Battery life prediction method and apparatus, computer device, and storage medium

Abstract

The application provides a battery life prediction method and device, computer equipment and a storage medium. Based on the aging life data of a target battery under multiple different test conditions, an acceleration model and first model parameters of the acceleration model are determined, and life conversion is performed to convert the aging life data of the target battery under each test condition into equivalent life data under a reference discharge current. The second model parameters of the target prediction life model are determined by using the equivalent life data corresponding to each test condition after conversion. The service life of the target battery under the reference discharge current is determined based on the target prediction life model. Based on the above technical solution, the service life of the battery can be efficiently and accurately predicted.

Description

Technical Field

[0001] This invention relates to the field of battery testing technology, and specifically to a battery life prediction method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Lithium-ion batteries, due to their high energy density, long cycle life, and low self-discharge rate, have become core power supply components for modern electronic devices, especially medical devices with extremely high reliability and safety requirements, such as implantable pacemakers, neurostimulators, and continuous monitoring instruments. In these applications, batteries not only need to provide sustained and stable power output within a limited volume, but also, because they are often implanted in the human body or used in critical life support systems, their lifespan and reliability are directly related to patient safety and treatment outcomes.

[0003] However, existing technologies for predicting the lifespan of batteries used in medical devices have significant limitations in accuracy. Firstly, the aging process of lithium-ion batteries used in medical devices is extremely slow, making full-lifespan testing in real-world scenarios, which can take several years, impractical. Secondly, if conventional methods of accelerated testing and model extrapolation are used, the complex microcurrent operating modes and pulsed load characteristics of medical devices result in aging mechanisms that differ significantly from the degradation patterns under conventional testing conditions. This leads to substantial discrepancies between the extrapolation results based on conventional accelerated models and the actual lifespan. Summary of the Invention

[0004] Embodiments of the present invention provide a battery life prediction method, apparatus, computer device, and storage medium, which can efficiently and accurately predict the lifespan of a battery.

[0005] In a first aspect, embodiments of the present invention provide a battery life prediction method, comprising: Based on the aging lifetime data of the target battery under multiple different test conditions, an accelerated model and the first model parameters of the accelerated model are determined, wherein each test condition includes a charge and discharge current, and the discharge current of a test condition is set as a reference discharge current. Based on the acceleration model and the parameters of the first model, the aging lifetime data of the target battery under various test conditions is converted into equivalent lifetime data under the reference discharge current. Using the equivalent lifetime data corresponding to each test condition, the pre-determined target predicted lifetime model is fitted to determine the second model parameters of the target predicted lifetime model. Based on the target predicted lifetime model and the second model parameters, a lifetime curve of the target battery is generated, and based on the lifetime curve, the lifetime of the target battery under the reference discharge current is determined.

[0006] In one embodiment, determining the accelerated model and the first model parameters of the accelerated model based on aging lifetime data of the target battery under multiple different test conditions includes: The acceleration model is determined based on the correlation between discharge current and battery life; Based on the discharge current and aging life data corresponding to each test condition, the accelerated model is fitted to determine the parameters of the first model.

[0007] Since the first model parameters are obtained by inputting the discharge current and aging lifetime data corresponding to each test condition into the accelerated model for solving, the accuracy of the obtained first model parameters is higher.

[0008] In one embodiment, the step of converting the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current based on the accelerated model and the first model parameters includes: Based on the acceleration model and the parameters of the first model, the acceleration factor of the discharge current relative to the reference discharge current for each test condition is determined. By using the acceleration factor corresponding to each test condition, the corresponding aging life data is converted to lifespan, and the equivalent lifespan data corresponding to each test condition is determined.

[0009] Thus, based on the accelerated model and the first model parameters of the accelerated model determined using the aging lifetime data under various test conditions, the aging lifetime data of the target battery under various test conditions is converted into equivalent lifetime data under the reference discharge current, making the accuracy of the converted equivalent lifetime data higher.

[0010] In one embodiment, before fitting a predetermined target predicted lifetime model using equivalent lifetime data corresponding to each test condition, the method further includes: From multiple predicted lifetime models, the target predicted lifetime model that matches the equivalent lifetime data corresponding to each test condition is determined.

[0011] Since the target predicted lifetime model is obtained from multiple predicted lifetime models and is matched with the equivalent lifetime data corresponding to each test condition, the obtained target predicted lifetime model has a higher degree of matching with the equivalent lifetime corresponding to each test condition.

[0012] In one embodiment, generating the lifetime curve of the target battery based on the target predicted lifetime model and the second model parameters includes: Based on the target predicted lifetime model and the second model parameters, the lifetime reliability function curve of the target battery is plotted, and the lifetime reliability function curve is used as the lifetime curve.

[0013] By using the parameters of the target prediction lifetime model and the second model, a lifetime reliability function curve is plotted, making the plotted lifetime reliability function curve more intuitive, and each value on the lifetime reliability function curve corresponds to a reliability.

[0014] In one embodiment, determining the lifespan of the target battery at the reference discharge current based on the lifespan curve includes: From the lifetime reliability function curve, select the lifetime value corresponding to the preset reliability index, and use the selected lifetime value as the lifetime.

[0015] Since the service life is selected from the service life reliability function curve and corresponds to the preset reliability index, the reliability of the determined service life is higher.

[0016] In one embodiment, before determining the acceleration model and the first model parameters of the acceleration model, the method further includes: Determine the multiple different test conditions; Based on the aforementioned multiple different test conditions, the target battery was subjected to aging tests to obtain multiple sets of battery aging data; Based on the battery aging data for each group, determine the battery aging model corresponding to each test condition. Based on the battery aging model corresponding to each test condition, the aging life data of the target battery under each test condition is determined.

[0017] By conducting aging tests on the target battery under multiple different test conditions, the battery aging model corresponding to each test condition is obtained. Then, based on the battery aging model corresponding to each test condition, the aging life data of the target battery under each test condition is obtained, which makes the accuracy of the aging life data of the target battery under each test condition higher.

[0018] Secondly, embodiments of the present invention provide a battery life prediction device, comprising: The first model and parameter acquisition module is used to determine the accelerated model and the first model parameters of the accelerated model based on the aging life data of the target battery under multiple different test conditions. Each test condition includes a charge and discharge current, and the discharge current of a test condition is set as a reference discharge current. The lifetime conversion module is used to convert the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current based on the accelerated model and the parameters of the first model. The second model and parameter acquisition module is used to fit the pre-determined target prediction lifetime model using the equivalent lifetime data corresponding to each test condition, and to determine the second model parameters of the target prediction lifetime model. The lifetime prediction module is used to generate a lifetime curve of the target battery based on the target predicted lifetime model and the second model parameters, and to determine the lifetime of the target battery under the reference discharge current based on the lifetime curve.

[0019] Thirdly, embodiments of the present invention provide a computer device, the computer device comprising: One or more processors; The memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor in accordance with the battery life prediction method described in any of the preceding claims.

[0020] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the battery life prediction method described above.

[0021] The beneficial effects of the embodiments of the present invention are as follows: In embodiments of the present invention, based on aging lifetime data of the target battery under multiple different test conditions, an accelerated model and its first model parameters are determined. Using the accelerated model and the first model parameters, the aging lifetime data under different test conditions are uniformly converted to equivalent lifetime data under a reference discharge current. Based on this, a pre-set target prediction lifetime model is fitted to the equivalent lifetime data to obtain its parameters, generating a battery lifetime curve. Finally, the lifespan of the target battery under reference conditions is extracted from the curve. Therefore, using multiple different test conditions for accelerated aging tests can significantly improve lifetime prediction efficiency. Furthermore, by using an accelerated model based on test data for lifetime conversion, the accuracy of the equivalent lifetime data is improved, making the final lifetime curve and lifespan prediction results more reliable, thus achieving efficient and accurate battery lifetime prediction. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of the battery life prediction method provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of an embodiment of the present invention for obtaining aging life data of a target battery under various test conditions. Figure 3a , Figure 3b , Figure 3c and Figure 3d This is a schematic diagram of the structure provided by an embodiment of the present invention for fitting multiple predicted lifetime models using equivalent lifetime data corresponding to each test condition. Figure 4 This is a schematic diagram of the life reliability function curve plotted based on the target predicted life model provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a battery life prediction device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, it should be understood that the specific embodiments described herein are only for illustration and explanation of the present invention and are not intended to limit the present invention. In the present invention, unless otherwise stated, directional terms such as "upper" and "lower" generally refer to the upper and lower positions of the device in actual use or operation, specifically the drawing directions in the accompanying drawings; while "inner" and "outer" refer to the outline of the device.

[0025] See Figure 1 This application provides a battery life prediction method, which includes steps S101 to S104, as follows: Step S101: Based on the aging lifetime data of the target battery under multiple different test conditions, determine the accelerated model and the first model parameters of the accelerated model; Each test condition includes a charge / discharge current, with the discharge current for one test condition set as the baseline discharge current. Furthermore, an accelerated model characterizes the functional relationship between the discharge current and battery life.

[0026] In some embodiments, before performing step S101, it is also necessary to obtain aging lifetime data of the target battery under multiple different test conditions, see [link to relevant documentation]. Figure 2The steps for obtaining aging lifetime data of the target battery under multiple different test conditions include steps S201-S204, as follows: Step S201: Determine multiple different test conditions.

[0027] In some embodiments, by analyzing the aging mechanism of batteries used in medical devices, the aging influencing factors are determined to include discharge current. Therefore, based on the aging influencing factors, multiple test conditions under different currents are designed, and each current in the different designs corresponds to a test condition. At this time, multiple different test conditions can be combined into an accelerated aging test scheme.

[0028] In some embodiments, multiple different test conditions can form an accelerated aging test matrix, wherein the charging current of each test condition in the accelerated aging test matrix is ​​the same, but the discharging current of each test condition is different, and the discharging current of one test condition is set as a reference discharging current to improve the accuracy of subsequent lifetime conversion. In some embodiments, the charging current of each test condition in the accelerated aging test matrix may also be different.

[0029] In some embodiments, multiple different test conditions are typically three or more test conditions. For example, taking six different test conditions as an example, the accelerated aging test scheme is shown in Table 1 below.

[0030] Table 1

[0031] For example, taking the test item with serial number 1 as an example, the charging and discharging current of this test item is C / 2-C / 50, where C is a rate unit, which is numerically equal to the nominal capacity of the battery. For example, for a battery with a capacity of 100mAh, 1C = 100mA current, C / 2 = (100mA) / 2 = 50mA current, and C / 50 = (100mA) / 50 = 2mA current. C / 2 represents the current rate used in the charging stage, and C / 50 represents the current rate used in the discharging stage. The discharge current of the test item with serial number 1 is the reference discharge current, that is, the reference discharge current is C / 50. C / 50 matches the most important small current state in actual use.

[0032] Step S202: Based on multiple different test conditions, perform aging tests on the target battery to obtain multiple sets of battery aging data.

[0033] In some embodiments, after determining multiple different test conditions, a target battery of the same specification is selected for each test condition, and then aging tests are performed on the target battery using multiple different test conditions to obtain multiple sets of battery aging data. The multiple sets of aging battery data correspond one-to-one with multiple test conditions. For example, if the test conditions include test items numbered 1-6, the multiple sets of aging battery data include 6 sets of aging battery data corresponding to test items numbered 1-6 respectively.

[0034] In some embodiments, the target battery may be a lithium cobalt oxide battery or a ternary lithium battery, etc. For each test condition, the target battery is first charged to a first specified voltage using the charging current under that test condition, and then discharged to a second specified voltage using the discharging current under that test condition. Then, the target battery is subjected to charge-discharge cycles at the same rate to obtain battery aging data under that test condition. This process is repeated for each test condition to obtain battery aging data for each test condition. The accelerated aging test is typically conducted in a constant temperature environment at a set temperature, where the set temperature ranges from 45℃ ± 2℃. The set temperature can be 43℃, 45℃, or 46℃, etc.

[0035] In some embodiments, taking a 100mA capacity lithium cobalt oxide battery as an example, the lithium cobalt oxide battery is charged to a first specified voltage at a fixed current C / 2 constant current and constant voltage, and then subjected to constant current discharge cycle tests at different rates such as C / 50, C / 24, C / 10, C / 5, C / 3, and C / 2. The accelerated aging test is carried out in a constant temperature environment at a set temperature, thereby obtaining the aging battery data corresponding to each test condition.

[0036] For example, taking the C / 2-C / 2 charge-discharge cycle test as an example, the process for testing the standard cycle life is as follows: First, the battery is left to stand for 6 hours in a high-temperature environment of 45℃±2℃, then charged at a constant current and constant voltage of C / 2 rate to 4.2V, with a cutoff current of C / 20. Second, after charging, the battery is left to stand for 5 minutes, then discharged at a C / 2 rate to 2.7V, and the battery capacity C1 is recorded. Third, the battery is charged and discharged in cycles at the same rate, and the capacity of each cycle is recorded as C2, C3, C4…Cn. Fourth, if the battery cycle capacity in the nth cycle is less than 20% of the initial capacity C1, the experiment is terminated. The 20% of the initial capacity C1 can be set according to actual conditions, such as 10%, 15%, 25%, or 30%. Cyclic experiments at C / 50, C / 24, C / 10, C / 5, and C / 3 rates are measured using the same method.

[0037] In some embodiments, the target battery may be a battery sample selected according to a 95% confidence interval and a 95% reliability. The target battery is then tested until its capacity decays to 80% of its initial capacity, at which point the experiment is terminated. Each of the values ​​in the 95% confidence interval, 95% reliability, and 80% of the initial capacity can be set according to actual needs.

[0038] Step S203: Based on each set of battery aging data, determine the battery aging model corresponding to each test condition.

[0039] In some embodiments, a battery aging model can be established first, and then the battery aging model can be fitted using each set of battery aging data to obtain the battery aging model and its parameters corresponding to each test condition through fitting.

[0040] In some embodiments, the battery aging model can be an exponential model or a polynomial model, etc. The following example uses an exponential model.

[0041] In some embodiments, taking the six test items in Table 1 as examples, the battery aging data corresponding to test item number 1 is obtained and denoted as C / 2-C / 50 aging data. The exponential model is fitted using the C / 2-C / 50 aging data to obtain the various model parameters in the exponential model. At this time, the battery aging model corresponding to test item number 1 is determined and denoted as M1. Similarly, the battery aging models corresponding to test item number 2 are obtained and denoted as M2, M3, M4, M5, and M6, respectively.

[0042] Thus, by fitting the battery aging model to the battery aging data corresponding to each test condition, the battery aging model corresponding to each test condition can be obtained, making the matching degree between the obtained battery aging model corresponding to each test condition and the battery aging data of the target battery higher.

[0043] Step S204: Based on the battery aging model corresponding to each test condition, determine the aging life data of the target battery under each test condition.

[0044] In some embodiments, after obtaining the battery aging model corresponding to each test condition, extrapolation is performed using the battery aging model corresponding to each test condition to obtain the aging life data of the target battery under each test condition.

[0045] In some embodiments, an extrapolation condition can be set, such as the rate-dependent battery cycle capacity decay to a set percentage. This set percentage can be configured according to actual needs, for example, 80%, 75%, or 70%. The set percentage is then input into the battery aging model corresponding to each test condition, thereby obtaining the aging life data of the target battery under each test condition. The following example uses a set percentage of 80%. For example, let's define the battery aging model corresponding to test item number 1 as M1. Based on M1, if the target battery capacity is set to 80%, then the 80% capacity of the target battery is input into M1 for calculation. The number of charge-discharge cycles required for the target battery to reach 80% capacity in test item number 1 is denoted as N1. This determined number of charge-discharge cycles is then used as the aging life data of the target battery under test item number 1. The same operation is performed for the subsequent five test items to obtain the aging life data of the target battery under test items numbered 2, 3, 4, 5, and 6, denoted as N2, N3, N4, N5, and N6, respectively.

[0046] In some embodiments, after obtaining the aging life data of the target battery under various test conditions through steps S201-S204 described above, step S101 is executed. Furthermore, in step S101, an acceleration model can be determined based on the correlation between discharge current and battery life; the acceleration model is fitted based on the discharge current and aging life data corresponding to each test condition to determine the first model parameters. The acceleration model can be an inverse power-law model.

[0047] Specifically, after determining the accelerated model, the discharge current and aging lifetime data corresponding to each test condition are obtained. These data are then input into the accelerated model for solving, determining the first model parameters. The first model parameters characterize the sensitivity of lifetime to current. Because the first model parameters are determined by inputting the discharge current and aging lifetime data corresponding to each test condition into the accelerated model for solving, the accuracy of the obtained first model parameters is higher.

[0048] In some embodiments, the acceleration model can be represented by the following formula (1), as follows: L(S) = C - ¹ × S -m (1) In formula (1), S represents the discharge current, L(S) represents the battery life under the discharge current, m is the acceleration factor, and C - ¹ is a constant.

[0049] For example, taking the test items numbered 1-6 in Table 1 as an example, the discharge currents of the test items numbered 1-6 are C / 50, C / 24, C / 10, C / 5, C / 3 and C / 2 respectively, and the corresponding aging life data are N1, N2, N3, N4, N5 and N6 respectively. Thus, we can obtain 6 sets of data pairs from discharge current to aging life data, denoted as (C / 50, N1), (C / 24, N2), (C / 10, N3), (C / 5, N4), (C / 3, N5), and (C / 2, N6). After taking the logarithm of formula (1), formula (1) is transformed into a linear equation. Thus, the above 6 sets of data pairs are fitted with a linear equation, and the slope of the linear equation is denoted as a. Thus, the value of m can be determined. At this time, m = -a, and m can be, for example, 0.25, 0.26 and 0.28, etc.

[0050] Step S102: Based on the accelerated model and the parameters of the first model, convert the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current. In some embodiments, an acceleration factor relative to a reference discharge current for each test condition can be determined based on an acceleration model and first model parameters. Using the acceleration factor for each test condition, the corresponding aging lifetime data is converted to lifetime data to determine the equivalent lifetime data for each test condition. The acceleration factor can be determined based on the target ratio of the target battery's lifetime at the reference discharge current to the target battery's lifetime at the discharge current corresponding to the test condition. Alternatively, the acceleration factor can be determined based on the sum or difference of the target ratio and a set value, or it can be determined based on the product of the target ratio and a set weight. The following example demonstrates how the acceleration factor is determined based on the target ratio.

[0051] Specifically, the acceleration factor can be expressed by the following formula (2), as follows: A= (2) In formula (2) This indicates the battery's lifespan at the reference discharge current. This indicates the battery's lifespan under the corresponding discharge current during the test conditions. Indicates the reference discharge current. This indicates the discharge current corresponding to the test conditions.

[0052] Therefore, the acceleration factor can also be determined by substituting formula (1) into formula (2), as shown in the following formula (3): A= (3) Thus, according to formula (3), the acceleration factor corresponding to each test condition can be determined, with m=0.26 and Taking C / 50 as an example, the discharge current under each test item is used as the formula (3). Thus, the acceleration factor corresponding to each test item in the test items numbered 1-6 in Table 1 can be calculated, as shown in Table 2 below.

[0053] Table 2

[0054] In some embodiments, after obtaining the acceleration factor corresponding to each test condition, the aging lifetime data corresponding to each test condition can be multiplied by the corresponding acceleration factor, and the product can be used as the equivalent lifetime data corresponding to each test condition. For example, taking test item number 3 in Table 2 as an example, the aging lifetime data of test item number 3 is N3, and its corresponding acceleration factor is 1.519610. Multiplying N3 and 1.519610, the resulting product is the equivalent lifetime data corresponding to test item number 3, denoted as D3. Performing the above operation for each test item yields 6 equivalent lifetime data corresponding to 6 test items.

[0055] Thus, based on the accelerated model and the first model parameters of the accelerated model determined using the aging lifetime data under various test conditions, the aging lifetime data of the target battery under various test conditions is converted into equivalent lifetime data under the reference discharge current, making the accuracy of the converted equivalent lifetime data higher.

[0056] Step S103: Using the equivalent lifetime data corresponding to each test condition, fit the predetermined target prediction lifetime model to determine the second model parameters of the target prediction lifetime model. In some embodiments, before fitting a predetermined target predicted lifetime model using equivalent lifetime data corresponding to each test condition, a target predicted lifetime model matching the equivalent lifetime data corresponding to each test condition is determined from multiple predicted lifetime models.

[0057] In some embodiments, multiple lifetime prediction models may include at least two models selected from Weibull distribution model, log-normal distribution model, exponential model and log-logistic distribution model. The following example illustrates that multiple lifetime prediction models may include Weibull distribution model, log-normal distribution model, exponential model and log-logistic distribution model.

[0058] Specifically, multiple predicted lifetime models are fitted using equivalent lifetime data corresponding to each test condition. Based on the fitting results, a target predicted lifetime model is determined from the multiple predicted lifetime models. This results in a higher degree of matching between the determined target predicted lifetime model and the equivalent lifetime data corresponding to each test condition, thereby improving the accuracy of the lifetime predicted by the target predicted lifetime model.

[0059] In some embodiments, since the Weibull distribution model, log-normal distribution model, and log-logistic distribution model provide better fitting results, the target predicted lifetime model can be determined to include at least one of the Weibull distribution model, log-normal distribution model, and log-logistic distribution model. In this case, the target predicted lifetime model can be a Weibull distribution model, or it can be a combination of a Weibull distribution model and a log-logistic distribution model, or of course, a log-logistic distribution model, etc., and this specification does not impose specific limitations.

[0060] For example, when fitting multiple predicted lifetime models using equivalent lifetime data corresponding to various test conditions, see [reference needed]. Figure 3a Curve 31, obtained by fitting the model using the Weibull distribution, can be seen in [reference]. Figure 3b Curve 32, obtained by fitting a log-normal distribution model, can be seen in [reference]. Figure 3c Curve 33, obtained by fitting using an exponential model, can be obtained (see [reference]). Figure 3d Curve 34, obtained by fitting the curves using the logistic distribution model, is obtained. Based on the fitting results of each curve with the equivalent lifetime data corresponding to each test condition, one, two, or three models with the highest fitting degree are determined as the target lifetime prediction models. Figures 3a to 3d The equivalent lifetime data is represented by pseudo-failure lifetime, which is in years. The following examples use the target prediction lifetime model as a Weibull distribution model and a log-normal distribution model.

[0061] In some embodiments, after determining the target predicted lifetime model, the target predicted lifetime model is fitted using equivalent lifetime data corresponding to each test condition to determine the second model parameters of the target predicted lifetime model. Specifically, the equivalent lifetime data corresponding to each test condition can be input into the target predicted lifetime model to obtain the second model parameters of the target predicted lifetime model.

[0062] In some embodiments, if the target predicted lifetime model may include one or more predicted lifetime models, when the target predicted lifetime model includes multiple predicted lifetime models, for each predicted lifetime model in the target predicted lifetime model, the equivalent lifetime data corresponding to each test condition is used for fitting to obtain the second model parameters of each predicted lifetime model. For example, taking the target predicted lifetime model as a Weibull distribution model and a log-normal distribution model as an example, the equivalent lifetime data corresponding to the 6 test items in Table 1 can be input into the Weibull distribution model to determine the second model parameters of the Weibull distribution model; and the equivalent lifetime data corresponding to the 6 test items in Table 1 can be input into the log-normal distribution model to determine the second model parameters of the log-normal distribution model.

[0063] Step S104: Based on the target predicted lifetime model and the parameters of the second model, generate the lifetime curve of the target battery, and based on the lifetime curve, determine the lifetime of the target battery under the reference discharge current.

[0064] In some embodiments, the lifetime reliability function curve of the target battery can be plotted based on the target predicted lifetime model and the parameters of the second model, and the lifetime reliability function curve can be used as the lifetime curve.

[0065] In some embodiments, the lifetime reliability function curve can be plotted based on the reliability function formula of the target lifetime prediction model. Taking the target lifetime prediction model as a Weibull distribution model as an example, the reliability function formula of the Weibull distribution model can be used... Plot the reliability curve, where t represents the equivalent lifetime data, η represents the scale parameter, and β represents the shape parameter. Correspondingly, if the target predicted lifetime model is a log-normal distribution model, the reliability curve can be plotted according to the reliability function formula of the log-normal distribution model.

[0066] In some embodiments, after determining the lifetime reliability function curve, a lifetime value corresponding to a preset reliability index can be selected from the lifetime reliability function curve, and the selected lifetime value can be used as the lifetime, thus making the reliability of the predicted lifetime higher.

[0067] Specifically, with the improved accuracy of the converted equivalent lifespan data, the accuracy of the lifespan curve generated based on the equivalent lifespan data will also be improved, thereby increasing the lifespan obtained from the lifespan curve. Furthermore, the use of accelerated aging test schemes can effectively improve the prediction efficiency of lifespan, thus enabling efficient and accurate prediction of battery lifespan.

[0068] In some embodiments, the reliability index can be set according to actual needs, such as 0.95, 0.96, etc., and this specification does not impose specific limitations.

[0069] For example, see Figure 4 The curves representing the lifetime reliability function plotted according to the target predicted lifetime model include a first curve 41 plotted according to the Weibull distribution model and a second curve 42 plotted according to the log-normal distribution model. Figure 4 When the readability reliability is 0.95, the service life corresponding to the first curve 41 is 4.65 years, and the service life corresponding to the second curve 42 is 5.96 years. Therefore, it can be determined that the predicted service life of the target battery is more than 4.65 years.

[0070] Thus, by fitting the aging life data under various test conditions, an accurate accelerated aging model and first model parameters are obtained. Based on the accelerated model and first model parameters, the aging life under different test conditions can be uniformly converted to equivalent life data under a reference discharge current. This conversion process is based on the empirical relationship between battery aging and current, thereby significantly improving the consistency and comparability of the equivalent life data. On this basis, the target life model and its parameters obtained by fitting the equivalent life data are more reliable, the life curve generated accordingly is more accurate, and the final target battery life prediction value extracted from it is also more accurate. At the same time, the accelerated aging test based on multiple different test conditions in this embodiment can significantly shorten the time required for life testing and significantly improve the prediction efficiency, thereby achieving efficient and accurate prediction of battery life overall.

[0071] like Figure 5 As shown, an embodiment of the present invention provides a battery life prediction device, comprising: The first model and parameter acquisition module 501 is used to determine the accelerated model and the first model parameters of the accelerated model based on the aging life data of the target battery under multiple different test conditions. The accelerated model characterizes the functional relationship between the discharge current and the battery life. Each test condition includes the charge and discharge current, and the discharge current of a test condition is set as the reference discharge current. The lifetime conversion module 502 is used to convert the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current based on the accelerated model and the parameters of the first model. The second model and parameter acquisition module 503 is used to fit the pre-determined target prediction lifetime model using the equivalent lifetime data corresponding to each test condition, and to determine the second model parameters of the target prediction lifetime model. The lifetime prediction module 504 is used to generate a lifetime curve of the target battery based on the target predicted lifetime model and the parameters of the second model, and to determine the lifetime of the target battery under the reference discharge current based on the lifetime curve.

[0072] In some embodiments, the first model and parameter acquisition module 501 is used to determine an accelerated model based on the correlation between discharge current and battery life; and to fit the accelerated model based on the discharge current and aging life data corresponding to each test condition to determine the parameters of the first model.

[0073] In some embodiments, the lifetime conversion module 502 is used to determine the acceleration factor of the discharge current relative to the reference discharge current for each test condition based on the acceleration model and the first model parameters; and to convert the corresponding aging lifetime data using the acceleration factor for each test condition to determine the equivalent lifetime data for each test condition.

[0074] In some embodiments, the acceleration model is an inverse power-law model.

[0075] In some embodiments, the battery life prediction device further includes: The target predicted lifetime model acquisition module is used to determine the target predicted lifetime model that matches the equivalent lifetime data corresponding to each test condition from multiple predicted lifetime models before fitting the pre-determined target predicted lifetime model using the equivalent lifetime data corresponding to each test condition.

[0076] In some embodiments, the target predicted lifetime model includes at least one of the Weibull distribution model, the log-normal distribution model, and the log-logistic distribution model.

[0077] In some embodiments, the lifetime prediction module 504 is used to plot the lifetime reliability function curve of the target battery based on the target predicted lifetime model and the parameters of the second model, and to use the lifetime reliability function curve as the lifetime curve.

[0078] In some embodiments, the life prediction module 504 is used to select a life value corresponding to a preset reliability index from the life reliability function curve, and use the selected life value as the service life.

[0079] In some embodiments, the battery life prediction device further includes: The test condition determination module is used to determine multiple different test conditions; The aging test module is used to perform aging tests on the target battery under multiple different test conditions to obtain multiple sets of battery aging data. The battery aging model acquisition module is used to determine the battery aging model corresponding to each test condition based on each set of battery aging data. The aging life data acquisition module is used to extrapolate the aging life data of the target battery under various test conditions based on the battery aging model corresponding to each test condition.

[0080] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0081] In some embodiments of this application, a computer device is provided, including one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processors as steps in the battery life prediction method described above. The steps in the road loss-based location debugging method described here can be steps in the battery life prediction method of the various embodiments described above.

[0082] The computer device can be a terminal, such as a smartphone, tablet, laptop, touchscreen, game console, personal computer (PC), personal digital assistant (PDA), or other similar device. Alternatively, the computer device can be a server.

[0083] like Figure 6 As shown, Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The computer device 1100 includes a processor 1101 with one or more processing cores, a memory 1102 with one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 and the memory 1102 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0084] The processor 1101 is the control center of the computer device 1100. It connects various parts of the computer device 1100 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102, and by calling data stored in the memory 1102, it executes various functions of the computer device 1100 and processes data, thereby providing overall monitoring of the computer device 1100. The processor 1101 can be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0085] In this embodiment, the processor 1101 in the computer device 1100 loads the instructions corresponding to the processes of one or more applications into the memory 1102 according to the following steps, and the processor 1101 runs the applications stored in the memory 1102 to realize various functions, such as: determining an accelerated model and a first model parameter of the accelerated model based on the aging life data of the target battery under multiple different test conditions, wherein each test condition includes a charge and discharge current, and the discharge current of a test condition is set as a reference discharge current; converting the aging life data of the target battery under each test condition into equivalent life data under the reference discharge current based on the accelerated model and the first model parameter; fitting a pre-determined target predicted life model using the equivalent life data corresponding to each test condition to determine the second model parameter of the target predicted life model; generating a life curve of the target battery based on the target predicted life model and the second model parameter, and determining the service life of the target battery under the reference discharge current based on the life curve.

[0086] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0087] Optional, such as Figure 6 As shown, the computer device 1100 also includes: a touch screen display 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. The processor 1101 is electrically connected to the touch screen display 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107. Those skilled in the art will understand that... Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0088] The touch display screen 1103 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 1103 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1101. It can also receive and execute commands from the processor 1101. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 1103 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 1103 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to achieve input functions.

[0089] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0090] Audio circuit 1105 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuit 1105 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuit 1105, converted back into audio data, and then processed by processor 1101 before being transmitted via radio frequency circuit 1104 to, for example, another electronic device, or output to memory 1102 for further processing. Audio circuit 1105 may also include an earphone jack to provide communication between peripheral headphones and electronic devices.

[0091] The input unit 1106 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0092] Power supply 1107 is used to supply power to various components of computer device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 1107 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0093] although Figure 6 As not shown in the diagram, computer device 1100 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0094] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0095] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0096] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of computer programs that can be loaded by a processor to execute any of the battery life prediction methods provided in embodiments of this application. The computer program can perform the steps of the aforementioned battery life prediction methods.

[0097] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0098] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0099] Since the computer program stored in the computer-readable storage medium can execute any of the battery life prediction methods provided in the embodiments of this application, the beneficial effects that any of the battery life prediction methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0100] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.

[0101] In the above embodiments of the battery life prediction device, computer-readable storage medium, electronic device, and computer program product, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and beneficial effects of the battery life prediction device, computer-readable storage medium, computer program product, electronic device, and their corresponding units described above can be referred to the description of the battery life prediction method in the above embodiments, and will not be repeated here.

[0102] The foregoing has provided a detailed description of a battery life prediction method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting battery life, characterized in that, include: Based on the aging lifetime data of the target battery under multiple different test conditions, an accelerated model and the first model parameters of the accelerated model are determined, wherein each test condition includes a charge and discharge current, and the discharge current of a test condition is set as a reference discharge current. Based on the acceleration model and the parameters of the first model, the aging lifetime data of the target battery under various test conditions is converted into equivalent lifetime data under the reference discharge current. Using the equivalent lifetime data corresponding to each test condition, the pre-determined target predicted lifetime model is fitted to determine the second model parameters of the target predicted lifetime model. Based on the target predicted lifetime model and the second model parameters, a lifetime curve of the target battery is generated, and based on the lifetime curve, the lifetime of the target battery under the reference discharge current is determined.

2. The battery life prediction method according to claim 1, characterized in that, The process of determining an accelerated model and its first model parameters based on aging lifetime data of the target battery under multiple different test conditions includes: The acceleration model is determined based on the correlation between discharge current and battery life; Based on the discharge current and aging life data corresponding to each test condition, the accelerated model is fitted to determine the parameters of the first model.

3. The battery life prediction method according to claim 2, characterized in that, The step of converting the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current based on the accelerated model and the parameters of the first model includes: Based on the acceleration model and the parameters of the first model, the acceleration factor of the discharge current relative to the reference discharge current for each test condition is determined. By using the acceleration factor corresponding to each test condition, the corresponding aging life data is converted to lifespan, and the equivalent lifespan data corresponding to each test condition is determined.

4. The battery life prediction method according to any one of claims 1-3, characterized in that, Before fitting the pre-determined target predicted lifetime model using the equivalent lifetime data corresponding to each test condition, the method further includes: From multiple predicted lifetime models, the target predicted lifetime model that matches the equivalent lifetime data corresponding to each test condition is determined.

5. The battery life prediction method according to claim 4, characterized in that, The step of generating the lifetime curve of the target battery based on the target predicted lifetime model and the second model parameters includes: Based on the target predicted lifetime model and the second model parameters, the lifetime reliability function curve of the target battery is plotted, and the lifetime reliability function curve is used as the lifetime curve.

6. The battery life prediction method according to claim 5, characterized in that, Determining the lifespan of the target battery at the reference discharge current based on the lifespan curve includes: From the lifetime reliability function curve, select the lifetime value corresponding to the preset reliability index, and use the selected lifetime value as the lifetime.

7. The battery life prediction method according to any one of claims 1-6, characterized in that, Before determining the acceleration model and the first model parameters of the acceleration model, the method further includes: Determine the accelerated aging test protocol; Based on the accelerated aging test scheme, the target battery was subjected to aging tests to obtain multiple sets of battery aging data; Based on the battery aging data for each group, determine the battery aging model corresponding to each test condition. Based on the battery aging model corresponding to each test condition, the aging life data of the target battery under each test condition is determined.

8. A battery life prediction device, characterized in that, include: The first model and parameter acquisition module is used to determine the accelerated model and the first model parameters of the accelerated model based on the aging life data of the target battery under multiple different test conditions. Each test condition includes a charge and discharge current, and the discharge current of a test condition is set as a reference discharge current. The lifetime conversion module is used to convert the aging lifetime data of the target battery under various test conditions into equivalent lifetime data under the reference discharge current based on the accelerated model and the parameters of the first model. The second model and parameter acquisition module is used to fit the pre-determined target prediction lifetime model using the equivalent lifetime data corresponding to each test condition, and to determine the second model parameters of the target prediction lifetime model. The lifetime prediction module is used to generate a lifetime curve of the target battery based on the target predicted lifetime model and the second model parameters, and to determine the lifetime of the target battery under the reference discharge current based on the lifetime curve.

9. A computer device, characterized in that, The computer device includes: One or more processors; The memory; and one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the battery life prediction method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to perform the steps in the battery life prediction method according to any one of claims 1 to 7.

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