Battery data processing method, electronic device and storage medium

Abstract

Provided in the present application are a battery data processing method, an electronic device and a storage medium. The method comprises: acquiring preset lifespan attenuation curves of a target battery under a plurality of different lifespan attenuation types; intercepting a curve segment from each preset lifespan attenuation curve; splicing a plurality of curve segments end to end, so as to obtain a predicted lifespan attenuation curve of the target battery; and determining a predicted lifespan of the target battery on the basis of the predicted lifespan attenuation curve.

Description

Battery data processing method, electronic device and storage medium

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 21, 2024, with application number 202410332223.1. The entire contents of the above application are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of battery technology, and in particular to a battery data processing method, electronic device, and storage medium. Background Art

[0003] Battery lifespan prediction is typically achieved through electrochemical prediction and genetic algorithm prediction. Electrochemical prediction, based on the properties of battery materials, studies the mechanism of battery degradation and can optimize battery performance by improving battery material design. Genetic algorithm prediction, on the other hand, infers battery lifespan by fitting existing data, offering a high degree of efficiency. SUMMARY OF THE INVENTION

[0004] However, electrochemical predictions often require large computational resources, complex models, and large prediction errors. Genetic algorithm predictions struggle to account for the decaying patterns of battery health, and also suffer from large prediction errors.

[0005] This application provides a method for processing battery data, which includes:

[0006] Obtaining preset life decay curves of a target battery under multiple different life decay types;

[0007] Obtaining predicted operating condition change information of the target battery, wherein the predicted operating condition change information includes multiple predicted operating conditions sorted in chronological order, and each predicted operating condition corresponds to a life attenuation type;

[0008] According to the life decay type corresponding to each predicted operating condition, a curve segment is intercepted from a preset life decay curve under the corresponding life decay type to obtain multiple curve segments;

[0009] According to the order of the multiple predicted operating conditions in the predicted operating condition change information, the multiple curve segments are sequentially spliced ​​end to end to obtain the predicted life attenuation curve of the target battery, and the battery health at the splicing points of adjacent curve segments in the predicted life attenuation curve is the same;

[0010] Based on the predicted life decay curve, the predicted life of the target battery is determined.

[0011] The present application provides a battery data processing device, which includes:

[0012] A first acquisition module is used to obtain a preset life decay curve of a target battery under multiple different life decay types;

[0013] a second acquisition module, configured to acquire predicted operating condition change information of the target battery, wherein the predicted operating condition change information includes a plurality of predicted operating conditions sorted in chronological order, each predicted operating condition corresponding to a life attenuation type;

[0014] A segment interception module is used to intercept a curve segment from a preset life decay curve under the corresponding life decay type according to the life decay type corresponding to each predicted working condition, thereby obtaining multiple curve segments;

[0015] A fragment splicing module is used to sequentially splice multiple curve fragments head to tail according to the order of multiple predicted operating conditions in the predicted operating condition change information to obtain a predicted life decay curve of the target battery. The battery health at the splicing point of adjacent curve fragments in the predicted life decay curve is the same;

[0016] The life determination module is used to determine the predicted life of the target battery based on the predicted life decay curve.

[0017] The present application provides an electronic device, which includes a processor and a memory. The memory stores a computer program, and the computer program is configured to be executed by the processor to implement any of the above battery data processing methods.

[0018] The present application provides a computer storage medium storing a computer program, wherein the computer program is configured to be executed by a processor to implement any of the above battery data processing methods.

[0019] The present application provides a computer program product, including a computer program or instructions, where the computer program or instructions are executed by a processor to implement any of the above battery data processing methods. Beneficial effects

[0020] In the present application, by obtaining the preset life decay curve of the target battery under multiple different life decay types, and then predicting the life decay type corresponding to the operating condition based on the predicted operating condition change information of the target battery, a curve segment is intercepted from the preset life decay curve under the corresponding life decay type, and then the curve segments of different life decay types are spliced ​​end to end to obtain the predicted life decay curve of the target battery, thereby obtaining the predicted life of the target battery. Compared with the current battery life prediction scheme, this application takes into account the attenuation law of battery health (SOH, State of Health) under different life decay types, and the calculation is simpler, thereby reducing the prediction error of the battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a flow chart of a method for processing battery data provided in some implementations of the present application;

[0022] FIG2a is a schematic diagram of a method for intercepting a curve segment provided in some implementations of the present application;

[0023] FIG2 b is another schematic diagram of a method for intercepting a curve segment provided in some implementations of the present application;

[0024] FIG2c is another schematic diagram of a method for splicing curve segments provided in some implementations of the present application;

[0025] FIG3 is a schematic diagram of a predicted lifespan attenuation curve provided in some implementations of the present application;

[0026] FIG4 is a flowchart of a method for processing battery data provided in some implementations of the present application;

[0027] FIG5 is a flowchart of a method for processing battery data provided in some implementations of the present application;

[0028] FIG6 is a schematic diagram of an expected lifespan attenuation curve and an actual lifespan attenuation curve provided in some implementations of the present application;

[0029] FIG7 is a flowchart of a method for processing battery data provided in some implementations of the present application;

[0030] FIG8 is a schematic diagram of a health extreme value curve provided in some implementations of the present application. Modes for Carrying Out the Invention

[0031] In the description of the present application, “plurality” means two or more, unless otherwise clearly defined.

[0032] In order to reduce the prediction error of battery life, the present application provides a battery data processing method, electronic device and storage medium, which obtains the preset life decay curve of the target battery under multiple different life decay types, and then predicts the life decay type corresponding to the working condition based on the predicted working condition change information of the target battery, intercepts the curve segment from the preset life decay curve under the corresponding life decay type, and then splices the curve segments of different life decay types from beginning to end to obtain the predicted life decay curve of the target battery, thereby obtaining the predicted life of the target battery. Compared with the current battery life prediction scheme, the present application takes into account the attenuation law of battery health under different life decay types, and the calculation is simpler, thereby reducing the prediction error of battery life. For the specific scheme, please refer to the following detailed description.

[0033] In a first aspect, the present application provides a method for processing battery data. Specifically, referring to FIG1 , FIG1 is a flow chart of a method for processing battery data provided in some implementations. In FIG1 , the method for processing battery data may include:

[0034] 101. Obtain preset life decay curves of a target battery under multiple different life decay types.

[0035] In a possible implementation of the present application, the target battery is generally a battery pack. Before the target battery leaves the factory, or after the target battery leaves the factory and is used in a designated vehicle as a power battery, the life of the target battery needs to be predicted.

[0036] Lifespan degradation type refers to the type of health degradation of the target battery. Examples include operating charge-discharge degradation, dormant static aging degradation, power consumption degradation, and vibration-mechanical degradation. Operating charge-discharge degradation refers to the health degradation of the target battery caused by charging and discharging during operation. Dormant static aging degradation refers to the health degradation of the target battery caused by aging during rest. Power consumption degradation refers to the health degradation caused by the target battery supplying very low current to power-consuming components in the vehicle where the target battery is located. Vibration-mechanical degradation refers to the vibration of the target battery during movement, which causes uneven distribution of chemical substances within the target battery, thereby reducing the target battery's performance and causing degradation of the target battery's health. Furthermore, when categorizing lifespan degradation types, further subdivisions can be made based on the ambient temperature of the target battery's environment. For example, the lifespan degradation types may include operating charge-discharge degradation at an ambient temperature of 25°C, dormant static aging degradation at an ambient temperature of 25°C, operating charge-discharge degradation at an ambient temperature of 45°C, and dormant static aging degradation at an ambient temperature of 45°C.

[0037] The target battery has a preset life decay curve under each life decay type. The preset life decay curve records the changes in the health of the target battery over time under the influence of the life decay factors of the corresponding life decay type. Each preset life decay curve can be obtained through preliminary tests on batteries of the same type as the target battery. Since the health decay of the target battery under a single life decay type generally conforms to the characteristics of the Arrhenius formula, in the process of obtaining the preset life decay curve through preliminary tests, the health decay situation over a short period of time can be tested, and then the subsequent health decay situation can be predicted using the Arrhenius formula. By connecting these two health decay situations, the corresponding preset life decay curve can be obtained, thereby reducing the time spent on generating the preset life decay curve.

[0038] 102. Obtain predicted operating condition change information of the target battery, wherein the predicted operating condition change information includes multiple predicted operating conditions sorted in chronological order, and each predicted operating condition corresponds to a life attenuation type.

[0039] In a possible implementation of the present application, the predicted operating condition change information is the predicted operating condition change of the target battery. The predicted operating condition change information includes multiple predicted operating conditions sorted in chronological order. Based on the predicted operating condition change information, it can be known at which time point the target battery may be in which predicted operating condition. Multiple predicted operating conditions can be divided according to different life attenuation types, so that each predicted operating condition can correspond to a life attenuation type. The predicted operating conditions may, for example, include the predicted operating conditions corresponding to working charge and discharge attenuation, the predicted operating conditions corresponding to dormant static aging attenuation, the predicted operating conditions corresponding to power consumption attenuation, the predicted operating conditions corresponding to vibration mechanical attenuation, etc. The predicted operating conditions may, for example, include the predicted operating conditions corresponding to working charge and discharge attenuation at an ambient temperature of 25°C, the predicted operating conditions corresponding to dormant static aging attenuation at an ambient temperature of 25°C, the predicted operating conditions corresponding to working charge and discharge attenuation at an ambient temperature of 45°C, the predicted operating conditions corresponding to dormant static aging attenuation at an ambient temperature of 45°C, etc.

[0040] In a possible implementation of the present application, the predicted operating condition change information of the target battery can be predicted based on historical operating condition change information. Specifically, obtaining the predicted operating condition change information of the target battery can include: identifying multiple different vehicles of the same model as the vehicle in which the target battery is located, where the target battery is located in a vehicle in which the target battery has been manufactured and used, or a vehicle in which the target battery has not been manufactured but is about to be used; obtaining historical operating condition change information of the battery packs of the multiple different vehicles, where the historical operating condition change information may include multiple historical operating conditions of the battery packs of the corresponding vehicles sorted in chronological order at historical time points, each historical operating condition corresponding to a life degradation type; performing statistical analysis on the historical operating condition change information of the battery packs of the multiple different vehicles (i.e., analyzing the distribution pattern of each historical operating condition in the historical operating condition change information based on the obtained historical operating condition change information of the battery packs of the multiple different vehicles), thereby obtaining a preset distribution satisfied by the historical operating condition change information of the battery packs of the multiple different vehicles, the preset distribution generally being a normal distribution; and randomly selecting values ​​according to the preset distribution to obtain the predicted operating condition change information.

[0041] 103. According to the life decay type corresponding to each predicted working condition, a curve segment is intercepted from the preset life decay curve under the corresponding life decay type to obtain multiple curve segments.

[0042] In a possible implementation of the present application, according to the life decay type corresponding to each predicted operating condition, the step of intercepting a curve segment from the preset life decay curve under the corresponding life decay type is specifically performed in sequence according to the order of multiple predicted operating conditions in the predicted operating condition change information. For example, as shown in Figure 2a, curve segment 1 is first intercepted from a preset life decay curve, and then curve segment 2 is intercepted from another preset life decay curve. For example, as shown in Figure 2b, curve segment 3 is first intercepted from a preset life decay curve, and then curve segment 4 is intercepted from another preset life decay curve. In order to ensure that the battery health at the splicing points of adjacent curve segments is the same when multiple curve segments are subsequently spliced ​​end to end, thereby ensuring the continuity of battery health attenuation, the interception position when intercepting the curve segments can be set so that the battery health at the splicing point of the preceding curve segment and the subsequent curve segment is the same. For example, as shown in Figure 2c, the battery health at the splicing point of curve segments 1 and 2 is the same, the battery health at the splicing point of curve segments 2 and 3 is the same, and the battery health at the splicing point of curve segments 3 and 4 is the same.

[0043] 104. According to the order of the multiple predicted operating conditions in the predicted operating condition change information, the multiple curve segments are spliced ​​end to end in sequence to obtain the predicted life attenuation curve of the target battery. The battery health at the splicing points of adjacent curve segments in the predicted life attenuation curve is the same.

[0044] In a possible implementation of this application, the order of the multiple predicted operating conditions in the predicted operating condition change information is used as the splicing order for the curve segments under the corresponding predicted operating conditions. By sequentially splicing the multiple curve segments end to end according to the splicing order, a predicted life decay curve for the target battery can be obtained. An example of the predicted life decay curve is shown in Figure 3. This predicted life decay curve integrates the different decay patterns of the target battery under different life decay types, thereby reducing the prediction error of the battery life.

[0045] 105. Determine the predicted life of the target battery based on the predicted life decay curve.

[0046] In a possible implementation of this application, if the target battery has not yet shipped, determining the target battery's predicted lifespan based on the predicted lifespan decay curve may include: determining the time span from 100% battery health to a preset health level in the predicted lifespan decay curve; and using the duration represented by this time span as the target battery's predicted lifespan. The preset health level may be, for example, 80%, but other values ​​are also possible and are not limited here.

[0047] In a possible implementation of the present application, if the target battery has been shipped and used in a designated vehicle as a power battery, determining the predicted life of the target battery based on the predicted life decay curve can include: obtaining the years of use of the target battery; substituting the years of use as the horizontal axis parameter into the predicted life decay curve to obtain the corresponding vertical axis parameter, which is the current predicted health of the target battery; in the predicted life decay curve, determining the time span of the battery health from the predicted health to the preset health; and using the duration represented by the time span as the predicted life of the target battery.

[0048] It can be seen that in a possible implementation of the present application, by splicing curve segments of different life decay types end to end, the predicted life decay curve of the target battery is obtained, thereby obtaining the predicted life of the target battery, reducing the prediction error of the battery life.

[0049] It should be noted that steps 102 to 105 can be implemented by specific codes or by a data model, such as the Simulink data model in MATLAB. The specific implementation method of steps 102 to 105 is not limited here.

[0050] In a possible implementation of this application, a target battery warranty life recommendation function may be provided to recommend to battery manufacturers how many years of warranty the target battery should have. As shown in FIG4 , after determining the predicted life of the target battery based on the predicted life decay curve, the following may also be included:

[0051] 401. After obtaining a predicted life based on each predicted operating condition change information, generate a plurality of normal distribution graphs of the predicted life.

[0052] In a possible implementation of the present application, multiple pieces of predicted operating condition change information may exist simultaneously. For example, 100 pieces of predicted operating condition change information may exist simultaneously. Based on each piece of predicted operating condition change information, a corresponding predicted lifespan can be calculated. After a predicted lifespan is obtained based on each piece of predicted operating condition change information, a normal distribution graph of multiple predicted lifespans can be generated based on the multiple predicted lifespans. This normal distribution graph records the distribution of the multiple predicted lifespans.

[0053] 402. In the normal distribution graph, determine the recommended warranty life of the target battery.

[0054] In a possible implementation of the present application, a suitable recommended warranty life of the target battery is determined based on the distribution of multiple predicted lifespans in a normal distribution graph. Battery manufacturers can provide warranty for the target battery based on the recommended warranty lifespan to make the battery warranty more reasonable.

[0055] In a possible implementation of the present application, determining the recommended warranty life of the target battery in a normal distribution graph may include: determining a confidence interval at a preset confidence level in the normal distribution graph, where the preset confidence level may be, for example, 0.95. It can be seen that most of the predicted lifespans are within the confidence interval; taking the minimum value of the confidence interval as the recommended warranty life of the target battery. Taking the minimum value of the confidence interval as 8 years as an example, 8 years may be used as the recommended warranty life of the target battery, thereby avoiding losses caused by too long or too short a battery warranty period.

[0056] It can be seen that in a possible implementation of the present application, by generating multiple normal distribution graphs of predicted lifespans and determining the recommended warranty lifespan of the target battery in the normal distribution graphs, the warranty of the battery can be made more reasonable.

[0057] In addition, if the target battery has been shipped and used in a designated vehicle as a power battery, there is generally no need to provide a recommendation function for the target battery's warranty life. At this time, in view of the fact that there may be multiple pieces of predicted operating condition change information at the same time, through steps 103 and 104, a plurality of predicted operating condition change information corresponding to the predicted life decay curves can be obtained, and then a predicted life decay curve is screened out from the predicted life decay curves corresponding to the multiple predicted operating condition change information, and step 105 is executed based on the screened predicted life decay curve. The screening rule can be, for example: among the predicted life decay curves corresponding to the multiple predicted operating condition change information, the predicted life decay curve with the largest battery health decay amplitude is used as the screened predicted life decay curve.

[0058] In a possible implementation of the present application, as shown in FIG5 , determining the predicted life of the target battery based on the predicted life decay curve may include:

[0059] 501. Obtain a correction coefficient associated with each predicted operating condition, where the correction coefficients associated with different predicted operating conditions are different.

[0060] In a possible implementation of the present application, since the predicted life attenuation curve obtained by splicing multiple curve segments may not be completely consistent with the actual life attenuation curve, for example, the actual life attenuation curve may have accelerated attenuation at some positions of the curve relative to the predicted life attenuation curve, different correction coefficients can be set for different life attenuation types, that is, different predicted working conditions are pre-associated with different correction coefficients, so that the correction coefficients can be used to correct the predicted life attenuation curve.

[0061] In a possible implementation of the present application, a process for determining the correction coefficient is described. Specifically, the correction coefficient can be obtained during the preliminary test phase of the target battery. During the preliminary test phase, the battery data processing method can also include the following steps 1 to 7:

[0062] Step 1: Obtain preset working condition change information, where the preset working condition change information includes multiple preset working conditions sorted in chronological order, and each preset working condition corresponds to a predicted working condition;

[0063] In a possible implementation, during the early stages of testing, preset operating condition change information can be obtained by manual configuration. Each preset operating condition corresponds to a predicted operating condition, meaning that the preset operating condition and the predicted operating condition corresponding to the preset operating condition simultaneously correspond to a lifespan attenuation type (e.g., operating charge-discharge attenuation, dormant static aging attenuation, power consumption attenuation, vibration mechanical attenuation, etc.; for example, operating charge-discharge attenuation at an ambient temperature of 25°C, dormant static aging attenuation at an ambient temperature of 25°C, operating charge-discharge attenuation at an ambient temperature of 45°C, dormant static aging attenuation at an ambient temperature of 45°C, etc.).

[0064] Step 2: According to the life decay type corresponding to each preset working condition, a curve segment is intercepted from the preset life decay curve under the corresponding life decay type, and the segment is used as the preset curve segment, thereby obtaining a plurality of preset curve segments;

[0065] Step 3: According to the order of the multiple preset operating conditions in the preset operating condition change information, the multiple preset curve segments are sequentially spliced ​​end to end to obtain the expected life decay curve of the target battery under the preset operating condition change information, and the battery health at the splicing point of adjacent preset curve segments in the expected life decay curve is the same;

[0066] In a possible implementation, the method for generating the expected life decay curve is similar to that for the predicted life decay curve. The difference is that the expected life decay curve is generated based on the preset operating condition change information from the early test phase, while the predicted life decay curve is generated based on the predicted operating condition change information. Therefore, the generation process of the expected life decay curve is not further described here. Referring to Figure 6, an example of the expected life decay curve is shown.

[0067] Step 4: Test the target battery according to the preset operating condition change information to obtain an actual life decay curve corresponding to the expected life decay curve;

[0068] In a possible implementation, when testing a target battery according to preset operating condition change information, the target battery's actual operating environment is sequentially changed according to multiple preset operating conditions in chronological order in the preset operating condition change information to perform actual testing on the target battery, thereby obtaining an actual life decay curve. It can be seen that the actual life decay curve is actual measured data on the life decay of the target battery. An example of an actual life decay curve is shown in FIG6 .

[0069] Step 5: determining a first curve segment in the expected life decay curve and a second curve segment in the actual life decay curve, wherein the first curve segment and the second curve segment are preset curve segments under the life decay type corresponding to the same preset working condition;

[0070] Step 6: using the first curve segment and the second curve segment under the life attenuation type corresponding to the same preset working condition, determine the correction coefficient associated with the preset working condition;

[0071] Step 7: Determine the correction coefficient associated with the predicted working condition corresponding to the preset working condition based on the correction coefficient associated with each preset working condition.

[0072] In a possible implementation, since the expected lifetime decay curve represents the expected lifetime decay curve data of the target battery under the preset operating condition change information, and the actual lifetime decay curve represents the actual measured lifetime decay curve data of the target battery under the preset operating condition change information, a correction coefficient associated with each preset operating condition in the preset operating condition change information can be calculated based on the difference between the expected data and the actual measured data. After obtaining the correction coefficient associated with the preset operating condition, the correction coefficient associated with the predicted operating condition corresponding to the preset operating condition can be determined based on the correction coefficient associated with the preset operating condition. For example, the correction coefficient associated with the preset operating condition can be directly used as the correction coefficient associated with the predicted operating condition corresponding to the preset operating condition.

[0073] In a possible implementation, determining a correction coefficient associated with the preset operating condition using a first curve segment and a second curve segment under a life decay type corresponding to the same preset operating condition may include: in the first curve segment and the second curve segment under the life decay type corresponding to the same preset operating condition, since both the first curve segment and the second curve segment record the change of battery health over time, the ratio of battery health at the same time point can be determined in the first curve segment and the second curve segment, and used as the ratio corresponding to the time point; after obtaining the ratios corresponding to all time points in the first curve segment and the second curve segment, determining the average value of the ratios corresponding to all time points; and using the average value as the correction coefficient associated with the same preset operating condition.

[0074] 502. Using the correction coefficient associated with each predicted operating condition, perform a first correction process on the curve segment under the corresponding predicted operating condition in the predicted life attenuation curve.

[0075] In a possible implementation of the present application, the first correction processing may be to multiply the correction coefficient associated with the predicted operating condition by each battery health in the curve segment under the corresponding predicted operating condition in the predicted life decay curve, thereby correcting each battery health in the curve segment to obtain the curve segment after the first correction processing.

[0076] 503. Determine the predicted life of the target battery based on the predicted life decay curve after the first correction process.

[0077] In a possible implementation of the present application, after performing a first correction process on each curve segment in the predicted life decay curve, a predicted life decay curve after the first correction process can be obtained. Based on the predicted life decay curve after the first correction process, the predicted life of the target battery can be determined.

[0078] In a possible implementation of the present application, if the target battery has not yet left the factory, determining the predicted life of the target battery based on the predicted life decay curve after the first correction processing may include: determining the time span of the battery health from 100% to the preset health in the predicted life decay curve after the first correction processing; and using the duration represented by the time span as the predicted life of the target battery.

[0079] In a possible implementation of the present application, if the target battery has been shipped and used in a designated vehicle as a power battery, determining the predicted life of the target battery based on the predicted life decay curve after the first correction processing can include: obtaining the years of use of the target battery; substituting the years of use as the horizontal axis parameter into the predicted life decay curve after the first correction processing to obtain the corresponding vertical axis parameter, which is the current predicted health of the target battery; in the predicted life decay curve after the first correction processing, determining the time span of the battery health from the predicted health to the preset health; and taking the duration represented by the time span as the predicted life of the target battery.

[0080] It can be seen that in a possible implementation of the present application, by performing a first correction processing on the curve segment under the corresponding predicted operating condition in the predicted life decay curve through the correction coefficient associated with each predicted operating condition, a life decay curve that is more in line with reality can be obtained, thereby improving the accuracy of the calculated predicted life of the target battery.

[0081] In a possible implementation of the present application, as shown in FIG7 , determining the predicted life of the target battery based on the predicted life decay curve may include:

[0082] 701. Perform a second correction process on the predicted life attenuation curve using the extreme health value curve.

[0083] In a possible implementation of the present application, in order to reduce the error in the predicted life decay curve, the predicted life decay curve may be subjected to a second correction process using a health extreme value curve. The health extreme value curve records the extreme value of the battery health and is used to determine whether the error of the battery health in the predicted life decay curve is large, and to correct the battery health in the predicted life decay curve when the error is large. The health extreme value curve may specifically include at least one of a health maximum value curve and a health minimum value curve. The health maximum value curve is shown as curve 1 in FIG8 , and the health minimum value curve is shown as curve 2 in FIG8 .

[0084] In a possible implementation of this application, the process for generating a health extreme value curve is described. Specifically, the battery data processing method may also include: identifying multiple different vehicles of the same model as the vehicle in which the target battery is located; obtaining battery pack charge and consumption information for the multiple different vehicles, as well as actual usage information for the vehicle in which the target battery is located; performing a life prediction simulation on the target battery based on the actual usage information and the battery pack charge and consumption information for the multiple different vehicles; and determining the health extreme value curve for the target battery based on the results of the life prediction simulation.

[0085] Among them, the battery pack charging and power consumption information includes but is not limited to the battery pack fast charging ratio, the battery pack storage SOC, etc., and the real usage information includes but is not limited to the real average monthly mileage, service life, etc. It should be noted that the real usage information can be obtained through online detection of the Battery Management System (BMS), or by any other means, which is not limited here. The average monthly mileage of the vehicle in the real usage information above is an example. In possible implementation methods, it can be the average mileage of the vehicle per quarter, the average annual mileage of the vehicle, etc.

[0086] In a possible implementation, battery pack charge and consumption information for multiple different vehicles is randomly generated according to a preset target distribution. The target distribution generation process may, for example, include obtaining actual battery pack charge and consumption information for multiple different vehicles of the same model as the target battery pack, performing statistical analysis on the actual charge and consumption information, and determining the target distribution satisfied by the actual charge and consumption information for the multiple different vehicles. The target distribution characterizes the distribution patterns that each piece of information within the actual charge and consumption information for the multiple different vehicles conforms to.

[0087] In a possible implementation, a life prediction simulation is performed on the target battery based on actual usage information and battery pack charging and consumption information of multiple different vehicles, which may include: taking the actual usage information and the battery pack charging and consumption information of each vehicle as a set of test data, thereby obtaining multiple sets of test data; inputting each set of test data into the battery life prediction software for battery life prediction simulation, and obtaining a battery health curve for each set of test data after simulation by the battery life prediction software; after obtaining multiple battery health curves, using the multiple battery health curves as the results of the life prediction simulation.

[0088] In a possible implementation, determining the extreme health curve of the target battery based on the results of the life prediction simulation may include: screening out the battery health curve with the largest decrease in battery health over time from the results of the life prediction simulation, and using it as the minimum health curve in the health extreme curve; screening out the battery health curve with the smallest decrease in battery health over time from the results of the life prediction simulation, and using it as the maximum health curve in the health extreme curve.

[0089] In a possible implementation, the predicted life decay curve is subjected to a second correction process using the health extreme value curve, which may include: at the same time point, if the battery health on the predicted life decay curve is less than the battery health on the health minimum value curve, then the battery health on the predicted life decay curve is modified to the battery health at the corresponding time point on the health minimum value curve; at the same time point, if the battery health on the predicted life decay curve is greater than the battery health on the health maximum value curve, then the battery health on the predicted life decay curve is modified to the battery health at the corresponding time point on the health maximum value curve; at the same time point, if the battery health on the predicted life decay curve is less than or equal to the battery health on the health maximum value curve, and greater than or equal to the battery health on the health minimum value curve, then the battery health at the corresponding time point on the predicted life decay curve remains unchanged.

[0090] 702. Determine a predicted life of the target battery based on the predicted life decay curve after the second correction process.

[0091] In a possible implementation of the present application, if the target battery has not yet left the factory, determining the predicted life of the target battery based on the predicted life decay curve after the second correction processing may include: determining the time span of the battery health from 100% to the preset health in the predicted life decay curve after the second correction processing; and using the duration represented by the time span as the predicted life of the target battery.

[0092] In a possible implementation of the present application, if the target battery has been shipped and used in a designated vehicle as a power battery, determining the predicted life of the target battery based on the predicted life decay curve after the second correction processing can include: obtaining the years of use of the target battery; substituting the years of use as the horizontal axis parameter into the predicted life decay curve after the second correction processing to obtain the corresponding vertical axis parameter, which is the current predicted health of the target battery; in the predicted life decay curve after the second correction processing, determining the time span of the battery health from the predicted health to the preset health; and taking the duration represented by the time span as the predicted life of the target battery.

[0093] In a possible implementation of the present application, determining the predicted life of the target battery based on the predicted life decay curve after the second correction processing may also include: performing a first correction processing on the predicted life decay curve after the second correction processing, and then determining the predicted life of the target battery based on the predicted life decay curve that has been first corrected and then processed by the first correction processing. Similarly, the step of determining the predicted life of the target battery based on the predicted life decay curve after the first correction processing may include: performing a second correction processing on the predicted life decay curve after the first correction processing, and then determining the predicted life of the target battery based on the predicted life decay curve that has been first corrected and then processed by the second correction processing. The specific calculation steps of the predicted life are not repeated here.

[0094] It can be seen that in a possible implementation of the present application, the predicted life decay curve is subjected to a second correction process through the health extreme value curve, which can reduce the error in the predicted life decay curve and thus improve the accuracy of the calculated predicted life of the target battery.

[0095] In a second aspect, based on the battery data processing method, the present application provides a battery data processing device, which is used to perform any step of the battery data processing method. For example, the battery data processing device may include:

[0096] A first acquisition module is used to obtain a preset life decay curve of a target battery under multiple different life decay types;

[0097] a second acquisition module, configured to acquire predicted operating condition change information of the target battery, wherein the predicted operating condition change information includes a plurality of predicted operating conditions sorted in chronological order, each predicted operating condition corresponding to a life attenuation type;

[0098] A segment interception module is used to intercept a curve segment from a preset life decay curve under the corresponding life decay type according to the life decay type corresponding to each predicted working condition, thereby obtaining multiple curve segments;

[0099] A fragment splicing module is used to sequentially splice multiple curve fragments head to tail according to the order of multiple predicted operating conditions in the predicted operating condition change information to obtain a predicted life decay curve of the target battery. The battery health at the splicing point of adjacent curve fragments in the predicted life decay curve is the same;

[0100] The life determination module is used to determine the predicted life of the target battery based on the predicted life decay curve.

[0101] In a third aspect, the present application provides an electronic device, which includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement a battery data processing method in any possible implementation manner as described above.

[0102] In a fourth aspect, the present application provides a computer storage medium storing a computer program, wherein the computer program is configured to be executed by a processor to implement any of the above battery data processing methods.

[0103] In a fifth aspect, the present application provides a computer program product or computer program, the computer program product or computer program including 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 implement any of the above battery data processing methods.

Claims

1. A method for processing battery data, wherein: The battery data processing method includes: Obtaining preset life decay curves of a target battery under multiple different life decay types; Acquiring predicted operating condition change information of the target battery, wherein the predicted operating condition change information includes a plurality of predicted operating conditions sorted in chronological order, and each predicted operating condition corresponds to one of the life attenuation types; According to the life decay type corresponding to each predicted operating condition, a curve segment is intercepted from a preset life decay curve under the corresponding life decay type to obtain a plurality of curve segments; According to the order of the multiple predicted operating conditions in the predicted operating condition change information, the multiple curve segments are sequentially spliced ​​end to end to obtain a predicted life decay curve of the target battery, wherein the battery health at the splicing points of adjacent curve segments in the predicted life decay curve is the same; and Based on the predicted life decay curve, a predicted life of the target battery is determined.

2. The method for processing battery data according to claim 1, wherein: There are a plurality of pieces of predicted operating condition change information at the same time. After determining the predicted life of the target battery based on the predicted life decay curve, the method further includes: After obtaining one predicted lifespan based on each piece of predicted operating condition change information, generating a plurality of normal distribution graphs of the predicted lifespans; and In the normal distribution graph, a recommended warranty life of the target battery is determined.

3. The method for processing battery data according to claim 2, wherein: Determining the recommended warranty life of the target battery in the normal distribution graph includes: In the normal distribution graph, determining a confidence interval at a preset confidence level; and The minimum value of the confidence interval is used as the recommended warranty life of the target battery.

4. The method for processing battery data according to claim 1, wherein: The determining the predicted life of the target battery based on the predicted life decay curve includes: Obtaining a correction coefficient associated with each of the predicted operating conditions, wherein the correction coefficients associated with different predicted operating conditions are different; Using the correction coefficient associated with each of the predicted operating conditions, a first correction process is performed on the curve segment under the corresponding predicted operating condition in the predicted life attenuation curve; and The predicted life of the target battery is determined based on the predicted life decay curve after the first correction process.

5. The method for processing battery data according to claim 4, wherein: The battery data processing method further includes: Acquire preset operating condition change information, wherein the preset operating condition change information includes a plurality of preset operating conditions sorted in chronological order, and each preset operating condition corresponds to one of the predicted operating conditions; According to the life decay type corresponding to each of the preset operating conditions, a curve segment is intercepted from the preset life decay curve under the corresponding life decay type, and the segment is used as the preset curve segment, thereby obtaining a plurality of preset curve segments; According to the order of the plurality of preset operating conditions in the preset operating condition change information, the plurality of preset curve segments are sequentially spliced ​​end to end to obtain an expected life decay curve of the target battery under the preset operating condition change information, wherein the battery health at the splicing points of adjacent preset curve segments in the expected life decay curve is the same; Testing the target battery according to the preset operating condition change information to obtain an actual life decay curve corresponding to the expected life decay curve; Determining a first curve segment in the expected life decay curve, and determining a second curve segment in the actual life decay curve, wherein the first curve segment and the second curve segment are preset curve segments under the same life decay type corresponding to the preset operating condition; Determining a correction coefficient associated with the preset operating condition by using the first curve segment and the second curve segment under the life decay type corresponding to the same preset operating condition; and According to the correction coefficient associated with each of the preset operating conditions, a correction coefficient associated with the predicted operating condition corresponding to the preset operating condition is determined.

6. The method for processing battery data according to claim 5, wherein: The determining of the correction coefficient associated with the preset operating condition by using the first curve segment and the second curve segment under the life attenuation type corresponding to the same preset operating condition includes: In the first curve segment and the second curve segment under the life decay type corresponding to the same preset operating condition, determining a ratio of the battery health at the same time point, and using the ratio corresponding to the time point; After obtaining the ratios corresponding to all the time points in the first curve segment and the second curve segment, determining an average value of the ratios corresponding to all the time points; and The average value is used as a correction coefficient associated with the preset working condition.

7. The method for processing battery data according to claim 1, wherein: The battery data processing method further includes: Identifying a plurality of different vehicles of the same model as the vehicle in which the target battery is located; Obtaining charging and consumption information of battery packs of the multiple different vehicles, as well as actual usage information of the vehicle where the target battery is located; Performing a life prediction simulation on the target battery based on the actual usage information and the battery pack charge and consumption information of the multiple different vehicles; Determine the health extreme value curve of the target battery according to the result of the life prediction simulation; The determining the predicted life of the target battery based on the predicted life decay curve includes: performing a second correction process on the predicted life decay curve using the health extreme value curve; and The predicted life of the target battery is determined based on the predicted life decay curve after the second correction process.

8. The method for processing battery data according to any one of claims 1 to 7, wherein: The obtaining of the predicted operating condition change information of the target battery includes: Identifying a plurality of different vehicles of the same model as the vehicle in which the target battery is located; Obtaining historical operating condition change information of battery packs of the multiple different vehicles; Performing statistical analysis on the historical operating condition change information of the battery packs of the multiple different vehicles to determine a preset distribution satisfied by the historical operating condition change information of the battery packs of the multiple different vehicles; and The predicted operating condition change information is randomly generated according to the preset distribution.

9. An electronic device, wherein: The electronic device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement the battery data processing method according to any one of claims 1 to 8.

10. A computer storage medium, wherein: The computer storage medium stores a computer program, and the computer program is configured to be executed by a processor to implement the battery data processing method according to any one of claims 1 to 8.

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