Battery capacity analysis and prediction method and system of storage battery

Abstract

The invention discloses a battery capacity analysis and prediction method and system of a storage battery, and relates to the technical field of data processing. The method comprises the following steps: acquiring operation data of a target storage battery in a preset time period, wherein the operation data comprises a voltage sequence, an internal resistance sequence, a temperature sequence and a charge-discharge cycle number sequence; determining a time interval corresponding to the plurality of operation conditions according to the operation data; calculating a temperature influence factor, a power loss factor and a cyclic aging factor of each time interval based on the sub-operation data in each time interval; receiving the battery capacity attenuation amount of the target storage battery in each time interval; generating a capacity attenuation curve of the target storage battery by combining the battery capacity attenuation amount, the temperature influence factor, the power loss factor and the cyclic aging factor of each time interval; and predicting the battery capacity in the next time period based on the capacity change curve. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of battery capacity analysis and prediction of the storage battery is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, system, electronic device and storage medium for analyzing and predicting battery capacity of a storage battery. Background Art

[0002] With the popularization of renewable energy and electric vehicles, batteries, as key energy storage devices, their performance reliability and life prediction play a vital role in ensuring system efficiency and economic benefits.

[0003] At present, the existing battery capacity analysis and prediction methods mainly use fixed models or evaluation standards to process the collected data to predict the battery capacity. However, in actual applications, different batteries exhibit different characteristics under various working conditions, and these differences will become more significant over time. Relying only on fixed models or evaluation standards to analyze battery data often ignores the differences between individual batteries that increase over time, resulting in errors in battery capacity prediction, thereby reducing the accuracy of battery capacity analysis and prediction. Summary of the invention

[0004] The present application provides a method and system for analyzing and predicting the battery capacity of a storage battery, which has the effect of improving the accuracy of analyzing and predicting the battery capacity of the storage battery.

[0005] In a first aspect, the present application provides a method for analyzing and predicting battery capacity of a battery, comprising: Acquire operating data of a target battery within a preset period of time, wherein the operating data includes a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge-discharge cycle number sequence; Determining time intervals corresponding to a plurality of operating conditions according to the operating data of the target battery within the preset time period; Calculating the temperature influence factor, power loss factor and cycle aging factor of each time interval based on the sub-operation data in each time interval; receiving a battery capacity attenuation of the target battery in each of the time intervals; Generate a capacity decay curve of the target battery by combining the battery capacity decay amount, temperature influence factor, power loss factor and cycle aging factor in each of the time intervals; Based on the capacity variation curve, the battery capacity of the target battery in the next period is predicted.

[0006] In a second aspect of the present application, a battery capacity analysis and prediction system for a storage battery is provided.

[0007] A data acquisition module, used to acquire the operating data of the target battery within a preset period of time, wherein the operating data includes a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge and discharge cycle number sequence; An interval division module, used for determining time intervals corresponding to a plurality of operating conditions according to the operating 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 of each time interval based on the sub-operation data in each time interval; receiving the battery capacity attenuation of the target battery in each time interval; and combining the battery capacity attenuation, temperature influence factor, power loss factor and cycle aging factor of each time interval to generate a capacity attenuation curve of the target battery; The capacity prediction module is used to predict the battery capacity of the target battery in the next period based on the capacity change curve.

[0008] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a method for analyzing and predicting the battery capacity of a storage battery when loaded and executed by the processor.

[0009] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a battery capacity analysis and prediction method for a storage battery.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical scheme, the operating data of the target battery such as the voltage sequence, internal resistance sequence, temperature sequence and charge and discharge cycle sequence in the preset period are obtained, and the accurate monitoring of the working state of the specific individual battery is realized. By dividing the preset period into multiple time intervals corresponding to different operating conditions, the method can capture the unique performance of the battery under different working conditions. On this basis, the temperature influence factor, power loss factor and cycle aging factor of each time interval are calculated respectively, and the personalized quantitative characterization of the influencing factors of the capacity decay of the specific battery is realized. By obtaining the battery capacity decay amount in each time interval and correlating it with the corresponding influencing 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 operation data of the individual battery can avoid the limitation that the fixed model is difficult to adapt to the individual differences of the battery. By establishing a mathematical relationship adapted to the individual characteristics of the battery, it can not only accurately predict the trend of battery capacity change, but also dynamically adapt to the evolution characteristics of battery performance over time, solve the prediction error problem caused by ignoring the individual differences of the battery, and further improve the accuracy of the battery capacity analysis and prediction of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of a battery capacity analysis and prediction method of a storage battery provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of a battery capacity analysis and prediction system for a storage battery provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The present application embodiment provides a battery capacity analysis and prediction method for a battery. In one embodiment, please refer to Figure 1 , Figure 1 The present invention provides a flow chart of a battery capacity analysis and prediction method for a storage battery provided in an embodiment of the present invention. The method can be implemented by a computer program, which can be integrated into an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run in a battery capacity analysis and prediction system for a storage battery based on a von Neumann system. Specifically, the method can include the following steps: Step 101: Acquire operating data of a target battery within a preset period of time, the operating data including a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge-discharge cycle number sequence.

[0017] The operating 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 embodiment of the present application, it can be understood as a voltage sequence collected by a voltage sampling device, an internal resistance sequence collected by an internal resistance measuring device, a temperature sequence collected by a temperature sensor, and a charge and discharge cycle number sequence recorded by a charge and discharge management system.

[0018] Specifically, a voltage sampling device is set at the positive and negative ends of the target battery, and the voltage data is collected by the voltage sampling device according to a preset sampling time interval, wherein the sampling time interval can be set to 1 minute, thereby obtaining a voltage sequence; an internal resistance measuring device is set inside the target battery, and the internal resistance measuring device collects internal resistance data according to a preset sampling time interval to obtain an internal resistance sequence; a temperature sensor is set on the surface or inside the target battery, and the temperature data is collected by the temperature sensor according to a preset sampling time interval to obtain a temperature sequence; the number of charge and discharge cycles of the target battery is recorded by the charge and discharge management system to obtain a charge and discharge cycle number sequence. Among them, the preset time period can be a continuous period of time such as a week or a month, or it can be a combination of multiple discrete time segments. These operating data reflect the performance change characteristics of the target battery during actual use from different dimensions: the voltage sequence reflects the charge and discharge state and the remaining capacity state of the battery, the internal resistance sequence reflects the internal loss state of the battery, the temperature sequence reflects the thermal characteristics of the battery, and the charge and discharge cycle number sequence reflects the use intensity of the battery. By collecting these multi-dimensional operating data, the performance characteristics of the battery under the actual working environment can be fully obtained, providing a data basis for the subsequent accurate prediction of the battery capacity. This collection method based on actual operating data avoids the errors that may be caused by using a unified fixed model, can better adapt to the differences between different individual batteries, and improve the accuracy of capacity prediction.

[0019] Step 102: Determine time intervals corresponding to a plurality of operating conditions according to the operating data of the target battery within a preset period of time.

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

[0021] The time interval refers to a period of time during which the target battery operates continuously in a relatively stable working state. In the embodiment of the present application, it can be understood as a plurality of time periods divided by the operating condition conversion point, wherein the operating condition conversion point includes the time point when the temperature exceeds the temperature threshold in the temperature sequence, the time point when the voltage exceeds the voltage threshold in the voltage sequence, and the time point when the internal resistance exceeds the internal resistance threshold in the internal resistance sequence.

[0022] Specifically, the temperature threshold, voltage threshold and internal resistance threshold are first set, and these thresholds can be determined according to the technical specifications of the battery and the actual application scenarios. Then, within the preset period, by judging whether the temperature data in the temperature sequence exceeds the temperature threshold, the time point of the temperature abnormality is determined as the operating condition conversion point; by judging whether the voltage data in the voltage sequence exceeds the voltage threshold, the time point of the voltage abnormality is determined as the operating condition conversion point; by judging whether the internal resistance data in the internal resistance sequence exceeds the internal resistance threshold, the time point of the internal resistance abnormality is determined as the operating condition conversion point. With these operating condition conversion points as the boundaries, the preset period is divided into time intervals corresponding to multiple operating conditions. In each time interval, the working state of the battery is relatively stable and has similar performance change characteristics. This operating condition division method based on multi-parameter thresholds can accurately identify changes in the working state of the battery, making subsequent performance analysis more targeted. By independently analyzing the battery performance under different working conditions, the performance attenuation law of the battery under various working conditions can be more accurately described, thereby improving the accuracy of capacity prediction.

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

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

[0025] The voltage sequence refers to a set of time-series voltage data obtained by continuously sampling the terminal voltage of the target battery at a preset sampling time interval. In the embodiment of the present application, it can be understood as a data sequence consisting of voltage data collected at a fixed sampling time interval within a preset period of time by a voltage sampling device set at the positive and negative terminals of the target battery.

[0026] The internal resistance sequence refers to a time series internal resistance data set obtained by continuously sampling the internal impedance of the target battery at a preset sampling time interval. In the embodiment of the present application, it can be understood as a data sequence consisting of internal resistance data collected at a fixed sampling time interval within a preset period of time by an internal resistance measurement device disposed inside the target battery.

[0027] The operating condition conversion point refers to the time point when the operating state of the target battery changes significantly. In the embodiment of the present application, it can be understood as the time point when the temperature data in the temperature sequence exceeds the upper temperature threshold or is lower than the lower temperature threshold, the time point when the voltage data in the voltage sequence exceeds the upper voltage threshold or is lower than the lower voltage threshold, and the time point when the internal resistance data in the internal resistance sequence exceeds the upper internal resistance threshold or is lower than the lower internal resistance threshold.

[0028] Specifically, firstly, a temperature threshold is set based on the technical specifications of the battery, and the temperature threshold includes an upper temperature threshold and a lower temperature threshold. By traversing each temperature data in the temperature sequence, when it is detected that the temperature data exceeds the upper temperature threshold or is lower than the lower temperature threshold, the corresponding time point is recorded as a temperature condition conversion point; secondly, a voltage threshold is set, and the voltage threshold includes an upper voltage threshold and a lower voltage threshold. By traversing each voltage data in the voltage sequence, when it is detected that the voltage data exceeds the upper voltage threshold or is lower than the lower voltage threshold, the corresponding time point is recorded as a voltage condition conversion point; similarly, an internal resistance threshold is set, and the internal resistance threshold includes an upper resistance threshold and a lower resistance threshold. By traversing each internal resistance data in the internal resistance sequence, when it is detected that the internal resistance data exceeds the upper resistance threshold or is lower than the lower resistance threshold, the corresponding time point is recorded as an internal resistance condition conversion point. These condition conversion points identify the moment when the battery working state changes significantly. The temperature condition conversion point reflects the change of the battery thermal environment, the voltage condition conversion point reflects the change of the charge and discharge state, and the internal resistance condition conversion point reflects the change of the internal characteristics of the battery. Through this multi-parameter threshold judgment method, the changing characteristics of the battery working state can be fully captured, providing a basis for the subsequent working condition division.

[0029] Step 202: Divide the preset time period into time intervals corresponding to a plurality of operating conditions based on the transition points of each operating condition.

[0030] Specifically, first, all the temperature condition conversion points, voltage condition conversion points and internal resistance condition conversion points within the preset time period are sorted in chronological order to form a condition conversion point sequence. Then, the preset time period is divided into multiple continuous time intervals with two adjacent condition conversion points as the start and end time. In each time interval, the temperature, voltage and internal resistance of the target battery are all within their corresponding threshold ranges, indicating that the battery is in a relatively stable working state within the time interval. This time interval division method based on multi-parameter condition conversion points makes each time interval correspond to a specific operating condition, which can accurately reflect the performance change characteristics of the battery under different working conditions. Through this division method, the complex working process of the battery in the entire preset time period can be decomposed into multiple stable condition stages, which is convenient for independent analysis of the performance changes under each condition, thereby improving the accuracy of subsequent capacity prediction. At the same time, this division method also provides a clear time range for calculating the temperature influence factor, power loss factor and cycle aging factor under different conditions, making the factor calculation more accurate and reliable.

[0031] Step 103: Calculate the temperature influence factor, power loss factor and cycle aging factor of each time interval based on the sub-operation data in each time interval.

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

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

[0034] The power loss factor refers to a quantitative indicator that characterizes the degree of energy loss of the target battery due to internal impedance and the charging and discharging process within a specific time interval. In the embodiment of the present 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. The factor is determined by comprehensively analyzing the product effect of voltage change and internal resistance change, and reflects the power loss of the battery during the charging and discharging process.

[0035] The cycle aging factor refers to a quantitative indicator that characterizes the degree of performance degradation of the target battery due to the charge and discharge cycle process within a specific time interval. In the embodiment of the present application, it can be understood as a comprehensive factor reflecting the cycle intensity of the battery calculated based on the subsequence of the number of charge and discharge cycles. The factor is determined by analyzing the depth and frequency of the charge and discharge cycle, and comprehensively considers the different degrees of influence of deep cycles and shallow cycles on the battery life.

[0036] Specifically, in order to comprehensively evaluate the performance changes of the target battery under different working conditions, it is necessary to calculate the corresponding influencing factors based on the sub-operating data in each time interval. First, the sub-operating data corresponding to each time interval is extracted from the operating data of the preset period, including the temperature sub-sequence, voltage sub-sequence, internal resistance sub-sequence and the charge and discharge cycle number sub-sequence. Then, the temperature influence factor is calculated based on the temperature sub-sequence, which reflects the degree of influence of temperature on battery performance. The calculation process takes into account the deviation of temperature from the standard operating temperature and the influence of temperature fluctuation; the power loss factor is calculated based on the voltage sub-sequence and the internal resistance sub-sequence, which characterizes the internal loss degree of the battery during the charge and discharge process. The calculation process comprehensively considers the joint influence of voltage change and internal resistance change on battery power loss; the cycle aging factor is calculated based on the charge and discharge cycle number sub-sequence, which reflects the degree of influence of charge and discharge cycle on battery performance attenuation. The calculation process takes into account the influence of cycle depth and cycle frequency. This analysis method based on multiple influencing factors can quantify the influencing factors of battery performance attenuation from different angles. Through the comprehensive analysis of these factors, the performance change law of the battery under various working conditions can be more accurately described. At the same time, the calculation results of these influencing factors provide reliable characteristic parameters for the subsequent establishment of a battery capacity prediction model, which helps to improve the accuracy and reliability of capacity prediction.

[0037] Based on the above embodiment, as an optional embodiment, in step 103: calculating the temperature influence factor of each time interval based on the sub-operation data in 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 used as the temperature fluctuation amplitude.

[0038] Specifically, in order to accurately evaluate the impact of temperature changes on the performance of the target battery within a specific time interval, it is necessary to quantitatively analyze the temperature fluctuation. First, a temperature subsequence within the time interval is obtained, which contains a series of temperature data collected at a preset sampling time interval. Then, the temperature data in the temperature subsequence is traversed, and the difference between two adjacent temperature sampling values ​​is calculated, and these differences are defined as the temperature fluctuation amplitude. This calculation method can reflect the degree of temperature change over time. The larger the temperature fluctuation amplitude, the more drastic the temperature change and the more significant the impact on battery performance. By calculating the temperature fluctuation amplitude, the dynamic characteristics of temperature change can be described in more detail, rather than just focusing on the absolute value of temperature. This temperature fluctuation analysis method based on the difference between adjacent sampling points can effectively capture the instantaneous change characteristics of temperature, provide more comprehensive temperature change information for the subsequent calculation of temperature influence factors, and thus improve the accuracy of temperature influence factor calculation. At the same time, this method is also convenient for identifying periods of drastic temperature fluctuations, which helps to analyze the influence of temperature fluctuations on battery performance.

[0039] Step 302: taking the ratio of the difference between the first temperature sampling value and the last temperature sampling value in the sub-temperature sequence to the length of the time interval as the fluctuation coefficient.

[0040] Specifically, in order to further quantify the temperature change trend of the target battery in a specific time interval, it is necessary to introduce a fluctuation coefficient to characterize the overall rate of temperature change. First, the first temperature sampling value and the last temperature sampling value are obtained from the temperature subsequence, and the difference between the two temperature values ​​is calculated. The difference reflects the overall change in temperature in the entire time interval; then the length of the time interval is obtained, that is, the time span from the start time to the end time of the time interval; finally, the temperature difference is divided by the length of the time interval to obtain the fluctuation coefficient. This calculation method can reflect the average rate of change of temperature in the entire time interval. The larger the fluctuation coefficient, the faster the temperature changes and the more significant the impact on battery performance. By introducing the fluctuation coefficient and combining the aforementioned temperature fluctuation amplitude, the temperature change characteristics can be described from two dimensions: local fluctuation and overall trend, providing more complete temperature change information for the calculation of temperature influencing factors. This fluctuation coefficient calculation method based on the difference between the first and last temperature and the length of the time interval can effectively reflect the continuity and directionality of temperature changes, and help to more accurately evaluate the long-term impact of temperature changes on battery performance.

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

[0042] Specifically, in order to comprehensively evaluate the overall impact of temperature changes on the target battery within a specific time interval, it is necessary to organically combine the local and global features of temperature fluctuations. First, all the temperature fluctuation amplitudes calculated within the time interval are arithmetic averaged to obtain the mean of the temperature fluctuation amplitude, which reflects the average intensity of the temperature fluctuation; then this mean is divided by the pre-calculated fluctuation coefficient, and the instantaneous change characteristics and continuous change characteristics of the temperature are combined through this mathematical operation, and finally the temperature influence factor of the time interval is obtained. This calculation method takes into account both the severity of the temperature change and the continuity of the temperature change, so that the temperature influence factor can more comprehensively characterize the impact of temperature change on battery performance. The temperature influence factor calculated in this way can effectively distinguish the degree of influence of temperature change on battery performance under different working conditions. The larger the temperature influence factor, the more significant the impact of temperature change on battery performance. This temperature influence factor calculation method based on the mean of temperature fluctuation amplitude and fluctuation coefficient can accurately reflect the comprehensive impact of temperature change on battery performance, provide reliable temperature characteristic parameters for subsequent capacity prediction, and help improve the accuracy of capacity prediction.

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

[0044] Specifically, in order to accurately evaluate the power loss of the target battery in a specific time interval, it is necessary to calculate the instantaneous power value at each sampling moment based on the internal resistance and voltage data. First, the internal resistance subsequence and voltage subsequence in the time interval are obtained. The data in these two sequences have the same sampling time interval and corresponding sampling time; then, at each sampling moment, the internal resistance sampling value at that moment is multiplied by the voltage sampling value to obtain the instantaneous power value at that sampling moment. This calculation method is based on the power loss principle of the battery. The product of internal resistance and voltage is used to characterize the energy loss of the battery during the charging and discharging process. The larger the instantaneous power value, the more serious 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 working conditions. This instantaneous power calculation method based on internal resistance and voltage can accurately characterize the power loss characteristics of the battery, provide basic data for the subsequent calculation of the power loss factor, and help analyze the characteristics of battery performance degradation.

[0045] Step 305: Calculate the average of the differences between any two adjacent internal resistance sampling values ​​in the sub-internal resistance sequence to obtain the internal resistance fluctuation average.

[0046] Specifically, in order to evaluate the overall trend of the internal resistance change of the target battery within a specific time interval, it is necessary to perform a statistical analysis on the fluctuation of the internal resistance. First, the internal resistance subsequence within the time interval is obtained, the internal resistance data in the sequence is traversed, and the difference between two adjacent internal resistance sampling values ​​is calculated to obtain a series of internal resistance fluctuation values; then these internal resistance fluctuation values ​​are arithmetic averaged to obtain the internal resistance fluctuation mean. This calculation method can reflect the average degree of internal resistance change. The larger the internal resistance fluctuation mean, the more drastic the change in the internal impedance of the battery, and the more unstable its power loss characteristics. By calculating the internal resistance fluctuation mean, the influence of random fluctuations in the internal resistance change can be eliminated, and a more representative internal resistance change feature can be obtained. This internal resistance fluctuation analysis method based on the difference between adjacent sampling values ​​can effectively characterize the dynamic change characteristics of the internal impedance of the battery, provide reliable internal resistance change parameters for the subsequent calculation of the power loss factor, and help to more accurately evaluate the power loss condition of the battery.

[0047] Step 306: Calculate the sum of each instantaneous power value and divide it by the mean value of the internal resistance fluctuation to obtain the power loss factor of the time interval.

[0048] Specifically, first, the instantaneous power values ​​at all sampling moments in the time interval are accumulated to obtain the total power loss value, which reflects the cumulative energy loss of the battery in the entire time interval; then the total power loss value is divided by the pre-calculated mean value of the internal resistance fluctuation. Through this mathematical operation, the cumulative effect of power loss and the dynamic change characteristics of the internal resistance are combined to finally obtain the power loss factor of the time interval. This calculation method takes into account both the overall energy loss of the battery and the fluctuation characteristics of the internal resistance change, so that the power loss factor can more comprehensively characterize the loss state of the battery. The power loss factor calculated in this way can effectively distinguish the degree of power loss of the battery under different working conditions. The larger the power loss factor, the more serious 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 energy conversion efficiency and internal loss characteristics of the battery, provide reliable power loss characteristic parameters for subsequent capacity prediction, and help improve the accuracy of capacity prediction.

[0049] Based on the above embodiment, as an optional embodiment, in step 102: calculating the cyclic aging factor of each time interval based on the sub-operation 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 and discharge cycle numbers in the sub-sequence of charge and discharge cycle numbers and the total number of cycles in the sub-sequence of charge and discharge cycle numbers.

[0050] Specifically, in order to accurately evaluate the cycle intensity of the target battery in a specific time interval, it is necessary to analyze the time characteristics and cumulative effects of the charge and discharge cycle at the same time. First, obtain the subsequence of the number of charge and discharge cycles in the time interval, traverse the cycle number data in the sequence, and calculate the time interval between two adjacent charge and discharge cycles. These time intervals reflect the frequency characteristics of the battery charge and discharge cycle; at the same time, count the total number of cycles in the time interval, which reflects the cumulative use intensity of the battery charge and discharge cycle. This analysis method not only pays attention to the time distribution characteristics of the charge and discharge cycle, but also considers the cumulative effect of the number of cycles. The shorter the time interval, the more frequent the charge and discharge cycle, and the greater the total number of cycles, the higher the battery usage intensity. By simultaneously obtaining these two types of characteristic parameters, the cycle usage mode of the battery in the time interval can be fully described, providing a sufficient data basis for the subsequent calculation of the cycle aging factor. This cycle characteristic analysis method based on time interval and total number of cycles can effectively distinguish the cycle load of the battery under different usage modes, which helps to more accurately evaluate the impact of charge and discharge cycles on battery performance degradation.

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

[0052] Specifically, first obtain the total number of cycles obtained within the time interval, and the length of the time interval, that is, the time span from the start time to the end time of the time interval; then divide the total number of cycles by the length of the time interval to obtain the cycle frequency. This calculation method can standardize the charge and discharge cycle conditions within different time length intervals, so that the cycle intensity of different time intervals is comparable. The higher the cycle frequency, the more frequent the battery charge and discharge cycle per unit time, and the higher the risk of performance degradation. By calculating the cycle frequency, the impact of the difference in time interval length can be eliminated, and a more universal cycle intensity characteristic can be obtained. This cycle frequency calculation method based on the total number of cycles and the length of the time interval can effectively reflect the time density characteristics of the battery charge and discharge cycle, provide standardized cycle intensity parameters for the subsequent calculation of the cycle aging factor, and help to more accurately evaluate the impact of the charge and discharge cycle on the battery life.

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

[0054] Specifically, firstly, the arithmetic average of all adjacent charge and discharge cycle time intervals calculated within the time interval is performed to obtain the mean time interval, which reflects the average time distribution characteristics of the charge and discharge cycle; then the mean time interval is multiplied by the pre-calculated cycle frequency, and the time distribution law and usage intensity of the cycle are integrated through this mathematical operation, and finally the cycle aging factor of the time interval is obtained. This calculation method takes into account both the uniformity of the time distribution of the charge and discharge cycle and the cumulative effect of the cycle intensity, so that the cycle aging factor can more comprehensively characterize the impact of the charge and discharge cycle on the battery performance. The cycle aging factor calculated in this way can effectively distinguish the degree of influence of the charge and discharge cycle on the battery life under different usage modes. The larger the cycle aging factor, the more significant the influence of the charge and discharge cycle on the battery performance degradation. This cycle aging factor calculation method based on the mean time interval and the cycle frequency can accurately reflect the aging characteristics of the battery under different cycle usage modes, provide reliable cycle aging characteristic parameters for subsequent capacity prediction, and help improve the accuracy of capacity prediction.

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

[0056] The battery capacity attenuation refers to the actual capacity loss value of the target battery within a specific time interval, which is specifically expressed as the reduction in the battery capacity at the end of the time interval relative to the battery capacity at the start of the time interval. In the embodiment of the present application, it can be understood as the degree of reduction in the available capacity of the battery due to the combined effects of factors such as temperature changes, power loss, and charge and discharge cycles during operation. The larger the value, the more serious the battery performance attenuation.

[0057] The capacity decay curve refers to a mathematical function curve that describes the law of change of the capacity decay of the target battery over time. The curve reflects the quantitative relationship between the battery capacity decay and the temperature influence factor, the power loss factor and the cycle aging factor. In the embodiment of the present application, it can be understood as a multivariable function model based on historical data, whose independent variables include temperature change characteristics, power loss characteristics and cycle use characteristics, and the dependent variable is the battery capacity decay. The model can reflect the dynamic change process of battery capacity decay under different working conditions.

[0058] Specifically, in order to establish a relationship model between the capacity decay of the target battery and each influencing factor, it is necessary to comprehensively analyze the capacity decay characteristics of each time interval. First, the battery capacity decay of the target battery in each time interval is obtained. These decay data reflect the actual degradation of the battery capacity over time; then these capacity decay data are correlated with the temperature influencing factor, power loss factor and cycle aging factor of the corresponding time interval. The mathematical relationship between the capacity decay and the three influencing factors is established by data fitting, so as to generate the capacity decay curve of the target battery. This analysis method comprehensively considers the effects of the three main influencing factors of temperature change, power loss and cycle aging, so that the generated capacity decay curve can more accurately reflect the inherent law of battery performance decay. The capacity decay curve established in this way can effectively describe the changing trend of battery capacity over time, and can reflect the degree of influence of different working 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, provide a reliable mathematical model for battery capacity prediction and life assessment, and help improve the prediction accuracy of the battery management system.

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

[0060] Specifically, the temperature influence factor, power loss factor and cycle aging factor calculated for the target battery in each time interval are first obtained, and then the weighted sum of these three factors on the battery performance attenuation is calculated to obtain the attenuation coefficient for each time interval. This calculation method takes into account the differences in the contribution of different influencing factors to the battery performance attenuation. By reasonably setting the weight coefficient, the relative importance of each factor can be more accurately reflected. The attenuation coefficient obtained by weighted summation can comprehensively characterize the various attenuation effects on the battery in a specific time interval. The larger the attenuation coefficient, the more significant the comprehensive impact of the battery performance attenuation in the time interval. This attenuation coefficient calculation method based on multi-factor weighted fusion can quantify and unify the effects of temperature change, power loss and cycle aging, provide key characteristic parameters for the subsequent establishment of a capacity prediction model, and help improve the accuracy of capacity prediction.

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

[0062] Specifically, in order to accurately characterize the actual battery capacity attenuation under the influence of various influencing factors, it is necessary to correlate the battery capacity attenuation with the attenuation coefficient. First, the battery capacity attenuation and the corresponding attenuation coefficient of the target battery in each time interval are obtained, and then the battery capacity attenuation in each time interval is multiplied by the corresponding attenuation coefficient to obtain the target attenuation in each time interval. This calculation method can more accurately characterize the actual situation of battery capacity attenuation by combining the actually observed capacity attenuation with the attenuation coefficient that reflects the comprehensive effect of various influencing factors. The target attenuation obtained by this product operation not only contains the information of the actual capacity loss of the battery, but also integrates the influence weights of factors such as temperature change, power loss and cycle aging, and can more comprehensively reflect the true state of battery performance attenuation. This target attenuation calculation method based on capacity attenuation and attenuation coefficient can effectively correct the capacity attenuation 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 a 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, in order to show the time evolution law of the target battery capacity decay, it is necessary to establish a functional relationship between the time interval and the target decay amount. First, in the two-dimensional coordinate system, each time interval is used as the horizontal axis, and the corresponding target decay amount is used as the vertical axis. Then, each time interval and its corresponding target decay amount are marked as discrete points in the coordinate system, and these discrete points are connected by data fitting to finally generate the capacity decay curve of the target battery. This drawing method can present the dynamic process of battery capacity decay in a visual form, making the battery performance decay trend more clearly visible. By analyzing the morphological characteristics of the obtained capacity decay curve, such as the slope, inflection point and overall trend of the curve, we can deeply understand the law of battery capacity decay and predict the capacity state at a future time point. This capacity decay curve generation method based on time interval and target decay amount can provide an intuitive performance evaluation basis for the battery management system, which helps to timely detect battery performance anomalies, optimize battery usage strategies, and extend battery life.

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

[0066] Specifically, in order to predict the future capacity state of the target battery, it is necessary to perform extrapolation analysis based on the established capacity decay curve. First, the capacity decay curve is mathematically modeled, and the mathematical function expression describing the capacity decay law is obtained through curve fitting. The expression includes multiple influencing parameters such as temperature influencing factor, power loss factor and cycle aging factor; then, according to the current usage status and working environment of the target battery, the change trend of each influencing factor in the next period is predicted, and these predicted values ​​are substituted into the mathematical model to calculate the battery capacity prediction value for the next period. This prediction method makes full use of the capacity decay law contained in the historical data, and quantifies the effects of various influencing factors through mathematical models. Through this prediction method based on the capacity decay curve, the capacity change trend of the battery under specific usage conditions can be accurately evaluated, providing a reliable decision-making basis for the optimization control and preventive maintenance of the battery management system.

[0067] Based on the above embodiment, as an optional embodiment, in step 105: based on the capacity change curve, predicting the battery capacity of the target battery in the next period, this step may also include the following steps: Step 501: Obtain target attenuation amounts for a preset number of time intervals in the capacity attenuation curve and the current battery capacity; determine the attenuation change rate based on the target attenuation amounts for the preset number of time intervals.

[0068] Specifically, in order to accurately grasp the rate of change of the target battery capacity attenuation, it is necessary to analyze the capacity attenuation in the recent period. First, select the nearest preset number of time intervals from the capacity attenuation curve to obtain the target attenuation data corresponding to these time intervals and the current actual capacity value of the battery; then, based on the target attenuation data in these continuous time intervals, the attenuation change rate of each time interval is obtained by calculating the difference between the target attenuation values ​​of adjacent time intervals and dividing by the time interval, and then these change rates are weighted averaged to finally determine the attenuation change rate that reflects the current capacity attenuation trend. This calculation method can more accurately reflect the current attenuation state and change trend of the battery by selecting the data of several recent time intervals for analysis. The attenuation change rate obtained in this way not only takes into account the continuity of historical data, but also highlights the attenuation characteristics of the recent period, and can better represent the real-time state of battery capacity attenuation. This attenuation change rate calculation method based on the data of the most recent time interval can timely capture the dynamic characteristics of battery performance attenuation, provide a more timely reference basis for subsequent capacity prediction, and help improve the accuracy of prediction.

[0069] Step 502: multiply the attenuation change rate by the target attenuation in the last time interval of the capacity attenuation curve to obtain the estimated attenuation of the target battery in the next time period.

[0070] Specifically, first obtain the target attenuation of the last time interval in the capacity attenuation curve, which reflects the actual attenuation state of the battery in the most recent time interval; then multiply this target attenuation by the pre-calculated attenuation change rate. Through this multiplication operation, the expected attenuation of the battery capacity in the next time period can be predicted. This calculation method takes into account both the current attenuation state of the battery and the changing trend of capacity attenuation, making the prediction result more reasonable. The expected attenuation obtained in this way can more accurately reflect the capacity loss of the battery in the next time period. The larger the expected attenuation, the faster the battery performance decays. This method of calculating the expected attenuation based on the latest target attenuation and the attenuation change rate can dynamically adapt to the changing characteristics of battery performance attenuation, provide a reliable numerical basis for battery capacity prediction, and help to detect battery abnormalities in a timely manner and take corresponding maintenance measures.

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

[0072] Specifically, first obtain the current capacity value of the battery, which reflects the actual available capacity state of the battery at this moment; then subtract the pre-calculated estimated attenuation value from the current capacity value, and through this subtraction operation, the predicted capacity value of the battery in the next period can be obtained. This calculation method intuitively reflects the attenuation process of the battery capacity over time by calculating the difference between the current capacity and the estimated attenuation amount. The battery capacity for the next period calculated in this way can provide the battery management system with a clear capacity prediction value, which helps the system to timely evaluate the health status and service life of the battery. This capacity prediction method based on the current capacity and the estimated attenuation amount can accurately reflect the dynamic change process of the battery capacity.

[0073] Reference Figure 2 , a battery capacity analysis and prediction system for a storage battery provided in an embodiment of the present application, the system comprises: a data acquisition module, an interval division module, a curve determination module, and a capacity prediction module, wherein: A data acquisition module is used to acquire the operating data of the target battery within a preset period of time, the operating data including a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge and discharge cycle number sequence; An interval division module, used to determine time intervals corresponding to multiple operating conditions according to the operating data of the target battery within a preset period; The curve determination module is used to calculate the temperature influence factor, power loss factor and cycle aging factor of each time interval based on the sub-operation data in each time interval; receive the battery capacity attenuation of the target battery in each time interval; and generate a capacity attenuation curve of the target battery by combining the battery capacity attenuation, temperature influence factor, power loss factor and cycle aging factor of each time interval; The capacity prediction module is used to predict the battery capacity of the target battery in the next period based on the capacity change curve.

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

[0075] On the basis of the above embodiment, 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 temperature sampling value and the last temperature sampling value 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 multiplying it by the fluctuation coefficient to obtain the temperature influence factor of the time interval.

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

[0077] On the basis of the above embodiment, the curve determination module is also used to determine, for each time interval, the time interval between any two adjacent charge and discharge cycle numbers in the sub-charge and discharge cycle number sequence and the total number of cycles of the sub-charge and 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.

[0078] On the basis of the above embodiment, the curve determination module is also used to perform weighted summation of the temperature influencing factor, power loss factor and cycle aging factor of each time interval to obtain the attenuation coefficient of each time interval; multiply the battery capacity attenuation of each time interval by the corresponding attenuation coefficient to obtain the target attenuation of 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 as the vertical axis.

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

[0080] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0081] The present application also discloses an electronic device. Figure 3 , Figure 3 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 realize the connection and 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] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0086] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 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 of a battery capacity analysis and prediction method.

[0087] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a battery capacity analysis and prediction method for a battery stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

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

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0093] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0094] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for analyzing and predicting battery capacity of a storage battery, characterized in that: include: Acquire operating data of a target battery within a preset period of time, wherein the operating data includes a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge-discharge cycle number sequence; Determining time intervals corresponding to a plurality of operating conditions according to the operating data of the target battery within the preset time period; Calculating the temperature influence factor, power loss factor and cycle aging factor of each time interval based on the sub-operation data in each time interval; receiving a battery capacity attenuation of the target battery in each of the time intervals; Generate a capacity decay curve of the target battery by combining the battery capacity decay amount, temperature influence factor, power loss factor and cycle aging factor in each of the time intervals; Based on the capacity variation curve, the battery capacity of the target battery in the next period is predicted.

2. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The step of determining time intervals corresponding to a plurality of operating conditions according to the operating data of the target battery within the preset time period includes: Within the preset time period, determining the time point when the temperature in the temperature sequence exceeds the temperature threshold as the operating condition switching point, the time point when the voltage in the voltage sequence exceeds the voltage threshold as the operating condition switching point, and the time point when the internal resistance in the internal resistance sequence exceeds the internal resistance threshold as the operating condition switching point; The preset time period is divided into time intervals corresponding to multiple operating conditions based on each operating condition conversion point.

3. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The sub-operation data includes a sub-temperature sequence, and the temperature influence factor of each time interval is calculated based on the sub-operation data in each time interval, including: For each of the time intervals, the difference between any two adjacent temperature sampling values ​​in the sub-temperature sequence is taken as the temperature fluctuation amplitude; Taking the ratio of the difference between the first temperature sampling value and the last temperature sampling value in the sub-temperature sequence and the length of the time interval as the fluctuation coefficient; The average of the temperature fluctuation amplitudes is calculated, and the average is divided and multiplied by the fluctuation coefficient to obtain the temperature influence factor of the time interval.

4. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The sub-operation data includes a sub-internal resistance sequence and a sub-voltage sequence, and the power loss factor of each time interval is calculated based on the sub-operation data in each time interval, including: For each of the time intervals, based on the sampling values ​​of the sub-internal resistance sequence and the sub-voltage sequence at multiple sampling moments, calculating the instantaneous power value at each of the sampling moments; Calculating the average of the differences between any two adjacent internal resistance sampling values ​​in the sub-internal resistance sequence to obtain the internal resistance fluctuation average; The power loss factor of the time interval is obtained by calculating the sum of the instantaneous power values ​​and dividing it by the internal resistance fluctuation mean.

5. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The sub-operation data includes a sub-charge and discharge cycle number sequence, and the calculation of the cycle aging factor of each time interval based on the sub-operation data in each time interval includes: For each of the time intervals, determining the time interval between any two adjacent charge and discharge cycle numbers in the sub-sequence of charge and discharge cycle numbers and the total number of cycles in the sub-sequence of charge and discharge cycle numbers; Dividing the total number of cycles by the length of the time interval to obtain a cycle frequency; The mean of each of the time intervals is multiplied by the cycle frequency to obtain the cycle aging factor of the time interval.

6. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The generating the capacity decay curve of the target battery by combining the battery capacity decay amount, the temperature influence factor, the power loss factor and the cycle aging factor in each of the time intervals includes: Performing weighted summation on the temperature influence factor, power loss factor and cycle aging factor of each of the time intervals to obtain an attenuation coefficient of each of the time intervals; Multiplying the battery capacity attenuation in each of the time intervals by the corresponding attenuation coefficient to obtain a target attenuation in each of the time intervals; A capacity decay curve of the target battery is generated with each of the time intervals as the horizontal axis and the target decay amount as the vertical axis.

7. The battery capacity analysis and prediction method of a storage battery according to claim 1, characterized in that: The predicting the battery capacity of the target battery in the next period of time based on the capacity change curve includes: Obtaining target attenuation amounts for a preset number of time intervals in the capacity attenuation curve and the current battery capacity; Determining a rate of change of attenuation based on the target attenuation in the preset number of time intervals; Multiplying the attenuation change rate by the target attenuation in the last time interval of the capacity attenuation curve to obtain the estimated attenuation of the target battery in the next time period; The predicted attenuation amount is subtracted from the current battery capacity to obtain the battery capacity of the target battery in the next period.

8. A battery capacity analysis and prediction system for a storage battery, characterized in that: The system comprises: A data acquisition module, used to acquire the operating data of the target battery within a preset period of time, wherein the operating data includes a voltage sequence, an internal resistance sequence, a temperature sequence, and a charge and discharge cycle number sequence; An interval division module, used for determining time intervals corresponding to a plurality of operating conditions according to the operating 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 of each time interval based on the sub-operation data in each time interval; receiving the battery capacity attenuation of the target battery in each time interval; and combining the battery capacity attenuation, temperature influence factor, power loss factor and cycle aging factor of each time interval to generate a capacity attenuation curve of the target battery; The capacity prediction module is used to predict the battery capacity of the target battery in the next period based on the capacity change curve.

9. An electronic device, characterized in that: It 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 so that the electronic device executes the battery capacity analysis and prediction method of the battery as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the battery capacity analysis and prediction method of the storage battery as described in any one of claims 1-7 is executed.

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