Cell cycle life prediction methods, systems and dielectrics

Abstract

This invention relates to the field of battery parameter measurement technology, and more particularly to a method, system, and medium for predicting the cycle life of a battery cell. The method includes the following steps: real-time monitoring of the current change rate of the battery cell during cyclic charging and discharging; identifying whether the battery cell has entered a high-load operating state through the dynamic changes in the current change rate; if the battery cell is identified as entering a high-load operating state, initiating voltage stability analysis; under high-load operating conditions, analyzing the voltage stability fluctuations of the battery cell and extracting voltage fluctuation parameters; and determining the current health status of the battery cell through the changing trend of the voltage fluctuation parameters. This invention identifies high-load states by real-time monitoring of the current change rate and calculates the recovery time difference to detect the capacity decay rate, thereby predicting the cycle life of the battery cell and promptly outputting replacement information, ultimately improving the safety and stability of battery cell use.

Description

Technical Field

[0001] This invention relates to the field of battery parameter measurement technology, and in particular to a method, system and medium for predicting the cycle life of a battery cell. Background Technology

[0002] As a core component of modern electronic devices and energy storage systems, the performance and lifespan of battery cells are crucial to the stability and safety of these devices. In practical applications, battery cells often operate under high load conditions, but most existing technologies do not fully consider the performance changes of battery cells under these conditions. Specifically, analysis is only performed after the battery cell has been fully charged or discharged, while in actual use, battery cells are often not fully charged or discharged. This assessment method, which ignores high load conditions, cannot detect the rapid performance decline of battery cells under high load conditions in a timely manner, and may even lead to safety hazards. In addition, when assessing the health status of battery cells, most existing technologies do not incorporate voltage stability fluctuation parameters, but only evaluate the cell's capacity difference or fitting slope, ignoring the key indicator of voltage fluctuation. Voltage stability fluctuation is an important manifestation of the performance changes of battery cells under high load conditions; failing to incorporate this parameter into the assessment leads to inaccurate health status judgments. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, system, and medium for predicting the cycle life of battery cells to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objective, a method for predicting the cycle life of a battery cell is provided, the method comprising the following steps: Step S1: Monitor the current change rate of the battery cell in real time during the cyclic charging and discharging process; identify whether the battery cell has entered a high-load operating state by the dynamic change of the current change rate; if the battery cell is identified to have entered a high-load operating state, start voltage stability analysis. Step S2: Under high load operating conditions, analyze the voltage stability fluctuation of the battery cell and extract the voltage fluctuation parameters; determine the current health status of the battery cell by the changing trend of the voltage fluctuation parameters; Step S3: When the battery cell recovers from a high-load state to a normal operating state, calculate the time difference from the high-load state to the recovery state as an indicator of the battery cell's recovery capability; detect the current capacity decay rate by measuring the value of the recovery capability indicator. Step S4: Obtain the initial capacity of the battery cell, and predict the remaining cycle life of the battery cell by combining the current health status and the current capacity decay rate; if the predicted remaining cycle life shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, then output the battery cell replacement information.

[0005] This specification also provides a cell cycle life prediction system for performing the cell cycle life prediction method described above. The cell cycle life prediction system includes: The current monitoring module is used to monitor the rate of change of current in the battery cell during the cyclic charging and discharging process in real time; it identifies whether the battery cell has entered a high-load operating state by the dynamic change of the rate of change of current; if the battery cell is identified to have entered a high-load operating state, it initiates voltage stability analysis. The voltage fluctuation detection module is used to analyze the voltage stability fluctuation of the battery cell under high load conditions, extract voltage fluctuation parameters, and determine the current health status of the battery cell by the changing trend of the voltage fluctuation parameters. The capacity decay identification module is used to calculate the time difference between the high load state and the recovery state when the battery cell recovers from the high load state to the normal operating state, and use it as an indicator of the battery cell's recovery capability; the current capacity decay rate is detected by the value of the recovery capability indicator. The cycle life prediction module is used to obtain the initial capacity of the battery cell, and predict the remaining cycle life of the battery cell by combining the current health status and the current capacity decay rate. If the prediction result of the remaining cycle life shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, the battery cell replacement information is output.

[0006] A computer-readable storage medium storing a computer program that, when executed, implements the above-described method for predicting the cycle life of a battery cell.

[0007] The beneficial effects of this invention are: On the one hand, this invention is based on the special operating condition of a battery cell entering a high-load working state during cyclic charging and discharging. In practical applications, the high-load state of a battery cell may lead to a rapid decline in its performance and even cause safety hazards. By monitoring the rate of change of current of the battery cell in real time during cyclic charging and discharging, it is possible to quickly identify whether the battery cell has entered a high-load working state. This monitoring method avoids excessive performance loss of the battery cell due to the failure to detect the high-load state in time.

[0008] On the other hand, under high-load operating conditions, this invention can accurately determine the current health status of the battery cell by analyzing the voltage stability fluctuations and extracting voltage fluctuation parameters. This method starts from the voltage characteristics of the battery cell, avoiding subjective judgment and improving the accuracy of health status assessment. When the battery cell recovers from a high-load state to a normal operating state, this invention calculates the recovery time difference as a recovery capability indicator, and then detects the current capacity decay rate. This process directly reflects the capacity decay of the battery cell with objective numerical indicators, improving the accuracy and repeatability of the detection results.

[0009] On the other hand, by combining the initial capacity of the battery cell, its current health status, and the current capacity decay rate, this invention can predict the remaining cycle life of the battery cell relatively accurately. When the prediction result shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, the battery cell replacement information is output in a timely manner, providing a scientific basis for the maintenance and replacement of the battery cell and effectively ensuring the safe and stable operation of the equipment or system using the battery cell. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the steps involved in a method for predicting the cycle life of a battery cell. Figure 2 The cell voltage curve and linear regression analysis graph are shown. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0013] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0014] To achieve the above objectives, please refer to Figures 1 to 2 A method for predicting the cycle life of a battery cell, the method comprising the following steps: Step S1: Monitor the current change rate of the battery cell in real time during the cyclic charging and discharging process; identify whether the battery cell has entered a high-load operating state by the dynamic change of the current change rate; if the battery cell is identified to have entered a high-load operating state, start voltage stability analysis. In this embodiment of the invention, a current monitoring device is deployed, which includes a high-precision current sensor for real-time acquisition of the current value of the battery cell during charging and discharging. The current sensor is connected to the battery cell via a sampling circuit, the sampling frequency of which is set to 10 times per second to obtain sufficiently dense current data points. The acquired current data first passes through a low-pass filter circuit with a cutoff frequency set to 0.5Hz to remove high-frequency noise and smooth the current signal. The filtered current data is then sent to a data processing unit, which performs differential calculations on the current values ​​of two adjacent sampling points to obtain the current change; if the current value at one sampling point is 10A and the current value at the next sampling point is 12A, then the current change is 2A.

[0015] It should be noted that the data processing unit records the time interval between each sampling. Assuming the time interval is 0.1 seconds, the current change rate can be obtained by dividing the current change by the time interval, i.e., 2A / 0.1 seconds = 20A / second. The system presets a current change rate threshold, for example, 15A / second; when the calculated current change rate exceeds this threshold, the system determines that the cell has entered a high-load operating state.

[0016] It should be noted that once the battery cell is determined to be in a high-load operating state, the system immediately triggers the voltage stability analysis module. The voltage stability analysis module also includes a high-precision voltage sensor, which is used to synchronously collect the voltage values ​​at both ends of the battery cell. The sampling frequency of the voltage sensor is also set to 10 times per second.

[0017] Step S2: Under high load operating conditions, analyze the voltage stability fluctuation of the battery cell and extract the voltage fluctuation parameters; determine the current health status of the battery cell by the changing trend of the voltage fluctuation parameters; In one embodiment of the present invention, a high-precision voltage sensor is deployed and connected to the positive and negative terminals of the battery cell to collect the voltage values ​​across the battery cell in real time. The voltage sensor should have an accuracy at the millivolt level to ensure accurate capture of minute voltage changes; the sampling frequency of the voltage sensor is set to 10 times per second to ensure capture of rapid voltage changes; each time a sample is taken, the voltage value and its corresponding timestamp are recorded; the collected voltage data is transmitted to a data processing unit, which can be an embedded system or a computer. It should be noted that the acquired voltage data is input into a low-pass filter with a cutoff frequency set to 0.5Hz. This means the filter will allow signals with frequencies below 0.5Hz to pass through, while attenuating signals with frequencies above 0.5Hz. The low-pass filter can be implemented using a digital filter, such as a Butterworth filter, and the filter order can be set according to actual needs.

[0018] In another embodiment of the present invention, differential calculation is performed on the filtered voltage data to calculate the voltage change between adjacent sampling points. The specific operations are as follows: 1) Read the filtered voltage value of the current sampling point and the filtered voltage value of the previous sampling point; 2) Calculate the voltage change, that is, the voltage value of the current sampling point minus the voltage value of the previous sampling point; 3) Calculate the voltage change rate by dividing the voltage change by the sampling time interval (0.1 seconds).

[0019] It should be noted that time series analysis is performed on the extracted voltage fluctuation parameters to record the changes in the absolute value, mean, and standard deviation of the voltage change rate over time; a threshold is set, and when the absolute value of the voltage change rate exceeds 0.5V / second, it is marked as an abnormal fluctuation; an empty list is initialized to record outliers, and the absolute value of the voltage change rate of all sampling points is traversed. If the absolute value of the voltage change rate of a certain sampling point exceeds 0.5V / second, the timestamp of that sampling point and the corresponding voltage change rate are recorded in the outlier list.

[0020] Step S3: When the battery cell recovers from a high-load state to a normal operating state, calculate the time difference from the high-load state to the recovery state as an indicator of the battery cell's recovery capability; detect the current capacity decay rate by measuring the value of the recovery capability indicator. In this embodiment of the invention, when the battery cell enters a high-load state, the timestamp of that moment is recorded and designated as the high-load start time. The criterion for determining a high-load state can be that the current exceeds a certain threshold, such as twice the rated capacity of the battery cell (2C), where C represents the rated capacity of the battery cell. When the battery cell recovers from the high-load state to the normal operating state, the timestamp of that moment is recorded again and designated as the recovery time. The criterion for determining the normal operating state can be that the current returns to 0.2 times the rated capacity of the battery cell (0.2C).

[0021] It should be noted that the time difference from the high load state to the recovery state is calculated, that is, the recovery time minus the high load start time. The resulting time difference is used as an indicator of the cell's recovery capability; this indicator reflects the time required for the cell to recover from the high load state to the normal operating state.

[0022] In one embodiment of the present invention, after the battery cell recovers from a high-load state to a normal working state, a complete charge-discharge cycle test is performed, and the total discharge amount of the battery cell is recorded as the current discharge amount; the rated capacity of the battery cell is obtained, which is usually provided by the battery cell manufacturer and is recorded as the rated capacity; the capacity decay rate is calculated by dividing the current discharge amount by the rated capacity and then multiplying by 100%, and the result is the capacity decay rate.

[0023] Step S4: Obtain the initial capacity of the battery cell, and predict the remaining cycle life of the battery cell by combining the current health status and the current capacity decay rate; if the predicted remaining cycle life shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, then output the battery cell replacement information.

[0024] In this embodiment of the invention, the initial capacity of the battery cell is obtained from the technical documentation of the battery cell or the data provided by the manufacturer. The initial capacity refers to the rated capacity of the battery cell in a brand new state, usually expressed in ampere-hours (Ah); or the initial capacity of the battery cell is determined by performing a complete cycle test on a brand new battery.

[0025] In one embodiment of the present invention, the remaining cycle life of the battery cell is predicted based on the cell's initial capacity, current capacity decay rate, and known cycle count. The specific method is as follows: 1) Calculate the total capacity decay of the battery cell, which is the initial capacity multiplied by the capacity decay rate; 2) Determine the relationship between capacity decay and cycle count based on historical or experimental data of the battery cell; for example, if the capacity of the battery cell decays by 10% every 100 cycles, the number of cycles the battery cell has undergone can be calculated based on the current capacity decay rate. 3) Subtract the number of cycles already completed from the total cycle life to obtain the remaining cycle life. The total cycle life refers to the number of cycles a cell undergoes from a brand new state to a point where its capacity decays to a certain unusable level (such as 80% of its initial capacity).

[0026] It should be noted that a safe operating standard is set, such as the remaining cycle life of the battery cell should not be less than 100 cycles. The predicted remaining cycle life is compared with the safe operating standard. If the remaining cycle life is lower than the safe operating standard, the battery cell is considered to be nearing the end of its service life and needs to be replaced. If the remaining cycle life is lower than the safe operating standard, the system will output battery cell replacement information. The replacement information can be displayed on the screen, accompanied by an audible alarm, or sent to the maintenance personnel's terminal device via the network. The replacement information should include the battery cell's identification information (such as serial number and model), current status (such as capacity degradation rate and remaining cycle life), and recommended replacement time.

[0027] Preferably, the real-time monitoring of the current change rate of the battery cell during the cyclic charging and discharging process in step S1 includes: During the charging phase of the battery cell, the charging process is divided into three stages; the first stage is from 0% to 30% of the charging capacity, and the current value is collected once every first sampling time. The second stage involves charging from 30% to 70% capacity. The current value is collected every second sampling time. After each sampling in the second stage, the sampling interval is increased proportionally by the amount of time in the first sampling time. The third stage is from 70% to 100% charging capacity, and the initial sampling interval is the third sampling time. After each sampling in the third stage, the sampling interval is adjusted according to the change in the current value of the previous sampling. If the change in current is less than the standard change in current, the sampling interval is increased by the amount of the second sampling time; otherwise, it remains unchanged. During the discharge phase of the battery cell, an alternating sampling method is used to alternately sample current values ​​from the positive and negative terminals of the battery cell, with the alternating cooling time paused after each sampling. Set an upper and lower threshold for the current value, and only retain current values ​​within the threshold range; current values ​​outside the threshold range are discarded. Calculate the ratio of the difference between two adjacent current values ​​to the time interval to determine the rate of change of current.

[0028] In this embodiment of the invention, in the first stage, the initial acquisition interval is set to 1 minute, that is, the current value is acquired once every 1 minute; in this stage, the acquisition interval remains unchanged, and after each acquisition of the current value, the current time point and the current value are recorded.

[0029] It should be noted that in the second stage, the initial sampling interval is set to 1 minute, that is, the current value is sampled once every 1 minute; after each current value sampling, the sampling interval is increased by 30 seconds. For example, the specific operation is as follows: after the first current value sampling, the current time point is recorded; the time point for the second current value sampling is the first sampling time point plus 1 minute and 30 seconds; the time point for the third current value sampling is the second sampling time point plus 2 minutes; and so on, after each sampling, the sampling interval is increased by 30 seconds.

[0030] It should be noted that in the third stage, the initial sampling interval is set to 2 minutes, meaning that the current value is sampled every 2 minutes. After each sampling, the variation range of the previous sampling current value is checked. For example, the specific operation is as follows: assuming the previous sampling current value is the previous current value and the current sampling current value is the current current value, the variation range of the current value is the absolute value of the difference between the current current value and the previous current value. If the variation range of the current value is less than the standard current variation range (e.g., 0.5A), the sampling interval is increased by 1 minute. If the variation range of the current value is greater than or equal to the standard current variation range, the sampling interval remains unchanged. If the previous sampling current value is 2A and the current sampling current value is 2.4A, the current variation range is 0.4A, which is less than 0.5A, then the next sampling interval is increased by 1 minute.

[0031] In another operation mode of this invention embodiment, an alternating acquisition method is adopted, in which current values ​​are alternately acquired from the positive and negative terminals of the battery cell; the current value is acquired from the positive terminal for the first time and the current time point is recorded; the current value is acquired from the negative terminal for the second time and the current time point is recorded; and so on, after each acquisition, the current time point is recorded, and a 30-second pause is made after each acquisition to allow for alternating cooling.

[0032] In another embodiment of the present invention, an upper threshold and a lower threshold are set for the current value. After each current value is collected, it is checked whether the current value is within the threshold range. If the current value is greater than or equal to 0.1A and less than or equal to 5A, the current value is retained; if the current value is less than 0.1A or greater than 5A, the current value is discarded. The current change rate is determined by calculating the ratio of the difference between two adjacent current values ​​to the time interval. If two adjacent current values ​​are the previous current value and the current current value, and the time interval is the collection interval, the current change rate is calculated as the difference between the current value and the previous current value divided by the collection interval.

[0033] Preferably, step S1, which involves real-time monitoring of the current change rate of the battery cell during cyclic charging and discharging, further includes: The first sampling time is set to 1 minute, the second sampling time is set to 2 minutes, the third sampling time is set to 3 minutes, the alternating cooling time is set to 10 minutes, the current change amplitude is set to 0.05A, and the upper and lower threshold values ​​of the current value are set to 120% and 80% of the rated current of the cell, respectively.

[0034] It is important to note that during the initial charging and discharging phase of the battery cell, current changes are drastic, and heat accumulates rapidly within the cell. At this stage, a higher sampling frequency can promptly capture significant current changes, providing an accurate data foundation for subsequent dynamic adjustments. Simultaneously, frequent sampling can detect rapid heat accumulation within the cell, preventing performance degradation or safety hazards caused by excessive heat buildup. When the battery cell reaches 30% to 70% charging capacity, current changes become relatively stable, but heat accumulation still needs monitoring. Setting the second sampling interval to 2 minutes ensures data accuracy while reducing data processing overhead. Furthermore, the 2-minute interval ensures that current values ​​are collected promptly when heat accumulation reaches a certain level. During the 70% to 100% charging capacity phase, current changes are typically more stable, and heat accumulation slows down as the cell approaches full charge. Setting the third sampling interval to 3 minutes further reduces data processing overhead while maintaining data accuracy. A 3-minute data acquisition interval ensures that current values ​​are collected promptly when heat accumulates to a certain level inside the battery cell, allowing for necessary adjustments.

[0035] It is important to note that during the discharge phase of the battery cell, heat is generated when alternating current values ​​are collected. To prevent overheating, appropriate cooling is necessary after each data collection. Setting the alternating cooling time to 10 minutes ensures sufficient time for the cell to dissipate heat during the data collection process, thus avoiding performance degradation or safety hazards caused by heat accumulation. The 10-minute cooling time effectively reduces the internal temperature of the battery cell, ensuring it operates within a safe temperature range.

[0036] It is important to note that the magnitude of current change during battery cell charging is a crucial indicator of cell condition. Setting the current change range to 0.05A ensures that minute current variations can be detected promptly during charging, allowing for accurate monitoring of the cell's condition. This 0.05A current change range effectively distinguishes subtle changes in internal heat accumulation within the cell, preventing the accumulation from being overlooked due to excessively small current variations.

[0037] It is important to note that the current value during charging and discharging of the battery cell needs to be strictly controlled to ensure the cell's safety and performance. Setting an upper threshold of 120% of the cell's rated current and a lower threshold of 80% ensures the cell operates within a safe range while avoiding performance problems caused by excessively high or low current. The 120% upper threshold prevents excessive heat generation due to overcurrent, while the 80% lower threshold ensures the cell will not malfunction due to excessively low current. These two threshold settings effectively control heat accumulation within the cell, ensuring it operates within a safe temperature range.

[0038] Preferably, step S1, which identifies whether the battery cell has entered a high-load operating state by the dynamic change of the current change rate, includes: During the charging and discharging process of the battery cell, the values ​​of the rate of change of current are arranged in chronological order to form a sequence; The time series is divided into multiple subsequences, each containing continuous current rate of change values; Calculate the absolute change in the difference between two adjacent current rate of change values ​​in each subsequence; calculate the number of times the sign of the current rate of change changes from positive to negative or from negative to positive in each subsequence; If the absolute value change in a subsequence exceeds 0.05 A / s and the number of sign changes exceeds 3, it is identified as a high dynamic change pattern. If three or more of the five consecutive subsequences are identified as high dynamic change modes, the cell is ultimately determined to have entered a high-load operating state.

[0039] In this embodiment of the invention, the values ​​of the current change rate are first collected and arranged sequentially according to time to form a complete current change rate time series. For example, assuming that in a certain cell charge and discharge test, the collected current change rate values ​​are 0.02A / s, 0.03A / s, -0.01A / s, -0.04A / s, 0.05A / s, 0.06A / s, -0.02A / s, -0.03A / s, etc., these values ​​are arranged in chronological order of collection time to constitute the current change rate time series.

[0040] It should be noted that the time series is divided into multiple subsequences, each containing several consecutive current change rate values. For example, the above time series can be divided into the following subsequences: the first subsequence is 0.02 A / s, 0.03 A / s, -0.01 A / s; the second subsequence is -0.04 A / s, 0.05 A / s, 0.06 A / s; the third subsequence is -0.02 A / s, -0.03 A / s, etc., and the values ​​within each subsequence are continuously collected.

[0041] In one embodiment of the present invention, two calculations are performed for each subsequence. First, the absolute change in the difference between two adjacent current rate of change values ​​in the subsequence is calculated. Taking the first subsequence as an example, the differences between two adjacent values ​​are 0.03 A / s - 0.02 A / s = 0.01 A / s and -0.01 A / s - 0.03 A / s = -0.04 A / s, respectively. The absolute values ​​are then taken to obtain absolute changes of 0.01 A / s and 0.04 A / s, respectively. Second, the number of times the sign of the current rate of change changes from positive to negative or from negative to positive is calculated. In the first subsequence, the sign remains unchanged from 0.02 A / s to 0.03 A / s, and changes from positive to negative from 0.03 A / s to -0.01 A / s, with a change count of 1. Based on the above calculation results, each subsequence is identified as having a high dynamic change pattern. If a subsequence contains pairs of values ​​with an absolute value change exceeding 0.05 A / s and the sign changes more than 3 times, then the subsequence is identified as having a high dynamic change pattern.

[0042] In one embodiment of the present invention, assuming that the absolute value changes of adjacent values ​​in a certain subsequence are 0.06 A / s, 0.02 A / s, and 0.07 A / s respectively, and the number of sign changes is 4, since there are value pairs (0.06 A / s, 0.07 A / s) in this subsequence with absolute value changes exceeding 0.05 A / s and the number of sign changes exceeding 3, this subsequence is identified as a high dynamic change mode. A comprehensive judgment is made on multiple consecutive subsequences; if 3 or more subsequences out of 5 consecutive subsequences are identified as high dynamic change modes, then the cell is ultimately determined to be in a high-load operating state. Assuming that 3 out of 5 consecutive subsequences are high dynamic change modes, then the cell is determined to be in a high-load operating state at this time.

[0043] Preferably, in step S2, the analysis of the voltage stability fluctuation of the battery cell and the extraction of voltage fluctuation parameters include: Under high load operating conditions, the voltage value of the battery cell is continuously collected; Plot the voltage values ​​as a curve and calculate the linear regression slope of the voltage value curve. If the linear regression slope is greater than 0, it is marked as an upward fluctuation trend stage; if the linear regression slope is less than 0, it is marked as a downward fluctuation trend stage. During the upward and downward fluctuation trends, calculate the absolute value of the difference between two adjacent voltage values ​​to determine the median of the absolute value; calculate the ratio of the absolute value of each voltage difference to the median to obtain the mean and standard deviation of the ratio. If the average value of the ratio is greater than 1.5 and the standard deviation of the ratio is greater than 0.5, it is judged as a voltage fluctuation state, and the voltage fluctuation parameters are recorded.

[0044] Please see Figure 2In this embodiment of the invention, the voltage value of the battery cell is continuously collected when the battery cell is under high load operation. It is assumed that the battery cell voltage values ​​collected in a certain period of time are 3.5V, 3.6V, 3.7V, 3.6V, 3.5V, 3.4V, 3.3V, 3.4V, etc.

[0045] In one embodiment of the present invention, the collected voltage values ​​are plotted on a coordinate graph in chronological order of collection time to form a voltage value curve; then, the voltage value curve is fitted using a linear regression method to calculate the linear regression slope of the curve.

[0046] It should be noted that if the slope of the linear regression is greater than 0, for example, 0.1, it indicates that the voltage value is increasing over time, and the current stage is marked as an upward fluctuation trend stage; if the slope of the linear regression is less than 0, for example, -0.2, it indicates that the voltage value is decreasing over time, and the current stage is marked as a downward fluctuation trend stage.

[0047] In one embodiment of the present invention, the absolute value of the difference between two adjacent voltage values ​​is calculated during both the upward and downward fluctuation trend phases. Taking the downward fluctuation trend phase as an example, assuming the voltage values ​​are successively 3.7V, 3.6V, 3.5V, 3.4V, and 3.3V, the absolute values ​​of the difference between two adjacent voltage values ​​are 0.1V, 0.1V, 0.1V, and 0.1V, respectively. Next, the median of these absolute values ​​is determined; in the example above, the median is 0.1V.

[0048] In one embodiment of the invention, the ratio of the absolute value of each voltage difference to the median is calculated. In the example above, the ratios are 1, 1, 1, and 1, respectively. Next, the mean and standard deviation of these ratios are calculated. It should be noted that the average value of the ratio is 1 and the standard deviation is 0. If the average value of the ratio is greater than 1.5 and the standard deviation of the ratio is greater than 0.5, the cell is determined to be in a voltage fluctuation state, and the voltage fluctuation parameters at this time are recorded, including the average value of the ratio, the standard deviation of the ratio, and the corresponding voltage value.

[0049] Preferably, step S2, determining the current health status of the battery cell by analyzing the changing trend of voltage fluctuation parameters, includes: Arrange the voltage fluctuation parameters in chronological order to form a voltage parameter sequence; Calculate the ratio of the difference between two adjacent parameter values ​​in each voltage parameter sequence to the previous parameter value to determine the rate of change of the fluctuation parameter value; Calculate the rate of change of each fluctuation parameter value, and identify the extreme points and inflection points of the rate of change; If the number of extreme points in the rate of change of a fluctuation parameter value exceeds 3, then the cell health status corresponding to that voltage parameter sequence is judged to be in the rapid aging stage. If the number of inflection points in the rate of change of a fluctuation parameter value is less than one, then the cell health status corresponding to that voltage parameter sequence is judged to be in the normal aging stage.

[0050] In this embodiment of the invention, the recorded voltage fluctuation parameters are arranged in chronological order of acquisition time to form a voltage parameter sequence. Assuming that the voltage fluctuation parameters recorded within a certain period of time are 0.2, 0.3, 0.5, 0.4, 0.6, 0.8, etc., these parameters are arranged in chronological order to form a voltage parameter sequence.

[0051] In one embodiment of the present invention, each voltage parameter sequence is analyzed; the ratio of the difference between two adjacent parameter values ​​to the previous parameter value is calculated to determine the rate of change of the fluctuation parameter value. Taking the sequence 0.2, 0.3, 0.5, 0.4, 0.6 as an example, firstly, the differences between two adjacent parameter values ​​are calculated as follows: 0.3-0.2=0.1, 0.5-0.3=0.2, 0.4-0.5=-0.1, and 0.6-0.4=0.2. Then, the ratio of each difference to the previous parameter value, i.e., the rate of change of the fluctuation parameter value, is calculated as follows: 0.1 / 0.2=0.5, 0.2 / 0.3≈0.67, -0.1 / 0.5=-0.2, and 0.2 / 0.4=0.5.

[0052] It should be noted that the distribution of the rate of change of each fluctuation parameter value is statistically analyzed to identify the extreme points and inflection points of the rate of change. Extreme points refer to the maximum or minimum values ​​in the rate of change sequence, while inflection points are the turning points where the rate of change changes from increasing to decreasing or vice versa. In the above rate of change sequence 0.5, 0.67, -0.2, 0.5, 0.67 is a maximum point, -0.2 is a minimum point, and -0.2 is also an inflection point because the rate of change changes from increasing to decreasing.

[0053] It should be noted that if the number of extreme points in the rate of change of a fluctuation parameter value exceeds 3, for example, if there are 4 extreme points in a certain rate of change sequence, then the cell health status corresponding to that voltage parameter sequence is judged to be in the rapid aging stage; if the number of inflection points in the rate of change of a fluctuation parameter value is less than 1, for example, if there are only 0 or 1 inflection points in a certain rate of change sequence, then the cell health status corresponding to that voltage parameter sequence is judged to be in the normal aging stage.

[0054] Preferably, step S3 includes the following steps: Step S31: When the battery cell recovers from a high-load state to a normal operating state, record the recovery start time and recovery end time; Step S32: Calculate the time difference from high load state to recovery state as an indicator of the cell's recovery capability; Step S33: Calculate the ratio of the recovery capability index to the rated recovery time of the battery cell; Step S34: If the ratio is less than the preset recovery threshold, it is determined that the current capacity decay rate of the battery cell is within the normal range. Step S35: If the ratio is greater than or equal to the preset recovery threshold, the current capacity decay rate of the battery cell is determined to be abnormal.

[0055] In this embodiment of the invention, when the battery cell recovers from a high-load operating state to a normal operating state, two time points need to be recorded: the recovery start time and the recovery end time. Assuming the battery cell begins to recover from a high-load state at one moment and fully recovers to a normal operating state at another moment, these two time points will be accurately recorded. The time difference taken for the battery cell to recover from the high-load state is calculated; this time difference is the battery cell's recovery capability index. Specifically, it is calculated by subtracting the recovery start time from the recovery end time. For example, if the recovery start time is 10:00 AM and the recovery end time is 10:05 AM, then the recovery capability index is 5 minutes. The calculated recovery capability index is compared with the battery cell's rated recovery time, and the ratio between them is calculated. Assuming the battery cell's rated recovery time is 3 minutes, then 5 minutes is divided by 3 minutes to obtain a ratio. In this example, the ratio is approximately 1.67. This ratio is then compared with a preset recovery threshold. If the ratio is less than the preset recovery threshold, for example, the preset recovery threshold is 1.5, then it can be determined that the current capacity decay rate of the battery cell is within the normal range; if the ratio is greater than or equal to the preset recovery threshold, for example, the ratio of 1.67 calculated above is greater than or equal to 1.5, then it can be determined that the current capacity decay rate of the battery cell is abnormal.

[0056] It is important to note that the rated recovery time of a battery cell refers to the ideal time required for the cell to recover from a high-load state to normal operating condition under standard test conditions. This is achieved by conducting multiple recovery tests on the same batch of cells under high-load conditions to normal operating condition, recording the recovery time each time, and taking the average value as the rated recovery time. The rated recovery time is determined based on the cell type, materials, design parameters, and relevant industry standards or specifications. For example, for some energy storage cells, after being stored at room temperature (25°C) for 28 days, the charge and discharge energy recovery rate is not less than 97%, and the corresponding recovery time can be used as a reference. By combining actual recovery time data from previous use of this cell model and analyzing its distribution, a reasonable rated recovery time can be determined.

[0057] It is important to note that the preset recovery threshold is a reference value used to determine whether the current capacity degradation rate of the battery cell is normal. When the ratio of the cell's recovery capability index to the rated recovery time exceeds this threshold, the cell's capacity degradation rate is considered abnormal. The recovery threshold is determined based on the cell's capacity degradation rate standard. For example, if the cell's capacity degradation rate reaches a certain preset value, the value corresponding to the ratio of its recovery capability index to the rated recovery time can be used as the recovery threshold. By experimentally testing the cell's recovery capability index under different capacity degradation rates, analyzing the distribution of its ratio to the rated recovery time, and selecting an appropriate threshold, the recovery threshold can be selected. The threshold setting experience of similar cells in the industry should be referenced, and appropriate adjustments should be made based on the characteristics of the product itself.

[0058] Preferably, step S4 includes the following steps: Step S41: In the initial state of the battery cell, perform a complete charge-discharge cycle and record the maximum capacity during the charging process as the initial capacity of the battery cell. Step S42: Calculate the cell capacity retention rate based on the initial cell capacity and the current capacity decay rate; Step S43: Determine the degree of cell aging in the current health status; Step S44: Multiply the cell capacity retention rate value by the cell aging degree value to determine the remaining cycle judgment factor; Step S45: Predict the remaining cycle life of the battery cell based on the remaining cycle judgment factor; Step S46: If the predicted remaining cycle life shows that the remaining cycle life of the cell is lower than the preset safe operation standard, then output cell replacement information.

[0059] In one embodiment of the present invention, a complete charge-discharge cycle is performed on the battery cell in its initial state. During charging, the maximum capacity that the battery cell can reach is recorded, and this maximum capacity is used as the initial capacity of the battery cell. The initial capacity is the capacity benchmark of the battery cell in its new state and is used to calculate the capacity decay subsequently. Based on the initial capacity and the current capacity decay rate of the battery cell, the capacity retention rate of the battery cell is calculated. The capacity retention rate is the ratio of the current capacity to the initial capacity, reflecting the capacity retention level of the battery cell in the current state, i.e., capacity retention rate = initial capacity / current capacity; where the current capacity is the maximum capacity recorded by the battery cell after one complete charge-discharge cycle in the current state.

[0060] In one embodiment of the invention, the aging degree of the battery cell in its current health state is determined. This aging degree is obtained through a comprehensive evaluation of a series of aging detection indicators, used to quantify the aging level of the battery cell. These aging detection indicators may include changes in the cell's internal resistance, voltage plateau changes, and self-discharge rate. These indicators are obtained through experiments and data analysis and can reflect the aging status of the battery cell in its current state. The cell capacity retention rate is multiplied by the cell aging degree to obtain the remaining cycle life judgment factor. The remaining cycle life judgment factor is a comprehensive indicator used to assess the remaining service life of the battery cell in its current state. The calculation formula is as follows: Remaining cycle life judgment factor = Capacity retention rate × Aging degree.

[0061] In one embodiment of the present invention, the remaining cycle life of the battery cell is predicted based on a remaining cycle life judgment factor. If the predicted remaining cycle life shows that the remaining cycle life of the battery cell is lower than a preset safe operation standard, then battery cell replacement information is output. The safe operation standard is a minimum remaining cycle life threshold set according to the application scenario and safety requirements of the battery cell. When the remaining cycle life of the battery cell is lower than this threshold, it indicates that the battery cell can no longer meet the safe operation requirements and needs to be replaced.

[0062] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0063] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for predicting the cycle life of a battery cell, characterized in that, Includes the following steps: Step S1: Monitor the rate of change of current in the battery cell during the cyclic charging and discharging process in real time; The dynamic changes in the rate of change of current are used to identify whether the battery cell has entered a high-load operating state; if the battery cell is identified to have entered a high-load operating state, voltage stability analysis is initiated. Step S2: Under high load operating conditions, analyze the voltage stability fluctuation of the battery cell and extract the voltage fluctuation parameters; determine the current health status of the battery cell by the changing trend of the voltage fluctuation parameters; Step S3: When the battery cell recovers from a high-load state to a normal operating state, calculate the time difference from the high-load state to the recovery state as an indicator of the battery cell's recovery capability; detect the current capacity decay rate by measuring the value of the recovery capability indicator. Step S4: Obtain the initial capacity of the battery cell, and predict the remaining cycle life of the battery cell by combining the current health status and the current capacity decay rate; if the predicted remaining cycle life shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, then output the battery cell replacement information.

2. The cell cycle life prediction method according to claim 1, characterized in that, Step S1 involves real-time monitoring of the current change rate of the battery cell during cyclic charging and discharging, including: During the charging phase of the battery cell, the charging process is divided into three stages; the first stage is from 0% to 30% of the charging capacity, and the current value is collected once every first sampling time. The second stage involves charging from 30% to 70% capacity. The current value is collected every second sampling time. After each sampling in the second stage, the sampling interval is increased proportionally by the amount of time in the first sampling time. The third stage is from 70% to 100% charging capacity, and the initial sampling interval is the third sampling time. After each sampling in the third stage, the sampling interval is adjusted according to the change in the current value of the previous sampling. If the change in current is less than the standard change in current, the sampling interval is increased by the amount of the second sampling time; otherwise, it remains unchanged. During the discharge phase of the battery cell, an alternating sampling method is used to alternately sample current values ​​from the positive and negative terminals of the battery cell, with the alternating cooling time paused after each sampling. Set an upper and lower threshold for the current value, and only retain current values ​​within the threshold range; current values ​​outside the threshold range are discarded. Calculate the ratio of the difference between two adjacent current values ​​to the time interval to determine the rate of change of current.

3. The cell cycle life prediction method according to claim 2, characterized in that, Step S1, which involves real-time monitoring of the current change rate of the battery cell during cyclic charging and discharging, also includes: The first sampling time is set to 1 minute, the second sampling time is set to 2 minutes, the third sampling time is set to 3 minutes, the alternating cooling time is set to 10 minutes, the current change amplitude is set to 0.05A, and the upper and lower threshold values ​​of the current value are set to 120% and 80% of the rated current of the cell, respectively.

4. The cell cycle life prediction method according to claim 1, characterized in that, Step S1, which identifies whether a battery cell has entered a high-load operating state by dynamically changing the rate of change of current, includes: During the charging and discharging process of the battery cell, the values ​​of the rate of change of current are arranged in chronological order to form a sequence; The time series is divided into multiple subsequences, each containing continuous current rate of change values; Calculate the absolute change in the difference between two adjacent current rate of change values ​​in each subsequence; calculate the number of times the sign of the current rate of change changes from positive to negative or from negative to positive in each subsequence; If the absolute value change in a subsequence exceeds 0.05 A / s and the number of sign changes exceeds 3, it is identified as a high dynamic change pattern. If three or more of the five consecutive subsequences are identified as high dynamic change modes, the cell is ultimately determined to have entered a high-load operating state.

5. The cell cycle life prediction method according to claim 1, characterized in that, Step S2 analyzes the voltage stability fluctuation of the battery cell and extracts voltage fluctuation parameters, including: Under high load operating conditions, the voltage value of the battery cell is continuously collected; Plot the voltage values ​​as a curve and calculate the linear regression slope of the voltage value curve. If the linear regression slope is greater than 0, it is marked as an upward fluctuation trend stage; if the linear regression slope is less than 0, it is marked as a downward fluctuation trend stage. During the upward and downward fluctuation trends, calculate the absolute value of the difference between two adjacent voltage values ​​to determine the median of the absolute value; calculate the ratio of the absolute value of each voltage difference to the median to obtain the mean and standard deviation of the ratio. If the average value of the ratio is greater than 1.5 and the standard deviation of the ratio is greater than 0.5, it is judged as a voltage fluctuation state, and the voltage fluctuation parameters are recorded.

6. The cell cycle life prediction method according to claim 1, characterized in that, Step S2, which involves determining the current health status of the battery cell based on the changing trend of voltage fluctuation parameters, includes: Arrange the voltage fluctuation parameters in chronological order to form a voltage parameter sequence; Calculate the ratio of the difference between two adjacent parameter values ​​in each voltage parameter sequence to the previous parameter value to determine the rate of change of the fluctuation parameter value; Calculate the rate of change of each fluctuation parameter value, and identify the extreme points and inflection points of the rate of change; If the number of extreme points in the rate of change of a fluctuation parameter value exceeds 3, then the cell health status corresponding to that voltage parameter sequence is judged to be in the rapid aging stage. If the number of inflection points in the rate of change of a fluctuation parameter value is less than one, then the cell health status corresponding to that voltage parameter sequence is judged to be in the normal aging stage.

7. The cell cycle life prediction method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: When the battery cell recovers from a high-load state to a normal operating state, record the recovery start time and recovery end time; Step S32: Calculate the time difference from high load state to recovery state as an indicator of the cell's recovery capability; Step S33: Calculate the ratio of the recovery capability index to the rated recovery time of the battery cell; Step S34: If the ratio is less than the preset recovery threshold, it is determined that the current capacity decay rate of the battery cell is within the normal range. Step S35: If the ratio is greater than or equal to the preset recovery threshold, the current capacity decay rate of the battery cell is determined to be abnormal.

8. The cell cycle life prediction method according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: In the initial state of the battery cell, perform a complete charge-discharge cycle and record the maximum capacity during the charging process as the initial capacity of the battery cell. Step S42: Calculate the cell capacity retention rate based on the initial cell capacity and the current capacity decay rate; Step S43: Determine the degree of cell aging in the current health status; Step S44: Multiply the cell capacity retention rate value by the cell aging degree value to determine the remaining cycle judgment factor; Step S45: Predict the remaining cycle life of the battery cell based on the remaining cycle judgment factor; Step S46: If the predicted remaining cycle life shows that the remaining cycle life of the cell is lower than the preset safe operation standard, then output cell replacement information.

9. A cell cycle life prediction system, characterized in that, For performing the cell cycle life prediction method as described in claim 1, the cell cycle life prediction system includes: The current monitoring module is used to monitor the rate of change of current in the battery cell during the cyclic charging and discharging process in real time; it identifies whether the battery cell has entered a high-load operating state by the dynamic change of the rate of change of current; if the battery cell is identified to have entered a high-load operating state, it initiates voltage stability analysis. The voltage fluctuation detection module is used to analyze the voltage stability fluctuation of the battery cell under high load conditions and extract voltage fluctuation parameters; the current health status of the battery cell is judged by the changing trend of the voltage fluctuation parameters. The capacity decay identification module is used to calculate the time difference between the high load state and the recovery state when the battery cell recovers from the high load state to the normal operating state, and use it as an indicator of the battery cell's recovery capability; the current capacity decay rate is detected by the value of the recovery capability indicator. The cycle life prediction module is used to obtain the initial capacity of the battery cell, combine it with the current health status and the current capacity decay rate, and predict the remaining cycle life of the battery cell. If the prediction result of the remaining cycle life shows that the remaining cycle life of the battery cell is lower than the preset safe operation standard, the battery cell replacement information is output.

10. A computer-readable medium storing a computer program that, when executed, implements the above-described method for predicting the cycle life of a battery cell.

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