Method and device for predicting the grouping force of a battery module and energy storage system

Abstract

The embodiment of the application relates to the energy storage technical field, and provides a battery module grouping force prediction method and device and an energy storage system, the method comprises the following steps: obtaining hardness parameters and thickness parameters of a selected foam; analyzing the hardness parameters and the thickness parameters based on a pre-established prediction model to obtain a grouping force prediction value corresponding to the hardness parameters and the thickness parameters; the pre-established prediction model is constructed through multiple sets of experimental data, the experimental data comprises grouping force values of foams with multiple hardness and multiple thickness combinations measured in an actual compression process, and the prediction model is established by analyzing a nonlinear synergistic influence relationship of the hardness and the thickness on the grouping force, so that the problem of how to predict a module grouping compression force without actual assembly in a design stage of an energy storage battery module is solved.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a method, apparatus and energy storage system for predicting the packing force of a battery module. Background Technology

[0002] In the assembly process of energy storage battery modules (such as residential energy storage systems), to ensure the structural stability, thermal expansion compensation, and mechanical buffering performance of the cells during charge-discharge cycles, elastic foam is usually placed between adjacent cells as an isolation buffer layer. The hardness (Shore C) and thickness (mm) of this foam are key physical parameters that determine the assembly pressing force of the module: hardness affects the rigidity resistance of the material, and thickness determines the amount of compression deformation. Both of these factors work together to exert the axial force required during the pressing process.

[0003] Currently, related technologies generally employ the "trial assembly verification method" for group force assessment: This involves actually assembling multiple modules with different foam specifications during the product development phase, using pressure sensors or pressing equipment to collect group force data in real time, and repeatedly adjusting the foam selection until the design goals are met (e.g., pressing force controlled within the range of 800–1500N). This method has the following serious drawbacks:

[0004] Highly reliant on trial and error: Designers cannot predict the group forces under a specific combination of foam parameters in the early stages of the design process and must rely on a large number of physical samples for actual measurement.

[0005] The development cycle is long and the cost is high: every time a type of foam is changed (such as changing the hardness from 40 Shore C to 60 Shore C, or increasing the thickness from 3mm to 5mm), it is necessary to re-make the mold, assemble and test. The cost of a single trial assembly exceeds 1,000 yuan and the total time taken can reach 2-4 weeks.

[0006] Currently, there is an urgent need to predict the assembly pressure of energy storage battery modules during the design phase, without requiring actual assembly. Summary of the Invention

[0007] This application provides a method, apparatus, and energy storage system for predicting the assembly force of battery modules, which at least solves the problem of predicting the assembly pressing force of modules during the design stage of energy storage battery modules without actual assembly.

[0008] According to some embodiments of this application, one aspect of this application provides a method for predicting the packing force of a battery module. The method includes: obtaining the hardness parameters and thickness parameters of a candidate foam, wherein the candidate foam is a foam structure disposed between the cells of the battery module; analyzing the hardness parameters and the thickness parameters based on a pre-established prediction model to obtain predicted packing force values ​​corresponding to the hardness parameters and the thickness parameters; wherein the pre-established prediction model is constructed through multiple sets of experimental data, the experimental data including packing force values ​​measured during actual pressing of multiple combinations of hardness and thickness of foam, and the prediction model is established by analyzing the nonlinear synergistic influence relationship between hardness and thickness on packing force.

[0009] In some embodiments, before obtaining the hardness and thickness parameters of the candidate foam, the method further includes: statistically fitting the experimental data to establish a grouped force response function under the combined effect of hardness and thickness; using the grouped force response function to perform logarithmic transformation on the thickness under the hardness segmentation condition, and comprehensively fitting to obtain a global prediction relationship, wherein the fitting objects include the regression coefficients extracted by independent fitting under each hardness segmentation condition, and the global prediction relationship is the relationship between the hardness parameter, the thickness parameter, and the grouped force prediction value.

[0010] In some embodiments, after obtaining the global prediction relationship through comprehensive fitting, the method further includes: performing a preset test on the global prediction relationship to obtain the confidence level and lack of fit value of the global prediction relationship, wherein the preset test includes residual normality test, equal variance test, independence test, and lack of fit test; and determining the current prediction model as the pre-established prediction model when the confidence level is greater than or equal to the confidence level threshold and the lack of fit value is less than the lack of fit threshold, wherein the pre-established prediction model includes the global prediction relationship.

[0011] In some embodiments, the method further includes: reconstructing the prediction model using multiple sets of new experimental data when the confidence level is less than a confidence threshold and / or the misfit value is greater than or equal to the misfit threshold.

[0012] In some embodiments, the pre-established prediction model is established using experimental data from at least three sets of multiple hardness and multiple thickness combinations, with each set of experimental data derived from the measured group force of the same structural module under standard press-fitting process conditions.

[0013] In some embodiments, the hardness of the experimental data is in the range of 30 Shore C to 70 Shore C, and the thickness of the experimental data is in the range of 2 mm to 6 mm.

[0014] According to some embodiments of this application, another aspect of this application provides a device for predicting the pack force of a battery module, applicable to any of the methods for predicting the pack force of the battery module. The device includes: an acquisition unit for acquiring the hardness and thickness parameters of a candidate foam, wherein the candidate foam is a foam structure disposed between the cells of the battery module; and a first processing unit for analyzing the hardness and thickness parameters based on a pre-established prediction model to obtain predicted pack force values ​​corresponding to the hardness and thickness parameters. The pre-established prediction model is constructed through multiple sets of experimental data, including pack force values ​​measured during actual pressing of multiple combinations of hardness and thickness of foam. The prediction model is established by analyzing the nonlinear synergistic influence relationship between hardness and thickness on the pack force.

[0015] According to some embodiments of this application, another aspect of this application provides a battery device, including: a plurality of battery modules, wherein the group force of each battery module is predicted using any of the group force prediction methods for battery modules.

[0016] According to some embodiments of this application, another aspect of this application provides an electrical device, including: a plurality of the aforementioned battery devices.

[0017] According to some embodiments of this application, another aspect of this application provides an energy storage system, including: a control device and at least one of the aforementioned electrical devices, the control device being used to execute a method for predicting the packing force of any of the aforementioned battery modules.

[0018] The technical solution provided in this application has at least the following advantages: It introduces a quantitative functional relationship between foam hardness (H), thickness (T) and assembly pressing force (F). This model can capture the nonlinear synergistic effect of hardness and thickness on assembly force. By inputting the hardness and thickness parameters of the target foam, the predicted assembly force value is directly output. It can determine whether its mechanical properties meet the standards without manufacturing a physical prototype. Through the pre-established model verification mechanism, the accuracy and reliability of the prediction results are ensured. It eliminates the need to re-conduct a large number of physical tests due to changes in foam specifications. Thus, it solves the problem of how to predict the assembly pressing force of the module without actual assembly during the design stage of the energy storage battery module. Attached Figure Description

[0019] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Unless otherwise stated, the drawings in the accompanying drawings do not constitute a limitation on scale. In order to more clearly illustrate the technical solutions in the embodiments of this application or in the conventional art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a method for predicting the pack force of a battery module.

[0021] Figure 2 This is a structural block diagram of a battery module group force prediction device. Detailed Implementation

[0022] The embodiments of this application will now be described in detail with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0023] This application provides a method for predicting the pack strength of a battery module, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S101: Obtain the hardness and thickness parameters of the foam to be selected. The foam to be selected is the foam structure set between the cells of the battery module.

[0025] Step S102: Analyze the hardness parameters and thickness parameters based on the pre-established prediction model to obtain the predicted group force values ​​corresponding to the hardness parameters and thickness parameters. The pre-established prediction model is constructed through multiple sets of experimental data, including the group force values ​​measured during the actual pressing process of foams with multiple hardness and multiple thickness combinations. The prediction model is established by analyzing the nonlinear synergistic influence relationship between hardness and thickness on the group force.

[0026] Related technologies mainly rely on repeated physical trial assembly to verify foam selection, resulting in long development cycles, high costs, and difficulty in quantitatively assessing the impact of assembly force on structural safety in the early design stages. This application introduces a quantitative functional relationship between foam hardness (H), thickness (T), and assembly pressing force (F). This model can capture the nonlinear synergistic effect of hardness and thickness on assembly force. By inputting the hardness and thickness parameters of the target foam, it directly outputs the predicted assembly force value, allowing for the determination of whether its mechanical properties meet standards without manufacturing a physical prototype. Through a pre-established model verification mechanism, the accuracy and reliability of the prediction results are ensured, eliminating the need for extensive physical testing due to changes in foam specifications. This solves the problem of predicting module assembly pressing force during the energy storage battery module design stage without actual assembly.

[0027] In some embodiments, before obtaining the hardness and thickness parameters of the foam to be selected, the method further includes: statistically fitting the experimental data to establish a grouped force response function under the combined effect of hardness and thickness; using the grouped force response function to perform logarithmic transformation on the thickness under the hardness segmentation condition, and comprehensively fitting to obtain a global prediction relationship, wherein the fitting objects include the regression coefficients extracted by independent fitting under each hardness segmentation condition, and the global prediction relationship is the relationship between the hardness parameter, the thickness parameter and the grouped force prediction value.

[0028] First, it significantly improves the model's generalization ability and prediction accuracy. By transforming the nonlinear and complex hardness-thickness-group force coupling relationship into two relatively simple subproblems—a linear / quadratic logarithmic relationship within hardness segments and a linear relationship of coefficients with respect to hardness—it effectively captures the nonlinear mechanical characteristics of foam materials during physical deformation. This hierarchical fitting strategy avoids the high errors caused by forced fitting in the global range, ensuring that the model maintains high prediction accuracy even with a large hardness span. Second, it achieves parameterized global mapping, reducing model maintenance costs. Traditional methods require extensive experimentation and the establishment of new models if hardness conditions change; however, this embodiment expresses the regression coefficients (a, b, c) as a function of hardness H (i.e., α0 + α1). (H, etc., where α0 and α1 are corresponding coefficients) establishes a continuous global prediction relationship. Once the functional mapping between hardness and thickness is determined, the predicted force can be directly calculated by inputting any hardness and thickness values, without having to repeatedly build independent local models for each new hardness specification, thus improving the algorithm's flexibility and engineering applicability.

[0029] Multiple regression analysis was performed on the experimental data to establish a predictive model (i.e., the construction of group force response functions):

[0030] Linear model: F = a + bH + cT;

[0031] Or a quadratic model: F = a + bH + cT + dHT + eH 2 +fT 2 ;

[0032] Where: a, b, c, d, e, and f are regression coefficients.

[0033] When the hardness range is too large, model misfit may occur. In this case, a layered fitting method can be used, that is, fitting the relationship between thickness and group force under a single hardness condition. Here, it is recommended to use ln transformation.

[0034] When H=H1: lnF1=a1+b1 lnT+c1 (lnT) 2 ;

[0035] When H=H2: lnF2=a2+b2 lnT+c2 (lnT) 2 ;

[0036] When H=H3: lnF3=a3+b3 lnT+c3 (lnT) 2 ;

[0037] H1, H2, and H3 can be set using a fixed step size.

[0038] The three equations above are then combined into a matrix:

[0039] ;

[0040] Fitting parameter function:

[0041] a(H) = α0 + α1 H;

[0042] b(H) = β0 + β1 H;

[0043] c(H)=γ0+γ1 H;

[0044] Then, substituting the parametric function into the original function: LnF = a(H) + b(H)lnT + c(H)(lnT) 2 ;

[0045] The global prediction relationship for the grouped force response function is LnF = α0 + α1 H+(β0+β1 H)lnT+(γ0+γ1 H)(lnT) 2 .

[0046] The coefficients mentioned above are all intermediate values ​​and will not be elaborated upon further here.

[0047] In some embodiments, after obtaining the global prediction relationship through comprehensive fitting, the method further includes: performing a preset test on the global prediction relationship to obtain the confidence level and lack of fit of the global prediction relationship, wherein the preset test includes residual normality test, equal variance test, independence test, and lack of fit test; and determining the current prediction model as the pre-established prediction model if the confidence level is greater than or equal to the confidence level threshold and the lack of fit is less than the lack of fit threshold, wherein the pre-established prediction model includes the global prediction relationship.

[0048] Residual normality test: Used to verify whether the error (residual) between the model's predicted values ​​and the actual values ​​follows a normal distribution. This is an important prerequisite assumption for many statistical inferences in regression analysis (such as confidence intervals and hypothesis testing).

[0049] Homoscedasticity test: This test verifies whether the variance of the residuals remains constant (i.e., homoscedasticity) under different levels of input variables. If the variance changes with the predicted value (heteroscedasticity), it indicates that the model may have missed important variables or the function form is incorrectly specified, and the ordinary least squares estimate is no longer the optimal linear unbiased estimate.

[0050] Independence test: Used to verify whether there is a correlation (such as autocorrelation) between residuals, especially suitable for time series or spatial data. If the residuals are not independent, it usually means that there is a trend or periodic structure in the data that has not been captured by the model, leading to a bias in the standard error estimation.

[0051] The lack-of-fit test is used to determine whether the currently selected regression model form (such as linear or quadratic) is insufficient to describe the true relationship in the data. If there are enough repeated experimental points, this test can distinguish between "pure error" and "error caused by model missing data," thereby determining whether it is necessary to add higher-order terms or transform variables to improve the model fit.

[0052] The confidence threshold can be 80%, and the lack of fit can be 95%. Pre-set tests are performed on the global prediction relationship (including residual normality test, equal variance test, independence test, and lack of fit test). This can scientifically and quantitatively verify the statistical validity and fit quality of the mathematical model, ensuring that the model not only has high explanatory power, but more importantly, it ensures that the residual distribution conforms to statistical assumptions and eliminates systematic biases caused by heteroscedasticity, autocorrelation, or model form errors.

[0053] In some embodiments, the method further includes: reconstructing the prediction model using multiple sets of new experimental data when the confidence level is less than a confidence threshold and / or the misfit value is greater than or equal to the misfit threshold.

[0054] By setting confidence and misfit thresholds as thresholds, low-quality models caused by high data noise, insufficient sample representativeness, or complex physical mechanisms can be effectively filtered out, ensuring that the final prediction model delivered is statistically significant and physically reasonable.

[0055] In some embodiments, the pre-established prediction model is established by at least three sets of experimental data with multiple combinations of hardness and thickness, and each set of experimental data is derived from the measured group force of the same structural module under standard press-fitting process conditions.

[0056] A predictive model is built using experimental data based on at least three sets of different combinations of hardness and thickness. Compared with single-point testing or simple linear assumptions, this model can significantly capture the influence of the nonlinear mechanical behavior of foam materials on group forces, ensuring that the model has sufficient fitting accuracy and generalization ability over a wide range of parameters.

[0057] In some embodiments, the hardness of the above experimental data is in the range of 30 Shore C to 70 Shore C, and the thickness of the above experimental data is in the range of 2 mm to 6 mm.

[0058] The selection of this data range provides the foundation for the subsequent hierarchical fitting strategy. When the large hardness range causes the single global model to fail, this widely distributed dataset can clearly distinguish the thickness-force response law within different hardness ranges. By verifying the effectiveness of hierarchical fitting (such as ln transform processing) within this range, the robustness of the present invention in handling wide parameter domains can be demonstrated.

[0059] According to some embodiments of this application, another aspect of this application provides a battery module pack force prediction device, applicable to any of the above-mentioned battery module pack force prediction methods, such as... Figure 2 As shown, the above-mentioned device includes: an acquisition unit 21, used to acquire the hardness parameters and thickness parameters of the foam to be selected, wherein the foam to be selected is a foam structure set between the cells of the battery module; a first processing unit 22, used to analyze the hardness parameters and the thickness parameters based on a pre-established prediction model to obtain the predicted values ​​of the group force corresponding to the hardness parameters and the thickness parameters; the pre-established prediction model is constructed through multiple sets of experimental data, including the group force values ​​measured during the actual pressing process of multiple combinations of hardness and thickness of foam, and the prediction model is established by analyzing the nonlinear synergistic influence relationship between hardness and thickness on the group force.

[0060] Existing technologies primarily rely on repeated physical trials to verify foam selection, resulting in long development cycles, high costs, and difficulty in quantifying the impact of assembly forces on structural safety during the early design phase. This application introduces a quantitative functional relationship between foam hardness (H), thickness (T), and assembly pressing force (F). This model can capture the nonlinear synergistic effect of hardness and thickness on assembly force. By inputting the hardness and thickness parameters of the target foam, it directly outputs the predicted assembly force value, allowing for the determination of whether the mechanical properties meet the standards without manufacturing a physical prototype. Through a pre-established model verification mechanism, the accuracy and reliability of the prediction results are ensured, eliminating the need for extensive physical testing due to changes in foam specifications. This solves the problem of predicting module assembly pressing force during the energy storage battery module design phase without actual assembly.

[0061] In some embodiments, the above-mentioned apparatus further includes: a second processing unit for statistically fitting the experimental data before obtaining the hardness and thickness parameters of the foam to be selected, and establishing a grouped force response function under the combined action of hardness and thickness; and a third processing unit for performing logarithmic transformation processing on the thickness under the hardness segmentation conditions using the grouped force response function, and comprehensively fitting to obtain a global prediction relationship, wherein the fitting objects include the regression coefficients extracted by independent fitting under each hardness segmentation condition, and the global prediction relationship is the relationship between the hardness parameter, the thickness parameter and the grouped force prediction value.

[0062] In some embodiments, the apparatus further includes: a fourth processing unit configured to perform a preset test on the global prediction relationship after obtaining the global prediction relationship through comprehensive fitting, thereby obtaining the confidence level and the lack of fit value of the global prediction relationship, wherein the preset test includes residual normality test, equal variance test, independence test, and lack of fit test; and if the confidence level is greater than or equal to the confidence level threshold and the lack of fit value is less than the lack of fit threshold, the current prediction model is determined to be the pre-established prediction model, wherein the pre-established prediction model includes the global prediction relationship.

[0063] In some embodiments, the apparatus further includes a fifth processing unit configured to reconstruct the prediction model using multiple sets of new experimental data when the confidence level is less than a confidence threshold and / or the misfit value is greater than or equal to the misfit threshold.

[0064] According to some embodiments of this application, another aspect of this application provides a battery device, including: a plurality of battery modules, wherein the group force of each of the battery modules is predicted using any of the above-mentioned methods for predicting the group force of battery modules.

[0065] According to some embodiments of this application, another aspect of this application provides an electrical device, including: a plurality of the above-described battery devices.

[0066] According to some embodiments of this application, another aspect of this application provides an energy storage system, including: a control device and at least one of the above-described electrical devices, wherein the control device is used to perform a prediction method for the packing force of any of the above-described battery modules.

[0067] Existing technologies primarily rely on repeated physical trial assembly to verify foam selection, resulting in long development cycles, high costs, and difficulty in quantifying the impact of assembly force on structural safety in the early design stages. This application introduces a quantitative functional relationship between foam hardness (H), thickness (T), and assembly pressing force (F). This model can capture the nonlinear synergistic effect of hardness and thickness on assembly force. By inputting the hardness and thickness parameters of the target foam, it directly outputs the predicted assembly force value, allowing for the determination of whether its mechanical properties meet the standards without manufacturing a physical prototype. Through a pre-established model verification mechanism, the accuracy and reliability of the prediction results are ensured, eliminating the need for extensive physical testing due to changes in foam specifications. This solves the problem of predicting module assembly pressing force during the energy storage battery module design stage without actual assembly.

[0068] In long-duration energy storage applications, the group force prediction model constructed in this invention also demonstrates significant auxiliary technical effects. As the energy density requirements for long-duration energy storage (such as 4h and 8h energy storage systems) continue to increase, systems tend to use high-capacity, large-cell batteries to reduce the number of modules and lower costs. However, high-capacity cells often experience greater volume expansion and deformation, as well as more complex stress distributions, during charge-discharge cycles, posing a greater challenge to the mechanical stability of the module's internal structure. Through this prediction model, engineers can accurately quantify the pressing force distribution under different foam parameters in the early design phase, ensuring that even under the large deformation conditions of high-capacity cells, the module's internal contact pressure remains uniform and stable. This not only effectively suppresses the risk of cell damage or connection failure caused by localized stress concentration but also ensures structural safety and cycle life during long-term operation, thus providing strong theoretical support and data assurance for the reliable deployment of long-duration energy storage systems ranging from 4h to 8h.

[0069] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0070] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0071] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists, A and B exist simultaneously, and B exists. In addition, the character " / " in this document generally indicates that the related objects before and after it have an "or" relationship.

[0072] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0073] In the description of the embodiments of this application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0074] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0075] In the accompanying drawings corresponding to the embodiments of this application, the thickness and area of ​​the layers are enlarged for better understanding and ease of description. When describing a component (such as a layer, film, region, or substrate) on or on the surface of another component, the component may be "directly" located on the surface of the other component, or there may be a third component between the two components. Conversely, when describing a component on the surface of another component, or when another component is formed or disposed on the surface of a component, it indicates that there is no third component between the two components. Furthermore, when describing a component as being "generally" formed on another component, it means that the component is not formed on the entire surface (or front surface) of the other component, nor is it formed on a portion of the edge of the entire surface.

[0076] In the description of the embodiments of this application, when a component "includes" another component, other components are not excluded unless otherwise stated, and other components may be further included. Furthermore, when a component such as a layer, film, region, or plate is referred to as being "on / located" on another component, it can be "directly on" the other component (i.e., located on the surface of the other component with no other components between them), or another component may be present therein. Moreover, when a component such as a layer, film, region, or plate is "directly located" on another component, or when a component such as a layer, film, region, or plate is located on the surface of another component, it indicates that no other components are located therein.

[0077] The terminology used in the description of the various embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments and the appended claims, the term "part" is also intended to include the plural form unless the context clearly indicates otherwise. Components include layers, films, regions, or plates, etc.

[0078] Those skilled in the art will understand that the above embodiments are specific examples of implementing this application, and in practical applications, various changes in form and detail can be made without departing from the spirit and scope of this application. Any person skilled in the art can make various alterations and modifications without departing from the spirit and scope of this application; therefore, the scope of protection of this application should be determined by the scope defined in the claims.

Claims

1. A method for predicting the pack strength of a battery module, characterized in that, include: Obtain the hardness and thickness parameters of the foam to be selected, wherein the foam to be selected is the foam structure set between the cells of the battery module; The hardness parameter and the thickness parameter are analyzed based on the pre-established prediction model to obtain the predicted values ​​of the group force corresponding to the hardness parameter and the thickness parameter. The pre-established prediction model is constructed using multiple sets of experimental data, including group force values ​​measured during actual pressing of foams with multiple combinations of hardness and thickness. The prediction model is established by analyzing the nonlinear synergistic influence of hardness and thickness on group force. The pre-established prediction model is built through experimental data of at least three sets of multiple hardness and multiple thickness combinations. Each set of experimental data comes from the measured group force of the same structural module under standard press-fitting process conditions. Before obtaining the hardness and thickness parameters of the candidate foam, the method further includes: statistically fitting the experimental data to establish a grouped force response function under the combined effect of hardness and thickness; using the grouped force response function to perform logarithmic transformation on the thickness under the hardness segmentation condition, and comprehensively fitting to obtain a global prediction relationship, wherein the fitting objects include the regression coefficients extracted by independent fitting under each hardness segmentation condition, and the global prediction relationship is the relationship between the hardness parameter, the thickness parameter and the grouped force prediction value.

2. The method for predicting the pack strength of a battery module according to claim 1, characterized in that, After obtaining the global prediction relationship through comprehensive fitting, the method further includes: The global prediction relationship is subjected to a pre-defined test to obtain the confidence level and lack of fit of the global prediction relationship. The pre-defined test includes residual normality test, equal variance test, independence test, and lack of fit test. If the confidence level is greater than or equal to the confidence threshold and the misfit value is less than the misfit threshold, the current prediction model is determined to be the pre-established prediction model, which includes the global prediction relationship.

3. The method for predicting the pack strength of a battery module according to claim 2, characterized in that, The method further includes: If the confidence level is less than the confidence threshold and / or the misfit value is greater than or equal to the misfit threshold, the prediction model is reconstructed using multiple sets of new experimental data.

4. The method for predicting the pack strength of a battery module according to claim 1, characterized in that, The hardness of the experimental data is in the range of 30 Shore C to 70 Shore C, and the thickness of the experimental data is in the range of 2 mm to 6 mm.

5. A device for predicting the pack force of a battery module, applied to the method for predicting the pack force of a battery module according to any one of claims 1 to 4, characterized in that, The device includes: The acquisition unit is used to acquire the hardness and thickness parameters of the foam to be selected, wherein the foam to be selected is a foam structure set between the cells of the battery module; The first processing unit is used to analyze the hardness parameter and the thickness parameter based on a pre-established prediction model to obtain the predicted group force value corresponding to the hardness parameter and the thickness parameter. The pre-established prediction model is constructed using multiple sets of experimental data, including group force values ​​measured during actual pressing of foams with multiple combinations of hardness and thickness. The prediction model is established by analyzing the nonlinear synergistic influence of hardness and thickness on the group force.

6. A battery device, characterized in that, include: Multiple battery modules are provided, and the group force of each battery module is predicted using the group force prediction method of any one of claims 1 to 4.

7. An electrical device, characterized in that, include: The battery device as described in several claims 6.

8. An energy storage system, characterized in that, include: A control device and at least one electrical device as claimed in claim 7, the control device being used to perform a method for predicting the packing force of a battery module as claimed in any one of claims 1 to 4.

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