Construction method of internal pressure prediction model in battery aging process, battery internal pressure prediction method and related device

Abstract

The invention provides a method for constructing an internal pressure prediction model in a battery aging process, a battery internal pressure prediction method and a related device, and the method comprises the steps: obtaining test data of batteries of the same kind at a plurality of temperatures, carrying out the fitting according to the plurality of temperatures and the test data corresponding to each temperature, obtaining a pre-exponential factor, an exponential coefficient and a time influence function, and carrying out the calculation of the pre-exponential factor, the exponential coefficient and the time influence function. Therefore, the change condition of the time influence coefficient along with the temperature can be described through the time influence function. According to the method, the predicted use duration, the time influence function and the temperature of the battery are introduced into the battery internal pressure prediction model, so that the influence of the battery use duration and the temperature on gas production of the battery is comprehensively considered, and the battery internal pressure in the storage and circulation process can be accurately predicted through the battery internal pressure prediction model.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method for constructing an internal pressure prediction model during battery aging, a battery internal pressure prediction method, and related devices. Background Technology

[0002] During storage and cycling, batteries inevitably generate gas, increasing their internal pressure. Since the amount of gas inside a battery can be used to determine its internal resistance, the extent of side reactions, and the reliability of its explosion-proof valve design throughout its lifespan, it is necessary to provide a solution that can accurately predict the internal pressure of a battery during storage and cycling. Summary of the Invention

[0003] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency in the prior art that cannot accurately predict the internal pressure of the battery during storage and cycling.

[0004] In a first aspect, embodiments of this application provide a method for constructing an internal pressure prediction model during battery aging, including:

[0005] Acquire test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging tests on the battery at the target temperature, and the target temperature is any one of the multiple temperatures;

[0006] The measured internal pressure data corresponding to the multiple temperatures and the target temperature are fitted to obtain the pre-exponential factor, exponential coefficient, and time influence function; wherein, the time influence function is used to describe the change of the time influence coefficient with temperature;

[0007] A battery internal pressure prediction model is constructed based on the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the battery internal pressure prediction model is:

[0008]

[0009] In the formula, For the predicted internal pressure growth rate of the battery, For the pre-exponential factor, It is a natural constant. The exponential coefficient is mentioned. As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery usage time, and z be the value based on the time-effect function and A definite time-related influence coefficient.

[0010] In some embodiments, the measured internal pressure data includes the battery’s actual internal pressure growth rate and the battery’s usage time;

[0011] The process of fitting the measured internal pressure data corresponding to the plurality of temperatures and the target temperature to obtain the pre-exponential factor, exponential coefficient, and time influence function includes:

[0012] Using the logarithm of the actual internal pressure growth rate as the dependent variable and the logarithm of the used time as the independent variable, a linear fit is performed on multiple measured internal pressure data corresponding to the target temperature to obtain the first slope and the first intercept corresponding to the target temperature.

[0013] Using the first slope as the dependent variable and temperature as the independent variable, the multiple temperatures and each of the first slopes are fitted to obtain the time influence function.

[0014] The pre-exponential factor and the exponential coefficient are determined based on the plurality of temperatures and each of the first intercepts.

[0015] In some embodiments, determining the pre-exponential factor and the exponential coefficient based on the plurality of temperatures and each of the first intercepts includes:

[0016] Using the first intercept as the dependent variable and the reciprocal of temperature as the independent variable, a linear fit is performed on the reciprocals of the multiple temperatures and each of the first intercepts to obtain the second slope and the second intercept.

[0017] The pre-exponential factor is determined based on the second intercept, and the second slope is used as the exponential coefficient.

[0018] In some embodiments, determining the pre-exponential factor based on the second intercept includes:

[0019] The exponent is used as the second intercept to perform a power operation, and the pre-exponential factor is obtained; wherein the base corresponding to the power operation is the same as the base corresponding to the logarithmic operation.

[0020] In some embodiments, the time-effect function is a linear function.

[0021] Secondly, embodiments of this application provide a method for predicting battery internal pressure, including:

[0022] The actual battery temperature and initial internal pressure of the target battery were obtained respectively;

[0023] The target time influence coefficient is determined based on the actual battery temperature and the pre-established time influence function;

[0024] Based on the target time influence coefficient, the actual battery temperature, and the pre-established battery internal pressure prediction model, the predicted internal pressure growth rate of the target battery is determined; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing an internal pressure prediction model during battery aging as described in any one of claims 1 to 5;

[0025] The predicted internal pressure value of the target battery is determined based on the predicted internal pressure growth rate and the initial internal pressure value.

[0026] In some embodiments, if there are multiple actual battery temperatures, then determining the predicted internal pressure growth rate of the target battery based on the target time influence coefficient, the actual battery temperature, and a pre-established battery internal pressure prediction model includes:

[0027] The usage time corresponding to each actual battery temperature is obtained, and the usage time is configured based on the total usage time.

[0028] For each actual battery temperature, the actual battery temperature, the usage time corresponding to the actual battery temperature, and the target time influence coefficient are substituted into the battery internal pressure prediction model to obtain the sub-predicted internal pressure growth rate corresponding to the actual battery temperature.

[0029] The predicted internal pressure growth rate of the target battery under the total usage time is obtained by summing the sub-predicted internal pressure growth rates corresponding to each actual battery temperature.

[0030] Thirdly, embodiments of this application provide an apparatus for constructing an internal pressure prediction model during battery aging, comprising:

[0031] The test data acquisition module is used to acquire test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging tests on the battery at the target temperature, and the target temperature is any one of the multiple temperatures;

[0032] The fitting module is used to fit the multiple measured internal pressure data corresponding to the multiple temperatures and the target temperature, and obtain the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the time influence function is used to describe the change of the time influence coefficient with temperature;

[0033] The construction module is used to construct a battery internal pressure prediction model based on the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the battery internal pressure prediction model is:

[0034]

[0035] In the formula, For the predicted internal pressure growth rate of the battery, For the pre-exponential factor, It is a natural constant. The exponential coefficient is mentioned. As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery usage time, and z be the value based on the time-effect function and A definite time-related influence coefficient.

[0036] Fourthly, embodiments of this application provide a battery internal pressure prediction device, comprising:

[0037] The battery data acquisition module is used to acquire the actual battery temperature and initial internal pressure of the target battery, respectively.

[0038] The target coefficient determination module is used to determine the target time influence coefficient based on the actual battery temperature and the pre-established time influence function;

[0039] The internal pressure growth rate determination module is used to determine the predicted internal pressure growth rate of the target battery based on the target time influence coefficient, the actual battery temperature, and a pre-established battery internal pressure prediction model; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing an internal pressure prediction model during battery aging as described in any one of claims 1 to 5;

[0040] The internal pressure prediction module is used to determine the predicted internal pressure value of the target battery based on the predicted internal pressure growth rate and the initial internal pressure value.

[0041] Fifthly, embodiments of this application provide a computer device, the computer device including: one or more processors, and a memory;

[0042] The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the method for constructing the internal pressure prediction model during battery aging as described in any of the above embodiments, and / or perform the steps of the battery internal pressure prediction method as described in any of the above embodiments.

[0043] In the battery aging process internal pressure prediction model construction method, battery internal pressure prediction method and related device provided in some embodiments of this application, test data of similar batteries at multiple temperatures are obtained, and the test data corresponding to each temperature are fitted to obtain the pre-exponential factor, exponential coefficient and time influence function. The time influence function can then describe the change of the time influence coefficient with temperature. This application introduces the predicted battery usage time, time influence function and temperature into the battery internal pressure prediction model to comprehensively consider the influence of battery usage time and temperature on battery gas production. Therefore, the battery internal pressure prediction model can accurately predict the battery internal pressure during storage and cycling. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0045] Figure 1 This is a flowchart illustrating the method for constructing an internal pressure prediction model during battery aging in some embodiments;

[0046] Figure 2 This is a schematic diagram showing the connection between the gas pressure sensor and the battery surface in some embodiments;

[0047] Figure 3 This is a flowchart illustrating the steps of fitting multiple measured internal pressure data corresponding to multiple temperatures and target temperatures to obtain the pre-exponential factor, exponential coefficient, and time influence function in some embodiments.

[0048] Figure 4 This is one of the schematic diagrams of the fitting results in some embodiments;

[0049] Figure 5 This is a second schematic diagram of the fitting results in some embodiments;

[0050] Figure 6 This is the third schematic diagram of the fitting results in some embodiments;

[0051] Figure 7 This is a flowchart illustrating the battery internal pressure prediction method in some embodiments;

[0052] Figure 8 This is one of the schematic diagrams comparing the predicted curve and the measured curve in some embodiments;

[0053] Figure 9 This is a fourth schematic diagram of the fitting results in some embodiments;

[0054] Figure 10 This is the fifth schematic diagram of the fitting results in some embodiments;

[0055] Figure 11 This is a second schematic diagram comparing the predicted curve and the measured curve in some embodiments;

[0056] Figure 12 This is a schematic diagram of the structure of the device for constructing an internal pressure prediction model during battery aging in some embodiments;

[0057] Figure 13 This is a schematic diagram of the battery internal pressure prediction device in some embodiments;

[0058] Figure 14 This is a schematic diagram of the structure of a computer device in some embodiments. Detailed Implementation

[0059] The technical solutions of 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. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0060] During battery storage and cycling, the main factors affecting the battery gas production rate include temperature and battery usage time. Increased temperature leads to a gradual increase in electrolyte gas production, accelerating the gas production process. Prolonged storage or cycling also increases the amount of gas produced by the battery. Therefore, temperature and battery usage time work together to affect the battery gas production rate, and consequently, the battery's internal pressure.

[0061] Specifically, the variation of the chemical reaction rate k with temperature T affects the Arrhenius equation: In the formula, As the apparent activation energy, This is the molar gas constant. The inventors discovered that gas production during battery storage and cycling is primarily caused by SEI (Solid Electrolyte Interface) reforming. Because the SEI film formed during formation is not dense, SEI reforming during battery aging generates gases such as carbon dioxide and alkenes, which in turn affect the battery's internal pressure.

[0062] The growth of the SEI film is strongly correlated with the battery's operating time and is influenced by various factors such as current density, temperature, and formation conditions. These factors collectively determine the growth rate, composition, and stability of the SEI film, making the relationship between SEI film growth and time complex.

[0063] It should be noted that the terms "battery usage time," "predicted battery usage time," and similar expressions used in this application can be understood as the interval between a target time (such as the predicted time or the measured time) and a preset start time. The preset start time, serving as the starting point for duration statistics, can be specifically set according to actual circumstances. For example, the preset start time could be the moment the battery is first put into use or the start time of testing. Alternatively, the preset start time could be determined based on the battery's production date.

[0064] Based on the above principles, this application can derive a time-effect function through data fitting, which describes the change of the time-effect coefficient with temperature. By incorporating the predicted usage time and the time-effect coefficient into the battery internal pressure prediction model, the battery internal pressure can be predicted based on the degree of influence of battery usage time on battery gas production at different temperatures. In this way, the internal pressure of the battery during storage and use can be accurately predicted, achieving internal pressure prediction throughout the battery's entire life cycle. Furthermore, it is possible to predict whether the battery has an explosion risk throughout its entire life cycle and assess the reliability of the explosion-proof valve throughout its entire life cycle.

[0065] The method provided in this application will be described in detail below.

[0066] In some embodiments, such as Figure 1 As shown, this application provides a method for constructing an internal pressure prediction model during battery aging, including the following steps:

[0067] S102: Obtain test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging test of the battery at the target temperature, and the target temperature is any one of the multiple temperatures.

[0068] Among them, "same type of battery" refers to a battery that has the same or similar performance as the target battery to be predicted, such as a battery of the same model or a battery with the same or similar battery structure.

[0069] Multiple temperatures refer to at least three temperatures, and the specific number of temperatures can be set according to actual conditions such as the difficulty of the test and the required prediction accuracy. In some examples, to improve the accuracy of subsequent data fitting, multiple temperatures can be at least five temperatures.

[0070] It is understood that among multiple temperatures, the temperature difference between any two adjacent temperatures can be the same or different. In other words, the multiple temperatures can be set with equal or unequal arithmetic progressions, and this application does not impose specific limitations in this regard. For ease of explanation, some embodiments of this application are described using 40°C, 50°C, and 60°C as examples.

[0071] In this step, aging test data of similar batteries at multiple temperatures can be obtained. The test data corresponding to each temperature includes multiple measured internal pressure data obtained from aging tests of similar batteries at that temperature. In other words, the test data corresponding to any temperature includes multiple measured internal pressure data for various usage durations. It is understood that different temperatures may correspond to the same or different numbers of measured internal pressure data. This application does not impose specific limitations on this, as long as each temperature corresponds to at least three measured internal pressure data. For example, multiple measured internal pressure data at one temperature can be measured data of similar batteries over a one-year usage period to improve testing efficiency.

[0072] It is understood that this application can acquire test data through any testing method. In some examples, during the testing process, a gas pressure sensor can first be installed on the surface of similar batteries, and then similar batteries can be stored or cycled at different temperatures. The battery pressure can then be measured using the gas pressure sensor to obtain the measured internal pressure data.

[0073] Furthermore, the gas pressure sensor can be used to... Figure 2 The spiral metal interface 202 shown is fixed to the battery surface 204. (As shown) Figure 2 As shown, one end of the spiral metal interface 202, fixed to the battery surface 204, can be sealed to the battery surface 204 using a method such as AB glue 206. The other end of the spiral metal interface 202 can be threadedly connected to a gas pressure sensor, thus establishing communication between the battery interior and the gas pressure sensor. The battery can apply a force to the gas pressure sensor through the spiral metal interface 202. The greater the internal pressure of the battery, the greater the force. In this way, the internal pressure of the battery can be measured using the gas pressure sensor.

[0074] S104: Fit multiple measured internal pressure data corresponding to multiple temperatures and target temperatures to obtain the pre-exponential factor, exponential coefficient, and time influence function; among which, the time influence function is used to describe the change of the time influence coefficient with temperature.

[0075] In this step, since multiple measured internal pressure data corresponding to the target temperature can reflect the change of battery internal pressure of similar batteries over time at the target temperature, and the test data of similar batteries at different temperatures can reflect the change of battery internal pressure with temperature, this application can perform data fitting on multiple temperatures and the test data corresponding to each temperature, and obtain the pre-exponential factor, exponential coefficient, and time influence function based on the fitting results. Among them, the time influence function can describe the change of the time influence coefficient with temperature, and the time influence coefficient is used to determine the degree of influence of battery usage time on battery internal pressure.

[0076] It should be noted that this application can select the corresponding fitting method for data fitting based on the function expression of the time influence function, and the function type of the time influence function can be determined according to the actual situation. This application does not impose specific restrictions on this.

[0077] In some examples, the time effect function can be a linear function, meaning it is a linear function with the time effect coefficient as the dependent variable and temperature as the independent variable. This accurately describes how the time effect coefficient changes with temperature, facilitates derivation, and reduces the data requirements for test data.

[0078] S106: Construct a battery internal pressure prediction model based on the pre-exponential factor, exponential coefficient, and time influence function; the battery internal pressure prediction model is as follows:

[0079]

[0080] In the formula, For the predicted internal pressure growth rate of the battery, Pre-exponential factor, It is a natural constant. For exponential coefficients, As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery life, and z be the value based on the time-dependent function. A definite time-related influence coefficient.

[0081] In this step, a semi-empirical model can be established using the pre-exponential factor, exponential coefficient, and time influence function to obtain a battery internal pressure prediction model. Using this battery internal pressure prediction model, this application can comprehensively consider the influence of time and temperature on gas production during battery aging, and accurately predict the battery internal pressure curve throughout its entire lifespan. This allows for prediction of whether the battery has an explosion risk throughout its lifespan and assessment of the reliability of the explosion-proof valve throughout its lifespan.

[0082] In some embodiments, each measured internal pressure data point includes the battery's actual internal pressure growth rate and the battery's usage time. The actual internal pressure growth rate can be calculated based on the battery's internal pressure collected by a gas pressure sensor, and the battery's usage time can be the interval between the internal pressure collection time and a preset start time.

[0083] The aging process of batteries includes storage aging, cycle aging, and thermal aging. Storage aging and cycle aging simulate the performance degradation of materials or devices under unused and actual use conditions, respectively. Although there are other aging processes, these two are the most important factors. To simplify the model and improve accuracy, tests are conducted at the target temperature, simultaneously performing storage and cycle tests. This reduces the impact of thermal aging while taking into account both storage and cycle aging, thus improving overall accuracy.

[0084] Considering the allocation of storage aging and cycle aging within the used time increases the overall complexity of the model and makes accurate evaluation difficult. Therefore, storage time and cycle time within the used time are pre-allocated, i.e., a preset allocation ratio for storage time and cycle time is set. The used time is allocated according to the preset ratio, and aging tests are conducted using the storage time and cycle time determined by the preset ratio to obtain measured data. The preset allocation ratio can be determined based on historical statistical data of battery storage and cycling during actual use, or it can be estimated according to different application scenarios. For example, in large-scale energy storage power station scenarios, the ratio of storage time to cycle time is estimated based on the operating mode and scheduling requirements of the energy storage system. This can be set as needed to ensure the test scenario closely matches the actual application scenario and improve prediction accuracy. For example, if the used time is 1 year and the preset allocation ratio is storage time:cycle time = 82:18, then the cycle time can be set to 525,600 * 18% = 94,608 minutes. To better reflect real-world application scenarios, storage aging and cycle aging were performed at intervals during the test, and the ratio of storage time to cycle time was set according to a preset allocation ratio. In each day, the cycle time was 1440 * 0.18% = 259.2 minutes, so that the test data would be more realistic and simulate real application scenarios.

[0085] like Figure 3 As shown, multiple measured internal pressure data corresponding to multiple temperatures and target temperatures are fitted to obtain the pre-exponential factor, exponential coefficient, and time influence function, including:

[0086] S302: Using the logarithm of the actual internal pressure growth rate as the dependent variable and the logarithm of the usage time as the independent variable, perform linear fitting on multiple measured internal pressure data corresponding to the target temperature, and obtain the first slope and the first intercept corresponding to the target temperature.

[0087] S304: Using the first slope as the dependent variable and temperature as the independent variable, fit multiple temperatures and each first slope to obtain the time influence function;

[0088] S306: Determine the pre-exponential factor and exponential coefficient based on multiple temperatures and each first intercept.

[0089] Specifically, by performing logarithmic calculations on the battery internal pressure prediction model, a first-order equation can be constructed. For example, using... As the logarithmic base, taking the logarithm of the battery internal pressure prediction model yields:

[0090]

[0091] like , Then the above formula can be transformed into: Therefore, it can be seen that when the temperature T is constant, Therefore It is a linear function of the independent variable, with a slope of The intercept is Therefore, this application is based on As the dependent variable, By performing linear fitting on the measured internal pressure data at each temperature, the first slope and the first intercept corresponding to each temperature can be obtained.

[0092] For example, such as Figure 4 As shown, when multiple temperatures are 40℃, 50℃, and 60℃, this application uses the logarithm of the actual internal pressure growth rate as the dependent variable and the logarithm of the usage time as the independent variable to fit multiple measured internal pressure data corresponding to 40℃, obtaining a first slope of 0.29 and a first intercept of -0.8487 for 40℃. Similarly, this application can fit multiple measured internal pressure data corresponding to 50℃, obtaining a first slope of 0.43 and a first intercept of -1.09 for 50℃; and fit multiple measured internal pressure data corresponding to 60℃, obtaining a first slope of 0.49 and a first intercept of -1.4609 for 60℃.

[0093] Due to the time influence coefficient Since the slope is strongly correlated with temperature, after obtaining the first slope at different temperatures, this application uses the first slope as the dependent variable and temperature as the independent variable to fit multiple temperatures and the first slope corresponding to each temperature, and obtains the time influence function, i.e., z=F(T), to obtain the time influence coefficient at different temperatures. Introducing a time-effect function to describe the change of the time-effect coefficient with temperature allows the model to more accurately reflect the variation of battery internal pressure with time and temperature. This function not only improves the model's accuracy but also facilitates derivation and calculation. Furthermore, the type of time-effect function can be determined based on actual conditions, giving the model strong adaptability and flexibility.

[0094] For example, such as Figure 5As shown, if the first slope corresponding to 40℃ is 0.29, the first slope corresponding to 50℃ is 0.43, and the first slope corresponding to 60℃ is 0.49, then the aforementioned three first slopes can form three points: (40, 0.29), (50, 0.43), and (60, 0.49). Fitting based on these three points yields... The function with T as the dependent variable is the time-dependent function.

[0095] Furthermore, since the first intercept is strongly correlated with temperature, this application can determine the pre-exponential factor based on multiple temperatures and the first intercept corresponding to each temperature after obtaining the first intercepts for multiple different temperatures. and pre-exponential coefficient Once the pre-exponential factor, pre-exponential coefficient, and time influence function are determined, the battery internal pressure prediction model can also be determined accordingly.

[0096] This embodiment can obtain the pre-exponential factor, exponential coefficient, and time influence function through three rounds of fitting. The stepwise fitting method can make full use of experimental data, ensure the accuracy of model parameters, and speed up the fitting process.

[0097] In some embodiments, determining the pre-exponential factor and the exponential coefficient based on multiple temperatures and respective first intercepts includes:

[0098] Using the first intercept as the dependent variable and the reciprocal of temperature as the independent variable, a linear fit is performed on the reciprocals of multiple temperatures and each first intercept to obtain the second slope and the second intercept.

[0099] The pre-exponential factor is determined based on the second intercept, and the second slope is used as the exponential coefficient.

[0100] Specifically, the first intercept ,like , Then the previous equation can be transformed into: Because of A, and All are constants, therefore Therefore It is a linear function of the independent variable, with a slope of The intercept is .

[0101] Based on this, after obtaining the first intercept at different temperatures, this application can use the first intercept as the dependent variable and the reciprocal of the temperature as the independent variable to perform linear fitting on the reciprocals of multiple temperatures and each first intercept, and derive the pre-exponential factor and exponential coefficient based on the second slope and second intercept obtained from the fitting. In this way, the model parameters of the battery internal pressure prediction model can be determined accurately and quickly.

[0102] For example, such as Figure 6 As shown, if the first intercept corresponding to 40℃ is -0.8487, the first intercept corresponding to 50℃ is -1.09, and the first intercept corresponding to 60℃ is -1.4609, then the aforementioned three first intercepts can form three points, namely: ( -0.8487), ( (, -1.09) and ( (-1.4609). Based on fitting three points, we can obtain the following: As the dependent variable, with Let be a function of the independent variable, and let the slope of the function be . The intercept of the function is .

[0103] In some embodiments, determining the pre-exponential factor based on the second intercept includes:

[0104] The exponent is used as the second intercept to perform a power operation, and the pre-exponential factor is obtained; the base corresponding to the power operation is the same as the base corresponding to the logarithmic operation.

[0105] For example, when the base of the logarithm is e, the second intercept is... hour, .

[0106] In some examples, according to Figure 4 , Figure 5 and Figure 6 After constructing the battery internal pressure prediction model based on the fitting results shown, the resulting battery internal pressure prediction model is as follows:

[0107]

[0108] In the formula, T is the temperature in °C; t is the predicted battery usage time in min. This is the time influence coefficient, which can be determined based on the temperature and time influence functions.

[0109] In some embodiments, such as Figure 7 As shown, this application provides a method for predicting the internal pressure of a battery, which may include the following steps:

[0110] S402: Obtain the actual battery temperature and initial internal pressure of the target battery respectively;

[0111] S404: Determine the target time influence coefficient based on the actual battery temperature and the pre-established time influence function;

[0112] S406: Based on the target time influence coefficient, the actual battery temperature, and the pre-established battery internal pressure prediction model, determine the predicted internal pressure growth rate of the target battery; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing the internal pressure prediction model during battery aging described in any of the above embodiments;

[0113] S408: Determine the predicted internal pressure value of the target battery based on the predicted internal pressure growth rate and the initial internal pressure value.

[0114] Specifically, in the process of predicting the internal pressure of a battery, the time influence coefficient corresponding to the actual battery temperature can be determined based on the actual battery temperature and time influence function of the target battery to be predicted, and the target time influence coefficient can be obtained.

[0115] Given a determined target influence coefficient, this application can calculate the predicted internal pressure growth rate of the target battery at the predicted time by substituting the actual battery temperature into the battery internal pressure prediction model (T), substituting the target influence coefficient into the battery internal pressure prediction model (z), and then using the battery internal pressure prediction model after substituting the parameters. And based on the initial internal pressure value and The predicted internal pressure value of the target battery at the predicted time is obtained.

[0116] For example, according to Figure 4 , Figure 5 and Figure 6 After constructing the battery internal pressure prediction model based on the fitting results shown, this application can calculate the time influence coefficients corresponding to 40℃, 50℃, and 60℃ according to the target time influence function, thereby obtaining the battery internal pressure prediction model for the target battery at the three temperatures of 40℃, 50℃, and 60℃. Substituting t into the battery internal pressure prediction model for calculation, the predicted internal pressure value of the target battery at time t can be obtained. A comparison chart of the battery internal pressure predicted using this battery internal pressure prediction model and the measured battery internal pressure is shown below. Figure 8 As shown, from Figure 8 It can be seen that the predicted curves of the battery at 40℃, 50℃ and 60℃ are in high agreement with the measured curves, indicating that the prediction results are highly accurate.

[0117] For example, this application can use data from the target battery over 0~100,000 minutes to solve for model parameters, and based on this, build a battery internal pressure prediction model to predict the production period gas pressure of the target battery from 0~137,000 minutes, thereby verifying the accuracy of the prediction. Specifically, such as Figure 9 As shown, with a time range of 0~100000 min The dependent variable is 0~100000 min. A linear fit was performed on the independent variable, yielding z-values ​​of 0.29, 0.455, and 0.534 for the target battery at 40℃, 50℃, and 60℃, with first intercepts of -0.72, -1.50, and -2.30, respectively. Figure 10 As shown, this application fits the data using the first intercept as the dependent variable and the reciprocal of temperature as the independent variable, and obtains... , ,therefore Therefore, after substituting the parameters into the model, the resulting battery internal pressure prediction model is: The aforementioned model was used to predict the internal pressure of the target battery from 0 to 137,000 min. The predicted curve and the measured curve are shown below. Figure 11 As shown. When Table 1 shows the predicted and measured values ​​for each temperature at 137,000 min. As can be seen from Table 1, the deviations between the predicted and measured values ​​are small at different temperatures, indicating that the model's predictions have high accuracy.

[0118] Table 1. Comparison of predicted and measured values ​​at 137,000 min.

[0119]

[0120] In some embodiments, if there are multiple actual battery temperatures, the predicted internal pressure growth rate of the target battery is determined based on the target time influence coefficient, the actual battery temperature, and a pre-established battery internal pressure prediction model, including:

[0121] The usage time corresponding to each actual battery temperature is obtained separately, and the usage time is configured based on the total usage time;

[0122] For each actual battery temperature, the actual battery temperature, the corresponding usage time, and the target time influence coefficient are substituted into the battery internal pressure prediction model to obtain the sub-predicted internal pressure growth rate corresponding to the actual battery temperature.

[0123] The predicted internal pressure growth rate of the target battery under the total usage time is obtained by summing the sub-predicted internal pressure growth rates corresponding to each actual battery temperature.

[0124] In this embodiment, the actual battery temperature may change over time during battery storage and cycling. In this case, this application can calculate the sub-predicted internal pressure growth rate for each temperature based on the usage time of the target battery at each temperature, and sum the multiple sub-predicted internal pressure growth rates to obtain the total predicted internal pressure growth rate. This makes the prediction more closely match the actual application scenario and improves the accuracy of internal pressure prediction under temperature changes.

[0125] Furthermore, the usage time corresponding to each actual battery temperature is obtained separately. The usage time is configured based on the total usage time and a preset allocation ratio. That is, the usage time corresponding to each actual temperature is allocated according to the preset allocation ratio of storage time and cycle time, and the total usage time. Specifically, the actual temperature is allocated according to the storage time (storage usage time) and the actual temperature is allocated according to the cycle time (cycle usage time). Different actual temperatures can be allocated to storage time and cycle time according to the actual application scenario, so that the actual application and test data are matched, thereby improving the prediction accuracy and reliability.

[0126] Furthermore, the loop time and storage time can be allocated to different actual temperatures, resulting in loop times or storage times corresponding to different actual temperatures, which can further adapt to different application scenarios. Specifically, the actual temperature corresponding to the storage time is lower than the actual temperature corresponding to the loop time, and the loop time is shorter than the storage time, with the ratio of storage time to loop time satisfying a preset allocation ratio.

[0127] For example, the data fitting situation is as follows Figures 4 to 6 As shown, the battery internal pressure prediction model is... The current requirement is to use this battery internal pressure prediction model to predict the internal pressure of a target battery over 25 years. Based on a preset allocation ratio of 82:18 for storage time and cycling time, the target battery is allocated the following proportions: 18% for cycling at 45°C, 45% for storage at 35°C, 20% for storage at 25°C, and 17% for storage at 20°C. These allocations can be confirmed based on historical statistical data or estimated according to actual application scenarios, and can be set as needed; this application does not impose any restrictions.

[0128] First, this application can be based on Figure 5 The time influence function is shown. The time influence coefficients at 45℃, 35℃, 25℃, and 20℃ are calculated respectively, and the coefficients are 0.3533, 0.2533, 0.1533, and 0.1033. Substituting the temperature, the corresponding usage time, and the corresponding time influence coefficient into the aforementioned battery internal pressure prediction model, we can obtain:

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] In the formula, The predicted internal pressure growth rate of the target battery at 45°C; The predicted internal pressure growth rate of the target battery at 35°C; The predicted internal pressure growth rate of the target battery at 25°C; The predicted internal pressure growth rate of the target battery at 20°C; The projected internal pressure growth rate over a 25-year battery lifespan.

[0135] If the initial internal pressure is 0.001 MPa, then the predicted internal pressure of the target battery after 25 years is ( *0.001+0.001).

[0136] The following describes the apparatus for constructing an internal pressure prediction model during battery aging provided in the embodiments of this application. The apparatus for constructing an internal pressure prediction model during battery aging described below and the method for constructing an internal pressure prediction model during battery aging described above can be referred to in correspondence.

[0137] In some embodiments, such as Figure 12 As shown, this application provides a device 500 for constructing an internal pressure prediction model during battery aging, comprising:

[0138] The test data acquisition module 502 is used to acquire test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging tests on the battery at the target temperature, and the target temperature is any one of the multiple temperatures;

[0139] The fitting module 504 is used to fit the multiple measured internal pressure data corresponding to the multiple temperatures and the target temperature, and obtain the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the time influence function is used to describe the change of the time influence coefficient with temperature;

[0140] Construction module 506 is used to construct a battery internal pressure prediction model based on the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the battery internal pressure prediction model is:

[0141]

[0142] In the formula, For the predicted internal pressure growth rate of the battery, For the pre-exponential factor, It is a natural constant. The exponential coefficient is mentioned. As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery usage time, and z be the value based on the time-effect function and A definite time-related influence coefficient.

[0143] In some embodiments, the measured internal pressure data includes the actual internal pressure growth rate of the battery and the battery's usage time. The fitting module 504 of this application includes:

[0144] The first fitting unit is used to linearly fit multiple measured internal pressure data corresponding to the target temperature with the logarithm of the actual internal pressure growth rate as the dependent variable and the logarithm of the used time as the independent variable, and to obtain the first slope and the first intercept corresponding to the target temperature.

[0145] The second fitting unit is used to fit the plurality of temperatures and each of the first slopes with the first slope as the dependent variable and temperature as the independent variable, and to obtain the time influence function.

[0146] The parameter calculation unit is used to determine the pre-exponential factor and the exponential coefficient based on the plurality of temperatures and each of the first intercepts.

[0147] In some embodiments, the parameter calculation unit of this application includes:

[0148] The third fitting unit is used to perform linear fitting on the reciprocals of the plurality of temperatures and each of the first intercepts, with the first intercept as the dependent variable and the reciprocal of the temperature as the independent variable, and to obtain the second slope and the second intercept.

[0149] The parameter determination unit is used to determine the pre-exponential factor based on the second intercept and to use the second slope as the exponential coefficient.

[0150] In some embodiments, the parameter determination unit of this application includes:

[0151] The exponentiation unit is used to perform exponentiation with the second intercept as the exponent and obtain the pre-exponential factor; wherein the base corresponding to the exponentiation operation is the same as the base corresponding to the logarithmic operation.

[0152] In some embodiments, the time-effect function is a linear function.

[0153] The battery internal pressure prediction device provided in the embodiments of this application is described below. The battery internal pressure prediction device described below can be referred to in correspondence with the battery internal pressure prediction method described above.

[0154] In some embodiments, such as Figure 13 As shown, this application provides a battery internal pressure prediction device 600, comprising:

[0155] The battery data acquisition module 602 is used to acquire the actual battery temperature and initial internal pressure value of the target battery, respectively.

[0156] The target coefficient determination module 604 is used to determine the target time influence coefficient based on the actual battery temperature and the pre-established time influence function;

[0157] The internal pressure growth rate determination module 606 is used to determine the predicted internal pressure growth rate of the target battery based on the target time influence coefficient, the actual battery temperature, and the pre-established battery internal pressure prediction model; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing the internal pressure prediction model during battery aging described in any of the above embodiments;

[0158] The internal pressure prediction module 608 is used to determine the predicted internal pressure value of the target battery based on the predicted internal pressure growth rate and the initial internal pressure value.

[0159] In some embodiments, if there are multiple actual battery temperatures, the internal pressure growth rate determination module 606 of this application includes:

[0160] The usage time acquisition unit is used to acquire the usage time corresponding to each actual battery temperature, and the usage time is configured based on the total usage time.

[0161] The sub-growth rate calculation unit is used to, for each actual battery temperature, substitute the actual battery temperature, the usage time corresponding to the actual battery temperature and the target time influence coefficient into the battery internal pressure prediction model to obtain the sub-predicted internal pressure growth rate corresponding to the actual battery temperature.

[0162] The summation unit is used to sum the sub-predicted internal pressure growth rates corresponding to each of the actual battery temperatures to obtain the predicted internal pressure growth rate of the target battery under the total usage time.

[0163] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method as described in any embodiment.

[0164] In one embodiment, this application also provides a computer device storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the method for constructing an internal pressure prediction model during battery aging as described in any embodiment, and / or perform the steps of the battery internal pressure prediction method as described in any embodiment.

[0165] Indicatively, Figure 14This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. (Refer to...) Figure 14 The computer device 900 includes a processing component 902, which further includes one or more processors, and memory resources represented by memory 901 for storing instructions executable by the processing component 902, such as application programs. The application programs stored in memory 901 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 902 is configured to execute instructions to perform the steps of the method for constructing an internal pressure prediction model during battery aging as described in any embodiment, and / or to perform the steps of the battery internal pressure prediction method as described in any embodiment.

[0166] The computer device 900 may also include a power supply component 903 configured to perform power management of the computer device 900, a wired or wireless network interface 904 configured to connect the computer device 900 to a network, and an input / output (I / O) interface 905. The computer device 900 may operate on an operating system stored in memory 901, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0167] Those skilled in the art will understand that the internal structure of the computer device shown in this application is merely a block diagram of a portion of the structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0168] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0169] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0170] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing an internal pressure prediction model during battery aging, characterized in that, include: Acquire test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging tests on the battery at the target temperature, and the target temperature is any one of the multiple temperatures; The measured internal pressure data corresponding to the multiple temperatures and the target temperature are fitted to obtain the pre-exponential factor, exponential coefficient, and time influence function; wherein, the time influence function is used to describe the change of the time influence coefficient with temperature; A battery internal pressure prediction model is constructed based on the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the battery internal pressure prediction model is: In the formula, For the predicted internal pressure growth rate of the battery, For the pre-exponential factor, It is a natural constant. The exponential coefficient is... As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery usage time, and z be the value based on the time-effect function and A definite time-related influence coefficient.

2. The method according to claim 1, characterized in that, The measured internal pressure data includes the battery's actual internal pressure growth rate and the battery's usage time; The process of fitting the measured internal pressure data corresponding to the plurality of temperatures and the target temperature to obtain the pre-exponential factor, exponential coefficient, and time influence function includes: Using the logarithm of the actual internal pressure growth rate as the dependent variable and the logarithm of the used duration as the independent variable, a linear fit is performed on multiple measured internal pressure data corresponding to the target temperature to obtain the first slope and the first intercept corresponding to the target temperature. Using the first slope as the dependent variable and temperature as the independent variable, the multiple temperatures and each of the first slopes are fitted to obtain the time influence function. The pre-exponential factor and the exponential coefficient are determined based on the plurality of temperatures and each of the first intercepts.

3. The method according to claim 2, characterized in that, The step of determining the pre-exponential factor and the exponential coefficient based on the plurality of temperatures and each of the first intercepts includes: Using the first intercept as the dependent variable and the reciprocal of temperature as the independent variable, a linear fit is performed on the reciprocals of the multiple temperatures and each of the first intercepts to obtain the second slope and the second intercept. The pre-exponential factor is determined based on the second intercept, and the second slope is used as the exponential coefficient.

4. The method according to claim 3, characterized in that, Determining the pre-exponential factor based on the second intercept includes: The exponent is used as the second intercept to perform a power operation, and the pre-exponential factor is obtained; wherein the base corresponding to the power operation is the same as the base corresponding to the logarithmic operation.

5. The method according to any one of claims 1 to 4, characterized in that, The time-effect function is a linear function.

6. A method for predicting the internal pressure of a battery, characterized in that, include: The actual battery temperature and initial internal pressure of the target battery were obtained respectively; The target time influence coefficient is determined based on the actual battery temperature and the pre-established time influence function; Based on the target time influence coefficient, the actual battery temperature, and the pre-established battery internal pressure prediction model, the predicted internal pressure growth rate of the target battery is determined; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing an internal pressure prediction model during battery aging as described in any one of claims 1 to 5; The predicted internal pressure value of the target battery is determined based on the predicted internal pressure growth rate and the initial internal pressure value.

7. The method according to claim 6, characterized in that, If there are multiple actual battery temperatures, then determining the predicted internal pressure growth rate of the target battery based on the target time influence coefficient, the actual battery temperature, and the pre-established battery internal pressure prediction model includes: The usage time corresponding to each actual battery temperature is obtained, and the usage time is configured based on the total usage time. For each actual battery temperature, the actual battery temperature, the usage time corresponding to the actual battery temperature, and the target time influence coefficient are substituted into the battery internal pressure prediction model to obtain the sub-predicted internal pressure growth rate corresponding to the actual battery temperature. The predicted internal pressure growth rate of the target battery under the total usage time is obtained by summing the sub-predicted internal pressure growth rates corresponding to each actual battery temperature.

8. A device for constructing an internal pressure prediction model during battery aging, characterized in that, include: The test data acquisition module is used to acquire test data of similar batteries at multiple temperatures; wherein, the test data corresponding to the target temperature includes multiple measured internal pressure data obtained by aging tests on the battery at the target temperature, and the target temperature is any one of the multiple temperatures; The fitting module is used to fit the multiple measured internal pressure data corresponding to the multiple temperatures and the target temperature, and obtain the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the time influence function is used to describe the change of the time influence coefficient with temperature; The construction module is used to construct a battery internal pressure prediction model based on the pre-exponential factor, the exponential coefficient, and the time influence function; wherein, the battery internal pressure prediction model is: In the formula, For the predicted internal pressure growth rate of the battery, For the pre-exponential factor, It is a natural constant. The exponential coefficient is... As the apparent activation energy, For molar gas constant, Let t be the temperature, t be the predicted battery usage time, and z be the value based on the time-effect function and A definite time-related influence coefficient.

9. A battery internal pressure prediction device, characterized in that, include: The battery data acquisition module is used to acquire the actual battery temperature and initial internal pressure of the target battery, respectively. The target coefficient determination module is used to determine the target time influence coefficient based on the actual battery temperature and the pre-established time influence function; The internal pressure growth rate determination module is used to determine the predicted internal pressure growth rate of the target battery based on the target time influence coefficient, the actual battery temperature, and a pre-established battery internal pressure prediction model; wherein, the time influence function and the battery internal pressure prediction model are both constructed using the method for constructing an internal pressure prediction model during battery aging as described in any one of claims 1 to 5; The internal pressure prediction module is used to determine the predicted internal pressure value of the target battery based on the predicted internal pressure growth rate and the initial internal pressure value.

10. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the method for constructing an internal pressure prediction model during battery aging as described in any one of claims 1 to 5, and / or perform the steps of the battery internal pressure prediction method as described in any one of claims 6 to 7.

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