Battery deformation prediction method, storage medium and electronic equipment

Abstract

The invention discloses a battery deformation prediction method, a storage medium and electronic equipment, and relates to the technical field of battery safety. The method comprises the following steps: acquiring operation data of a target battery in a plurality of charge-discharge cycle periods; according to a target prediction model and the operation data in the plurality of charge-discharge cycle periods, predicted gas production data of the target battery in the whole life cycle is calculated, and the target prediction model is obtained by performing fitting calculation on test data collected by performing different gas production tests on the target battery; according to the predicted gas production data and a target curve graph, a predicted deformation value of the target battery is determined, and the target curve graph is used for representing the corresponding relation between the battery internal pressure value and the deformation value. The technical problem that the prediction accuracy of battery deformation is relatively low when gas production data obtained by a high-temperature storage battery is adopted to predict battery size change in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of battery safety technology, and in particular to a method for predicting battery deformation, a storage medium, and an electronic device. Background Art

[0002] Battery gas production is an important factor affecting battery performance and safety. At the same time, severe gas production will also affect the core stress and shell deformation at the end of the battery life, causing the battery to expand and deform, affecting the module size and safety. Therefore, it is very necessary to predict the gas production during the entire life cycle under the actual application conditions of the battery and the cell deformation at the end of its life.

[0003] At present, the existing technology often adopts high-temperature storage gas production actual measurement data verification. However, the situation in actual vehicle application is more complicated. The high-temperature storage method can accelerate the gas production, but it is too different from the actual working conditions. It cannot reflect the actual gas production and size changes of the battery, and the prediction accuracy of battery deformation is low. Summary of the invention

[0004] The embodiments of the present application provide a method for predicting battery deformation, a storage medium, and an electronic device to at least solve the technical problem that the prior art uses gas production data obtained from high-temperature storage batteries to predict battery size changes, but has a low accuracy in predicting battery deformation.

[0005] According to one aspect of an embodiment of the present application, a method for predicting battery deformation is provided, comprising: obtaining operating data of a target battery during multiple charge and discharge cycles; calculating predicted gas production data of the target battery during its entire life cycle based on a target prediction model and the operating data during multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating test data collected from different gas production tests on the target battery; determining a predicted deformation value of the target battery based on the predicted gas production data and a target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0006] Furthermore, the target prediction model includes a first prediction model and a second prediction model, the first prediction model and the second prediction model correspond to different gas production tests, wherein the predicted gas production data of the target battery in the full life cycle is calculated based on the target prediction model and the operating data in multiple charge and discharge cycles, including: for each charge and discharge cycle, the first predicted gas production data in the current charge and discharge cycle is calculated based on the first prediction model and the average discharge depth data, average charge and discharge rate data, average temperature data and number of cycles in the operating data in the current charge and discharge cycle, and the second predicted gas production data in the current charge and discharge cycle is calculated based on the second prediction model and the average temperature data, average battery remaining charge state data and calendar time in the operating data in the current charge and discharge cycle; the first predicted gas production data in each charge and discharge cycle and the second predicted gas production data in each charge and discharge cycle are accumulated to obtain the predicted gas production data of the target battery in the full life cycle.

[0007] Furthermore, based on the predicted gas production data and the target curve graph, the predicted deformation value of the target battery is determined, including: matching the predicted gas production data with multiple battery internal pressure values ​​on the target curve graph to obtain the target internal pressure value; obtaining the target deformation value corresponding to the target internal pressure value, and using the target deformation value as the predicted deformation value of the target battery.

[0008] Furthermore, the first prediction model is obtained through the following steps: according to the application scenario of the target battery, the first test condition matrix of the cyclic gas production test is determined, wherein the first test condition matrix includes temperature conditions, battery remaining state of charge range conditions and charge and discharge rate conditions; based on the first test condition matrix, a cyclic gas production test is performed on the target battery to obtain test data of the cyclic gas production test; fitting calculation is performed based on the test data of the cyclic gas production test to obtain the first prediction model.

[0009] Furthermore, a second prediction model is obtained through the following steps: based on the application scenario of the target battery, a second test condition matrix of the storage gas production test is determined, wherein the second test condition matrix includes temperature conditions and battery remaining state of charge conditions; based on the second test condition matrix, a storage gas production test is performed on the target battery to obtain test data of the storage gas production test; and a fitting calculation is performed based on the test data of the storage gas production test to obtain a second prediction model.

[0010] Furthermore, the target curve graph is obtained through the following steps: using a pressure tester to perform pressure tests on test batteries corresponding to the target batteries according to different pressure intensities, to obtain pressurized batteries corresponding to each pressure intensity, wherein the pressure intensity is used to represent the internal pressure value of the battery; measuring the dimensions of the pressurized batteries corresponding to each pressure intensity, to obtain current battery dimension data corresponding to each pressure intensity; generating a target curve graph based on each pressure intensity, the current battery dimension data corresponding to each pressure intensity, and the initial battery dimension data corresponding to each pressure intensity.

[0011] Furthermore, a target curve graph is generated based on each pressure intensity, the current battery size data corresponding to each pressure intensity, and the initial battery size data corresponding to each pressure intensity, including: performing difference calculation on the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity to obtain a deformation value corresponding to each pressure intensity; and drawing based on each pressure intensity and the deformation value corresponding to each pressure intensity to obtain a target curve graph.

[0012] Furthermore, after determining the predicted deformation value of the target battery, the method further includes: determining a module design strategy for the target battery according to the predicted deformation value of the target battery.

[0013] According to another aspect of an embodiment of the present application, a battery deformation prediction device is also provided, including: an acquisition unit, used to acquire operating data of a target battery within multiple charge and discharge cycles; a first calculation unit, used to calculate the predicted gas production data of the target battery within its full life cycle based on a target prediction model and the operating data within multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating test data collected from different gas production tests on the target battery; a first determination unit, used to determine the predicted deformation value of the target battery based on the predicted gas production data and a target curve graph, wherein the target curve graph is used to represent the correspondence between the battery internal pressure value and the deformation value.

[0014] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned battery deformation prediction method when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, which includes one or more processors; a memory for storing one or more programs, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to run the programs, wherein the programs are configured to execute the above-mentioned battery deformation prediction method when running.

[0016] In an embodiment of the present application, the operating data of the target battery in multiple charge and discharge cycles is first obtained, and then the predicted gas production data of the target battery in its full life cycle is calculated based on the target prediction model and the operating data in multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating the test data collected from different gas production tests on the target battery, and then the predicted deformation value of the target battery is determined based on the predicted gas production data and the target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0017] In the above process, based on the target prediction model obtained by fitting the test data collected from different gas production tests on the target battery and the operating data in multiple charge and discharge cycles, the predicted gas production data of the target battery over its entire life cycle can be calculated. Therefore, the predicted deformation value of the target battery can be determined based on the predicted gas production data and the target curve, which is closer to the actual application scenario, realizes the effective prediction of the battery deformation, and improves the prediction accuracy of the battery deformation.

[0018] It can be seen that through the technical solution of the present application, the purpose of effectively predicting the gas production of the battery under actual operating conditions and the dimensional changes at the end of its life is achieved, thereby achieving the technical effect of improving the accuracy of prediction of battery deformation, and further solving the technical problem of low accuracy in predicting battery deformation by using gas production data obtained from high-temperature storage batteries to predict battery dimensional changes in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a flow chart of an optional method for predicting battery deformation according to an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of an optional internal pressure-size curve according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of an optional gas production prediction curve according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of an optional cyclic gas production test matrix according to an embodiment of the present application;

[0024] Figure 5 is a schematic diagram of an optional storage gas production test matrix according to an embodiment of the present application;

[0025] Figure 6 is a schematic diagram of an optional pressure test process according to an embodiment of the present application;

[0026] Figure 7 is a schematic diagram of an optional battery deformation prediction device according to an embodiment of the present application;

[0027] Figure 8 is a schematic diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0031] Example 1

[0032] According to an embodiment of the present application, an embodiment of a method for predicting battery deformation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] Figure 1 is a flowchart of an optional method for predicting battery deformation according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0034] Step S101, obtaining operation data of a target battery in a plurality of charge and discharge cycles.

[0035] In the above steps, the operating data of the target battery in multiple charge and discharge cycles can be obtained through application systems, processors, electronic devices and other devices. Optionally, the operating data of the target battery in multiple charge and discharge cycles can be obtained through a battery deformation prediction system. The target battery can be a large cylindrical lithium-ion battery with a steel shell. The operating data at least includes average discharge depth data, average charge and discharge rate data, average temperature data, number of cycles, average battery remaining charge state data, calendar time, etc.

[0036] For example, according to the actual scenario, taking N days (N is a positive integer, for example, 7 days) as a cycle period, the average temperature T1 within N days is calculated, the average current is recorded as I1, and the charge and discharge rate is calculated based on the average current, and the number of cycles n1 and calendar time t1 within the statistical period are calculated.

[0037] Step S102, based on the target prediction model and the operating data in multiple charge and discharge cycles, the predicted gas production data of the target battery in the whole life cycle is calculated, wherein the target prediction model is obtained by fitting and calculating the test data collected from different gas production tests on the target battery.

[0038] Optionally, different gas production tests include at least a battery cycle gas production test and a battery storage gas production test, and the target prediction model includes a first prediction model and a second prediction model, the first prediction model is obtained by fitting and calculating test data collected from a cycle gas production test of a target battery, and the second prediction model is obtained by fitting and calculating test data collected from a storage gas production test of a target battery, and the target prediction model is expressed as follows:

[0039] P=P cycle +P calenddr

[0040] Among them, P cycle is the first prediction model, P calenddrThe second prediction model.

[0041] P cycle =e f(DOD，C，T) ×n f(C，T)

[0042] P calenddr =e f(SOC，T) ×t f(SOC，T)

[0043] Wherein, P is the internal pressure of the battery cell, e is a natural constant, f(·) represents a fitting relationship, such as a linear relationship, a polynomial relationship, etc., DOD is the depth of discharge, C is the charge and discharge rate, T is the temperature, n is the number of cycles, SOC is the remaining state of charge of the battery, and t is the calendar time.

[0044] Optionally, based on the target prediction model and the operating data in multiple charge and discharge cycles, the predicted gas production data (i.e., the predicted internal pressure value) of the target battery in the whole life cycle can be calculated. For example, the actual scene gas production situation is fitted, and the cycle gas production and calendar gas production in the cycle are calculated respectively, and the total gas production in the cycle is calculated, that is: P = P cycle +P calendar , the gas production of each cycle is solved and accumulated respectively, and the total gas production of the life cycle (i.e. the predicted gas production data within the whole life cycle) can be obtained, which is expressed as follows:

[0045]

[0046] Where M is the number of cycles.

[0047] Step S103, determining the predicted deformation value of the target battery according to the predicted gas production data and the target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0048] Optionally, based on the predicted gas production data and the target curve graph, the predicted deformation value of the target battery can be determined, for example, based on P 总 The value of the deformation variable of the battery steel shell, the deformation variable of the positive end point or the deformation variable of the negative end point can be queried on the target curve graph as the predicted deformation value. Figure 2 is a schematic diagram of an optional internal pressure-size curve according to an embodiment of the present application. The target curve diagram may be as follows: Figure 2 The graph shown shows the effect of the internal pressure of the battery on the deformation of the terminals and the casing.

[0049] Based on the scheme defined by the above steps S101 to S103, it can be known that in an embodiment of the present application, the operating data of the target battery in multiple charge and discharge cycles is first obtained, and then the predicted gas production data of the target battery in the whole life cycle is calculated based on the target prediction model and the operating data in multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating the test data collected from different gas production tests on the target battery, and then the predicted deformation value of the target battery is determined based on the predicted gas production data and the target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0050] It is easy to notice that in the above process, based on the target prediction model obtained by fitting the test data collected from different gas production tests on the target battery and the operating data in multiple charge and discharge cycles, the predicted gas production data of the target battery over its entire life cycle can be calculated, so that the predicted deformation value of the target battery can be determined based on the predicted gas production data and the target curve, which is closer to the actual application scenario, realizes the effective prediction of the battery deformation, and improves the prediction accuracy of the battery deformation.

[0051] It can be seen that through the technical solution of the present application, the purpose of effectively predicting the gas production of the battery under actual operating conditions and the dimensional changes at the end of its life is achieved, thereby achieving the technical effect of improving the accuracy of prediction of battery deformation, and further solving the technical problem of low accuracy in predicting battery deformation by using gas production data obtained from high-temperature storage batteries to predict battery dimensional changes in the prior art.

[0052] In an optional embodiment, the target prediction model includes a first prediction model and a second prediction model, the first prediction model and the second prediction model correspond to different gas production tests, wherein the predicted gas production data of the target battery in the full life cycle is calculated based on the target prediction model and the operating data in multiple charge and discharge cycles, including: for each charge and discharge cycle, the first predicted gas production data in the current charge and discharge cycle is calculated based on the first prediction model and the average discharge depth data, average charge and discharge rate data, average temperature data and number of cycles in the operating data in the current charge and discharge cycle, and the second predicted gas production data in the current charge and discharge cycle is calculated based on the second prediction model and the average temperature data, average battery remaining charge state data and calendar time in the operating data in the current charge and discharge cycle; the first predicted gas production data in each charge and discharge cycle and the second predicted gas production data in each charge and discharge cycle are accumulated to obtain the predicted gas production data of the target battery in the full life cycle.

[0053] Optionally, the first prediction model is a cycle gas production model P cycleThe second prediction model is the calendar gas production model P calenddr , for each charge and discharge cycle, according to P cycle The first predicted gas production data in the current charge-discharge cycle can be calculated by combining the average discharge depth data, average charge-discharge rate data, average temperature data and number of cycles in the operation data in the current charge-discharge cycle. For example, these data are brought into P cycle In the formula, we get P cycle The value of is the first predicted gas production data.

[0054] According to P calenddr The second predicted gas production data in the current charge-discharge cycle can be calculated by combining the average temperature data, the average battery remaining charge state data and the calendar time in the operation data in the current charge-discharge cycle. For example, these data are brought into P calenddr In the formula, we get P calenddr The value of is the second predicted gas production data.

[0055] Furthermore, by accumulating the first predicted gas production data in each charge and discharge cycle and the second predicted gas production data in each charge and discharge cycle, the predicted gas production data of the target battery in the whole life cycle can be obtained, which is expressed as follows:

[0056] P=P cycle +P calenddr

[0057]

[0058] Figure 3 is a schematic diagram of an optional gas production prediction curve according to an embodiment of the present application. According to the predicted gas production data obtained by fitting the actual scene gas production situation, a curve such as Figure 3 The curve shown.

[0059] It should be noted that, based on the target prediction model and the operating data in multiple charge and discharge cycles, the predicted gas production data of the target battery during its entire life cycle is calculated, which can predict whether the internal pressure of the battery will exceed the valve opening pressure in actual scenarios, providing a reference basis for battery design. Moreover, it is closer to the actual application scenario and realizes the effective prediction of the battery's gas production, thereby improving the accuracy of the prediction of battery deformation.

[0060] In an optional embodiment, the predicted deformation value of the target battery is determined based on the predicted gas production data and the target curve graph, including: matching the predicted gas production data with multiple battery internal pressure values ​​on the target curve graph to obtain the target internal pressure value; obtaining the target deformation value corresponding to the target internal pressure value, and using the target deformation value as the predicted deformation value of the target battery.

[0061] Optionally, in the process of determining the predicted deformation value of the target battery based on the predicted gas production data and the target curve, the predicted gas production data can be matched with multiple battery internal pressure values ​​on the target curve to obtain the target internal pressure value (i.e., the same internal pressure value as the predicted gas production data), and then the target deformation value corresponding to the target internal pressure value is obtained, and the target deformation value is used as the predicted deformation value of the target battery. For example, the predicted gas production data is 0.8MPa, according to Figure 2 It can be determined that the predicted deformation value of the negative terminal of the battery is 0.1mm, the predicted deformation value of the positive terminal is 0.2mm, and the predicted deformation value of the steel shell is 0.6mm.

[0062] It should be noted that determining the predicted deformation value of the target battery based on the predicted gas production data and the target curve is closer to the actual application scenario, achieves effective prediction of the battery deformation, and improves the accuracy of the prediction of the battery deformation.

[0063] In an optional embodiment, the first prediction model is obtained by the following steps: determining a first test condition matrix for a cyclic gas production test based on an application scenario of a target battery, wherein the first test condition matrix includes temperature conditions, battery remaining state of charge range conditions, and charge and discharge rate conditions; performing a cyclic gas production test on the target battery based on the first test condition matrix to obtain test data of the cyclic gas production test; and performing fitting calculations based on the test data of the cyclic gas production test to obtain a first prediction model.

[0064] Optionally, according to the application scenario of the target battery, a first test condition matrix of the cyclic gas production test is determined, and then the cyclic gas production test is performed on the target battery based on the first test condition matrix to obtain test data of the cyclic gas production test. A fitting calculation is performed based on the test data of the cyclic gas production test to obtain a first prediction model, that is:

[0065] P cycle =e f(DOD,C,T) ×n f(C,T)

[0066] Figure 4 is a schematic diagram of an optional cyclic gas production test matrix according to an embodiment of the present application, such as Figure 4 As shown, the cyclic gas production test matrix (i.e., the first test condition matrix) includes temperature conditions (Temp. / °C), battery remaining state of charge range conditions (SOC Range / %), and charge and discharge rate conditions (DCH(C), indicating discharge rate; CH(C), indicating charge rate).

[0067] For example, a test matrix is ​​designed according to the battery application scenario, cyclic gas production test data is collected, and a cyclic gas production model is fitted and calculated to obtain a cyclic gas production model.

[0068] In an optional embodiment, the second prediction model is obtained by the following steps: determining a second test condition matrix for the storage gas production test based on an application scenario of the target battery, wherein the second test condition matrix includes temperature conditions and battery remaining state of charge conditions; performing a storage gas production test on the target battery based on the second test condition matrix to obtain test data of the storage gas production test; and performing fitting calculations based on the test data of the storage gas production test to obtain the second prediction model.

[0069] Optionally, according to the application scenario of the target battery, a second test condition matrix of the storage gas production test is determined, and then the storage gas production test is performed on the target battery based on the second test condition matrix, and test data of the storage gas production test can be obtained. A fitting calculation is performed based on the test data of the storage gas production test to obtain a second prediction model, that is:

[0070] P calendar =e f(SOC,T) ×t f(SOC,T)

[0071] Figure 5 is a schematic diagram of an optional storage gas production test matrix according to an embodiment of the present application, such as Figure 5 As shown, the storage gas generation test matrix (ie, the second test condition matrix) includes a temperature condition (Temp.) and a battery remaining state of charge condition (SOC).

[0072] For example, a test matrix is ​​designed according to the battery application scenario, storage gas production test data is collected, and a storage gas production model is fitted and calculated to obtain a storage gas production model.

[0073] It should be noted that the cyclic gas production model and the storage gas production model obtained through fitting calculation provide a basis for calculating the predicted gas production data of the target battery throughout its life cycle, which is closer to the actual application scenario, thereby enabling effective prediction of the battery's gas production and thus improving the accuracy of the prediction of battery deformation.

[0074] In an optional embodiment, the target curve graph is obtained by the following steps: using a pressure tester to perform pressure tests on test batteries corresponding to the target battery according to different pressure intensities, to obtain pressurized batteries corresponding to each pressure intensity, wherein the pressure intensity is used to represent the internal pressure value of the battery; measuring the dimensions of the pressurized batteries corresponding to each pressure intensity, to obtain current battery dimension data corresponding to each pressure intensity; generating a target curve graph based on each pressure intensity, the current battery dimension data corresponding to each pressure intensity, and the initial battery dimension data corresponding to each pressure intensity.

[0075] In an optional embodiment, a target curve graph is generated based on each pressure intensity, the current battery size data corresponding to each pressure intensity, and the initial battery size data corresponding to each pressure intensity, including: performing a difference calculation between the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity to obtain a deformation value corresponding to each pressure intensity; and drawing based on each pressure intensity and the deformation value corresponding to each pressure intensity to obtain a target curve graph.

[0076] Optionally, a pressure tester is used to perform pressure tests on test batteries corresponding to the target batteries (for example, fake batteries containing a core but not an electrolyte) according to different pressure intensities, so that the pressurized batteries corresponding to each pressure intensity can be obtained. Then, the dimensions of the pressurized batteries corresponding to each pressure intensity are measured to obtain the current battery size data corresponding to each pressure intensity. Then, the difference between the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity (i.e., the battery size data before pressurization) is calculated to obtain the deformation value corresponding to each pressure intensity. Each pressure intensity and the deformation value corresponding to each pressure intensity are plotted to obtain the target curve graph.

[0077] Figure 6 is a schematic diagram of an optional pressure test process according to an embodiment of the present application, such as Figure 6 As shown, a hole is punched on the side wall of the fake battery, and a pressure fixture is used to connect the fake battery to a pressure tester. The pressure tester is used to perform a pressure test on the fake battery according to different pressure intensities (for example, 0.3MPa, 0.4MPa, 0.5MPa, 0.6MPa, 0.8MPa, 1.0MPa, 1.2MPa, and 1.4MPa). Three or more parallel samples are taken for each pressure intensity, and then a three-dimensional coordinate measuring machine is used to measure the dimensions of the pressurized battery. The battery deformation is calculated based on the battery size data after pressurization and the battery size data before pressurization, and the result is plotted. Figure 2 The internal pressure-size curve is shown.

[0078] It should be noted that the target curve graph is obtained by plotting the deformation values ​​collected through the pressure test, which provides a basis for determining the predicted deformation value of the target battery, is closer to the actual application scenario, and can thus achieve effective prediction of the battery deformation and improve the accuracy of the prediction of the battery deformation.

[0079] In an optional embodiment, after the predicted deformation value of the target battery is determined, a module design strategy for the target battery is determined based on the predicted deformation value of the target battery.

[0080] Optionally, by predicting the gas generation and deformation process of the battery cell during its entire life, a reference basis can be provided for module design.

[0081] In an embodiment of the present application, the operating data of the target battery in multiple charge and discharge cycles is first obtained, and then the predicted gas production data of the target battery in its full life cycle is calculated based on the target prediction model and the operating data in multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating the test data collected from different gas production tests on the target battery, and then the predicted deformation value of the target battery is determined based on the predicted gas production data and the target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0082] In the above process, based on the target prediction model obtained by fitting the test data collected from different gas production tests on the target battery and the operating data in multiple charge and discharge cycles, the predicted gas production data of the target battery over its entire life cycle can be calculated. Therefore, the predicted deformation value of the target battery can be determined based on the predicted gas production data and the target curve, which is closer to the actual application scenario, realizes the effective prediction of the battery deformation, and improves the prediction accuracy of the battery deformation.

[0083] It can be seen that through the technical solution of the present application, the purpose of effectively predicting the gas production of the battery under actual operating conditions and the dimensional changes at the end of its life is achieved, thereby achieving the technical effect of improving the accuracy of prediction of battery deformation, and further solving the technical problem of low accuracy in predicting battery deformation by using gas production data obtained from high-temperature storage batteries to predict battery dimensional changes in the prior art.

[0084] Example 2

[0085] According to an embodiment of the present application, an embodiment of a device for predicting battery deformation is provided, wherein: Figure 7 is a schematic diagram of an optional battery deformation prediction device according to an embodiment of the present application, such as Figure 7 As shown, the device includes: an acquisition unit 701, used to acquire the operating data of the target battery in multiple charge and discharge cycles; a first calculation unit 702, used to calculate the predicted gas production data of the target battery in the whole life cycle according to the target prediction model and the operating data in multiple charge and discharge cycles, wherein the target prediction model is obtained by fitting and calculating the test data collected from different gas production tests on the target battery; a first determination unit 703, used to determine the predicted deformation value of the target battery according to the predicted gas production data and the target curve diagram, wherein the target curve diagram is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0086] It should be noted that the above-mentioned acquisition unit 701, first calculation unit 702 and first determination unit 703 correspond to steps S101 to S103 in the above-mentioned embodiment, and the examples and application scenarios implemented by the three units and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0087] Optionally, the first calculation unit includes: a first calculation subunit, used to calculate, for each charge and discharge cycle, the first predicted gas production data in the current charge and discharge cycle based on the first prediction model and the average discharge depth data, the average charge and discharge rate data, the average temperature data and the number of cycles in the operating data in the current charge and discharge cycle, and to calculate the second predicted gas production data in the current charge and discharge cycle based on the second prediction model and the average temperature data, the average battery remaining state of charge data and the calendar time in the operating data in the current charge and discharge cycle; a second calculation subunit, used to accumulate the first predicted gas production data in each charge and discharge cycle and the second predicted gas production data in each charge and discharge cycle to obtain the predicted gas production data of the target battery over its entire life cycle.

[0088] Optionally, the first determination unit includes: a first determination subunit, used to match the predicted gas production data with multiple battery internal pressure values ​​on the target curve graph to obtain a target internal pressure value; a second determination subunit, used to obtain a target deformation value corresponding to the target internal pressure value, and use the target deformation value as the predicted deformation value of the target battery.

[0089] Optionally, the battery deformation prediction device also includes the following units, which are used to obtain a first prediction model through the following steps: a second determination unit, used to determine a first test condition matrix of a cyclic gas generation test based on an application scenario of the target battery, wherein the first test condition matrix includes temperature conditions, battery remaining state of charge range conditions, and charge and discharge rate conditions; a first processing unit, used to perform a cyclic gas generation test on the target battery based on the first test condition matrix to obtain test data of the cyclic gas generation test; a second calculation unit, used to perform fitting calculations based on the test data of the cyclic gas generation test to obtain a first prediction model.

[0090] Optionally, the battery deformation prediction device also includes the following units, which are used to obtain a second prediction model through the following steps: a third determination unit, used to determine a second test condition matrix for the storage gas production test based on the application scenario of the target battery, wherein the second test condition matrix includes temperature conditions and battery remaining state of charge conditions; a second processing unit, used to perform a storage gas production test on the target battery based on the second test condition matrix to obtain test data of the storage gas production test; a third calculation unit, used to perform fitting calculations based on the test data of the storage gas production test to obtain a second prediction model.

[0091] Optionally, the battery deformation prediction device also includes the following units, which are used to obtain a target curve graph through the following steps: a third processing unit, used to use a pressure tester to perform pressure tests on the test batteries corresponding to the target batteries according to different pressure intensities, and obtain pressurized batteries corresponding to each pressure intensity, wherein the pressure intensity is used to represent the internal pressure value of the battery; a fourth processing unit, used to measure the dimensions of the pressurized batteries corresponding to each pressure intensity, and obtain current battery size data corresponding to each pressure intensity; a generation unit, used to generate a target curve graph based on each pressure intensity, the current battery size data corresponding to each pressure intensity, and the initial battery size data corresponding to each pressure intensity.

[0092] Optionally, the generation unit includes: a third calculation subunit, used to perform difference calculation between the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity, so as to obtain the deformation value corresponding to each pressure intensity; and a drawing subunit, used to draw according to each pressure intensity and the deformation value corresponding to each pressure intensity, so as to obtain a target curve graph.

[0093] Optionally, the battery deformation prediction device further includes: a fourth determination unit, configured to determine a module design strategy for the target battery according to the predicted deformation value of the target battery after determining the predicted deformation value of the target battery.

[0094] Example 3

[0095] According to another aspect of an embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned battery deformation prediction method is implemented.

[0096] Example 4

[0097] According to another aspect of an embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned battery deformation prediction method when running.

[0098] Example 5

[0099] According to another aspect of the embodiment of the present application, an electronic device is also provided, wherein: Figure 8 is a schematic diagram of an optional electronic device according to an embodiment of the present application, such as Figure 8As shown, the electronic device includes one or more processors; a memory for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are used to run the programs, wherein the programs are set to execute the above-mentioned battery deformation prediction method when running. When the processor executes the program, the following steps are implemented: obtaining the operating data of the target battery in multiple charge and discharge cycles; based on the target prediction model and the operating data in multiple charge and discharge cycles, calculating the predicted gas production data of the target battery in the whole life cycle, wherein the target prediction model is obtained by fitting and calculating the test data collected by performing different gas production tests on the target battery; based on the predicted gas production data and the target curve graph, determining the predicted deformation value of the target battery, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

[0100] Optionally, the processor also implements the following steps when executing the program: for each charge and discharge cycle, the first predicted gas production data for the current charge and discharge cycle is calculated based on the first prediction model and the average discharge depth data, average charge and discharge rate data, average temperature data and number of cycles in the operating data for the current charge and discharge cycle; and the second predicted gas production data for the current charge and discharge cycle is calculated based on the second prediction model and the average temperature data, average battery remaining state of charge data and calendar time in the operating data for the current charge and discharge cycle; the first predicted gas production data for each charge and discharge cycle and the second predicted gas production data for each charge and discharge cycle are accumulated to obtain the predicted gas production data for the target battery over its entire life cycle.

[0101] Optionally, when the processor executes the program, the following steps are also implemented: matching the predicted gas production data with multiple battery internal pressure values ​​on the target curve graph to obtain a target internal pressure value; obtaining a target deformation value corresponding to the target internal pressure value, and using the target deformation value as the predicted deformation value of the target battery.

[0102] Optionally, the processor also implements the following steps when executing the program: determining a first test condition matrix for a cyclic gas production test based on an application scenario of the target battery, wherein the first test condition matrix includes temperature conditions, battery remaining state of charge range conditions, and charge and discharge rate conditions; performing a cyclic gas production test on the target battery based on the first test condition matrix to obtain test data for the cyclic gas production test; and performing fitting calculations based on the test data of the cyclic gas production test to obtain a first prediction model.

[0103] Optionally, the processor also implements the following steps when executing the program: determining a second test condition matrix for the storage gas production test based on the application scenario of the target battery, wherein the second test condition matrix includes temperature conditions and battery remaining state of charge conditions; performing a storage gas production test on the target battery based on the second test condition matrix to obtain test data of the storage gas production test; and performing fitting calculations based on the test data of the storage gas production test to obtain a second prediction model.

[0104] Optionally, the processor also implements the following steps when executing the program: using a pressure tester to perform pressure tests on test batteries corresponding to the target battery according to different pressure intensities, to obtain pressurized batteries corresponding to each pressure intensity, wherein the pressure intensity is used to represent the internal pressure value of the battery; measuring the dimensions of the pressurized batteries corresponding to each pressure intensity, to obtain current battery dimension data corresponding to each pressure intensity; and generating a target curve graph based on each pressure intensity, the current battery dimension data corresponding to each pressure intensity, and the initial battery dimension data corresponding to each pressure intensity.

[0105] Optionally, when the processor executes the program, the following steps are also implemented: performing difference calculation on the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity to obtain the deformation value corresponding to each pressure intensity; and drawing based on each pressure intensity and the deformation value corresponding to each pressure intensity to obtain the target curve graph.

[0106] Optionally, when the processor executes the program, the following steps are also implemented: after determining the predicted deformation value of the target battery, determining a module design strategy for the target battery based on the predicted deformation value of the target battery.

[0107] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0108] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0109] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0111] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0112] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0114] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for predicting battery deformation, characterized in that: include: Obtaining the operating data of the target battery during multiple charge and discharge cycles; According to the target prediction model and the operation data in the multiple charge and discharge cycles, the predicted gas production data of the target battery in the whole life cycle is calculated, wherein the target prediction model is obtained by fitting and calculating the test data collected by performing different gas production tests on the target battery; The predicted deformation value of the target battery is determined based on the predicted gas production data and the target curve graph, wherein the target curve graph is used to represent the corresponding relationship between the battery internal pressure value and the deformation value.

2. The method according to claim 1, characterized in that The target prediction model includes a first prediction model and a second prediction model, wherein the first prediction model and the second prediction model correspond to different gas production tests, wherein the predicted gas production data of the target battery in the full life cycle is calculated based on the target prediction model and the operation data in the multiple charge and discharge cycles, including: For each charge and discharge cycle, the first predicted gas production data in the current charge and discharge cycle is calculated based on the first prediction model and the average discharge depth data, the average charge and discharge rate data, the average temperature data and the number of cycles in the operation data in the current charge and discharge cycle, and the second predicted gas production data in the current charge and discharge cycle is calculated based on the second prediction model and the average temperature data, the average battery remaining state of charge data and the calendar time in the operation data in the current charge and discharge cycle; The first predicted gas production data in each charge and discharge cycle and the second predicted gas production data in each charge and discharge cycle are accumulated to obtain the predicted gas production data of the target battery in the full life cycle.

3. The method according to claim 1, characterized in that Determining the predicted deformation value of the target battery according to the predicted gas production data and the target curve graph includes: Matching the predicted gas production data with a plurality of battery internal pressure values ​​on the target curve graph to obtain a target internal pressure value; A target deformation value corresponding to the target internal pressure value is obtained, and the target deformation value is used as a predicted deformation value of the target battery.

4. The method according to claim 2, characterized in that: The first prediction model is obtained by following the steps: Determine a first test condition matrix for a cyclic gas generation test according to an application scenario of the target battery, wherein the first test condition matrix includes temperature conditions, battery residual state of charge range conditions, and charge and discharge rate conditions; Performing the cycle gas generation test on the target battery based on the first test condition matrix to obtain test data of the cycle gas generation test; The first prediction model is obtained by performing fitting calculation based on the test data of the cycle gas production test.

5. The method according to claim 2, characterized in that: The second prediction model is obtained by following the steps below: Determine a second test condition matrix for storage gas generation test according to the application scenario of the target battery, wherein the second test condition matrix includes temperature conditions and battery residual state of charge conditions; Performing the storage gas generation test on the target battery based on the second test condition matrix to obtain test data of the storage gas generation test; A fitting calculation is performed based on the test data of the storage gas production test to obtain the second prediction model.

6. The method according to any one of claims 1 to 5, characterized in that The target curve graph is obtained by following the steps: Using a pressure tester to perform pressure tests on the test battery corresponding to the target battery according to different pressure intensities, to obtain a pressurized battery corresponding to each pressure intensity, wherein the pressure intensity is used to represent the internal pressure value of the battery; Measuring the size of the pressurized battery corresponding to each pressure intensity to obtain current battery size data corresponding to each pressure intensity; The target curve graph is generated according to each pressure intensity, the current battery size data corresponding to each pressure intensity, and the initial battery size data corresponding to each pressure intensity.

7. The method according to claim 6, characterized in that Generating the target curve graph according to each pressure intensity, the current battery size data corresponding to each pressure intensity, and the initial battery size data corresponding to each pressure intensity includes: Performing difference calculation on the current battery size data corresponding to each pressure intensity and the initial battery size data corresponding to each pressure intensity to obtain a deformation value corresponding to each pressure intensity; The target curve graph is obtained by plotting according to each pressure intensity and the deformation value corresponding to each pressure intensity.

8. The method according to any one of claims 1 to 5, characterized in that After determining the predicted deformation value of the target battery, the method further includes: A module design strategy for the target battery is determined based on the predicted deformation value of the target battery.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the battery deformation prediction method described in any one of claims 1 to 8 when running.

10. An electronic device, characterized in that: The electronic device includes one or more processors; A memory for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to run the programs, wherein the program is configured to execute the battery deformation prediction method described in any one of claims 1 to 8 when running.

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