A battery life prediction method and device, computer equipment and medium

Abstract

The application discloses a battery life prediction method and device, computer equipment and medium, the battery life prediction method comprises: obtaining the N times of effective charging times of the battery to be measured and a plurality of effective charging data in each effective charging time; wherein N is a positive integer; the initial calibration capacity of the battery to be measured is determined by calculating the capacity of the battery to be measured according to the effective charging data corresponding to the first n charging cycles in the N times of effective charging times; wherein 5 < n < N; and n is an integer; the current capacity of the battery to be measured is determined according to the state of charge, the effective charging data, the N times of effective charging times and the charging efficiency of the battery to be measured; the degradation characteristic parameters of the battery to be measured are determined according to the initial calibration capacity, the current capacity and the N times of effective charging times; the remaining life of the battery to be measured is determined according to the degradation characteristic parameters. By using the above technical means, the prediction accuracy of the battery life can be improved, and a scientific basis is provided for battery quality guarantee management.

Description

Technical Field

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

[0002] As the core component of energy storage equipment, the lifespan of batteries directly affects the operational stability and operating costs of the equipment. Especially in the fields of power batteries and energy storage batteries, accurate prediction of battery lifespan is the key to carrying out battery quality assurance management, maintenance and cascade utilization.

[0003] Existing battery life prediction methods are mostly based on accelerated aging tests in laboratories. They simulate the charge-discharge cycles of batteries to obtain the degradation patterns and then extrapolate them to actual use scenarios. However, there are significant differences between the laboratory environment and the actual use of batteries in terms of temperature, humidity, charge-discharge frequency, and charging methods, resulting in low accuracy of the prediction results and failing to accurately reflect the true degradation state of the battery.

[0004] Meanwhile, existing battery life prediction technologies lack effective integration and filtering of actual user usage data. A large amount of invalid and scattered charging data will interfere with capacity calculation and degradation trend analysis. Furthermore, they do not provide differentiated predictions for batteries with different warranty periods and different initial capacities, making it difficult to meet the personalized needs of enterprises for battery warranty management. Summary of the Invention

[0005] This invention provides a battery life prediction method, apparatus, computer equipment, and medium to improve the accuracy of battery life prediction and provide a scientific basis for battery quality assurance management.

[0006] In a first aspect, embodiments of the present invention provide a battery life prediction method, comprising: Obtain N valid charge counts of the battery under test and multiple valid charge data within each valid charge count; where N is a positive integer; The capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles in the N effective charging times, and the initial calibrated capacity of the battery under test is determined; where 5 < n < N, and n is an integer; The current capacity of the battery under test is determined based on the battery status, the valid charging data, the N valid charging times, and the charging efficiency. The degradation characteristic parameters of the battery under test are determined based on the initial calibrated capacity, the current capacity, and the N effective charging cycles. The remaining life of the battery under test is determined based on the initial calibrated capacity and the degradation characteristic parameters.

[0007] Optionally, obtain N valid charge counts of the battery under test and multiple valid charge data within each valid charge count, including: Obtain the initial charging data of the battery under test; The initial charging data is filtered based on the warranty information of the battery under test and the charging time and duration of each set of charging data in the initial charging data, to select N valid charging times and multiple valid charging data within each valid charging time.

[0008] Optionally, the capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles out of the N effective charging counts, and the initial calibrated capacity of the battery under test is determined, including: Obtain the valid charging data corresponding to the first n charging cycles out of the N valid charging counts; The average battery capacity of the battery under test is determined based on the effective charging data corresponding to the first n charging cycles. The initial calibrated capacity of the battery under test is determined based on the average battery capacity and the preset battery capacity range.

[0009] Optionally, the initial calibration capacity of the battery under test is determined based on the average battery capacity and a preset battery capacity range, including: When the average battery capacity is less than the first preset battery capacity, the initial calibration capacity of the battery under test is determined to be the first target calibration capacity; the first target calibration capacity is less than the first preset battery capacity. When the average battery capacity is greater than the first preset battery capacity and less than the second preset battery capacity, the initial calibration capacity of the battery under test is determined to be the second target calibration capacity; the second target calibration capacity is greater than the first target calibration capacity. When the average battery capacity is greater than the second preset battery capacity and less than the third preset battery capacity, the initial calibration capacity of the battery under test is determined to be the third target calibration capacity; the third target calibration capacity is greater than the second target calibration capacity. When the average battery capacity is greater than the fourth preset battery capacity, the initial calibration capacity of the battery under test is determined to be the fourth target calibration capacity; the fourth preset battery capacity is greater than or equal to the third preset battery capacity; the fourth target calibration capacity is greater than the fourth preset battery capacity and greater than the third target calibration capacity.

[0010] Optionally, the current capacity of the battery under test is determined based on the battery's state of charge, the valid charging data, the N valid charging cycles, and the charging efficiency, including: S11. When the battery under test is not fully charged, acquire the cumulative charging amount corresponding to the third set of charging data to the last set of charging data in each effective charging count. S12. Determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the charging ratio corresponding to the last set of charging data. S13. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency. S14. Select at least 5 consecutive valid charging data points from the N valid charging times as the target valid charging data group. S15. The target effective charging data in the target effective charging data group are sequentially executed in steps S11-S13, and the current capacity of the battery under test is determined according to the average value of the standard discharge amount corresponding to multiple target effective charging data in the target effective charging data group. or, S21. When the battery under test is fully charged, acquire the third set of charging data from the valid charging data within each valid charging count to the cumulative charging amount corresponding to the full charge. S22. Determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the full charge ratio. S23. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency. S24. Select at least 5 consecutive valid charging data points from the N valid charging times as the target valid charging data group; S25. The target effective charging data in the target effective charging data group are sequentially executed in steps S21-S23, and the current capacity of the battery under test is determined according to the average value of the standard discharge amount corresponding to each target effective charging data in the target effective charging data group.

[0011] Optionally, the degradation characteristic parameters of the battery under test are determined based on the initial calibration capacity, the current capacity, and the N effective charging cycles, including: The capacity degradation value of the battery under test is determined based on the initial calibration capacity and the current capacity. The capacity degradation rate of the battery under test is determined based on the capacity degradation value and the number of effective charging cycles (N). The charging frequency of the battery under test is determined based on the number of days the battery was used and the number of N valid charging cycles.

[0012] Optionally, determining the remaining lifespan of the battery under test based on the initial calibrated capacity and the degradation characteristic parameters includes: The capacity degradation threshold of the battery under test is determined based on the initial calibration capacity. The remaining lifespan of the battery under test is determined based on the capacity degradation threshold, the capacity degradation value, the capacity degradation rate, and the charging frequency.

[0013] Secondly, embodiments of the present invention also provide a battery life prediction device, comprising: The effective charging data acquisition module is used to acquire N effective charging times of the battery under test and multiple effective charging data within each effective charging time; where N is a positive integer; The initial calibration capacity determination module is used to calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles in the N effective charging times, and to determine the initial calibration capacity of the battery under test; where 5 < n < N, and n is an integer; The current capacity determination module is used to determine the current capacity of the battery under test based on the battery status, the valid charging data, the N valid charging times, and the charging efficiency. The degradation characteristic parameter determination module is used to determine the degradation characteristic parameters of the battery under test based on the initial calibration capacity, the current capacity, and the N effective charging times. The remaining lifetime determination module is used to determine the remaining lifetime of the battery under test based on the initial calibrated capacity and the degradation characteristic parameters.

[0014] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the battery life prediction method as described in any one of the first aspects.

[0015] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the battery life prediction method as described in any one of the first aspects.

[0016] The technical solution provided by this invention obtains N effective charging cycles of the battery under test and multiple effective charging data points within each effective charging cycle. This allows for subsequent battery life prediction based on the effective charging data, rather than relying on a large amount of invalid and fragmented charging data, thereby improving the accuracy of battery life prediction. The N effective charging cycles are determined based on the effective charging data; the capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles within the N effective charging cycles, and the initial calibrated capacity of the battery under test is determined; the current capacity of the battery under test is determined based on the battery's state of charge, effective charging data, N effective charging cycles, and charging efficiency. This facilitates subsequent calculation of degradation characteristic parameters, thereby predicting battery life and improving the accuracy of battery life prediction, providing a scientific basis for battery warranty management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention 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 the content of the embodiments of the present invention and these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first battery life prediction method provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the second battery life prediction method provided in this embodiment of the invention; Figure 3 A flowchart illustrating the third battery life prediction method provided in this embodiment of the invention; Figure 4 A flowchart illustrating the fourth battery life prediction method provided in this embodiment of the invention; Figure 5 A flowchart illustrating the fifth battery life prediction method provided in this embodiment of the invention; Figure 6 A flowchart illustrating the sixth battery life prediction method provided in this embodiment of the invention; Figure 7 This is a schematic diagram of the structure of a battery life prediction device provided in an embodiment of the present invention; Figure 8 A schematic diagram of the structure of a computer device applied to a battery life prediction method according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0020] It should be noted that the terms "first" and "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising," "having," and "etc.", and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Figure 1 A flowchart illustrating the first battery life prediction method provided in this embodiment of the invention is shown below. Figure 1 As shown, the battery life prediction method includes: S101. Obtain the N valid charging times of the battery under test and multiple valid charging data within each valid charging time; where N is a positive integer.

[0022] Specifically, the battery under test can be understood as the target battery whose lifespan needs to be predicted. The effective charge count is the number of charging cycles performed on the battery under test; that is, the effective charge count is incremented by 1 for each effective charge completed. Each effective charge count includes multiple effective charge data points. For example, N=30 or N=20, etc. This embodiment of the invention does not specifically limit the value of the effective charge count.

[0023] It should be noted that the N valid charging counts and the multiple valid charging data within each valid charging count are data that meet the requirements and are selected from the actual charging data based on the user's registered APP.

[0024] It should also be noted that each charging process for the battery under test includes multiple charging stages, from the first charging stage to the end of charging. If the battery under test is fully charged, the end of charging is the fifth charging stage; if the battery under test is not fully charged, the end of charging can be the fourth stage. During each charging process, each charging stage includes multiple valid charging data points. These multiple valid charging data points within each valid charging cycle represent the valid charging data across all charging stages for completing one valid charge.

[0025] For example, valid charging counts and multiple valid charging data points within each valid charging count can be filtered from a large amount of charging data using warranty information and data validity criteria. The warranty information includes user information registered with the app, charging data uploaded by the battery under test via a smart charger with communication capabilities, and data uploads within the last three months. Data validity criteria require that the first charging phase of the battery under test includes at least eight sets of charging data, excluding the first two sets of data from the first charging phase and data from charging cycles with a charging duration of less than three hours.

[0026] In contrast, existing technologies use a large amount of invalid and fragmented charging data for battery life prediction, which affects the accuracy of battery life prediction. However, the technical solution provided by this invention addresses this issue by removing unstable and invalid data—specifically, the first two sets of data from the first charging stage and charging cycle data with a charging time of less than 3 hours—to retain valid charging data. This lays the foundation for subsequent battery life prediction and improves prediction accuracy.

[0027] It should be noted that valid charging data includes charging stage, charging time, charging duration, and charging amount, meaning that valid charging data is a table showing the corresponding charging time, charging duration, and charging amount. For example, the first set of charging data in the valid charging data includes the first charging time, the first charging duration, and the first charging amount of the battery under test.

[0028] S102. Calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times, and determine the initial calibrated capacity of the battery under test; where 5 < n < N, and n is an integer.

[0029] Specifically, taking N=30 and n=10 as an example, the capacity of the battery under test is calculated based on the effective charging data corresponding to the first 10 charging cycles out of 30 effective charging times, and the average value of the first 10 capacity calculations is calculated so as to determine the initial calibrated capacity of the battery under test based on the average value of the first 10 capacity calculations.

[0030] For example, the charging capacity corresponding to the first effective charging cycle is the total charge input in the first cycle, which is the integral of the current over the first charging time. The charging capacity corresponding to the second effective charging cycle is the total charge input in the second cycle, which is the integral of the current over the second charging time, and so on. The average battery capacity corresponding to the effective charging data of the first n charging cycles can be calculated. Then, the initial calibrated capacity is determined based on the average battery capacity.

[0031] S103. Determine the current capacity of the battery under test based on its state of charge, valid charging data, N valid charging cycles, and charging efficiency.

[0032] Specifically, the state of charge of a battery under test can be understood as either fully charged or partially charged. Charging efficiency can be understood as the ratio of the amount of electricity actually charged into the battery to the amount delivered by the charger. It should be noted that due to heat generation and other energy loss during the charging process, the charging efficiency is generally less than 100%. For example, charging efficiency is typically related to parameters such as the type of battery under test, its internal resistance, and its temperature.

[0033] Specifically, the current capacity of the battery under test is determined based on its state of charge, effective charging data, N effective charging cycles, and charging efficiency, so that degradation characteristic parameters can be calculated based on the current capacity.

[0034] S104. Determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, current capacity, and N effective charging cycles.

[0035] Specifically, degradation characteristic parameters can include parameters such as capacity degradation value, capacity degradation rate, and charging frequency, which are used to predict the degree of battery aging.

[0036] Specifically, the degradation characteristic parameters of the battery under test are determined based on the initial calibrated capacity, the current capacity, and N effective charging cycles, so as to determine the remaining life of the battery under test in the future.

[0037] S105. Determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0038] Specifically, the capacity degradation threshold of the battery under test can be determined based on the initial calibrated capacity. Then, the remaining lifespan of the battery under test can be determined based on the capacity degradation threshold and degradation characteristic parameters. In this way, the number of days that the battery under test can still be used can be determined, so as to complete the prediction of the battery life.

[0039] The battery life prediction method provided in this invention acquires N effective charging cycles of the battery under test and multiple effective charging data points within each effective charging cycle. This allows for subsequent battery life prediction based on the effective charging data, rather than relying on a large amount of invalid and fragmented charging data, thereby improving the accuracy of battery life prediction. The capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles out of the N effective charging cycles, determining the initial calibrated capacity of the battery. The current capacity of the battery under test is determined based on its state of charge, effective charging data, N effective charging cycles, and charging efficiency. This facilitates the subsequent calculation of degradation characteristic parameters, thereby predicting battery life and improving the accuracy of battery life prediction, providing a scientific basis for battery warranty management.

[0040] Optional, Figure 2 This is a flowchart illustrating the second battery life prediction method provided in an embodiment of the present invention. Figure 2 Based on the above embodiments, the operation of acquiring valid charging data of the battery under test is described in detail, such as... Figure 2 As shown, the battery life prediction method includes: S201. Obtain the initial charging data of the battery under test.

[0041] Specifically, initial charging data can be understood as all charging data that has not been filtered.

[0042] S202. Based on the warranty information of the battery under test and the charging time and charging duration of each set of charging data in the initial charging data, the initial charging data is filtered to select N valid charging times and multiple valid charging data within each valid charging time.

[0043] Specifically, the warranty information includes the user information registered with the APP, charging data of the battery under test uploaded through a smart charger with communication capabilities, and charging data uploaded within the last 3 months. Initial charging data includes charging stage, charging time, charging duration, and charging amount. Since the charger is unaware of the battery status at the beginning of charging, the charging data can be unstable, leading to calculation errors. By removing unstable and invalid data—specifically, removing the first two sets of data from the first charging stage of each charging cycle based on charging time, and removing charging cycle data with a charging duration of less than 3 hours based on charging duration—we can filter out valid charging times and multiple valid charging data points within each valid charging cycle. This lays the foundation for subsequent battery life prediction, thereby improving prediction accuracy.

[0044] S203. Calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times, and determine the initial calibrated capacity of the battery under test; where 5 < n < N, and n is an integer.

[0045] S204. Determine the current capacity of the battery under test based on its state of charge, valid charging data, N valid charging cycles, and charging efficiency.

[0046] S205. Determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, current capacity, and N effective charging cycles.

[0047] S206. Determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0048] The battery life prediction method provided in this invention filters valid charging data based on the warranty information of the battery under test and the charging time and charging duration of each set of charging data in the initial charging data. In this way, battery life prediction can be performed based on the valid charging data instead of directly using the initial charging data, thereby improving the accuracy of battery life prediction.

[0049] Optional, Figure 3 This is a flowchart illustrating the third battery life prediction method provided in an embodiment of the present invention. Figure 3 Based on the above embodiments, the operation of calculating the capacity of the battery under test and determining the initial calibrated capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times is described in detail, such as... Figure 3 As shown, the battery life prediction method includes: S301. Obtain the N valid charging times of the battery under test and multiple valid charging data within each valid charging time; where N is a positive integer.

[0050] S302. Obtain the valid charging data corresponding to the first n charging cycles out of N valid charging times; where 5 < n < N, and n is an integer.

[0051] Specifically, the effective charging data corresponding to the first n charging cycles out of N effective charging cycles is obtained. In other words, the first n effective charging data out of N effective charging cycles are extracted so that the average battery capacity can be calculated based on the extracted data later.

[0052] S303. Determine the average battery capacity of the battery under test based on the valid charging data corresponding to the previous n charging cycles.

[0053] Specifically, the charging capacity corresponding to the first effective charging cycle is the total charge input in the first cycle, which is the integral of the current over the first charging time. The charging capacity corresponding to the second effective charging cycle is the total charge input in the second cycle, which is the integral of the current over the second charging time, and so on. The average battery capacity corresponding to the effective charging data of the first n charging cycles can be calculated. The initial calibrated capacity is then determined based on the average battery capacity.

[0054] S304. Determine the initial calibration capacity of the battery under test based on the average battery capacity and the preset battery capacity range.

[0055] Furthermore, when the average battery capacity is less than the first preset battery capacity, the initial calibration capacity of the battery under test is determined to be the first target calibration capacity; the first target calibration capacity is less than the first preset battery capacity. When the average battery capacity is greater than the first preset battery capacity and less than the second preset battery capacity, the initial calibration capacity of the battery under test is determined to be the second target calibration capacity; the second target calibration capacity is greater than the first target calibration capacity. When the average battery capacity is greater than the second preset battery capacity and less than the third preset battery capacity, the initial calibration capacity of the battery under test is determined to be the third target calibration capacity; the third target calibration capacity is greater than the second target calibration capacity. When the average battery capacity is greater than the fourth preset battery capacity, the initial calibration capacity of the battery under test is determined to be the fourth target calibration capacity; the fourth preset battery capacity is greater than or equal to the third preset battery capacity; the fourth target calibration capacity is greater than the fourth preset battery capacity and greater than the third target calibration capacity.

[0056] As a feasible implementation method, when the average battery capacity is less than a first preset battery capacity, the initial calibration capacity of the battery under test is determined to be the first target calibration capacity. For example, taking the average battery capacity as Q0 and the first preset battery capacity as 22Ah as an example, when Q0 < 22Ah, the initial calibration capacity of the battery under test is determined to be 21Ah, that is, the first target calibration capacity is 21Ah.

[0057] As another feasible implementation, when the average battery capacity is greater than a first preset battery capacity and less than a second preset battery capacity, the initial calibration capacity of the battery under test is determined to be the second target calibration capacity. That is, when the average battery capacity is between the first preset battery capacity and the second preset battery capacity, the initial calibration capacity of the battery under test is the second target calibration capacity. For example, taking an average battery capacity of Q0, a first preset battery capacity of 22Ah, and a second preset battery capacity of 24Ah as an example, when 22Ah < Q0 < 24Ah, the initial calibration capacity of the battery under test is determined to be 23Ah, i.e., the second target calibration capacity is 23Ah.

[0058] As another feasible implementation, when the average battery capacity is greater than the second preset battery capacity and less than the third preset battery capacity, the initial calibration capacity of the battery under test is determined to be the third target calibration capacity. That is, when the average battery capacity is between the second and third preset battery capacities, the initial calibration capacity of the battery under test is the third target calibration capacity. For example, taking an average battery capacity of Q0, a second preset battery capacity of 24Ah, and a third preset battery capacity of 30Ah as an example, when 24Ah < Q0 < 30Ah, the initial calibration capacity of the battery under test is determined to be 26Ah, i.e., the third target calibration capacity is 26Ah.

[0059] As another feasible implementation, when the average battery capacity is greater than the fourth preset battery capacity, the initial calibration capacity of the battery under test is determined to be the fourth target calibration capacity. For example, taking the average battery capacity as Q0 and the fourth preset battery capacity as 30Ah as an example, when Q0 > 30Ah, the initial calibration capacity of the battery under test is determined to be 35Ah, that is, the fourth target calibration capacity is 35Ah.

[0060] Among them, the first preset battery capacity, the second preset battery capacity, the third preset battery capacity, and the fourth preset battery capacity are all preset battery capacity values.

[0061] S305. Determine the current capacity of the battery under test based on its state of charge, valid charging data, N valid charging cycles, and charging efficiency.

[0062] S306. Determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, current capacity, and N effective charging cycles.

[0063] S307. Determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0064] The battery life prediction method provided in this invention determines the average battery capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles; and determines the initial calibration capacity of the battery under test based on the average battery capacity and a preset battery capacity range. Compared with determining the initial calibration capacity of the battery under test based on only one charging data, determining the initial calibration capacity of the battery under test based on the average battery capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles and a preset battery capacity range avoids the randomness of a single test, thereby improving the accuracy of the initial calibration capacity calculation and further improving the accuracy of the battery life prediction method.

[0065] Optional, Figure 4 This is a flowchart illustrating the fourth battery life prediction method provided in an embodiment of the present invention. Figure 4Based on the above embodiments, the operation of determining the current capacity of the battery under test according to the battery's state of charge, effective charging data, N effective charging cycles, and charging efficiency is described in detail, such as... Figure 4 As shown, the battery life prediction method includes: S401. Obtain the N valid charging times of the battery under test and multiple valid charging data within each valid charging time; where N is a positive integer.

[0066] S402. Calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times, and determine the initial calibrated capacity of the battery under test; where 5 < n < N, and n is an integer.

[0067] As a possible implementation method, the current capacity is calculated when the battery under test is not fully charged.

[0068] S11. When the battery under test is not fully charged, obtain the cumulative charge amount corresponding to the third set of charging data to the last set of charging data in each valid charging count.

[0069] Specifically, when the battery under test is not fully charged, that is, when the battery capacity is less than 100%, the first two sets of charging data in the valid charging data are the data at the beginning of charging, and the charging data is unstable at this time. Therefore, in order to ensure the accuracy of subsequent calculations, starting from the third set of charging data, the charging amount corresponding to each set of charging data is obtained sequentially, and the charging amounts corresponding to the third set of charging data to the last set of charging data are summed to obtain the cumulative charging amount corresponding to the third set of charging data to the last set of charging data.

[0070] S12. Determine the full charge capacity of the battery under test based on the cumulative charge amount, the charging ratio corresponding to the third set of charging data, and the charging ratio corresponding to the last set of charging data.

[0071] Specifically, when the battery under test is not fully charged, the charging ratio corresponding to the last set of charging data is 77%. Let the cumulative charging amount be Q1, the charging ratio corresponding to the third set of charging data be SOC1, and the full charge capacity of the battery under test be Qcharge, then Qcharge = Q1 / (77% - SOC1).

[0072] S13. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency.

[0073] Specifically, taking the charging efficiency as η, with a value ranging from 90% to 95%, and the standard discharge capacity as Q_discharge, as an example, Q_discharge = Q_charge × η. This allows us to calculate the standard discharge capacity corresponding to one effective charging cycle.

[0074] S14. Select at least 5 consecutive valid charging data points from N valid charging attempts as the target valid charging data set.

[0075] Specifically, the target effective charging data set includes multiple sets of continuous effective charging data, that is, at least 5 consecutive effective charging data are selected from N effective charging times as the target effective charging data set.

[0076] S15. Repeat steps S11-S13 sequentially for the target effective charging data in the target effective charging data group, and determine the current capacity of the battery under test based on the average value of the standard discharge amount corresponding to multiple target effective charging data in the target effective charging data group.

[0077] Specifically, steps S11-S13 are executed sequentially for each target valid charging data in the target valid charging data group, thereby obtaining multiple standard discharge amounts. The current capacity of the battery under test is determined based on the average of the multiple standard discharge amounts.

[0078] For example, when N=30, five consecutive valid charging data points are selected from the 30 valid charging attempts as the target valid charging data set. For instance, valid charging data from the first to the fifth valid charging attempts can be selected as the target valid charging data set. That is, the target valid charging data set includes the first target valid charging data, the second target valid charging data, the third target valid charging data, the fourth target valid charging data, and the fifth target valid charging data. The cumulative charging amount, full charge capacity, and standard discharge capacity corresponding to the third set of charging data from the first target valid charging data up to full charge are obtained sequentially. Similarly, the cumulative charging amount, full charge capacity, and standard discharge capacity corresponding to the third set of charging data from the second target valid charging data up to full charge are obtained, and so on. In other words, a standard discharge capacity can be determined based on each set of target valid charging data in the target valid charging data set. Multiple standard discharge capacities can be obtained in this way, and then the average of these multiple standard discharge capacities is calculated as the current capacity of the battery under test.

[0079] S403. Determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, current capacity, and N effective charging cycles.

[0080] S404. Determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0081] Figure 5 This is a flowchart illustrating the fifth battery life prediction method provided in this embodiment of the invention. Figure 5Based on the above embodiments, the operation of determining the current capacity of the battery under test according to the battery's state of charge, effective charging data, N effective charging cycles, and charging efficiency is described in detail, such as... Figure 5 As shown, the battery life prediction method includes: S501. Obtain the N valid charging times of the battery under test and multiple valid charging data within each valid charging time; where N is a positive integer.

[0082] S502. Calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times, and determine the initial calibrated capacity of the battery under test; where 5 < n < N, and n is an integer.

[0083] As another feasible implementation method, the current capacity is calculated when the battery under test is fully charged.

[0084] S21. When the battery under test is fully charged, obtain the third set of charging data from the valid charging data within each valid charging cycle up to the cumulative charging amount corresponding to the full charge.

[0085] Specifically, the battery under test is considered fully charged when it is at 100%. Since the first two sets of charging data in the valid charging data are from the very beginning of charging and the data is unstable at this point, to ensure the accuracy of subsequent calculations, starting from the third set of charging data, the charging amount corresponding to each set of charging data is obtained sequentially, and the charging amounts corresponding to the third to the last set of charging data are summed to obtain the cumulative charging amount corresponding to the third to the last set of charging data.

[0086] S22. Determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the full charge ratio.

[0087] Specifically, when the battery under test is fully charged, the charging ratio corresponding to the last set of charging data is 100%, that is, the full charge is 100%. Let the cumulative charge be Q1, the charging ratio corresponding to the third set of charging data be SOC1, and the full charge capacity of the battery under test be Qcharge, then Qcharge = Q1 / (100% - SOC1).

[0088] S23. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency.

[0089] Specifically, taking the charging efficiency as η, with a value ranging from 90% to 95%, and the standard discharge capacity as Q_discharge, as an example, Q_discharge = Q_charge × η. This allows us to calculate the standard discharge capacity corresponding to one effective charging cycle.

[0090] S24. Select at least 5 consecutive valid charging data points from N valid charging attempts as the target valid charging data group.

[0091] Specifically, the target effective charging data set includes multiple sets of continuous effective charging data, that is, at least 5 consecutive sets of effective charging data are selected from N effective charging times as the target effective charging data set.

[0092] S25. Repeat steps S21-S23 sequentially for the target effective charging data in the target effective charging data group, and determine the current capacity of the battery under test based on the average value of the standard discharge amount corresponding to each target effective charging data in the target effective charging data group.

[0093] Specifically, steps S21-S23 are executed sequentially for each target valid charging data in the target valid charging data group, thereby obtaining multiple standard discharge amounts. The current capacity of the battery under test is determined based on the average of the multiple standard discharge amounts.

[0094] For example, when N=30, five consecutive valid charging data points are selected from the 30 valid charging attempts as the target valid charging data set. For instance, valid charging data from the first to the fifth valid charging attempts can be selected as the target valid charging data set. That is, the target valid charging data set includes the first target valid charging data, the second target valid charging data, the third target valid charging data, the fourth target valid charging data, and the fifth target valid charging data. The cumulative charging amount, full charge capacity, and standard discharge capacity corresponding to the third set of charging data from the first target valid charging data up to full charge are obtained sequentially. Similarly, the cumulative charging amount, full charge capacity, and standard discharge capacity corresponding to the third set of charging data from the second target valid charging data up to full charge are obtained, and so on. In other words, a standard discharge capacity can be determined based on each set of target valid charging data in the target valid charging data set. Multiple standard discharge capacities can be obtained in this way, and then the average of these multiple standard discharge capacities is calculated as the current capacity of the battery under test.

[0095] S503. Determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, current capacity, and N effective charging cycles.

[0096] S504. Determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0097] The battery life prediction method provided in this invention determines the current capacity of the battery under test by judging whether it is fully charged and then selecting different calculation rules, thereby improving the prediction accuracy of battery life. Furthermore, by selecting the average of the standard discharge amounts from at least five consecutive valid charging data points to determine the current capacity of the battery under test, the influence of fluctuations caused by single charging data points can be mitigated.

[0098] Optional, Figure 6 This is a flowchart illustrating the sixth battery life prediction method provided in this embodiment of the invention. Figure 6 Based on the above embodiments, the operation of determining the degradation characteristic parameters of the battery under test according to the initial calibration capacity, current capacity, and N effective charging cycles is described in detail, such as... Figure 6 As shown, the battery life prediction method includes: S601. Obtain the N valid charging times of the battery under test and multiple valid charging data within each valid charging time; where N is a positive integer.

[0099] S602. Calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles out of N effective charging times, and determine the initial calibrated capacity of the battery under test; where 5 < n < N, and n is an integer.

[0100] S603. Determine the current capacity of the battery under test based on its state of charge, valid charging data, N valid charging cycles, and charging efficiency.

[0101] S604. Determine the capacity degradation value of the battery under test based on the initial calibrated capacity and the current capacity.

[0102] Specifically, taking an initial calibrated capacity of Q0, a current capacity of Q-average, and a capacity decay value of ΔQ1 as an example, ΔQ1 = Q0 - Q-average.

[0103] S605. Determine the capacity degradation rate of the battery under test based on the capacity degradation value and N effective charging cycles.

[0104] Specifically, taking the capacity decay rate K as an example, K = △Q1 / N.

[0105] S606. Determine the charging frequency of the battery under test based on the number of days of use and N valid charging cycles.

[0106] Specifically, the number of days the battery under test has been used can be understood as the number of days between the user's registration date and the current date. Charging frequency reflects the actual charging and discharging frequency of the battery under test used by the user.

[0107] Exemplarily, taking the number of days of use as W and the charging frequency as Dn, Dn = W / N.

[0108] S607. Determine the capacity degradation threshold of the battery under test according to the initial calibrated capacity.

[0109] Exemplarily, if the remaining capacity of the battery under test not being lower than 70% of the initial capacity is taken as the quality assurance qualified standard, and the corresponding capacity degradation value is △Q2, then △Q2 = (1 - 70%) × Q0.

[0110] S608. Determine the remaining life of the battery under test according to the capacity degradation threshold, the capacity degradation value, the capacity degradation rate, and the charging frequency.

[0111] Specifically, the remaining life can be understood as the remaining number of days of use of the battery under test. Exemplarily, the remaining life is D, then D = (△Q2 - △Q1) / K × Dn.

[0112] It should be noted that if △Q1 ≥ △Q2, it means that the battery under test has reached the quality assurance degradation threshold, and the remaining number of days of use is 0 days. If △Q1 < △Q2, it means that the battery under test has not reached the quality assurance degradation threshold and can still be used.

[0113] As a feasible implementation method, taking the number of effective charging times N = 30, the quality assurance years of the battery under test being 3 years, and the number of days from user registration to the current time being 180 days as an example. Calculate the capacity of the battery under test for the first 10 charging cycles of this user, and take the average value to obtain the average battery capacity Q0 = 27.5 Ah. According to the calibration rule, 24 Ah < Q0 < 30 Ah, and the initial capacity is calibrated to 26 Ah. The majority of the battery under test for this user is in a state of being not fully charged conventionally. Record the charging ratio corresponding to the third group of charging data in each effective charging data as SOC1 = 30%, and the charging ratio corresponding to the last group of charging data as 77%. The cumulative charging amount Q1 = 11.28 Ah; calculate the full charge capacity Q charge = 11.28 / (77% - 30%) = 24 Ah; take the charging efficiency η = 92%, and calculate the standard discharge capacity Q discharge = 24 × 92% = 22.08 Ah; select the Q discharge calculated values of 30 consecutive effective chargings and calculate the average to obtain the current capacity Q average = 23.4 Ah. The capacity degradation value △Q1 = 26 - 23.4 = 2.6 Ah; the capacity degradation rate K = 2.6 / 30 ≈ 0.0867 Ah / time; the charging frequency Dn = 180 / 30 = 6 days / time, that is, this user charges once every 6 days on average. The capacity degradation threshold △Q2 at the expiration of the quality assurance = (1 - 70%) × 26 = 7.8 Ah; the remaining number of days D at the expiration of the quality assurance = (7.8 - 2.6) / 0.0867 × 6 ≈ 360 days, that is, this battery can still be used for about 360 days and still meets the 3-year quality assurance requirement.

[0114] As another implementation method, taking an effective charging cycle of N=20, a warranty period of 2 years for the battery under test, and 100 days since the user registered as of now as an example.

[0115] The capacity of the user's battery under test was calculated after the first 10 charging cycles, and the average value was taken to obtain the average battery capacity Q0 = 21.8Ah. According to the calibration rules, Q0 < 22Ah, so the initial calibration capacity is 21Ah. The user's battery under test was mostly in a fully charged state. The charging ratio corresponding to the third set of charging data in each effective charging session was SOC1 = 25%, and the charging ratio corresponding to the last set of charging data was 100%, i.e., the full charge was 100%, with a cumulative charging amount Q1 = 16.8Ah. The full charge capacity Qcharge was calculated as Qcharge = 16.8 / (100% - 25%) = 22.4Ah. Taking the charging efficiency η = 90%, the standard discharge capacity Qdischarge was calculated as Qdischarge = 22.4 × 90% = 20.16Ah. The average of the Qdischarge values ​​from 20 consecutive effective charging sessions was taken to obtain the current capacity Qaverage = 19.5Ah. The capacity degradation value △Q1 = 21 - 19.5 = 1.5 Ah; the capacity degradation rate K = 1.5 / 20 = 0.075 Ah / cycle; the charging frequency Dn = 100 / 20 = 5 days / cycle, meaning the user charges the battery on average once every 5 days. The capacity degradation threshold before the warranty expires △Q2 = (1 - 70%) × 21 = 6.3 Ah; the remaining days before the warranty expires D = (6.3 - 1.5) / 0.075 × 5 ≈ 320 days, meaning the battery can still be used for approximately 320 days.

[0116] The battery life prediction method provided in this invention accurately calculates the current capacity and degradation characteristic parameters of the battery under test using effective charging data, and then establishes a degradation trend model to predict the remaining service life of the battery before the warranty expires, thus solving the problems of low prediction accuracy and poor integration with actual scenarios in existing methods.

[0117] Based on the same inventive concept, embodiments of the present invention also provide a battery life prediction device for performing the battery life prediction method described in the above embodiments. Figure 7 This is a schematic diagram of a battery life prediction device provided in an embodiment of the present invention, as shown below. Figure 7 As shown, the battery life prediction device includes: The effective charging data acquisition module 100 acquires N effective charging times of the battery under test and multiple effective charging data within each effective charging time; where N is a positive integer.

[0118] The initial calibration capacity determination module 200 is used to calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles in N effective charging times, and to determine the initial calibration capacity of the battery under test; where 5 < n < N; and n is an integer.

[0119] The current capacity determination module 300 is used to determine the current capacity of the battery under test based on the battery's state of charge, valid charging data, N valid charging cycles, and charging efficiency.

[0120] The degradation characteristic parameter determination module 400 is used to determine the degradation characteristic parameters of the battery under test based on the initial calibrated capacity, the current capacity, and N effective charging cycles.

[0121] The remaining life determination module 500 is used to determine the remaining life of the battery under test based on the initial calibrated capacity and degradation characteristic parameters.

[0122] Optionally, the effective charging data acquisition module includes an initial charging data acquisition unit and a filtering unit.

[0123] The initial charging data acquisition unit is used to acquire the initial charging data of the battery under test.

[0124] The filtering unit is used to filter the initial charging data based on the warranty information of the battery under test and the charging time and duration of each set of charging data in the initial charging data, and to filter out N valid charging times and multiple valid charging data within each valid charging time.

[0125] Optionally, the initial calibration capacity determination module includes an effective charging data acquisition unit, a battery capacity average value determination unit, and an initial calibration capacity determination unit.

[0126] The effective charging data acquisition unit is used to acquire the effective charging data corresponding to the first n charging cycles out of N effective charging times.

[0127] The battery capacity average value determination unit is used to determine the average battery capacity of the battery under test based on the valid charging data corresponding to the previous n charging cycles.

[0128] The initial calibration capacity determination unit is used to determine the initial calibration capacity of the battery under test based on the average battery capacity and a preset battery capacity range.

[0129] Optionally, the initial calibration capacity determination unit may further include a first target calibration capacity determination subunit, a second target calibration capacity determination subunit, a third target calibration capacity determination subunit, and a fourth target calibration capacity determination subunit.

[0130] The first target calibration capacity determination subunit is used to determine the initial calibration capacity of the battery under test as the first target calibration capacity when the average battery capacity is less than the first preset battery capacity; the first target calibration capacity is less than the first preset battery capacity.

[0131] The second target calibration capacity determination subunit is used to determine the initial calibration capacity of the battery under test as the second target calibration capacity when the average battery capacity is greater than the first preset battery capacity and less than the second preset battery capacity; the second target calibration capacity is greater than the first target calibration capacity.

[0132] The third target calibration capacity determination subunit is used to determine the initial calibration capacity of the battery under test as the third target calibration capacity when the average battery capacity is greater than the second preset battery capacity and less than the third preset battery capacity; the third target calibration capacity is greater than the second target calibration capacity.

[0133] The fourth target calibration capacity determination subunit is used to determine the initial calibration capacity of the battery under test as the fourth target calibration capacity when the average battery capacity is greater than the fourth preset battery capacity; the fourth preset battery capacity is greater than or equal to the third preset battery capacity; the fourth target calibration capacity is greater than the fourth preset battery capacity and greater than the third target calibration capacity.

[0134] Optionally, the current capacity determination module includes a first cumulative charging amount acquisition unit, a first full charge capacity determination unit, a first standard discharge capacity determination unit, a first target valid charging data group selection unit, and a first current capacity determination unit.

[0135] The first cumulative charge acquisition unit is used to acquire the cumulative charge amount corresponding to the third set of charging data to the last set of charging data in each valid charging count when the battery under test is not fully charged.

[0136] The first full charge capacity determination unit is used to determine the full charge capacity of the battery under test based on the cumulative charge amount, the charging ratio corresponding to the third set of charging data, and the charging ratio corresponding to the last set of charging data.

[0137] The first standard discharge capacity determination unit is used to determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency.

[0138] The first target valid charging data group selection unit is used to select at least 5 consecutive valid charging data points from N valid charging times as the target valid charging data group.

[0139] The first current capacity determination unit is used to sequentially execute steps S11-S13 on the target valid charging data in the target valid charging data group, and determine the current capacity of the battery under test based on the average value of the standard discharge amount corresponding to multiple target valid charging data in the target valid charging data group.

[0140] Alternatively, the current capacity determination module may include a second cumulative charging amount acquisition unit, a second full charge capacity determination unit, a second standard discharge capacity determination unit, a second target valid charging data group selection unit, and a second current capacity determination unit.

[0141] The second cumulative charge acquisition unit is used to acquire the third set of charging data from the valid charging data within each valid charging cycle to the cumulative charge corresponding to the full charge when the battery under test is fully charged.

[0142] The second full charge capacity determination unit is used to determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the full charge ratio.

[0143] The second standard discharge capacity determination unit is used to determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency.

[0144] The second target effective charging data group selection unit is used to select at least 5 consecutive effective charging data points from N effective charging times as the target effective charging data group.

[0145] The second current capacity determination unit is used to sequentially execute steps S21-S23 on the target valid charge data in the target valid charge data group, and determine the current capacity of the battery under test based on the average value of the standard discharge amount corresponding to each target valid charge data in the target valid charge data group.

[0146] Optionally, the degradation characteristic parameter determination module includes a capacity degradation value determination unit, a capacity degradation rate determination unit, and a charging frequency determination unit.

[0147] The capacity degradation value determination unit is used to determine the capacity degradation value of the battery under test based on the initial calibrated capacity and the current capacity.

[0148] The capacity degradation rate determination unit is used to determine the capacity degradation rate of the battery under test based on the capacity degradation value and N effective charging cycles.

[0149] The charging frequency determination unit is used to determine the charging frequency of the battery under test based on the number of days of use and N valid charging cycles.

[0150] Optionally, the remaining lifetime determination module includes a capacity decay threshold determination unit and a remaining lifetime determination unit.

[0151] The capacity degradation threshold determination unit is used to determine the capacity degradation threshold of the battery under test based on the initial calibrated capacity.

[0152] The remaining life determination unit is used to determine the remaining life of the battery under test based on the capacity degradation threshold, capacity degradation value, capacity degradation rate, and charging frequency.

[0153] The battery life prediction device provided in this invention employs an effective charging data acquisition module to acquire N effective charging cycles and multiple effective charging data points within each effective charging cycle of the battery under test; an initial calibration capacity determination module to calculate the battery's capacity based on the effective charging data corresponding to the first n charging cycles out of the N effective charging cycles, and to determine the initial calibration capacity of the battery under test; a current capacity determination module to determine the current capacity of the battery under test based on its state of charge, effective charging data, N effective charging cycles, and charging efficiency; a degradation characteristic parameter determination module to determine the degradation characteristic parameters of the battery under test based on the initial calibration capacity, current capacity, and N effective charging cycles; and a remaining lifespan determination module to determine the remaining lifespan of the battery under test based on the degradation characteristic parameters. This improves the accuracy of battery life prediction and provides a scientific basis for battery warranty management.

[0154] Figure 8 This is a schematic diagram of a computer device used in a battery life prediction method to implement an embodiment of the present invention. The computer device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computer device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0155] like Figure 8 As shown, the computer device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the computer device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0156] Multiple components in computer device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows computer device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0157] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, for example, applied to a battery life prediction method.

[0158] In some embodiments, an application to a battery life prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on a computer device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the application to a battery life prediction method described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform an application to a battery life prediction method by any other suitable means (e.g., by means of firmware).

[0159] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0160] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] In the context of embodiments of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0162] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0163] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0164] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0165] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will be able to make various obvious changes, readjustments, and substitutions without departing from the scope of protection of the present invention. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting battery life, characterized in that, include: Obtain N valid charge counts of the battery under test and multiple valid charge data within each valid charge count; where N is a positive integer; The capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles in the N effective charging times, and the initial calibrated capacity of the battery under test is determined; where 5 < n < N, and n is an integer; The current capacity of the battery under test is determined based on the battery status, the valid charging data, the N valid charging times, and the charging efficiency. The degradation characteristic parameters of the battery under test are determined based on the initial calibrated capacity, the current capacity, and the N effective charging cycles. The remaining life of the battery under test is determined based on the initial calibrated capacity and the degradation characteristic parameters.

2. The battery life prediction method according to claim 1, characterized in that, Obtain N valid charge counts of the battery under test and multiple valid charge data points within each valid charge count, including: Obtain the initial charging data of the battery under test; The initial charging data is filtered based on the warranty information of the battery under test and the charging time and duration of each set of charging data in the initial charging data, to select N valid charging times and multiple valid charging data within each valid charging time.

3. The battery life prediction method according to claim 1, characterized in that, The capacity of the battery under test is calculated based on the effective charging data corresponding to the first n charging cycles out of the N effective charging counts, and the initial calibrated capacity of the battery under test is determined, including: Obtain the valid charging data corresponding to the first n charging cycles out of the N valid charging counts; The average battery capacity of the battery under test is determined based on the valid charging data corresponding to the first n charging cycles. The initial calibrated capacity of the battery under test is determined based on the average battery capacity and the preset battery capacity range.

4. The battery life prediction method according to claim 3, characterized in that, Determining the initial calibration capacity of the battery under test based on the average battery capacity and a preset battery capacity range includes: When the average battery capacity is less than the first preset battery capacity, the initial calibration capacity of the battery under test is determined to be the first target calibration capacity; the first target calibration capacity is less than the first preset battery capacity. When the average battery capacity is greater than the first preset battery capacity and less than the second preset battery capacity, the initial calibration capacity of the battery under test is determined to be the second target calibration capacity; the second target calibration capacity is greater than the first target calibration capacity. When the average battery capacity is greater than the second preset battery capacity and less than the third preset battery capacity, the initial calibration capacity of the battery under test is determined to be the third target calibration capacity; the third target calibration capacity is greater than the second target calibration capacity. When the average battery capacity is greater than the fourth preset battery capacity, the initial calibration capacity of the battery under test is determined to be the fourth target calibration capacity; the fourth preset battery capacity is greater than or equal to the third preset battery capacity; the fourth target calibration capacity is greater than the fourth preset battery capacity and greater than the third target calibration capacity.

5. The battery life prediction method according to claim 1, characterized in that, The current capacity of the battery under test is determined based on the battery status, the valid charging data, the N valid charging cycles, and the charging efficiency, including: S11. When the battery under test is not fully charged, acquire the cumulative charging amount corresponding to the third set of charging data to the last set of charging data in each effective charging count. S12. Determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the charging ratio corresponding to the last set of charging data. S13. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency. S14. Select at least 5 consecutive valid charging data points from the N valid charging times as the target valid charging data group. S15. The target effective charging data in the target effective charging data group are sequentially executed in steps S11-S13, and the current capacity of the battery under test is determined according to the average value of the standard discharge amount corresponding to multiple target effective charging data in the target effective charging data group. or, S21. When the battery under test is fully charged, acquire the third set of charging data from the valid charging data within each valid charging count to the cumulative charging amount corresponding to the full charge. S22. Determine the full charge capacity of the battery under test based on the cumulative charging amount, the charging ratio corresponding to the third set of charging data, and the full charge ratio. S23. Determine the standard discharge capacity of the battery under test based on the full charge capacity and charging efficiency. S24. Select at least 5 consecutive valid charging data points from the N valid charging times as the target valid charging data group; S25. The target effective charging data in the target effective charging data group are sequentially executed in steps S21-S23, and the current capacity of the battery under test is determined according to the average value of the standard discharge amount corresponding to each target effective charging data in the target effective charging data group.

6. The battery life prediction method according to claim 1, characterized in that, The degradation characteristic parameters of the battery under test are determined based on the initial calibrated capacity, the current capacity, and the N effective charging cycles, including: The capacity degradation value of the battery under test is determined based on the initial calibration capacity and the current capacity. The capacity degradation rate of the battery under test is determined based on the capacity degradation value and the N effective charging cycles. The charging frequency of the battery under test is determined based on the number of days the battery was used and the number of N valid charging cycles.

7. The battery life prediction method according to claim 6, characterized in that, Determining the remaining life of the battery under test based on the initial calibrated capacity and the degradation characteristic parameters includes: The capacity degradation threshold of the battery under test is determined based on the initial calibration capacity. The remaining lifespan of the battery under test is determined based on the capacity degradation threshold, the capacity degradation value, the capacity degradation rate, and the charging frequency.

8. A battery life prediction device, characterized in that, include: The effective charging data acquisition module is used to acquire N effective charging times of the battery under test and multiple effective charging data within each effective charging time; where N is a positive integer; The initial calibration capacity determination module is used to calculate the capacity of the battery under test based on the effective charging data corresponding to the first n charging cycles in the N effective charging times, and to determine the initial calibration capacity of the battery under test; where 5 < n < N, and n is an integer; The current capacity determination module is used to determine the current capacity of the battery under test based on the battery status, the valid charging data, the N valid charging times, and the charging efficiency. The degradation characteristic parameter determination module is used to determine the degradation characteristic parameters of the battery under test based on the initial calibration capacity, the current capacity, and the N effective charging times. The remaining lifetime determination module is used to determine the remaining lifetime of the battery under test based on the initial calibrated capacity and the degradation characteristic parameters.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the battery life prediction method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the battery life prediction method as described in any one of claims 1-7.

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