy of a sample at a temperature of 45°C versus the increase in sample expansion force after linear fitting in an embodiment of the present application;

[0028] Figure 9C This is a schematic diagram of a curve showing the cumulative discharge energy of a sample at 60°C after linear fitting versus the increase in sample expansion force;

[0029] Figure 10 A schematic diagram of a curve showing the cumulative discharge energy of a sample versus the increase in the sample expansion force when the preset working conditions of the embodiment of the present application include a preset discharge depth;

[0030] Figure 11A This is a graph showing the cumulative discharge energy of a sample with an actual discharge depth of 75% DOD after linear fitting versus the increase in sample expansion force.

[0031] Figure 11B This is a graph showing the cumulative discharge energy of a sample with an actual discharge depth of 95% DOD after linear fitting versus the increase in sample expansion force.

[0032] Figure 11C This is a curve diagram of the cumulative discharge energy of the sample with an actual discharge depth of 100% DOD after linear fitting in the embodiment of the present application - the increase in sample expansion force;

[0033] Figure 12 Schematic diagram of a module of the expansion force prediction system according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0035] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0036] In the description of this application, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0037] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0038] In the description of this application, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0039] A battery's service life consists of cycle life and storage life. Cycle life refers to the battery's usable life during cyclic charge and discharge cycles, while storage life refers to the battery's usable life when not in use. During room-temperature storage, the increase in battery expansion force is minimal and almost negligible. Therefore, over the battery's entire life cycle, the increase in expansion force generated during cyclic charge and discharge cycles is the primary factor affecting battery service life.

[0040] In related technologies, the expansion force of batteries is predicted by the following two methods. The first is to establish a linear relationship between the battery's state of health (SOH) and stress; the second is to perform three-dimensional simulation based on a mechanical model to obtain the distribution of stress on the battery's large surface. However, in actual applications, the health state (SOH) of the first method varies with factors such as the battery's charge and discharge rate and temperature, making the first method less universal; the simulation parameters required by the second method are more difficult to obtain, and the method is limited to the distribution of battery stress in a certain state (such as EOL (End of life)). Therefore, the second method cannot predict the changing trend of the battery's expansion force growth over its entire life cycle.

[0041] Based on this, the embodiments of the present application provide an expansion force prediction method, an expansion force prediction system, an electronic device, and a storage medium, which can predict the expansion force of a battery under different working conditions, thereby improving the universality of the battery expansion force prediction and realizing the expansion force prediction of the battery's cyclic charge and discharge process (i.e., the entire life cycle).

[0042] It should be noted that in the following embodiments, the battery may include a single cell or multiple cells, which is not specifically limited in the present embodiment. When the battery includes a single cell, the expansion force of the battery is the expansion force of the single cell.

[0043] Reference Figure 1 The present invention provides an expansion force prediction method for batteries. The expansion force prediction method includes the following steps:

[0044] S110, obtaining the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset working conditions;

[0045] S120, calculating model parameters according to the sample expansion force growth, the sample cumulative discharge energy, and a preset prediction model;

[0046] S130 , obtaining the actual working condition of the battery, and predicting the expansion force of the battery based on the actual working condition, model parameters, and a preset prediction model.

[0047] Specifically, a battery cyclic expansion force growth prediction model is established. The battery is subjected to cyclic charge and discharge tests under different preset operating conditions to collect the expansion force growth of multiple samples and the cumulative discharge energy of multiple samples. The model parameters in the preset prediction model are solved based on the expansion force growth of multiple samples and the cumulative discharge energy of multiple samples, and the preset prediction model is updated based on the solved model parameters. In actual application, the actual operating conditions of the battery are obtained. Based on the actual operating conditions of the battery and the updated preset prediction model, the expansion force growth of the battery under these operating conditions can be calculated. The expansion force of the battery is then predicted based on the expansion force growth, and the remaining battery life can be predicted based on the expansion force. For example, a preset prediction model is established as shown in the following formula (1).

[0048]

[0049] Among them, ΔF represents the growth of the sample expansion force, A represents the initial parameter of the sample, B represents the growth rate parameter of the sample expansion force, and E n represents the cumulative discharge energy of the sample, z represents the cumulative discharge parameter of the sample, T represents the temperature, and DOD represents the depth of discharge.

[0050] The expansion force prediction method provided in the embodiment of the present application establishes a preset prediction model based on the sample expansion force growth and the sample cumulative discharge energy during the battery cyclic charge and discharge process. Therefore, the battery expansion force growth under different preset operating conditions (including any one of a preset temperature and a preset discharge depth) can be predicted according to the preset prediction model. The battery expansion force is predicted based on the expansion force growth, thereby improving the universality of the preset prediction model and the expansion force prediction accuracy.

[0051] In some embodiments, the preset operating condition includes any one of a preset temperature and a preset depth of discharge. Correspondingly, the actual operating condition includes any one of an actual temperature and an actual depth of discharge. Below, the solution of the model parameters in the embodiment of the present application is specifically described using the example of the preset operating condition including the preset temperature.

[0052] Reference Figure 2 In some embodiments, step S110 includes the following sub-steps:

[0053] S210, performing a cyclic charge and discharge process on the battery under a preset working condition;

[0054] S220, collecting candidate expansion values for each cycle according to a preset period, and collecting candidate discharge energies for each cycle;

[0055] S230, obtaining the sample expansion force growth amount according to the maximum value of the candidate expansion values of the sample cycle and the maximum value of the candidate expansion value of the first cycle;

[0056] S240 , accumulating the candidate discharge energies of all cycles before the sample cycle to obtain the sample cumulative discharge energy.

[0057] Specifically, in order to predict the expansion force of the battery throughout its life cycle, the battery is subjected to cyclic charge and discharge treatments at different preset temperatures. Each cycle includes a charging process and a discharging process. When the battery is in the charging process, the expansion force of the battery will continue to increase; when the battery is in the discharging process, the expansion force of the battery will continue to decrease. According to a preset period (for example: 30s), multiple candidate expansion values in each cycle are collected, and the expansion force growth amount ΔF of a sample is calculated according to the following formula (2). It can be understood that the specific value of the preset period can also be adaptively adjusted according to actual conditions, and the embodiments of the present application do not make specific limitations.

[0058] ΔF=F i -F0......Formula (2)

[0059] Where i represents the sample cycle, F i represents the maximum value among multiple candidate expansion values collected in the i-th cycle, and F0 represents the maximum value among multiple candidate expansion values collected in the first cycle. At the same time, the candidate discharge energy of the battery at the end of each cycle is collected, and all candidate discharge energies before the i-th cycle are accumulated to obtain the cumulative discharge energy E of the sample in the i-th cycle. n Repeat the above steps to obtain the expansion force growth ΔF of multiple samples and the cumulative discharge energy E of multiple samples. n , thus according to the expansion force growth ΔF of the multiple samples, the cumulative discharge energy E of the multiple samples n The model parameters are calculated using the preset prediction model.

[0060] In some embodiments, to improve the prediction accuracy of a preset prediction model, the sample expansion force growth ΔF is preprocessed before solving the model parameters. Specifically, the sample expansion force growth ΔF that does not meet a preset condition is deleted. The preset condition includes either the deviation of the sample expansion force growth ΔF being less than a preset threshold or the sample expansion force growth ΔF showing an upward trend.

[0061] Specifically, the expansion force increase ΔF of the sample in the i-th cycle is compared with the expansion force increase ΔF of the sample in the i+1th cycle and the expansion force increase ΔF of the sample in the i-1th cycle. If the deviation of the expansion force increase ΔF of the sample in the i-th cycle is greater than or equal to a preset threshold (e.g., 10%), the expansion force increase ΔF of the sample in the i-th cycle is determined to be abnormal data. In this case, the expansion force increase ΔF of the sample in the i-th cycle should not be used to solve the model parameters. Alternatively, if the expansion force increase ΔF of the sample in the i-th cycle shows a downward trend compared to the expansion force increase ΔF of the sample in the i-1th cycle, the expansion force increase ΔF of the sample in the i-th cycle is determined to be abnormal data.

[0062] Reference Figure 3 In some embodiments, step S120 includes the following sub-steps:

[0063] S310, obtaining a plurality of candidate cumulative discharge parameters according to the sample expansion force growth, the sample cumulative discharge energy, and a preset prediction model;

[0064] S320, performing mean processing on multiple candidate cumulative discharge parameters to obtain a sample cumulative discharge parameter;

[0065] S330, obtaining sample initial parameters and sample expansion force growth rate parameters according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and a preset prediction model.

[0066] Specifically, the battery is subjected to cyclic charge and discharge tests at different preset temperatures, and the expansion force growth ΔF and the cumulative discharge energy E of multiple samples are collected according to the above method. n The expansion force growth ΔF of multiple samples and the cumulative discharge energy E of multiple samples are n Substituting into equation (1) to calculate multiple candidate cumulative discharge parameters, the multiple candidate cumulative discharge parameters are averaged to obtain a sample cumulative discharge parameter z. The sample cumulative discharge parameter z is used as a fixed value to update the preset prediction model (i.e., equation (1)). Based on the updated preset prediction model, the expansion force growth of the multiple samples, and the cumulative discharge energy of the multiple samples, the sample initial parameter A and the sample expansion force growth rate parameter B are calculated. Based on the calculated sample initial parameter A and sample expansion force growth rate parameter B, the preset prediction model is updated again to predict the expansion force growth under actual battery operating conditions based on the updated preset prediction model.

[0067] When the preset prediction model is updated for the first time, that is, when the candidate cumulative discharge parameters are solved for the first time, an initial value for calculation needs to be set. For example, the initial value for calculation includes the initial value of the initial parameter A 初值 and initial cumulative discharge parameter z 初值 . Reference Figure 4The initial parameter A is determined based on the valley value of the battery sample cumulative discharge energy-sample expansion force growth curve obtained by the above method. 初值 , that is, select the initial value initial parameter A 初值 Equal to 0 or negative value, initial value cumulative discharge parameter z 初值 Equal to 0.1 is the initial value of the calculation.

[0068] Reference Figure 5 In some embodiments, step S330 includes the following sub-steps:

[0069] S510, obtaining candidate initial parameters and candidate expansion force growth rate parameters according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and a preset prediction model;

[0070] S520, performing mean processing on the candidate expansion force growth rate parameters of the batteries tested at the same preset temperature; and obtaining calibrated expansion force growth rate parameters of the batteries at different preset temperatures by testing the batteries at different preset temperatures;

[0071] S530, performing linear fitting on the calibrated expansion force growth rate parameter and the corresponding preset working condition parameter to obtain the sample expansion force growth rate parameter;

[0072] S540, performing mean processing on candidate initial parameters of batteries with the same preset operating condition to obtain calibrated initial parameters of batteries with different preset operating conditions;

[0073] S550: Perform linear fitting on the calibration initial parameters and the corresponding preset working conditions to obtain the sample initial parameters.

[0074] Specifically, according to the preset prediction model updated by the cumulative discharge parameters of the samples, the expansion force growth ΔF of the multiple samples, the cumulative discharge energy E of the multiple samples nMultiple candidate expansion force growth rate parameters and multiple candidate initial parameters are calculated. The multiple candidate expansion force growth rate parameters include parameters for batteries subjected to cyclic charge-discharge tests at the same preset temperature, as well as parameters for batteries subjected to cyclic charge-discharge tests at different preset temperatures. Similarly, the multiple candidate initial parameters include parameters for batteries subjected to cyclic charge-discharge tests at the same preset temperature, as well as parameters for batteries subjected to cyclic charge-discharge tests at different preset temperatures. Therefore, the candidate expansion force growth rate parameters and candidate initial parameters for batteries subjected to cyclic charge-discharge tests at the same preset temperature are averaged to obtain the average values of the candidate expansion force growth rate parameters and the average values of the candidate initial parameters for batteries subjected to the same test conditions (i.e., the same preset temperature), thereby obtaining calibrated expansion force growth rate parameters and calibrated initial parameters for batteries subjected to different test conditions (i.e., different preset temperatures). Linear fitting is performed on the multiple calibrated expansion force growth rate parameters and the corresponding preset temperatures, as well as on the multiple calibrated initial parameters and the corresponding preset temperatures, to calculate the sample expansion force growth rate parameter B and the sample initial parameter A, respectively.

[0075] For example, multiple calibration expansion force growth rate parameters are processed logarithmically to obtain In(B). Then, In(B) and Perform linear fitting (such as Figure 6 As shown, the goodness of fit R 2 >99%), and the fitting parameters β0 and β1 are obtained. Then, based on the fitting parameters β0 and β1, the expansion force growth rate parameter B of the sample can be calculated as follows (3).

[0076]

[0077] Furthermore, when the selected initial value initial parameter A 初值 ≠0, perform linear fitting on the calibration initial parameters and preset temperature (such as Figure 7 As shown, the goodness of fit R 2 >99%), and the fitting parameters a and b are obtained. Then, the sample initial parameter A can be calculated based on the fitting parameters a and b and the following formula (4).

[0078] A=aT+b......Formula (4)

[0079] Thus, when the preset working conditions include the preset temperature, the model parameters of the preset prediction model (including the sample initial parameter A, the sample expansion force growth rate parameter B and the sample cumulative discharge parameter z) can be solved. It can be understood that when the preset working conditions include the preset discharge depth, the sample expansion force growth amount ΔF, the sample cumulative discharge energy E nThe solution of the sample initial parameter A and the sample cumulative discharge parameter z is the same as the above method. Therefore, the following only describes the solution of the sample expansion force growth rate parameter B when the preset working conditions include the preset discharge depth.

[0080] When the preset working condition includes a preset discharge depth, the multiple calibration expansion force growth rate parameters and the preset discharge depth are logarithmically processed to obtain In(B) and In(DOD) respectively. Then, linear fitting processing is performed on In(B) and In(DOD) (e.g. Figure 8 As shown, the goodness of fit R 2 >99%), and the fitting parameters θ0 and θ1 are obtained. According to the fitting parameters θ0 and θ1, the expansion force growth rate parameter B of the sample can be calculated according to the following formula (5).

[0081]

[0082] In a specific embodiment, a ternary lithium-ion power battery is used as an example. When the preset operating conditions include a preset temperature, the battery's depth of discharge is controlled to be 0-100% DOD, the charge and discharge rate is 1C / 1C, and the initial preload is 300 kgf. The battery is subjected to cyclic charge and discharge tests at different preset temperatures. The preset temperatures include 25°C, 45°C, and 60°C. Figure 4 , is the battery sample cumulative discharge energy-sample expansion force growth curve obtained according to the above method, among which some curves are missing due to instrument abnormality. Figures 9A to 9C , is the sample cumulative discharge energy-sample expansion force growth curve obtained after linear fitting according to the above method. Figure 4 and Figures 9A to 9C By comparison, it can be seen that the fitted curve is highly consistent with the original test curve. Therefore, the expansion force prediction method provided in the embodiment of the present application can improve the accuracy of battery expansion force prediction.

[0083] According to the method described in the above embodiment, the model parameters shown in Table 1 below are calculated.

[0084] Table 1:

[0085] <![CDATA[β0]]> <![CDATA[β1]]> a b z 11.31 -6960.72 43.83 -1.53 2.5

[0086] By updating the preset prediction model based on the aforementioned model parameters, the sample expansion force growth ΔF of batteries of the same system and design at a specific operating temperature (i.e., actual temperature) can be predicted. Based on this sample expansion force growth ΔF, the battery's expansion force can be predicted, and thus the battery's remaining useful life can be predicted. It is understood that "same system" refers to batteries with the same model and capacity, and "same design" refers to batteries with the same interlayer design, housing design, and other features, which are not specifically limited in this embodiment of the application.

[0087] Similarly, when the preset operating conditions include a preset depth of discharge, the battery cycle temperature is controlled to be 25°C, the charge and discharge rate is 1C / 1C, and the initial preload is 300kgf, and the battery is subjected to cycle charge and discharge tests at different preset depths of discharge. The preset depths of discharge include 100% DOD, 95% DOD, and 75% DOD. Figure 10 , is the cumulative discharge energy-sample expansion force growth curve of the battery sample obtained according to the above method. Figures 11A to 11C , is the sample cumulative discharge energy-sample expansion force growth curve obtained after linear fitting according to the above method. Figure 10 and Figures 11A to 11C The comparison shows that the fitted curve has a high degree of agreement with the original test curve. Therefore, the expansion force prediction method provided by the embodiment of the present application can improve the accuracy of battery expansion force prediction. It can be understood that when the selected initial value initial parameter A 初值 ≠0, the calibration initial parameters and the preset temperature are linearly fitted to obtain fitting parameters a and b. In the embodiment of the present application, the initial value initial parameter A is selected. 初值 =0, so no linear fitting is required.

[0088] According to the method described in the above embodiment, the model parameters shown in Table 2 below are calculated.

[0089] Table 2:

[0090] <![CDATA[θ0]]> <![CDATA[θ1]]> z 2.1747 1.8559 0.7

[0091] By updating the preset prediction model based on the above model parameters, the sample expansion force growth ΔF of batteries with the same system and design at a specific discharge depth (i.e., the actual discharge depth) can be predicted. The battery's expansion force can be predicted based on the sample expansion force growth ΔF, thereby realizing the prediction of the remaining service life of the battery.

[0092] Reference Figure 12 , an embodiment of the present application further provides an expansion force prediction system, which is applied to a battery, and the expansion force prediction system includes:

[0093] The first module 100 is used to obtain the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating condition includes any one of a preset temperature and a preset discharge depth;

[0094] The second module 200 is used to calculate the model parameters according to the sample expansion force growth, the sample cumulative discharge energy and the preset prediction model;

[0095] The third module 300 is used to obtain the actual working condition of the battery and predict the expansion force of the battery based on the actual working condition, model parameters and a preset prediction model.

[0096] It can be seen that the contents of the above-mentioned expansion force prediction method embodiment are all applicable to the embodiments of the present expansion force prediction system. The functions specifically implemented by the present expansion force prediction system embodiment are the same as those of the above-mentioned expansion force prediction method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned ground stress prediction method embodiment.

[0097] An embodiment of the present application further provides an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores instructions, which are executed by the at least one processor so that the at least one processor implements the expansion force prediction method described in any of the above embodiments when executing the instructions.

[0098] An embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the expansion force prediction method described in any of the above embodiments.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0100] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0101] The embodiments of the present application have been described in detail above with reference to the accompanying drawings. However, the present application is not limited to the above embodiments. Various modifications can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the purpose of the present application. In addition, the embodiments of the present application and the features of the embodiments can be combined with each other unless there is a conflict.

Claims

1. An expansion force prediction method, applied to batteries, characterized in that: The expansion force prediction method comprises: Obtaining the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating conditions include any one of a preset temperature and a preset depth of discharge; The model parameters are calculated based on the sample expansion force growth, the sample cumulative discharge energy and a preset prediction model; wherein the model parameters include sample initial parameters, sample expansion force growth rate parameters and sample cumulative discharge parameters; Obtaining an actual operating condition of the battery, predicting an expansion force growth of the battery based on the actual operating condition, the model parameters, and the preset prediction model, and predicting the expansion force based on the predicted expansion force growth; wherein the actual operating condition includes any one of an actual temperature and an actual depth of discharge; The preset prediction model is ΔF represents the growth of the sample expansion force, A represents the initial parameters of the sample, B represents the growth rate parameter of the sample expansion force, E n represents the cumulative discharge energy of the sample, and z represents the cumulative discharge parameter of the sample.

2. The expansion force prediction method according to claim 1, characterized in that: The model parameters are calculated based on the growth of the sample expansion force, the cumulative discharge energy of the sample, and a preset prediction model, including: Obtaining a plurality of candidate cumulative discharge parameters according to the sample expansion force growth, the sample cumulative discharge energy, and the preset prediction model; Performing mean processing on the plurality of candidate cumulative discharge parameters to obtain a sample cumulative discharge parameter; The sample initial parameters and the sample expansion force growth rate parameters are obtained according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model.

3. The expansion force prediction method according to claim 2, characterized in that: The method of obtaining the sample initial parameters and the sample expansion force growth rate parameters according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model includes: Obtaining candidate initial parameters and candidate expansion force growth rate parameters according to the sample cumulative discharge parameters, the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model; performing averaging processing on the candidate expansion force growth rate parameters of the battery obtained by testing at the same preset temperature; or performing averaging processing on the candidate expansion force growth rate parameters of the battery obtained by testing at the same preset depth of discharge; By testing the battery at different preset temperatures, calibrated expansion force growth rate parameters of the battery at different preset temperatures are obtained; or by testing the battery at different preset depths of discharge, calibrated expansion force growth rate parameters of the battery at different preset depths of discharge are obtained; Performing linear fitting on the calibrated expansion force growth rate parameter and the corresponding parameter of the preset working condition to obtain the expansion force growth rate parameter of the sample; Performing mean processing on the candidate initial parameters of the battery under the same preset operating condition to obtain calibrated initial parameters of the battery under different preset operating conditions; Perform linear fitting on the calibration initial parameters and the corresponding preset working conditions to obtain the sample initial parameters.

4. The expansion force prediction method according to claim 2, characterized in that: Before obtaining a plurality of candidate cumulative discharge parameters according to the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model, the calculating and obtaining model parameters according to the sample expansion force growth amount, the sample cumulative discharge energy, and the preset prediction model further includes: The expansion force growth of the sample that does not meet the preset conditions is deleted; wherein the preset conditions include any one of the following: the deviation value of the expansion force growth of the sample is less than a preset threshold value, and the expansion force growth of the sample is on an upward trend.

5. The expansion force prediction method according to any one of claims 1 to 4, characterized in that: The obtaining of the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions includes: Under the preset working conditions, the battery is subjected to a cyclic charge and discharge process; Collecting candidate expansion values for each cycle according to a preset period, and collecting candidate discharge energies for each cycle; Obtaining the sample expansion force growth amount according to the maximum value of the candidate expansion value of the sample cycle and the maximum value of the candidate expansion value of the first cycle; The candidate discharge energies of all cycles before the sample cycle are accumulated to obtain the sample accumulated discharge energy.

6. The expansion force prediction method according to claim 3, characterized in that: The preset working condition includes a preset temperature, and performing linear fitting on the calibrated expansion force growth rate parameter and the corresponding parameter of the preset working condition to obtain the sample expansion force growth rate parameter includes: The calibrated expansion force growth rate parameter is logarithmically processed, and a linear fit is performed on the logarithmically processed calibrated expansion force growth rate parameter and the preset temperature to obtain the sample expansion force growth rate parameter.

7. The expansion force prediction method according to claim 3, characterized in that: The preset working condition includes a preset discharge depth, and the linear fitting of the calibrated expansion force growth rate parameter and the corresponding parameter of the preset working condition to obtain the sample expansion force growth rate parameter includes: The preset discharge depth and the calibrated expansion force growth rate parameter are logarithmically processed respectively, and the calibrated expansion force growth rate parameter after logarithmic processing and the preset discharge depth after logarithmic processing are linearly fitted to obtain the sample expansion force growth rate parameter.

8. An expansion force prediction system, applied to batteries, characterized in that: The expansion force prediction system comprises: The first module is used to obtain the sample expansion force growth and the sample cumulative discharge energy of the battery under different preset operating conditions; wherein the preset operating conditions include any one of a preset temperature and a preset discharge depth; The second module is used to calculate model parameters based on the sample expansion force growth, the sample cumulative discharge energy and a preset prediction model; wherein the model parameters include sample initial parameters, sample expansion force growth rate parameters and sample cumulative discharge parameters; a third module, configured to obtain an actual operating condition of the battery, predict an expansion force growth of the battery based on the actual operating condition, the model parameters, and the preset prediction model, and predict the expansion force based on the predicted expansion force growth; wherein the actual operating condition includes any one of an actual temperature and an actual depth of discharge; The preset prediction model is ΔF represents the growth of the sample expansion force, A represents the initial parameters of the sample, B represents the growth rate parameter of the sample expansion force, E n represents the cumulative discharge energy of the sample, and z represents the cumulative discharge parameter of the sample.

9. An electronic device, characterized in that include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the expansion force prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having processor-executable instructions stored therein, characterized in that: The processor-executable instructions are used to implement the expansion force prediction method according to any one of claims 1 to 7 when executed by the processor.

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