Battery capacity analysis and predication method and system for storage battery

Abstract

A battery capacity analysis and predication method and system for a storage battery, relating to the technical field of data processing. The method comprises: acquiring operation data of a target storage battery within a preset time period, wherein the operation data comprises a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge and discharge cycle number sequence; on the basis of the operation data, determining time intervals corresponding to a plurality of operation conditions; on the basis of operation sub-data within each time interval, calculating a temperature influence factor, a power loss factor, and a cycle aging factor within each time interval; acquiring a battery capacity attenuation amount of the target storage battery within each time interval; on the basis of the battery capacity attenuation amount, temperature influence factor, power loss factor, and cycle aging factor within each time interval, generating a capacity attenuation amount curve of the target storage battery; and on the basis of the capacity attenuation amount curve, predicting battery capacity in the next time period.

Description

A method and system for analyzing and predicting the capacity of a storage battery Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, electronic device, and storage medium for analyzing and predicting the capacity of a storage battery. Background Technology

[0002] With the increasing adoption of renewable energy and electric vehicles, batteries, as key energy storage devices, play a crucial role in ensuring system efficiency and economic benefits through their reliability and lifespan prediction.

[0003] Currently, existing methods for analyzing and predicting battery capacity mainly use fixed models or evaluation standards to process collected data in order to predict battery capacity. However, in practical applications, different batteries exhibit different characteristics under various operating conditions, and these differences become more significant over time. Relying solely on fixed models or evaluation standards to analyze battery data often overlooks the increasing differences between individual batteries over time, resulting in errors in battery capacity prediction and thus reducing the accuracy of battery capacity analysis and prediction. Summary of the Invention

[0004] This application provides a method and system for analyzing and predicting the capacity of a storage battery, which improves the accuracy of battery capacity analysis and prediction.

[0005] In a first aspect, this application provides a method for analyzing and predicting the battery capacity of a storage battery, comprising: acquiring operating data of a target storage battery within a preset time period, the operating data including a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge-discharge cycle count sequence; determining time intervals corresponding to multiple operating conditions based on the operating data of the target storage battery within the preset time period; calculating a temperature influence factor, a power loss factor, and a cycle aging factor for each time interval based on sub-operating data within each time interval; acquiring the battery capacity decay of the target storage battery within each time interval; generating a capacity decay curve of the target storage battery by combining the battery capacity decay, temperature influence factor, power loss factor, and cycle aging factor for each time interval; and predicting the battery capacity of the target storage battery in the next time period based on the capacity decay curve.

[0006] A second aspect of this application provides a battery capacity analysis and prediction system for a storage battery, comprising:

[0007] The system includes a data acquisition module for acquiring operational data of the target battery within a preset time period, including voltage, internal resistance, temperature, and charge / discharge cycle count sequences; an interval division module for determining time intervals corresponding to multiple operating conditions based on the operational data of the target battery within the preset time period; a curve determination module for calculating the temperature influence factor, power loss factor, and cycle aging factor for each time interval based on the sub-operational data within each time interval; acquiring the battery capacity decay of the target battery within each time interval; and generating a capacity decay curve of the target battery by combining the battery capacity decay, temperature influence factor, power loss factor, and cycle aging factor for each time interval; and a capacity prediction module for predicting the battery capacity of the target battery in the next time period based on the capacity decay curve.

[0008] A third aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, the program being loaded and executed by the processor to implement a battery capacity analysis and prediction method for a storage battery.

[0009] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a method for analyzing and predicting the capacity of a storage battery.

[0010] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By adopting the above technical solutions, operational data such as voltage sequence, internal resistance sequence, temperature sequence, and charge / discharge cycle number sequence of the target battery within a preset time period are obtained, realizing accurate monitoring of the working state of a specific battery. By dividing the preset time period into multiple time intervals corresponding to different operating conditions, the unique performance of the battery under different operating conditions can be captured. On this basis, the temperature influence factor, power loss factor, and cycle aging factor of each time interval are calculated respectively, realizing personalized quantitative characterization of the factors affecting the capacity decay of the specific battery. By obtaining the battery capacity decay amount of each time interval and performing correlation analysis with the corresponding influence factors, the generated capacity decay curve can accurately reflect the unique decay characteristics of the individual battery. This adaptive prediction method based on the actual operating data of the individual battery can avoid the limitations of fixed models that are difficult to adapt to individual battery differences. By establishing a mathematical relationship adapted to the characteristics of the individual battery, it can not only accurately predict the battery capacity change trend, but also dynamically adapt to the evolution characteristics of battery performance over time, solving the prediction error problem caused by ignoring individual battery differences, and further improving the accuracy of battery capacity analysis and prediction. Attached Figure Description

[0011] Figure 1 is a flowchart illustrating a battery capacity analysis and prediction method for a storage battery according to an embodiment of this application; Figure 2 is a structural diagram illustrating a battery capacity analysis and prediction system for a storage battery according to an embodiment of this application; Figure 3 is a structural diagram illustrating an electronic device according to an embodiment of this application.

[0012] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0015] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0016] This application provides a method for analyzing and predicting the capacity of a storage battery. In one embodiment, please refer to Figure 1, which is a flowchart illustrating the method for analyzing and predicting the capacity of a storage battery provided in this application. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone utility application. The method can also be implemented using a microcontroller or run in a battery capacity analysis and prediction system based on the von Neumann architecture. Specifically, the method may include the following steps: Step 101: Obtain the operating data of the target storage battery within a preset time period. The operating data includes a voltage sequence, internal resistance sequence, temperature sequence, and charge / discharge cycle count sequence.

[0017] Among them, operational data refers to various time-series sampling data that can reflect the performance characteristics and health status of the target battery during its actual operation. In the embodiments of this application, it can be understood as the voltage sequence collected by the voltage sampling device, the internal resistance sequence collected by the internal resistance measurement device, the temperature sequence collected by the temperature sensor, and the charge-discharge cycle number sequence recorded by the charge-discharge management system.

[0018] Specifically, voltage sampling devices are installed at the positive and negative terminals of the target battery. These devices collect voltage data at preset sampling time intervals, such as 1 minute, to obtain a voltage sequence. An internal resistance measuring device is installed inside the target battery, collecting internal resistance data at preset sampling time intervals to obtain an internal resistance sequence. A temperature sensor is installed on the surface or inside the target battery, collecting temperature data at preset sampling time intervals to obtain a temperature sequence. A charge-discharge management system records the number of charge-discharge cycles of the target battery to obtain a charge-discharge cycle count sequence. The preset time period can be a continuous period, such as a week or a month, or a combination of multiple discrete time segments. These operational data reflect the performance changes of the target battery during actual use from different dimensions: the voltage sequence reflects the battery's charge-discharge state and remaining capacity; the internal resistance sequence reflects the battery's internal losses; the temperature sequence reflects the battery's thermal characteristics; and the charge-discharge cycle count sequence reflects the battery's usage intensity. By collecting this multi-dimensional operational data, the performance characteristics of the battery under actual working conditions can be comprehensively obtained, providing a data foundation for accurate subsequent prediction of battery capacity. This data collection method, based on actual operating data, avoids the errors that may arise from using a uniform, fixed model. It can better adapt to the differences between individual batteries and improve the accuracy of capacity prediction.

[0019] Step 102: Based on the operating data of the target battery within a preset time period, determine the time intervals corresponding to multiple operating conditions.

[0020] The operating condition refers to the combination of the target battery's working state and environmental conditions in actual application. In the embodiments of this application, it can be understood as the battery's operating state characterized by three key parameters: temperature, voltage, and internal resistance. When any of these parameters exceeds its corresponding threshold, it indicates that the battery has entered a new operating condition.

[0021] A time interval refers to a period of time during which the target battery operates continuously under relatively stable working conditions. In the embodiments of this application, it can be understood as multiple time periods divided by operating condition transition points, where operating condition transition points include time points in the temperature sequence where the temperature exceeds a temperature threshold, time points in the voltage sequence where the voltage exceeds a voltage threshold, and time points in the internal resistance sequence where the internal resistance exceeds an internal resistance threshold.

[0022] Specifically, firstly, temperature thresholds, voltage thresholds, and internal resistance thresholds are set. These thresholds can be determined based on the battery's technical specifications and actual application scenarios. Then, within a preset time period, the time points of temperature anomalies are identified as operating condition transition points by judging whether the temperature data in the temperature sequence exceeds the temperature threshold; the time points of voltage anomalies are identified as operating condition transition points by judging whether the voltage data in the voltage sequence exceeds the voltage threshold; and the time points of internal resistance anomalies are identified as operating condition transition points by judging whether the internal resistance data in the internal resistance sequence exceeds the internal resistance threshold. Using these operating condition transition points as boundaries, the preset time period is divided into multiple time intervals corresponding to various operating conditions. Within each time interval, the battery's operating state is relatively stable, exhibiting similar performance change characteristics. This operating condition division method based on multi-parameter thresholds can accurately identify changes in the battery's operating state, making subsequent performance analysis more targeted. By independently analyzing the battery performance under different operating conditions, the performance degradation patterns of the battery under various operating states can be described more accurately, thereby improving the accuracy of capacity prediction.

[0023] Based on the above embodiments, as an optional embodiment, step 102: Based on the operating data of the target battery within a preset time period, determine the time intervals corresponding to multiple operating conditions. This step may also include the following steps: Step 201: Within the preset time period, determine the time points in the temperature sequence where the temperature exceeds the temperature threshold as operating condition transition points, the time points in the voltage sequence where the voltage exceeds the voltage threshold as operating condition transition points, and the time points in the internal resistance sequence where the internal resistance exceeds the internal resistance threshold as operating condition transition points.

[0024] The temperature sequence refers to a time-series temperature data set obtained by continuously sampling the temperature of the target battery at preset sampling time intervals. In this embodiment, it can be understood as a data sequence composed of temperature data collected at fixed sampling time intervals within a preset period by a temperature sensor installed on or inside the target battery.

[0025] A voltage sequence refers to a time-series voltage data set obtained by continuously sampling the terminal voltage of a target battery at preset sampling time intervals. In the embodiments of this application, it can be understood as a data sequence composed of voltage data collected at fixed sampling time intervals within a preset period by voltage sampling devices installed at the positive and negative terminals of the target battery.

[0026] An internal resistance sequence refers to a time-series internal resistance data set obtained by continuously sampling the internal impedance of a target battery at preset sampling time intervals. In the embodiments of this application, it can be understood as a data sequence composed of internal resistance data collected at fixed sampling time intervals within a preset time period by an internal resistance measuring device installed inside the target battery.

[0027] The operating condition transition point refers to the time point at which the operating state of the target battery undergoes a significant change. In the embodiments of this application, it can be understood as the time point in the temperature sequence where the temperature data exceeds the upper temperature threshold or falls below the lower temperature threshold, the time point in the voltage sequence where the voltage data exceeds the upper voltage threshold or falls below the lower voltage threshold, and the time point in the internal resistance sequence where the internal resistance data exceeds the upper internal resistance threshold or falls below the lower internal resistance threshold.

[0028] Specifically, firstly, temperature thresholds are set based on the battery's technical specifications. These temperature thresholds include an upper temperature threshold and a lower temperature threshold. By iterating through each temperature data point in the temperature sequence, when a temperature data point is detected to exceed the upper temperature threshold or fall below the lower temperature threshold, the corresponding time point is recorded as a temperature condition transition point. Secondly, voltage thresholds are set, including an upper voltage threshold and a lower voltage threshold. By iterating through each voltage data point in the voltage sequence, when a voltage data point is detected to exceed the upper voltage threshold or fall below the lower voltage threshold, the corresponding time point is recorded as a voltage condition transition point. Similarly, internal resistance thresholds are set, including an upper internal resistance threshold and a lower internal resistance threshold. By iterating through each internal resistance data point in the internal resistance sequence, when an internal resistance data point is detected to exceed the upper internal resistance threshold or fall below the lower internal resistance threshold, the corresponding time point is recorded as an internal resistance condition transition point. These condition transition points identify moments when the battery's operating state changes significantly. Temperature condition transition points reflect changes in the battery's thermal environment, voltage condition transition points reflect changes in the charge / discharge state, and internal resistance condition transition points reflect changes in the battery's internal characteristics. This multi-parameter threshold judgment method can comprehensively capture the changing characteristics of battery operating status, providing a basis for subsequent operating condition classification.

[0029] Step 202: Divide the preset time period into multiple time intervals corresponding to different operating conditions, using the transition points of each operating condition as boundaries.

[0030] Specifically, firstly, all temperature, voltage, and internal resistance transition points within a preset time period are sorted chronologically to form a sequence of transition points. Then, using two adjacent transition points as the start and end times, the preset time period is divided into multiple consecutive time intervals. Within each time interval, the target battery's temperature, voltage, and internal resistance are all within their corresponding threshold ranges, indicating that the battery is in a relatively stable operating state within that time interval. This time interval division method based on multi-parameter transition points ensures that each time interval corresponds to a specific operating condition, accurately reflecting the battery's performance changes under different operating conditions. This division method decomposes the complex working process of the battery throughout the preset time period into multiple stable operating condition stages, facilitating independent analysis of performance changes under each condition and improving the accuracy of subsequent capacity prediction. Simultaneously, this division method also provides a clear time range for calculating temperature influence factors, power loss factors, and cycle aging factors under different operating conditions, making factor calculations more accurate and reliable.

[0031] Step 103: Based on the sub-operation data within each time interval, calculate the temperature influence factor, power loss factor, and cycle aging factor for each time interval.

[0032] Here, sub-operational data refers to various types of time-series sampling data recorded by the target battery within a specific time interval. In the embodiments of this application, it can be understood as the temperature sub-sequence, voltage sub-sequence, internal resistance sub-sequence, and charge / discharge cycle number sub-sequence corresponding to each time interval extracted from the operation data of a preset time period.

[0033] The temperature influence factor is a quantitative indicator that characterizes the degree to which temperature changes affect the performance of a target battery within a specific time interval. In the embodiments of this application, it can be understood as a comprehensive factor calculated based on a temperature subsequence, reflecting the deviation of the battery temperature from the standard operating temperature and the impact of temperature fluctuations. This factor is determined by calculating the degree of deviation of the temperature data from the standard operating temperature and the severity of temperature changes.

[0034] The power loss factor is a quantitative indicator that characterizes the degree of energy loss of a target battery due to its internal impedance and the charging and discharging process within a specific time interval. In the embodiments of this application, it can be understood as a comprehensive factor reflecting the internal energy loss of the battery, calculated based on the voltage subsequence and the internal resistance subsequence. This factor is determined by comprehensively analyzing the product effect of voltage change and internal resistance change, reflecting the power loss status of the battery during the charging and discharging process.

[0035] Cyclic aging factor refers to a quantitative indicator characterizing the degree of performance degradation of a target battery due to charge-discharge cycles within a specific time interval. In the embodiments of this application, it can be understood as a comprehensive factor reflecting the intensity of battery cyclic use, calculated based on a subsequence of charge-discharge cycle counts. This factor is determined by analyzing the depth and frequency of charge-discharge cycles, comprehensively considering the different impacts of deep and shallow cycles on battery life.

[0036] Specifically, to comprehensively evaluate the performance changes of the target battery under different operating conditions, it is necessary to calculate the corresponding influencing factors based on sub-operational data within each time interval. First, sub-operational data corresponding to each time interval is extracted from the operating data of the preset time period, including temperature subsequence, voltage subsequence, internal resistance subsequence, and charge / discharge cycle count subsequence. Then, a temperature influencing factor is calculated based on the temperature subsequence, reflecting the degree of temperature's impact on battery performance. The calculation process considers the deviation of temperature from the standard operating temperature and the impact of temperature fluctuations. A power loss factor is calculated based on the voltage and internal resistance subsequences, characterizing the degree of internal loss of the battery during charging and discharging. The calculation process comprehensively considers the combined effects of voltage and internal resistance changes on battery power loss. Finally, a cycle aging factor is calculated based on the charge / discharge cycle count subsequence, reflecting the degree of impact of charge / discharge cycles on battery performance degradation. The calculation process considers the effects of cycle depth and cycle frequency. This analysis method based on multiple influencing factors can quantify the influencing factors of battery performance degradation from different perspectives. Through comprehensive analysis of these factors, the performance change patterns of the battery under various operating conditions can be described more accurately. Meanwhile, the calculation results of these influencing factors provide reliable characteristic parameters for the subsequent establishment of battery capacity prediction models, which helps to improve the accuracy and reliability of capacity prediction.

[0037] Based on the above embodiments, as an optional embodiment, in step 103: calculating the temperature influence factor of each time interval based on the sub-running data within each time interval, this step may also include the following steps: Step 301: For each time interval, the difference between any two adjacent temperature sampling values ​​in the sub-temperature sequence is taken as the temperature fluctuation amplitude.

[0038] Specifically, to accurately assess the impact of temperature changes on the performance of a target battery within a specific time interval, a quantitative analysis of temperature fluctuations is required. First, a temperature subsequence within the time interval is obtained, containing a series of temperature data collected at preset sampling time intervals. Then, the temperature data in the subsequence are iterated through, and the difference between two adjacent temperature samples is calculated. These differences are defined as the temperature fluctuation amplitude. This calculation method reflects the drastic change in temperature over time; the larger the temperature fluctuation amplitude, the more drastic the temperature change and the more significant its impact on battery performance. By calculating the temperature fluctuation amplitude, the dynamic characteristics of temperature changes can be described in more detail, rather than simply focusing on the absolute value of the temperature. This temperature fluctuation analysis method based on the difference between adjacent sampling points can effectively capture the instantaneous characteristics of temperature changes, providing more comprehensive temperature change information for subsequent calculations of temperature influence factors, thereby improving the accuracy of temperature influence factor calculations. Simultaneously, this method also facilitates the identification of periods of drastic temperature fluctuations, helping to analyze the impact of temperature fluctuations on battery performance.

[0039] Step 302: Use the ratio of the difference between the first and last temperature sample values ​​in the sub-temperature sequence to the length of the time interval as the fluctuation coefficient.

[0040] Specifically, to further quantify the temperature change trend of the target battery within a specific time interval, a fluctuation coefficient needs to be introduced to characterize the overall rate of temperature change. First, the first and last temperature samples are obtained from the temperature subsequence, and the difference between these two values ​​is calculated. This difference reflects the overall temperature change over the entire time interval. Then, the length of the time interval is obtained, i.e., the time span from the start to the end of the interval. Finally, the temperature difference is divided by the time interval length to obtain the fluctuation coefficient. This calculation method reflects the average rate of temperature change over the entire time interval; a larger fluctuation coefficient indicates a faster temperature change and a more significant impact on battery performance. By introducing the fluctuation coefficient, combined with the aforementioned temperature fluctuation amplitude, the temperature change characteristics can be described simultaneously from both local fluctuation and overall trend dimensions, providing more complete temperature change information for calculating temperature influence factors. This fluctuation coefficient calculation method, based on the temperature difference between the beginning and end of the interval and the time interval length, effectively reflects the persistence and directionality of temperature changes, helping to more accurately assess the long-term impact of temperature changes on battery performance.

[0041] Step 303: Calculate the mean value of each temperature fluctuation amplitude, and divide the mean value by the fluctuation coefficient to obtain the temperature influence factor of the time interval.

[0042] Specifically, to comprehensively assess the overall impact of temperature changes on a target battery within a specific time interval, it is necessary to organically combine the local and global characteristics of temperature fluctuations. First, the arithmetic mean of all calculated temperature fluctuation amplitudes within the time interval is obtained, reflecting the average intensity of the temperature fluctuations. Then, this mean is divided by a pre-calculated fluctuation coefficient. This mathematical operation integrates the instantaneous and continuous temperature change characteristics, ultimately yielding the temperature influence factor for that time interval. This calculation method considers both the severity and persistence of temperature changes, enabling the temperature influence factor to more comprehensively characterize the impact of temperature changes on battery performance. The temperature influence factor calculated in this way can effectively distinguish the degree of influence of temperature changes on battery performance under different operating conditions; a larger temperature influence factor indicates a more significant impact of temperature changes on battery performance. This method of calculating the temperature influence factor based on the average temperature fluctuation amplitude and fluctuation coefficient accurately reflects the comprehensive impact of temperature changes on battery performance, providing reliable temperature characteristic parameters for subsequent capacity prediction and helping to improve the accuracy of capacity prediction.

[0043] Based on the above embodiments, as an optional embodiment, in step 103: calculating the power loss factor of each time interval based on the sub-operation data within each time interval, this step may also include the following steps: Step 304: For each time interval, calculating the instantaneous power value at each sampling time based on the sampling values ​​of the sub-internal resistance sequence and the sub-voltage sequence at multiple sampling times.

[0044] Specifically, to accurately assess the power loss of a target battery within a specific time interval, it is necessary to calculate the instantaneous power value at each sampling moment based on internal resistance and voltage data. First, the internal resistance subsequence and voltage subsequence for that time interval are obtained. The data in these two sequences have the same sampling time interval and corresponding sampling moments. Then, at each sampling moment, the internal resistance sample value and the voltage sample value at that moment are multiplied to obtain the instantaneous power value for that sampling moment. This calculation method is based on the principle of battery power loss, characterizing the energy loss of the battery during charging and discharging through the product of internal resistance and voltage. The larger the instantaneous power value, the more severe the energy loss at that moment. By calculating the instantaneous power value at each sampling moment, detailed information on the change of battery power loss over time can be obtained. This information reflects the energy conversion efficiency of the battery under different operating conditions. This instantaneous power calculation method based on internal resistance and voltage can accurately characterize the power loss characteristics of the battery, providing basic data for subsequent calculation of the power loss factor and helping to analyze the characteristics of battery performance degradation.

[0045] Step 305: Calculate the mean of the difference between any two adjacent internal resistance samples in the sub-internal resistance sequence to obtain the mean of internal resistance fluctuation.

[0046] Specifically, to assess the overall trend of internal resistance change in a target battery over a specific time interval, statistical analysis of internal resistance fluctuations is required. First, an internal resistance subsequence within that time interval is obtained. The internal resistance data in the sequence is then iterated through, and the difference between two adjacent internal resistance samples is calculated, yielding a series of internal resistance fluctuation values. These fluctuation values ​​are then arithmetically averaged to obtain the mean internal resistance fluctuation. This calculation method reflects the average degree of internal resistance change; a larger mean internal resistance fluctuation indicates more drastic changes in the battery's internal impedance and more unstable power loss characteristics. By calculating the mean internal resistance fluctuation, the influence of random fluctuations in internal resistance changes can be eliminated, resulting in more representative internal resistance change characteristics. This internal resistance fluctuation analysis method based on the difference between adjacent sample values ​​can effectively characterize the dynamic changes in the battery's internal impedance, providing reliable internal resistance change parameters for subsequent calculations of the power loss factor, and helping to more accurately assess the battery's power loss status.

[0047] Step 306: Calculate the sum of the instantaneous power values ​​and divide by the average internal resistance fluctuation to obtain the power loss factor for the time interval.

[0048] Specifically, the instantaneous power values ​​at all sampling moments within the time interval are first summed to obtain the total power loss value, which reflects the cumulative energy loss of the battery throughout the entire time interval. Then, this total power loss value is divided by the pre-calculated average internal resistance fluctuation. This mathematical operation integrates the cumulative effect of power loss and the dynamic characteristics of internal resistance changes, ultimately yielding the power loss factor for that time interval. This calculation method considers both the overall energy loss of the battery and the fluctuation characteristics of internal resistance changes, allowing the power loss factor to more comprehensively characterize the battery's loss state. The power loss factor calculated in this way can effectively distinguish the degree of power loss of the battery under different operating conditions. The larger the power loss factor, the more severe the energy loss of the battery and the more significant the performance degradation. This power loss factor calculation method based on total power loss and internal resistance fluctuation can accurately reflect the battery's energy conversion efficiency and internal loss characteristics, providing reliable power loss characteristic parameters for subsequent capacity prediction and helping to improve the accuracy of capacity prediction.

[0049] Based on the above embodiments, as an optional embodiment, in step 103: calculating the cycle aging factor of each time interval based on the sub-running data in each time interval, this step may also include the following steps: Step 307: For each time interval, determine the time interval between any two adjacent charge-discharge cycles in the sub-charge-discharge cycle sequence and the total number of cycles in the sub-charge-discharge cycle sequence.

[0050] Specifically, to accurately assess the cyclic usage intensity of a target battery within a specific time interval, it is necessary to simultaneously analyze the temporal characteristics and cumulative effects of charge-discharge cycles. First, a subsequence of charge-discharge cycle counts within this time interval is obtained. The cycle count data in the sequence is then iterated through to calculate the time intervals between two adjacent charge-discharge cycles. These time intervals reflect the frequency characteristics of the battery's charge-discharge cycles. Simultaneously, the total number of cycles within this time interval is calculated, reflecting the cumulative usage intensity of the battery's charge-discharge cycles. This analytical method considers both the temporal distribution characteristics of charge-discharge cycles and the cumulative effect of cycle counts. Shorter time intervals indicate more frequent charge-discharge cycles, while a larger total number of cycles indicates higher battery usage intensity. By simultaneously acquiring these two types of characteristic parameters, a comprehensive description of the battery's cyclic usage pattern within this time interval can be obtained, providing a sufficient data foundation for subsequent calculations of the cycle aging factor. This cycle characteristic analysis method based on time intervals and total number of cycles can effectively distinguish the battery's cyclic load under different usage modes, helping to more accurately assess the impact of charge-discharge cycles on battery performance degradation.

[0051] Step 308: Divide the total number of loops by the length of the time interval to obtain the loop frequency.

[0052] Specifically, the method first obtains the total number of cycles within the time interval and the length of the time interval, i.e., the time span from the start to the end of the time interval. Then, the total number of cycles is divided by the length of the time interval to obtain the cycle frequency. This calculation method standardizes the charge-discharge cycle characteristics within different time intervals, making the cycle intensity comparable across different time intervals. A higher cycle frequency indicates more frequent charge-discharge cycles per unit time, and a higher risk of performance degradation. By calculating the cycle frequency, the influence of differences in time interval length can be eliminated, resulting in a more generalized characteristic of cycle intensity. This cycle frequency calculation method based on the total number of cycles and the length of the time interval effectively reflects the time density characteristics of battery charge-discharge cycles, providing standardized cycle intensity parameters for subsequent calculations of cycle aging factors, and helping to more accurately assess the impact of charge-discharge cycles on battery life.

[0053] Step 309: Multiply the mean of each time interval by the cycle frequency to obtain the cycle aging factor for the time interval.

[0054] Specifically, the arithmetic mean of all adjacent charge-discharge cycle time intervals calculated within the given time interval is first obtained, reflecting the average time distribution characteristics of the charge-discharge cycles. Then, this average time interval is multiplied by the pre-calculated cycle frequency. This mathematical operation integrates the time distribution pattern of the cycles with the usage intensity, ultimately yielding the cycle aging factor for that time interval. This calculation method considers both the uniformity of the time distribution of charge-discharge cycles and the cumulative effect of cycle intensity, allowing the cycle aging factor to more comprehensively characterize the impact of charge-discharge cycles on battery performance. The cycle aging factor calculated in this way can effectively distinguish the degree of impact of charge-discharge cycles on battery life under different usage modes. A larger cycle aging factor indicates a more significant impact of charge-discharge cycles on battery performance degradation. This method of calculating the cycle aging factor based on the average time interval and cycle frequency accurately reflects the aging characteristics of the battery under different usage modes, providing reliable cycle aging characteristic parameters for subsequent capacity prediction and helping to improve the accuracy of capacity prediction.

[0055] Step 104: Obtain the battery capacity decay of the target battery in each time interval; combine the battery capacity decay, temperature influence factor, power loss factor and cycle aging factor in each time interval to generate the capacity decay curve of the target battery.

[0056] Here, battery capacity degradation refers to the actual capacity loss of the target battery within a specific time interval, specifically the reduction in battery capacity at the end of the time interval compared to the start of the time interval. In this embodiment, it can be understood as the degree of reduction in usable capacity caused by the combined effects of factors such as temperature changes, power loss, and charge-discharge cycles during battery operation. The larger this value, the more severe the battery performance degradation.

[0057] The capacity decay curve refers to a mathematical function curve describing the change in the capacity decay of a target battery over time. This curve reflects the quantitative relationship between battery capacity decay and temperature influence factors, power loss factors, and cycle aging factors. In the embodiments of this application, it can be understood as a multivariate function model based on historical data. Its independent variables include temperature change characteristics, power loss characteristics, and cycle usage characteristics, while the dependent variable is the battery capacity decay. This model can reflect the dynamic change process of battery capacity decay under different operating conditions.

[0058] Specifically, to establish a model relating the capacity decay of a target battery to various influencing factors, it is necessary to comprehensively analyze the capacity decay characteristics across different time intervals. First, the battery capacity decay amount within each time interval is obtained; this decay data reflects the actual degradation of battery capacity over time. Then, this capacity decay data is correlated with the temperature influence factor, power loss factor, and cycle aging factor for the corresponding time intervals. A mathematical relationship between capacity decay and these three influencing factors is established through data fitting, thereby generating the capacity decay curve for the target battery. This analytical method comprehensively considers the effects of the three main influencing factors—temperature change, power loss, and cycle aging—making the generated capacity decay curve more accurately reflect the inherent laws of battery performance degradation. The capacity decay curve established in this way can effectively describe the trend of battery capacity change over time and reflect the degree of influence of different operating conditions on capacity decay. This capacity decay curve generation method based on multi-factor comprehensive analysis can accurately characterize the dynamic process of battery capacity decay, providing a reliable mathematical model for battery capacity prediction and life assessment, and helping to improve the prediction accuracy of battery management systems.

[0059] Based on the above embodiments, as an optional embodiment, in step 104: combining the battery capacity decay amount, temperature influence factor, power loss factor and cycle aging factor of each time interval to generate the capacity decay curve of the target battery, this step may also include the following steps: Step 401: weighted summation of temperature influence factor, power loss factor and cycle aging factor of each time interval to obtain the decay coefficient of each time interval.

[0060] Specifically, the method first obtains the temperature influence factor, power loss factor, and cycle aging factor of the target battery within each time interval. Then, based on the weighted impact of these three factors on battery performance degradation, they are weighted and summed to obtain the degradation coefficient for each time interval. This calculation method takes into account the different contributions of various influencing factors to battery performance degradation, and by reasonably setting the weighting coefficients, it can more accurately reflect the relative importance of each factor. The degradation coefficient obtained by weighted summation can comprehensively characterize the various degradation effects experienced by the battery within a specific time interval. The larger the degradation coefficient, the more significant the comprehensive impact of battery performance degradation within that time interval. This degradation coefficient calculation method based on multi-factor weighted fusion can quantify and unify the effects of temperature change, power loss, and cycle aging, providing key feature parameters for subsequent capacity prediction model building and helping to improve the accuracy of capacity prediction.

[0061] Step 402: Multiply the battery capacity decay amount in each time interval by the corresponding decay coefficient to obtain the target decay amount in each time interval.

[0062] Specifically, to accurately characterize the actual battery capacity degradation under the influence of various factors, it is necessary to correlate the battery capacity degradation with the degradation coefficient. First, the battery capacity degradation and corresponding degradation coefficient of the target battery in each time interval are obtained. Then, the battery capacity degradation in each time interval is multiplied by its corresponding degradation coefficient to obtain the target degradation for each time interval. This calculation method, by combining the actually observed capacity degradation with the degradation coefficient reflecting the combined effect of various influencing factors, can more accurately depict the actual situation of battery capacity degradation. The target degradation obtained through this multiplication operation includes information on the actual battery capacity loss and incorporates the influence weights of factors such as temperature changes, power loss, and cycle aging, thus more comprehensively reflecting the true state of battery performance degradation. This target degradation calculation method based on capacity degradation and degradation coefficient can effectively correct the capacity degradation prediction model, improve the reliability of the prediction results, and provide more accurate data support for battery life assessment and management decisions.

[0063] Step 403: Generate the capacity decay curve of the target battery with each time interval as the horizontal axis and the target decay amount as the vertical axis.

[0064] Specifically, to demonstrate the temporal evolution of the target battery's capacity degradation, a functional relationship needs to be established between time intervals and the target degradation amount. First, in a two-dimensional coordinate system, each time interval is used as the horizontal axis, and the corresponding target degradation amount as the vertical axis. Then, each time interval and its corresponding target degradation amount are marked as discrete points on the coordinate system. These discrete points are connected through data fitting to ultimately generate the target battery's capacity degradation curve. This plotting method can visually present the dynamic process of battery capacity degradation, making the battery performance degradation trend clearer. By analyzing the morphological characteristics of the obtained capacity degradation curve, such as the slope, inflection points, and overall trend, a deeper understanding of the battery capacity degradation pattern can be gained, and the capacity status at future points in time can be predicted. This method of generating capacity degradation curves based on time intervals and target degradation amounts can provide battery management systems with intuitive performance evaluation data, helping to promptly detect battery performance anomalies, optimize battery usage strategies, and extend battery life.

[0065] Step 105: Based on the capacity decay curve, predict the battery capacity of the target battery in the next period.

[0066] Specifically, to predict the future capacity state of a target battery, extrapolation analysis based on the established capacity decay curve is required. First, a mathematical model of the capacity decay curve is constructed. Curve fitting yields a mathematical function expression describing the capacity decay law, which includes multiple influencing parameters such as temperature influence factors, power loss factors, and cycle aging factors. Then, based on the current usage status and operating environment of the target battery, the changing trends of various influencing factors in the next time period are predicted. These predicted values ​​are substituted into the mathematical model to calculate the predicted battery capacity for the next time period. This prediction method fully utilizes the capacity decay patterns inherent in historical data, quantifying the effects of various influencing factors through a mathematical model. This capacity decay curve-based prediction method can accurately assess the battery's capacity change trend under specific usage conditions, providing a reliable decision-making basis for the optimized control and preventative maintenance of the battery management system.

[0067] Based on the above embodiments, as an optional embodiment, step 105: predicting the battery capacity of the target battery in the next period based on the capacity change curve may further include the following steps: Step 501: obtaining the target decay amount and the current battery capacity in a preset number of time intervals in the capacity decay curve; determining the decay change rate based on the target decay amount in the preset number of time intervals.

[0068] Specifically, to accurately grasp the rate of change of target battery capacity degradation, it is necessary to analyze the capacity degradation situation over a recent period. First, a predetermined number of recent time intervals are selected from the capacity degradation curve, and the target degradation data and the current actual battery capacity value corresponding to these time intervals are obtained. Then, based on the target degradation data within these consecutive time intervals, the difference in target degradation between adjacent time intervals is calculated and divided by the time interval to obtain the rate of change of degradation for each time interval. These rates of change are then weighted and averaged to finally determine the rate of change of degradation reflecting the current capacity degradation trend. This calculation method, by analyzing data from several recent time intervals, can more accurately reflect the current degradation state and trend of the battery. The rate of change of degradation obtained in this way considers both the continuity of historical data and highlights the degradation characteristics of recent periods, thus better representing the real-time state of battery capacity degradation. This method of calculating the rate of change of degradation based on recent time interval data can promptly capture the dynamic characteristics of battery performance degradation, providing a more timely reference for subsequent capacity prediction and helping to improve the accuracy of predictions.

[0069] Step 502: Multiply the rate of change of the degradation amount by the target degradation amount in the last time interval of the capacity degradation curve to obtain the expected degradation amount of the target battery in the next time period.

[0070] Specifically, the target degradation amount for the last time interval in the capacity degradation curve is first obtained, reflecting the actual degradation state of the battery in the most recent time interval. Then, this target degradation amount is multiplied by a pre-calculated rate of change of degradation. This product operation predicts the expected degradation amount of the battery capacity in the next time interval. This calculation method considers both the current degradation state of the battery and the trend of capacity degradation, making the prediction results more reasonable. The expected degradation amount obtained in this way can accurately reflect the capacity loss of the battery in the next time interval; the larger the expected degradation amount, the faster the battery performance degrades. This method of calculating the expected degradation amount based on the latest target degradation amount and the rate of change of degradation can dynamically adapt to the changing characteristics of battery performance degradation, providing a reliable numerical basis for battery capacity prediction and helping to promptly detect battery anomalies and take corresponding maintenance measures.

[0071] Step 503: Subtract the predicted degradation from the current battery capacity to obtain the target battery capacity in the next time period.

[0072] Specifically, the current capacity of the battery is first obtained, reflecting its actual usable capacity at this moment. Then, the pre-calculated expected degradation amount is subtracted from this current capacity. This subtraction operation yields the predicted capacity for the next time period. This calculation method, using the difference between the current capacity and the expected degradation amount, intuitively reflects the battery capacity degradation process over time. The battery capacity for the next time period calculated in this way provides the battery management system with a clear capacity prediction value, helping the system to timely assess the battery's health and lifespan. This capacity prediction method based on current capacity and expected degradation accurately reflects the dynamic changes in battery capacity.

[0073] Referring to Figure 2, a battery capacity analysis and prediction system for a storage battery is provided according to an embodiment of this application. The system includes: a data acquisition module, an interval division module, a curve determination module, and a capacity prediction module. Specifically: The data acquisition module acquires operating data of the target storage battery within a preset time period, including voltage sequences, internal resistance sequences, temperature sequences, and charge / discharge cycle count sequences; the interval division module determines time intervals corresponding to multiple operating conditions based on the operating data of the target storage battery within the preset time period; the curve determination module calculates the temperature influence factor, power loss factor, and cycle aging factor for each time interval based on the sub-operating data within each time interval; calculates the battery capacity decay of the target storage battery within each time interval; and generates a capacity decay curve for the target storage battery by combining the battery capacity decay, temperature influence factor, power loss factor, and cycle aging factor for each time interval; the capacity prediction module predicts the battery capacity of the target storage battery in the next time period based on the capacity decay curve.

[0074] Based on the above embodiments, the interval division module is also used to determine, within a preset time period, the time point in the temperature sequence where the temperature exceeds the temperature threshold as the operating condition transition point, the time point in the voltage sequence where the voltage exceeds the voltage threshold as the operating condition transition point, and the time point in the internal resistance sequence where the internal resistance exceeds the internal resistance threshold as the operating condition transition point; and divide the preset time period into multiple time intervals corresponding to multiple operating conditions with each operating condition transition point as the boundary.

[0075] Based on the above embodiments, the curve determination module is also used to, for each time interval, take the difference between any two adjacent temperature sampling values ​​in the sub-temperature sequence as the temperature fluctuation amplitude; take the ratio of the difference between the first and last temperature sampling values ​​in the sub-temperature sequence to the length of the time interval as the fluctuation coefficient; calculate the mean of each temperature fluctuation amplitude, and divide the mean by the fluctuation coefficient to obtain the temperature influence factor of the time interval.

[0076] Based on the above embodiments, the curve determination module is also used to calculate the instantaneous power value at each sampling time interval based on the sampling values ​​of the sub-internal resistance sequence and the sub-voltage sequence at multiple sampling times; calculate the mean of the difference between any two adjacent internal resistance sampling values ​​in the sub-internal resistance sequence to obtain the mean internal resistance fluctuation; and calculate the sum of each instantaneous power value divided by the mean internal resistance fluctuation to obtain the power loss factor of the time interval.

[0077] Based on the above embodiments, the curve determination module is also used to determine the time interval between any two adjacent charge-discharge cycles in the sub-charge-discharge cycle sequence and the total number of cycles in the sub-charge-discharge cycle sequence for each time interval; divide the total number of cycles by the length of the time interval to obtain the cycle frequency; and multiply the mean of each time interval by the cycle frequency to obtain the cycle aging factor of the time interval.

[0078] Based on the above embodiments, the curve determination module is also used to perform weighted summation of the temperature influence factor, power loss factor and cycle aging factor for each time interval to obtain the attenuation coefficient for each time interval; multiply the battery capacity attenuation amount for each time interval by the corresponding attenuation coefficient to obtain the target attenuation amount for each time interval; and generate the capacity attenuation curve of the target battery with each time interval as the horizontal axis and the target attenuation amount as the vertical axis.

[0079] Based on the above embodiments, the capacity prediction module is also used to obtain the target degradation amount and the current battery capacity in a preset number of time intervals in the capacity degradation curve; determine the degradation change rate based on the target degradation amount in the preset number of time intervals; multiply the degradation change rate by the target degradation amount in the last time interval in the capacity degradation curve to obtain the expected degradation amount of the target battery in the next period; and subtract the predicted degradation amount from the current battery capacity to obtain the battery capacity of the target battery in the next period.

[0080] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0081] This application also discloses an electronic device. Referring to FIG3, FIG3 is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0082] The communication bus 302 is used to enable communication between these components.

[0083] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0084] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0085] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0086] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Referring to FIG3, the memory 305, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a battery capacity analysis and prediction method.

[0087] In the electronic device 300 shown in Figure 3, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a battery capacity analysis and prediction method. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0088] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0089] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0090] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0093] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0094] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A method of predicting battery capacity analysis of a storage battery, characterized by, include: The operating data of the target battery within a preset time period is obtained, including voltage sequence, internal resistance sequence, temperature sequence, and charge / discharge cycle count sequence. Based on the operating data of the target battery during the preset time period, time intervals corresponding to multiple operating conditions are determined; Based on the sub-operational data within each of the aforementioned time intervals, the temperature influence factor, power loss factor, and cycle aging factor for each of the aforementioned time intervals are calculated. When the sub-operation data includes a sub-temperature sequence, the step of calculating the temperature influence factor for each time interval based on the sub-operation data within each time interval includes: for each time interval, taking the difference between any two adjacent temperature sampling values ​​in the sub-temperature sequence as the temperature fluctuation amplitude; taking the ratio of the difference between the first and last temperature sampling values ​​in the sub-temperature sequence to the length of the time interval as the fluctuation coefficient; calculating the mean of each temperature fluctuation amplitude, and dividing the mean by the fluctuation coefficient to obtain the temperature influence factor for the time interval; When the sub-operation data includes a sub-internal resistance sequence and a sub-voltage sequence, the step of calculating the power loss factor for each time interval based on the sub-operation data within each time interval includes: for each time interval, calculating the instantaneous power value at each sampling time based on the sampled values ​​of the sub-internal resistance sequence and the sub-voltage sequence at multiple sampling times; calculating the mean of the difference between any two adjacent internal resistance sampled values ​​in the sub-internal resistance sequence to obtain the mean internal resistance fluctuation; and calculating the sum of each instantaneous power value divided by the mean internal resistance fluctuation to obtain the power loss factor for the time interval. When the sub-running data includes a sub-charge-discharge cycle number sequence, the step of calculating the cycle aging factor for each time interval based on the sub-running data within each time interval includes: for each time interval, determining the time interval between any two adjacent charge-discharge cycles in the sub-charge-discharge cycle number sequence and the total number of cycles in the sub-charge-discharge cycle number sequence; dividing the total number of cycles by the length of the time interval to obtain the cycle frequency; and multiplying the mean of each time interval by the cycle frequency to obtain the cycle aging factor for the time interval. Obtain the battery capacity decay of the target battery in each of the time intervals; By combining the battery capacity decay rate, temperature influence factor, power loss factor, and cycle aging factor for each of the aforementioned time intervals, a capacity decay curve for the target battery is generated; and Based on the capacity decay curve, the battery capacity of the target battery in the next time period is predicted.

2. The method of claim 1, wherein The step of determining time intervals corresponding to multiple operating conditions based on the operating data of the target battery within the preset time period includes: Within the preset time period, the time points in the temperature sequence where the temperature exceeds a temperature threshold are determined as operating condition transition points, the time points in the voltage sequence where the voltage exceeds a voltage threshold are determined as operating condition transition points, and the time points in the internal resistance sequence where the internal resistance exceeds an internal resistance threshold are determined as operating condition transition points; and Using the aforementioned operating condition transition points as boundaries, the preset time period is divided into time intervals corresponding to the multiple operating conditions.

3. The method of claim 1, wherein the method further comprises: The process of generating the capacity decay curve of the target battery by combining the battery capacity decay, temperature influence factor, power loss factor, and cycle aging factor for each time interval includes: The attenuation coefficient for each time interval is obtained by weighted summation of the temperature influence factor, power loss factor, and cycle aging factor for each time interval. Multiply the battery capacity degradation amount for each time interval by the corresponding degradation coefficient to obtain the target degradation amount for each time interval; and Using each of the aforementioned time intervals as the horizontal axis and the target attenuation amount as the vertical axis, a capacity attenuation curve for the target battery is generated.

4. The method of claim 1, wherein the method further comprises: determining a state of charge of the battery; and determining a state of health of the battery. The method of predicting the battery capacity of the target battery in the next time period based on the capacity decay curve includes: Obtain the target degradation amount and the current battery capacity for a preset number of time intervals in the capacity degradation curve; Based on the target attenuation amount for the preset number of time intervals, determine the rate of change of attenuation amount; Multiplying the rate of change of the degradation amount by the target degradation amount in the last time interval of the capacity degradation curve yields the expected degradation amount of the target battery in the next time period; and The target battery capacity in the next time period is obtained by subtracting the expected degradation from the current battery capacity.

5. A battery capacity analysis prediction system for a storage battery, characterized by comprising: The system includes: The data acquisition module is configured to acquire the operating data of the target battery within a preset time period, the operating data including voltage sequence, internal resistance sequence, temperature sequence and charge / discharge cycle number sequence; The interval division module is configured to determine time intervals corresponding to multiple operating conditions based on the operating data of the target battery within the preset time period; The curve determination module is configured to calculate the temperature influence factor, power loss factor, and cycle aging factor for each time interval based on the sub-operation data within each time interval; obtain the battery capacity decay of the target battery within each time interval; and generate the capacity decay curve of the target battery by combining the battery capacity decay, temperature influence factor, power loss factor, and cycle aging factor for each time interval. The capacity prediction module is configured to predict the battery capacity of the target battery in the next time period based on the capacity decay curve. The curve determination module is further configured to: when the sub-running data includes a sub-temperature sequence, for each time interval, take the difference between any two adjacent temperature sampling values ​​in the sub-temperature sequence as the temperature fluctuation amplitude; take the ratio of the difference between the first and last temperature sampling values ​​in the sub-temperature sequence to the length of the time interval as the fluctuation coefficient; calculate the mean of each temperature fluctuation amplitude, and divide the mean by the fluctuation coefficient to obtain the temperature influence factor of the time interval; The curve determination module is further configured to: when the sub-operation data includes a sub-internal resistance sequence and a sub-voltage sequence, for each time interval, calculate the instantaneous power value at each sampling time based on the sampled values ​​of the sub-internal resistance sequence and the sub-voltage sequence at multiple sampling times; calculate the mean of the difference between any two adjacent internal resistance sampled values ​​in the sub-internal resistance sequence to obtain the mean internal resistance fluctuation; and calculate the sum of each instantaneous power value divided by the mean internal resistance fluctuation to obtain the power loss factor of the time interval. The curve determination module is further configured to: when the sub-running data includes a sub-charge-discharge cycle number sequence, for each time interval, determine the time interval between any two adjacent charge-discharge cycle numbers in the sub-charge-discharge cycle number sequence and the total number of cycles in the sub-charge-discharge cycle number sequence; divide the total number of cycles by the length of the time interval to obtain the cycle frequency; multiply the mean of each time interval by the cycle frequency to obtain the cycle aging factor of the time interval.

6. An electronic device, comprising: The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the battery capacity analysis and prediction method for a storage battery as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the battery capacity analysis and prediction method for a storage battery as described in any one of claims 1-4.

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