A battery rupture risk assessment method, device, equipment and readable storage medium

Abstract

This application relates to the field of battery risk assessment technology, and discloses a method, apparatus, device, and readable storage medium for assessing battery rupture risk. The method includes: acquiring a baseline parameter set for a baseline operating condition; performing simulation processing on the baseline parameter set using a preset simulation model to obtain a corresponding strain energy density reference value characterizing the tendency of electrode particles to rupture; acquiring a set of operating condition parameters for the operating condition to be assessed, and performing simulation based on the simulation model to obtain the corresponding peak strain energy density; calculating a risk coefficient characterizing the relative change in rupture risk based on the strain energy density reference value and the peak strain energy density; and determining the rupture risk level of the operating condition to be assessed based on the risk coefficient and level rules. This application achieves quantitative comparison, trend prediction, and sensitivity ranking of rupture risks under different battery designs and complex operating conditions, significantly improving the engineering robustness, cross-scheme comparability, and decision support capabilities for design optimization of simulation results.

Description

Technical Field

[0001] This application relates to the field of battery risk assessment technology, and in particular to a method, apparatus, device and readable storage medium for assessing battery rupture risk. Background Technology

[0002] With the rapid development of lithium-ion batteries towards high energy density and high-rate fast charging, mechanical cracking, pulverization, and interfacial debonding of electrode active particles (such as silicon-based anodes and high-nickel cathodes) during cycling have become core failure mechanisms leading to capacity decay, increased internal resistance, and heightened risk of thermal runaway. Currently, the industry primarily employs two technical approaches for failure research: one is to use advanced characterization methods such as FIB-SEM, XCT, and in-situ TEM to perform offline morphology and composition analysis of electrodes after cycling; the other is to construct electrochemical-mechanical coupled simulation models (such as the P2D-diffusion-induced stress model) to quantitatively simulate the spatiotemporal evolution of internal stress, strain, and strain energy density of the electrodes during charging and discharging. The latter approach, due to its advantages of low cost, high reproducibility, strong parameterization capabilities, and deep mechanism revelation, has been widely applied in battery material selection, structural design, and lifespan prediction, and is deeply integrated into the R&D processes of mainstream battery companies.

[0003] However, when this simulation technology is used for engineering-oriented "fracture risk assessment," it faces a triple contradiction: First, the assessment logic is disconnected from engineering implementation—existing methods require comparing the stress / strain / strain energy density output by the simulation with a certain "absolute fracture threshold" to determine the risk. This threshold is heavily dependent on the material's microstructure, process conditions, and batch differences. Calibration requires expensive and difficult-to-reproduce in-situ mechanical experiments, making it difficult for simulation results to be incorporated into mass production design. Second, the assessment results lack comparability and decision support—different projects have incompatible risk conclusions due to model simplification, mesh settings, or different threshold assumptions, making it impossible to achieve cross-scheme quantitative comparison or cross-parameter attribution analysis. Third, the analysis output and optimization closed-loop fracture—simulation often stops at cloud map display or single-condition "pass / fail" judgment, lacking systematic parameter scanning, sensitivity quantification, and multi-objective trade-off capabilities. This prevents engineers from identifying key sensitive factors, and optimization work is often trapped in trial and error. Summary of the Invention

[0004] In view of this, embodiments of this application provide a battery rupture risk assessment method, apparatus, device, and readable storage medium, which can effectively solve key problems in the prior art such as poor engineering applicability due to reliance on the absolute rupture threshold of materials, incomparability of risk results between different design schemes, and lack of quantitative sensitivity and optimization guidance capability in simulation analysis.

[0005] In a first aspect, embodiments of this application provide a method for assessing the risk of battery rupture, including: Obtain the baseline parameter set for the baseline operating condition; The baseline parameter set is simulated using a preset simulation model to obtain the strain energy density reference value that characterizes the tendency of electrode particles to fracture corresponding to the baseline working condition. Obtain the set of working parameters for the working condition to be evaluated, and perform simulation processing on the set of working parameters based on the simulation model to obtain the peak value of strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to break. Based on the strain energy density reference value and the strain energy density peak value, a risk coefficient characterizing the relative change in fracture risk is calculated. Based on the risk coefficient and the preset level rules, the rupture risk level of the working condition to be evaluated is determined.

[0006] In some embodiments, before performing simulation processing on the baseline parameter set using a preset simulation model, the method further includes: Determine the baseline parameter set based on engineering benchmarks; Based on the baseline parameter set, a simulation model supporting dynamic configuration of multiple parameters is constructed.

[0007] In some embodiments, the method further includes: For at least one parameter that affects the tendency of electrode particles to break, a single-factor trend analysis is performed to obtain the breakage risk relationship curve; Based on the fracture risk relationship curve, the parameter with the highest impact on fracture risk is identified, and corresponding parameter adjustment suggestions are generated.

[0008] In some embodiments, the step of performing simulation processing on the baseline parameter set using a preset simulation model to obtain a strain energy density reference value characterizing the tendency of electrode particle fracture corresponding to the baseline operating condition includes: The simulation model is run based on the baseline parameter set to obtain the first stress field and strain field distribution data of the electrode region during the charging and discharging process. Based on the first stress field and strain field data, the strain energy density of the electrode region is calculated, and the global maximum value of the strain energy density during the entire charging and discharging process is determined. The global maximum value is then determined as the strain energy density reference value characterizing the tendency of electrode particles to break.

[0009] In some embodiments, obtaining the set of working condition parameters for the working condition to be evaluated, and performing simulation processing on the set of working condition parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to fracture, includes: The simulation model is run based on the set of operating parameters to obtain the second stress field and strain field distribution data of the electrode region during the charging and discharging process. Based on the second stress field and strain field data, the strain energy density of the electrode region is calculated, and the global maximum value of the strain energy density during the entire charging and discharging process is determined. The global maximum value is then determined as the strain energy density peak value characterizing the tendency of electrode particles to break.

[0010] In some embodiments, calculating the risk coefficient characterizing the relative change in fracture risk based on the strain energy density reference value and the strain energy density peak value includes: Obtain the peak value of the strain energy density and the reference value of the strain energy density; The difference between the peak strain energy density and the reference strain energy density is calculated, and the difference is divided by the reference strain energy density to obtain the risk coefficient.

[0011] In some embodiments, determining the fracture risk level of the working condition to be evaluated based on the risk coefficient and a preset level rule includes: The risk coefficient is compared with the risk level thresholds in the preset level rules; Based on the comparison results, the rupture risk level of the working condition to be evaluated is determined.

[0012] Secondly, embodiments of this application provide a battery rupture risk assessment device, comprising: The parameter acquisition module is used to acquire the baseline parameter set of the baseline operating condition; The simulation processing module is used to perform simulation processing on the baseline parameter set using a preset simulation model to obtain the strain energy density reference value corresponding to the baseline working condition, which characterizes the tendency of electrode particles to break. The peak acquisition module is used to acquire the set of working parameters of the working condition to be evaluated, and perform simulation processing on the set of working parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to break. The risk acquisition module is used to calculate a risk coefficient characterizing the relative change in fracture risk based on the strain energy density reference value and the strain energy density peak value. The risk level determination module is used to determine the rupture risk level of the working condition to be evaluated based on the risk coefficient and the preset risk level rules.

[0013] Thirdly, embodiments of this application provide a terminal device, the terminal device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the battery rupture risk assessment method of the first aspect described above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium, wherein when the computer program is executed on a processor, it implements the battery rupture risk assessment method of the first aspect described above.

[0015] The embodiments of this application have the following beneficial effects: By obtaining the baseline parameter set of the baseline operating condition, a baseline simulation is performed using a preset simulation model to obtain a strain energy density reference value characterizing the tendency of electrode particles to fracture; then, the parameter set of the operating condition to be evaluated is obtained and the corresponding simulation is performed to obtain its strain energy density peak value; then, a normalized risk coefficient is calculated based on the two, and the fracture risk level is determined according to a preset level rule. This method completely abandons the dependence on the absolute fracture threshold of the material, and uses the relative change under the same model as the criterion to ensure that the risk results between different designs and different operating conditions are strictly comparable; the generated risk coefficient can directly support trend analysis, sensitivity ranking and graded early warning across parameter dimensions; it avoids misjudgment caused by threshold uncertainty, and significantly improves the engineering usability and decision reliability of simulation results in rapid battery design, BMS strategy formulation and material selection. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a battery rupture risk assessment method according to an embodiment of this application is shown; Figure 2 Another flowchart of the battery rupture risk assessment method according to an embodiment of this application is shown; Figure 3 This paper illustrates another flowchart of the battery rupture risk assessment method according to an embodiment of the present application; Figure 4 The diagram shows strain energy density maps of different charging rates in the battery rupture risk assessment method of this application embodiment; Figure 5 This paper illustrates the trend of risk coefficients at different rate in the battery breakage risk assessment method according to an embodiment of this application. Figure 6 The diagram shows strain energy density maps of different particle sizes during the charging process in the battery rupture risk assessment method of this application embodiment; Figure 7 This paper illustrates the trend of risk coefficients for different particle sizes in the battery rupture risk assessment method according to an embodiment of this application. Figure 8The diagram shows strain energy density maps of the charging process at different temperatures in the battery rupture risk assessment method of this application embodiment; Figure 9 This paper illustrates the trend of risk coefficients at different temperatures in the battery rupture risk assessment method according to an embodiment of this application. Figure 10 A schematic diagram of a battery rupture risk assessment method according to an embodiment of this application is shown. Detailed Implementation

[0018] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] In the following text, the terms "comprising," "having," and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more combinations thereof. Furthermore, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0021] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0022] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] Considering the key problems in existing technologies, such as poor engineering applicability due to reliance on the absolute fracture threshold of materials, incomparable risk results between different design schemes, and lack of quantitative sensitivity and optimization guidance in simulation analysis, a battery fracture risk assessment method is proposed. This method uses a single, engineering-reproducible baseline condition as a reference benchmark. Using the same simulation model, it obtains the reference value of strain energy density under the baseline condition and the peak value of strain energy density under the condition to be assessed, respectively. Based on these two values, a risk coefficient is calculated, and the fracture risk level is directly determined according to preset level rules. This method fundamentally decouples risk assessment from intrinsic material fracture parameters, ensuring that risk results under different design parameters (such as particle size and conductive agent content) and different operating conditions (such as temperature and charging rate) have strictly consistent dimensions, comparable benchmarks, and definite level mappings. This elevates the simulation output from a qualitative cloud map to a quantifiable engineering indicator that can be ranked, provides early warnings, and drives design decisions.

[0024] The following examples illustrate the battery rupture risk assessment method.

[0025] Figure 1 A flowchart of a battery rupture risk assessment method according to an embodiment of this application is shown. Exemplarily, the battery rupture risk assessment method includes the following steps: Step S100: Obtain the baseline parameter set of the baseline operating condition.

[0026] The baseline operating condition refers to the battery operating state considered safe in engineering or serving as a design benchmark. Its purpose is to provide a unified and reproducible reference standard for all subsequent fracture risk assessments, thereby completely avoiding dependence on the absolute fracture threshold of materials. The baseline parameter set is a collection of defined design and operating condition parameters that have a dominant influence on the fracture tendency of electrode particles, including key design and operating condition parameters such as the baseline charge rate (C), baseline temperature (T), and baseline active particle size (R). The acquisition process involves extracting parameter values ​​from the nominal operating conditions clearly defined in industry standards, publicly available product technical specifications, or recognized testing standards, ensuring that the benchmark has engineering consensus and feasibility.

[0027] For example, the baseline parameter set is taken as: reference charge rate. Reference ambient temperature Reference active particle size .

[0028] In other implementations, the parameters in the baseline parameter set can be adjusted within a reasonable engineering range, for example: charging rate. Available from to For any nominal value within the range, ambient temperature Available from to For any nominal value within the range, the particle size of the active particles Available from Any nominal value within the range, but all parameters must together constitute a nominal working state that is recognized as safe and stable in engineering.

[0029] Step S200: Perform simulation processing on the baseline parameter set using a preset simulation model to obtain the strain energy density reference value corresponding to the baseline working condition, which characterizes the tendency of electrode particles to fracture.

[0030] The simulation model refers to a parametric electrochemical-mechanical coupling model. Its electrochemical component employs a pseudo-two-dimensional P2D model or a simplified form, while the mechanical component uses a diffusion-induced stress model. This model is capable of receiving input parameters such as charge rate, ambient temperature, and active particle size. It can receive parameters and output the stress tensor and strain tensor field distributions of the negative electrode coating region. The strain energy density reference value refers to the global maximum strain energy density appearing in the negative electrode coating region during a complete charge-discharge cycle under baseline conditions. This serves as the energy benchmark for all subsequent risk assessments, ensuring that the assessment results do not depend on the absolute fracture parameters of the material. Simulation processing involves using this set of parameters as initial conditions to drive the model to complete a full charge-discharge transient solution from SOC=0% to 100% and back to 0%.

[0031] As an example, with (C0 = 1C, T0 = 25°C, R0 = 6 μm) as input, the model performs a constant current charging process from SOC = 0% to 100% and a subsequent constant current discharge process, and outputs full-field stress and strain evolution data of the entire negative electrode coating.

[0032] For example, the electrochemical-mechanical coupled simulation model can input and flexibly adjust key design and operating parameters such as charging rate (C), ambient temperature (T), and active particle size (R).

[0033] In other implementations, the simulation model may use a single-particle model for the electrochemical part and an isotropic elastoplastic constitutive model for the mechanical part, but the model must maintain the ability to explicitly input the charging rate, ambient temperature, and particle size of the active particles, and be able to output stress and strain fields.

[0034] In one alternative embodiment, such as Figure 2 As shown, step S200 includes the following sub-steps: S201, based on the baseline parameter set, runs the simulation model to obtain the first stress field and strain field distribution data of the electrode region during the charging and discharging process.

[0035] The first stress and strain field distribution data refers to the spatiotemporally resolved stress and strain tensor field data output by the model under the drive of the baseline parameter set. Its spatial resolution is determined by the finite element mesh, and its temporal resolution is the simulation time step. This data is the direct physical basis for calculating the strain energy density. The electrode region specifically refers to the negative electrode coating. The charging and discharging process refers to the complete cycle from the initial state of charge through constant current charging to full charge, and then through constant current discharging to cutoff. Its duration is determined by the reference charging rate. Decide.

[0036] As an example, with C0=1 C, T0=25°C, and R0=6 μm as inputs, the model performs the entire process of constant current charging from SOC=0% to 100% and subsequent constant current discharging, and outputs full-field stress and strain evolution data of the entire negative electrode coating.

[0037] In other embodiments, the first stress field and strain field data may be extracted only during the constant current charging stage, or only the cross-sectional data of the center of the negative electrode active layer may be extracted. The obtained data characterizes the key areas and key time periods of fracture risk.

[0038] S202, based on the first stress field and strain field data, calculate the strain energy density of the electrode region, and determine the global maximum value of the strain energy density throughout the charging and discharging process. The global maximum value is then determined as the strain energy density reference value characterizing the tendency of electrode particles to break.

[0039] Among them, strain energy density is the standard physical field output variable generated by the finite element simulation software after completing the stress-strain field solution in response to the user enabling the strain energy density output function; the global maximum value refers to the peak value of strain energy density that appears in the entire negative electrode coating area during a complete charge-discharge simulation cycle.

[0040] As an example, for the simulation results of the working condition C0=1 C, T0=25°C, R0=6 μm, the user enables the strain energy density output function in the simulation software, and the software returns the full-field strain energy density data; the system performs a full-domain spatiotemporal scan on the data and obtains the global maximum value U_base=28840 J / m³, which is the strain energy density reference value under the baseline working condition.

[0041] In an optional embodiment, the following sub-steps are included before step S200: Determine the baseline parameter set based on engineering benchmarks.

[0042] Among them, engineering benchmarks refer to the nominal operating conditions clearly defined in industry standards, publicly available product technical specifications, or recognized testing standards. Their role is to ensure that baseline parameters have engineering consensus, reproducibility, and safety, thereby providing a reliable starting point for subsequent relative risk assessment. Industry standards include the room temperature test conditions specified in standards such as GB / T 31484 "Cycle Life Requirements and Test Methods for Power Batteries for Electric Vehicles". Publicly available product technical specifications refer to technical documents released by battery manufacturers that specify the nominal charging rate and operating temperature range. Recognized testing standards refer to internationally recognized battery testing standards such as the IEC62660 series.

[0043] As an example, based on the publicly available technical specifications of a certain mass-produced power battery, its nominal standard charging rate is selected as... According to Appendix A of GB / T 31484, the recommended room temperature test standard was selected as... Based on the product data sheet provided by the negative electrode material supplier, the median particle size of its typical active particles was selected. As These three elements together constitute the baseline parameter set.

[0044] For example, you can select the standard charging rate (1C) specified in the battery datasheet, room temperature environment (25°C), and the median particle size (D) of typical active particles provided by the material supplier. 50 The baseline is defined by using a common standard such as 6μm.

[0045] In other implementations, the engineering reference can be other nominal values ​​that conform to industry practice, such as... However, all parameters must together constitute a nominal working state that has been verified by engineering practice as safe and stable.

[0046] Based on the baseline parameter set, a simulation model that supports dynamic configuration of multiple parameters is constructed.

[0047] Among them, the simulation model that supports dynamic configuration of multiple parameters refers to an electrochemical-mechanical coupled simulation model with independent parameter input ports. It allows users to input values ​​to three ports: charging rate (C), ambient temperature (T), and active particle size (R). The inputs to the three ports of C, T, and R are independent of each other. Changing any parameter does not change the values ​​of the other parameters. Without modifying the underlying control equations of the model, it completes the mapping of parameters to the corresponding physical fields, thereby generating simulation results adapted to the set of parameters. The model can be reused for simulation calculations of baseline conditions and all conditions to be evaluated, ensuring the consistency of the numerical basis on which the risk assessment depends.

[0048] As an example, a model is constructed with C0=1 C, T0=25°C, and R0=6 μm as initial inputs. Then, through parameter assignment operations, the input values ​​at port C are updated to 2 C, port T to 0°C, and port R to 3 μm, and the model outputs stress and strain field data for the corresponding working conditions.

[0049] Step S300: Obtain the set of working parameters for the working condition to be evaluated, and perform simulation processing on the set of working parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to fracture.

[0050] The operating condition to be evaluated refers to any non-baseline operating state that requires a fracture risk assessment; the operating condition parameter set is a collection of parameters of the same type as the baseline parameters, including the charging rate. Ambient temperature With active particle size This configuration maintains dimensional consistency with the baseline parameter set, ensuring that the two can be equivalently calculated under the same simulation model; the peak strain energy density refers to the global maximum strain energy density that occurs in the electrode region during a complete charge-discharge cycle under this operating condition. As a numerator in the risk calculation, it, together with the reference value obtained from S200, forms the basis for relative evolution assessment.

[0051] As an example, the working condition to be evaluated is taken as follows: , , With the other parameters unchanged, input this set of parameters into the model, run a 0%–100% SOC charge-discharge simulation, and output the full-field stress and strain data of the negative electrode coating.

[0052] In one alternative embodiment, such as Figure 3 As shown, step S300 includes the following sub-steps: S301, based on the operating condition parameter set, runs the simulation model to obtain the second stress field and strain field distribution data of the electrode area during the charging and discharging process.

[0053] Among them, the second stress field and strain field distribution data refer to the spatiotemporal resolved stress tensor and strain tensor field data output by the simulation model under the drive of the parameter set of the working condition to be evaluated.

[0054] In an exemplary manner, with Using the input as input, the simulation model completes the simulation of the entire charging and discharging process, and outputs full-field stress and strain evolution data of the entire negative electrode coating.

[0055] S302, based on the second stress field and strain field data, calculate the strain energy density of the electrode region, and determine the global maximum value of the strain energy density throughout the charging and discharging process. The global maximum value is determined as the strain energy density peak value characterizing the tendency of electrode particles to break.

[0056] As an example, for the simulation results of the working condition C=2C, T=25°C, R=6 μm, the user enables the strain energy density output function in the simulation software, and the software returns the full-field strain energy density data; the system performs a full-domain spatiotemporal scan on the data: traversing all finite element elements in the negative electrode coating area and all time steps in the complete charge and discharge cycle, extracting the maximum value of the strain energy density values, and obtaining U_max=64034 J / m³, which is the peak value of the strain energy density under the working condition to be evaluated.

[0057] In other embodiments, the peak strain energy density can be obtained by directly extracting the maximum value of the central node of the negative electrode active layer; or by taking the maximum value during the constant current charging stage instead of the maximum value of the entire cycle.

[0058] Step S400: Based on the reference value and peak value of strain energy density, calculate the risk coefficient characterizing the relative change in fracture risk.

[0059] The strain energy density reference value is the global maximum strain energy density obtained under baseline conditions, denoted as . The peak strain energy density is the global maximum strain energy density obtained under the condition being evaluated, denoted as . The risk coefficient is a dimensionless quantitative indicator that characterizes the increase in fracture risk of the assessed operating condition relative to the baseline operating condition. Its physical meaning lies in stripping away the influence of intrinsic material parameters and reflecting only the energy evolution trend caused by design or operating condition variations. Its function is to enable direct risk comparison across parameters and schemes. Its calculation formula is: η=[(U_max [U_base) / U_base]×100%; Exemplary, , Substituting into the formula, the risk coefficient is calculated. This value will be used as an input parameter for subsequent risk level determination.

[0060] In an optional embodiment, step S400 includes the following sub-steps: S401, obtain the peak value of strain energy density and the reference value of strain energy density.

[0061] Here, "acquisition" refers to extracting the calculated U_max and U_base values ​​from the output of the simulation model. These values ​​may take the form of, but are not limited to, structured data files exported by the simulation software, variable values ​​in runtime memory, data streams returned by external systems through standardized interfaces (such as RESTful APIs or OPC UAs), or pre-stored results in the enterprise product data management (PLM) system. The values ​​are in floating-point format and are used in subsequent calculations.

[0062] For example, U_base and U_max can be extracted from the simulation result datasets of the baseline condition and the condition to be evaluated, respectively, for example, by calling the result reading function provided by the simulation platform.

[0063] S402, calculate the difference between the peak strain energy density and the reference strain energy density, divide the difference by the reference strain energy density to obtain the risk coefficient.

[0064] Wherein, the difference is Its physical meaning is the absolute increase in strain energy density of the condition under evaluation compared to the baseline condition; dividing by the strain energy density reference value is the normalization process, which removes the dimensionless constraint from the result, making it a dimensionless ratio that purely reflects the degree of relative change; the risk coefficient is finally expressed as a percentage (×100%) (e.g., η=122%, η=317%, η= (65%), which forms the direct basis for risk level classification.

[0065] Exemplary, The difference is 35194, normalized to 35194 / 28840≈1.2199, multiplied by 100% to get η=122% (rounded to the nearest integer); this result is directly used for subsequent level determination (100%<η≤200%→medium risk).

[0066] In other implementations, the risk factor may retain decimal places (e.g., 122.0%) or use scientific notation (1.22 × 10²%), but all forms must ensure that the numerical precision is not less than four significant figures to meet the robustness requirements of the engineering criteria.

[0067] Step S500: Determine the rupture risk level of the working condition to be evaluated based on the risk coefficient and the preset level rules.

[0068] Among them, the preset level rule refers to the judgment logic that maps the risk coefficient to the operational level of the project. Its function is to transform the abstract numerical results into clear action basis for BMS strategy, thermal management instructions or design decisions. The rupture risk level is divided into three categories: low risk, medium risk, and high risk. For example: low risk area: η≤100%; medium risk area: 100%<η≤200%; high risk area: η>200%.

[0069] Exemplary, take Compare it with the preset level rules: because Therefore, it was determined to be a "medium-risk area"; this conclusion is directly used to guide the "recommendation that the maximum sustainable fast charging rate should not exceed approximately 2.6C".

[0070] In other implementations, the grading rules can be extended to four levels (e.g., adding "extremely high risk") or the threshold can be adjusted (e.g., medium risk can be changed to 80% < η ≤ 180%).

[0071] In an optional embodiment, step S500 includes the following sub-steps: S501 compares the risk coefficient with the threshold values ​​of each risk level in the preset risk level rules.

[0072] Among them, the thresholds for each risk level refer to the numerical points that constitute the boundary of the level, including the upper limit of low risk (100%), the upper limit of medium risk (200%), etc.; comparison refers to performing standard numerical comparison operations (>, <, ≥, ≤), judging in a fixed order from low to high or from high to low, ensuring that a single input falls into only one level range, avoiding logical overlap or missed judgment.

[0073] Exemplary, for Execution comparison: Step 1, 122% > 100%, does not meet the low risk requirement; Step 2, 122% ≤ 200%, meets the medium risk requirement, and the judgment ends.

[0074] In other implementations, the comparison order can be changed to high to low (first judging high risk), or a lookup table (LUT) method can be used, or function mapping can be used, etc.

[0075] S502. Based on the comparison results, determine the rupture risk level of the working condition to be evaluated.

[0076] Determining the rupture risk level involves converting the identifier into a semantically clear engineering level label and persistently storing or outputting it in real time. Its function is to complete the final transformation from mathematical indicators to engineering decisions. For example, S501 outputs the matching medium-risk range, and S502 writes the result to a standard JSON format report. This report can trigger a "limit to 2.6C" command or be archived by the PLM system for design review.

[0077] In other implementations, the risk level may be accompanied by a confidence level marker or associated with specific optimization recommendations (such as "medium risk - recommend reducing the multiplier or increasing the temperature").

[0078] In an optional embodiment, the battery rupture risk assessment method further includes the following steps: For at least one parameter affecting the fracture tendency of electrode particles, a single-factor trend analysis is performed to obtain a fracture risk relationship curve. Based on the fracture risk relationship curve, the parameter with the highest impact on fracture risk is identified, and corresponding parameter adjustment suggestions are generated.

[0079] Among them, at least one parameter affecting the tendency of electrode particles to break is the charging rate. Ambient temperature With active particle size Any of the following; single-factor trend analysis refers to keeping all other parameters fixed at the baseline value, only changing the target parameter to take multiple discrete values ​​within its reasonable engineering range, and repeating the entire S100–S500 process for each value to obtain a set of risk coefficients. "Sequence; Rupture Risk Relationship Curve" refers to a curve with the target parameter as the horizontal axis and corresponding... The value is a two-dimensional chart plotted on the vertical axis, which is used to intuitively reveal the quantitative relationship between parameter changes and risk evolution; the parameter adjustment suggestion refers to the allowable range of parameters based on the intersection of the curve slope or threshold, so as to make the risk level meet the preset target (such as control it below medium risk).

[0080] The reasonable range for engineering is specifically: charging rate Ambient temperature Active particle size "Identifying the highest degree of influence" refers to calculating the local slope of the curve. The parameter with the largest absolute value is the most sensitive parameter.

[0081] Exemplary, fixed ,Will Set as follows Perform S100–S500 on each group to obtain The values ​​are respectively ;draw The curve was calculated, and the slope of each interval was found to be... The slope of the segment is as high as This is the most sensitive factor in the current design; based on this, the following suggestion is generated: "To ensure..." (Medium-risk upper limit), the charging rate must be controlled within the following".

[0082] In other implementations, a multi-factor combined scan (e.g.) can be performed. (Joint changes), and construct a three-dimensional response surface or Pareto optimal front based on the obtained dataset to identify those that simultaneously satisfy multiple constraints (such as... The feasible domain of the design.

[0083] This embodiment achieves a decoupled diagnosis of the battery rupture risk driving mechanism by performing single-factor trend analysis and identifying the parameters with the highest impact on rupture risk. This allows the assessment results to move beyond simply determining the overall risk level and accurately pinpoint the dominant influencing factors. Based on this, the generated parameter adjustment suggestions transform abstract risk indicators into explicit design constraints, providing executable engineering inputs for material selection, operating condition settings, and system strategy formulation. Furthermore, this analytical capability supports collaborative optimization solutions among multiple objectives, enabling battery design to determine the feasible design domain that satisfies multiple constraints within mutually constraining performance boundaries.

[0084] In an optional embodiment, the specific implementation process of the present invention can be further illustrated by the following examples: Example 1: Sensitivity analysis of charging rate on breakage risk and optimization of fast charging strategy Baseline settings: Taking graphite anode as an example, the baseline operating conditions are set as follows: T0=25°C, R0=6um, C0=1C (charged to SOC=100%). The simulation yields U_base=28840 J / m³.

[0085] Trend scan: With T0 and R0 fixed, simulations were performed with the charging rate C set to 0.5C, 1C (baseline), 1.5C, and 2C respectively, to obtain the strain energy density change, such as... Figure 4 As shown.

[0086] risk assessment: C=0.5C: U_max=14900 J / m³, η=(14900-28840) / 28840=-48% (low risk) C=2C:U_max=64034 J / m³, η=(64034-28840) / 28840=122% (Medium risk) C=4C:U_max=120150 J / m³, η=(120150-28840) / 28840=317% (High risk) Optimization guidance: Risk coefficient trend as follows Figure 5 As shown, the risk increases almost linearly with the charging rate. To ensure that the risk does not enter the "high-risk zone" (η<200%), the maximum sustainable fast charging rate of this battery at room temperature should be recommended not to exceed approximately 2.6C.

[0087] Example 2: The Influence of Active Particle Size on Fragmentation Risk and Material Selection Guidance Baseline setting: Same as Example 1, with R0=6um as the baseline particle size.

[0088] Trend scanning: With T0 and C0 fixed, simulations were performed with particle sizes R set to 6 μm (baseline), 9 μm, and 12 μm, respectively, to obtain the strain energy density changes as follows: Figure 6 As shown.

[0089] risk assessment: R=9μm:U_max=80900 J / m³,η=(80900-28840) / 28840=181% (Medium risk) R=12μm:U_max=175450 J / m³,η=(175450-28840) / 28840=508% (High risk) Optimization guidance: The risk of fracture increases sharply with increasing particle size (e.g., Figure 7 (As shown). If the design goal is to withstand 4C fast charging (see Example 1, where η has reached 317%), reducing the particle size is the most effective material design method to reduce the risk of breakage.

[0090] Example 3: The impact of ambient temperature on fracture risk and the formulation of thermal management strategies Baseline setting: Same as Example 1, with T0=25℃ as the baseline temperature.

[0091] Trend scan: Fix C0 and R0, and set the temperature T to 45℃, 25℃ (baseline), and 0℃ respectively. Figure 8 (As shown).

[0092] risk assessment: T=45°C: U_max=10140 J / m³, η=(10140-28840) / 28840=-65% (low risk) T=0°C: U_max=748350 J / m³, η=(748350-28840) / 28840=2495% (extremely high risk) Optimization guidance: Low temperatures have an extremely aggravating effect on the risk of rupture (e.g., Figure 9 (As shown). Even with a 1C charging rate at 0°C, the risk is already far beyond the baseline. Therefore, the BMS strategy must include low-temperature current limiting functionality. For example, according to the trend chart, to keep η below the high-risk level, the charging rate at 0°C needs to be limited to approximately 0.08C or lower.

[0093] Example 4: Multi-factor comprehensive trade-off analysis For example, if a battery is to achieve 2C fast charging capability while ensuring safe operation in a low temperature environment of 0℃, it is difficult to meet the requirements by adjusting any one parameter alone (e.g., in Example 1, η=122% for 2C at 25℃; in Example 3, η=2495% for 1C at 0℃).

[0094] At this point, the method of the present invention employs a combinatorial optimization strategy: First, thermal management is implemented: by preheating, the battery temperature is raised from 0°C to 25°C, which alone can reduce the risk by approximately 2373% (from η=2495% to η=122%).

[0095] Meanwhile, optimizing the material design: reducing the negative electrode particle size from 6µm to 3µm can further reduce the risk (expected to reduce η by about 169%).

[0096] By combining the strategies of "increasing temperature and reducing particle size", the total risk of 2C fast charging in low-temperature environments is reduced from "absolute danger" to a controllable "medium-high risk" range, providing a clear and quantifiable optimization path for engineering implementation.

[0097] Figure 10 A schematic diagram of a battery rupture risk assessment device according to an embodiment of this application is shown. Exemplarily, the device 100 includes: The parameter acquisition module 110 is used to acquire the baseline parameter set of the baseline working condition; The simulation processing module 120 is used to perform simulation processing on the baseline parameter set using a preset simulation model to obtain the strain energy density reference value corresponding to the baseline working condition, which characterizes the tendency of electrode particles to break. Peak acquisition module 130 is used to acquire the set of working condition parameters of the working condition to be evaluated, and perform simulation processing on the set of working condition parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to break. The risk acquisition module 140 is used to calculate a risk coefficient characterizing the relative change in fracture risk based on the strain energy density reference value and the strain energy density peak value. The risk level determination module 150 is used to determine the rupture risk level of the working condition to be evaluated based on the risk coefficient and the preset risk level rules.

[0098] It is understood that the apparatus of this embodiment corresponds to the method of the above embodiments, and the options in the above embodiments are also applicable to this embodiment, so they will not be described again here.

[0099] This application also provides a terminal device, exemplary of which includes a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to enable the terminal device to perform the functions of the various modules in the above-described method or apparatus.

[0100] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0101] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0102] This application also provides a computer-readable storage medium for storing the computer program used in the aforementioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0104] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0105] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for assessing the risk of battery rupture, characterized in that, The method includes: Obtain the baseline parameter set for the baseline operating condition; The baseline parameter set is simulated using a preset simulation model to obtain the strain energy density reference value that characterizes the tendency of electrode particles to fracture corresponding to the baseline working condition. Obtain the set of working parameters for the working condition to be evaluated, and perform simulation processing on the set of working parameters based on the simulation model to obtain the peak value of strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to break. Based on the strain energy density reference value and the strain energy density peak value, a risk coefficient characterizing the relative change in fracture risk is calculated. Based on the risk coefficient and the preset level rules, the rupture risk level of the working condition to be evaluated is determined.

2. The battery rupture risk assessment method according to claim 1, characterized in that, Before performing simulation processing on the baseline parameter set using a preset simulation model, the method further includes: Determine the baseline parameter set based on engineering benchmarks; Based on the baseline parameter set, a simulation model supporting dynamic configuration of multiple parameters is constructed.

3. The battery rupture risk assessment method according to claim 1, characterized in that, The method further includes: For at least one parameter that affects the tendency of electrode particles to break, a single-factor trend analysis is performed to obtain the breakage risk relationship curve; Based on the fracture risk relationship curve, the parameter with the highest impact on fracture risk is identified, and corresponding parameter adjustment suggestions are generated.

4. The battery rupture risk assessment method according to claim 1, characterized in that, The step of performing simulation processing on the baseline parameter set using a preset simulation model to obtain the strain energy density reference value characterizing the tendency of electrode particle fracture corresponding to the baseline working condition includes: The simulation model is run based on the baseline parameter set to obtain the first stress field and strain field distribution data of the electrode region during the charging and discharging process. Based on the first stress field and strain field data, the strain energy density of the electrode region is calculated, and the global maximum value of the strain energy density during the entire charging and discharging process is determined. The global maximum value is then determined as the strain energy density reference value characterizing the tendency of electrode particles to break.

5. The battery rupture risk assessment method according to claim 1, characterized in that, The process of acquiring the set of working condition parameters for the working condition to be evaluated, and performing simulation processing on the set of working condition parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to fracture, includes: The simulation model is run based on the set of operating parameters to obtain the second stress field and strain field distribution data of the electrode region during the charging and discharging process. Based on the second stress field and strain field data, the strain energy density of the electrode region is calculated, and the global maximum value of the strain energy density during the entire charging and discharging process is determined. The global maximum value is then determined as the strain energy density peak value characterizing the tendency of electrode particles to break.

6. The battery rupture risk assessment method according to claim 1, characterized in that, The calculation of the risk coefficient, characterizing the relative change in fracture risk, based on the strain energy density reference value and the strain energy density peak value, includes: Obtain the peak value of the strain energy density and the reference value of the strain energy density; The difference between the peak strain energy density and the reference strain energy density is calculated, and the difference is divided by the reference strain energy density to obtain the risk coefficient.

7. The battery rupture risk assessment method according to claim 1, characterized in that, The step of determining the fracture risk level of the working condition to be evaluated based on the risk coefficient and the preset level rules includes: The risk coefficient is compared with the risk level thresholds in the preset level rules; Based on the comparison results, the rupture risk level of the working condition to be evaluated is determined.

8. A battery rupture risk assessment device, characterized in that, include: The parameter acquisition module is used to acquire the baseline parameter set of the baseline operating condition; The simulation processing module is used to perform simulation processing on the baseline parameter set using a preset simulation model to obtain the strain energy density reference value corresponding to the baseline working condition, which characterizes the tendency of electrode particles to break. The peak acquisition module is used to acquire the set of working parameters of the working condition to be evaluated, and perform simulation processing on the set of working parameters based on the simulation model to obtain the peak strain energy density corresponding to the working condition to be evaluated, which characterizes the tendency of electrode particles to break. The risk acquisition module is used to calculate a risk coefficient characterizing the relative change in fracture risk based on the strain energy density reference value and the strain energy density peak value. The risk level determination module is used to determine the rupture risk level of the working condition to be evaluated based on the risk coefficient and the preset risk level rules.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the battery rupture risk assessment method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed on a processor, implements the battery rupture risk assessment method according to any one of claims 1-7.

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