A battery energy storage system life prediction method

Abstract

This invention relates to the field of battery energy storage system technology, specifically to a battery energy storage system lifespan prediction method, comprising the following steps: S1, conducting experimental tests on specific battery models for both cyclic and static usage scenarios, obtaining experimental test results, and acquiring real-time operational data of the energy storage power station; when in use, this invention combines experimental testing with a data-driven rapid battery lifespan estimation method, by statistically analyzing the daily operational data of the battery energy storage system; when calculating capacity decay, both cyclic decay and calendar decay are considered simultaneously to achieve rapid assessment and lifespan prediction of the battery's state of equilibrium (SOH); by using parallel calculation of daily operating conditions and all possible decay amounts, replacing the previous serial iterative calculation method, the algorithm efficiency is significantly improved; when data is missing, a missing value completion method is used to fill in the overall daily operating conditions, rather than directly interpolating to complete a specific variable, which helps improve the accuracy of the prediction.

Description

Technical Field

[0001] This invention relates to the field of battery energy storage system technology, and more specifically to a method for predicting the lifespan of a battery energy storage system. Background Technology

[0002] During long-term operation, the capacity and performance of batteries in energy storage systems will undergo irreversible degradation. However, inconsistencies in material distribution and structural design may exist during the manufacturing process of lithium batteries; in addition, the working environment of energy storage systems is complex and variable, such as temperature and charge / discharge rate, which will affect the aging process of batteries.

[0003] State of Health (SOH) is a quantitative indicator used to assess the current performance of an energy storage system compared to its initial performance, reflecting the overall health of the energy storage device. SOH is typically determined by comparing the device's current capacity with its rated capacity (or initial capacity), or it can be comprehensively evaluated in conjunction with other parameters such as internal resistance and cycle efficiency. The SOH value generally ranges from 0% to 100%, with higher values ​​indicating better device health and performance closer to the initial state. In this invention, SOH specifically refers to the percentage of the device's current capacity to its rated capacity. State of Charge (SOC) represents the ratio of the battery's current remaining charge to its rated capacity, a key parameter for measuring the degree of battery charging, usually expressed as a percentage. SOC ranges from 0% (fully discharged) to 100% (fully charged). Depth of Discharge (DOD) measures the proportion of a battery's discharged capacity to its total rated capacity, describing the battery's state of use. It represents the degree of discharge of the battery from a fully charged state to a given point in time, usually expressed as a percentage. DOD ranges from 0% (no charge / discharge) to 100% (fully charged / discharged). Lithium iron phosphate (LFPB) batteries are lithium-ion batteries that use lithium iron phosphate as the cathode material. They offer excellent safety and thermal stability, with a lower risk of overheating or fire. LFPB batteries are commonly used in applications requiring high safety, such as electric vehicles and energy storage systems. Calendar degradation refers to the capacity loss and performance degradation that occurs over time when a battery is not used or only minimally used. Calendar degradation is related to battery storage time, temperature, state of charge (SOC), and state of equilibrium (SOH). Cycle degradation refers to the capacity loss and performance degradation that occurs during charge-discharge cycles due to electrochemical reactions and mechanical stress. Cycle degradation is related to battery charge / discharge rate, rate capability, and state of equilibrium (SOH).

[0004] Patent publication number CN114545272A describes in its specification that "this application provides a battery life prediction method, apparatus, and device. The method obtains a battery to be processed and determines the corresponding evaluation conditions. Then, based on the evaluation conditions, it performs charge-discharge cycle tests on the battery to be processed to obtain the battery degradation rate. Based on the relationship curve between the number of cycles and the degradation rate, it determines the lifespan of the battery to be processed, solving the problem of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Furthermore, the embodiments of this application, based on the relationship curve between the number of cycles and the degradation rate, can predict the battery lifespan under different degradation rates, making battery life prediction more comprehensive. In addition, the embodiments of this application can also predict the lifespan of batteries under different evaluation conditions, meeting various application needs." However, this technology obtains the battery degradation rate by performing charge-discharge cycle tests on the battery during experimental testing, and estimates the state of harm (SOH) only from an experimental testing perspective, resulting in a narrow focus and affecting the accuracy of SOH estimation.

[0005] Patent publication number CN116224119A describes in its specification that "This application provides a method, apparatus, device, and medium for rapid prediction of battery life. The method includes: obtaining a battery cycle stage based on the number of cycle factors to be tested and battery cycle factors; testing the battery according to the battery cycle stage, obtaining the battery SOH decay value corresponding to each test sub-stage, and obtaining the equivalent cycle number corresponding to each test sub-stage based on the charge and discharge energy of the battery in each test sub-stage; and determining the equivalent cycle number based on the battery SOH decay value and the equivalent cycle number in each test sub-stage." The method involves counting cycles to obtain the battery degradation rate for each test sub-stage; obtaining the value of each test cycle factor based on the degradation rate for each test sub-stage; and predicting the cycle life of the battery when it degrades to the target battery SOH value based on the value of each test cycle factor and the target battery SOH value. The method described in this application can conveniently and quickly predict battery life. However, the aforementioned techniques only consider the capacity degradation during the battery's use phase, affecting the accuracy of the prediction results. Furthermore, predicting capacity degradation requires serial iterative calculations, which can lead to low efficiency and high resource consumption when the time span to be predicted is large.

[0006] Patent publication number CN112595980A describes in its specification that "This application provides a method, apparatus, and device for predicting the lifespan of a battery energy storage system. In this method, operating condition data of a battery energy storage system comprising multiple battery cells is acquired over a preset time period. Based on the operating condition data, a pre-acquired table of cycle life decay rate parameters and calendar life decay rate parameters for the battery cells are used to calculate the total lifespan decay of the battery energy storage system within the preset time period. Then, based on the state of health (SOH) of the battery energy storage system at the beginning of the preset time period and the total lifespan decay, the SOH of the battery energy storage system at the end of the preset time period is calculated. Finally, based on the SOH of the battery energy storage system at the end of the preset time period, the remaining lifespan of the battery energy storage system is obtained. In this method, the SOH at the end of the preset time period is determined by the total lifespan decay within the preset time period and the SOH at the beginning of the preset time period, achieving a more accurate prediction of the lifespan of the battery energy storage system." However, this technology requires serial iterative calculations when predicting capacity decay, which can lead to low efficiency and high resource consumption when the time span to be predicted is large.

[0007] In conclusion, developing a method for predicting the lifespan of battery energy storage systems remains a critical issue that urgently needs to be addressed in the field of battery energy storage system technology. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies that rely on charge-discharge cycle testing during experimental testing to obtain battery degradation rates. Estimating SOH solely from an experimental perspective leads to a narrow focus, affecting the accuracy of SOH estimation. Furthermore, considering only the capacity decay during battery use impacts the accuracy of prediction results. Additionally, predicting capacity decay requires sequential iterative calculations, which can result in low efficiency and high resource consumption when the prediction time span is large.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] This invention provides a method for predicting the lifetime of a battery energy storage system, comprising the following steps:

[0011] S1. Conduct experimental tests on specific battery models for both cyclic and static usage scenarios, obtain experimental test results, and acquire real-time operational data of the energy storage power station.

[0012] S2. Based on the actual operating data, obtain statistical indicators of daily operating conditions through parallel analysis;

[0013] S3. During the period when any of the operational data mentioned in the statistical indicators is missing, the missing value completion method is used to fill in the missing values ​​to obtain the statistical indicators of the daily operating conditions within the statistical period.

[0014] S4. Based on the statistical indicators and experimental test results, calculate in parallel the daily cycle decay under charge and discharge conditions, calendar decay under static conditions, and daily total capacity decay for all SOH starting segments within the statistical period, and record them in the daily result vector.

[0015] S5. Based on the initial SOH of the battery, the daily result vector is aggregated within the statistical period to obtain the final SOH result.

[0016] Furthermore, in step S1, the method for conducting experimental tests on specific battery models for both cyclic and static usage scenarios, obtaining experimental test results, and acquiring real-time operational data of the energy storage power station is as follows:

[0017] The experimental tests include two aspects: cell cycle degradation test and cell calendar degradation test. Based on the cell cycle degradation test, a cycle life degradation rate parameter table is established to represent the cycle life degradation rate per unit energy flux of the cell under different combinations of SOH, temperature, and rate. Without loss of generality, cell capacity degradation curves are measured at 1C rated rate and temperatures of 10℃, 20℃, 25℃, 35℃, and 45℃. The corresponding rate is calculated using the derivative at each data point on this cell capacity degradation curve. ,temperature , Below, capacity decay per unit energy flux The corresponding cyclic decay test results are obtained, and the calculation expression is as follows:

[0018]

[0019] In the formula, This represents the capacity decay per unit of energy flux. This indicates the change in SOH. Indicates the depth of charge and discharge. For the increment of the cycle number, Indicates the first Without loss of generality, the capacity decay curves of each battery cell were measured at 20℃ at rates of 0.5, 1, and 2 times the rated power. Cyclic decay test results were obtained at different temperatures at 1C rate and at different rates at 20℃.

[0020] Furthermore, in step S1, the method for conducting experimental tests on specific battery models for both cyclic and static usage scenarios, obtaining experimental test results, and acquiring real-time operational data of the energy storage power station is as follows:

[0021] A calendar life degradation rate parameter table was established based on cell calendar degradation testing. This table is used to calculate the total life degradation of the battery energy storage system within a statistical period, representing the calendar life degradation rate of the battery cell per unit resting time under different SOH, temperature, and SOC conditions. Without loss of generality, cell capacity degradation curves were measured at 1C rated rate and temperatures of 10℃, 15℃, 20℃, 25℃, and 30℃. The derivative at each data point on the cell capacity degradation curve was used to calculate the corresponding rate. ,temperature , Below, capacity decay per unit energy flux The calculation expression is as follows:

[0022]

[0023] In the formula, To measure the calendar time increments without loss of generality, the cell capacity decay curves were measured at 20℃ with SOCs of 20%, 35%, 50%, 65%, 80%, 90%, and 100%. Calendar decay test results were obtained for different temperatures at 80% SOC and for different SOCs at 20℃.

[0024] Furthermore, in step S2, the method for obtaining statistical indicators of daily operating conditions through parallel analysis based on the actual operating data is as follows:

[0025] The actual operating data generated by the battery energy storage system during operation is stored locally and simultaneously uploaded and stored in the cloud. When it is necessary to retrieve actual operating data from a point in time prior, it is obtained from both local records and the cloud. The statistical indicators of daily operating conditions obtained through parallel analysis include the cumulative charge / discharge amount. The average charge / discharge temperature is Average charge / discharge rate The resting time after charging / discharging is The SOC after charging / discharging and resting is The average temperature after charging / discharging and resting is .

[0026] Furthermore, in step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values, and the method for obtaining the statistical indicators of continuous daily operating conditions within the statistical period is as follows:

[0027] The missing value completion methods include the nearest neighbor method and the linear interpolation method, for time series. Set any position involving statistical indicators For periods of missing runtime data, the nearest neighbor interpolation method will replace the missing values ​​with the nearest known data point, as shown in the following formula:

[0028]

[0029] In the formula, Indicates separation The most recent known data points, Indicates position Missing values ​​at the specified location.

[0030] Furthermore, in step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values, and the method for obtaining the statistical indicators of continuous daily operating conditions within the statistical period is as follows:

[0031] The linear interpolation method is a method for estimating missing data based on the linear relationship between adjacent known data points. It sets... and These are two known data points. These are the missing values ​​between them. The formula for the linear interpolation method is as follows:

[0032]

[0033] In the formula, Indicates a point in time Missing values, It is the time point where the missing value is located. and These represent the time intervals for the known data points to the left and right of the missing value, respectively. and These are the known data values ​​at these two points in time.

[0034] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0035] According to the statistical indicators And a list of initial SOH intervals under all SOH starting segments for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. :

[0036]

[0037] In the formula, This represents the total daily cycle decay under the nth SOH range, obtained by adding the charging cycle decay component and the discharging cycle decay component, where charging decay is the primary factor. This represents the cyclic decay component generated during the charging process in the nth SOH range, and the discharge decay. This represents the cyclic decay component generated during the discharge process in the nth SOH range. and They are respectively:

[0038] ,

[0039] In the formula, , Indicates the first Under the SOH range, the cyclic decay components generated during the charging and discharging processes are as follows: This indicates the charging temperature under the nth SOH range. Charging rate The basic coefficient of degradation due to charging cycle effects, similarly, on the discharging side... It is the basic attenuation coefficient under discharge conditions.

[0040] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0041] Based on the statistically obtained working condition indicators , , And the initial SOH range list for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. :

[0042]

[0043] In the formula, It is the first The total attenuation under static conditions in the SOH range, the first After charging in the SOH range, the static decay occurs. and the After discharge in the SOH range, the temperature is allowed to drop and then allowed to decay. They are respectively:

[0044]

[0045] In the formula, , No. The attenuation components generated after charging and resting, and after discharging and resting, under the SOH range. It is the initial static condition after charging. The attenuation baseline coefficient affected by discharge SOH factor, and the static side after discharge. Similarly, it is the basic attenuation coefficient after being left to stand still after discharge.

[0046] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0047] The initial SOH range list for each day is calculated based on the daily operating conditions. The total possible attenuation is shown in the following formula:

[0048]

[0049] In the formula, This indicates that the nth type of total decay is the sum of charge-discharge cycle decay and rest calendar decay. This represents the daily cycle degradation under charge-discharge conditions in the nth case. It is the first The total attenuation under static conditions in the SOH range.

[0050] Further, in step S5, based on the initial SOH of the battery, the method for aggregating the daily result vectors within the statistical period to obtain the final SOH result is as follows:

[0051] The initial SOH for the first day of the statistical period is set to 100% or any other specified value. The initial SOH for each day is derived from the SOH calculation result of the previous day. This SOH is set to be the third interval in the SOH interval list. Obtained from the total decay vector of that day As the decay value for that day, the final SOH value for that day is:

[0052]

[0053] In the formula, This represents the decay value obtained from the total decay vector on that day, corresponding to the interval where SOH is located. It is used to calculate the final SOH value for that day. The process is iterated from the first day to the last day of the statistical period to obtain the final SOH result, which is the estimated SOH value for the statistical period.

[0054] Beneficial effects

[0055] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0056] In use, this invention combines experimental testing with a data-driven method for rapid battery life estimation. It statistically analyzes the daily operating data of the battery energy storage system. When calculating capacity decay, it considers both cycle decay and calendar decay to achieve rapid assessment of battery state of health (SOH) and life prediction. By parallel calculation of daily operating conditions and all possible decay amounts, it replaces the previous serial iterative calculation method, thereby significantly improving algorithm efficiency. When data is missing, it uses a missing value completion method to fill in the overall daily operating conditions, rather than directly interpolating to complete a specific variable, which helps improve the accuracy of prediction. Attached Figure Description

[0057] Figure 1 This is a flowchart of a battery energy storage system lifetime prediction method according to the present invention.

[0058] Figure 2 The graph shows the cycle decay test results at different temperatures at 1C rate for the battery energy storage system lifetime prediction method of the present invention.

[0059] Figure 3 The graph shows the cyclic degradation test results at 20°C for different rates, based on the battery energy storage system lifetime prediction method of this invention.

[0060] Figure 4 This is a graph showing the calendar decay test results at different temperatures under 80% SOC, based on the battery energy storage system lifetime prediction method of this invention.

[0061] Figure 5 This is a graph showing the calendar decay test results at 20°C for different SOCs in a battery energy storage system lifetime prediction method according to the present invention. Detailed Implementation

[0062] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0063] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but includes other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0064] The present invention will now be described in further detail with reference to the accompanying drawings:

[0065] Example:

[0066] like Figures 1-5 As shown, the present invention provides a method for predicting the lifetime of a battery energy storage system, comprising the following steps:

[0067] S1. Conduct experimental tests on specific battery models for both cyclic and static usage scenarios, obtain experimental test results, and acquire real-time operational data of the energy storage power station.

[0068] Furthermore, in step S1, the method for conducting experimental tests on specific battery models for both cyclic and static usage scenarios, obtaining experimental test results, and acquiring real-time operational data of the energy storage power station is as follows:

[0069] The experimental tests include two aspects: cell cycle degradation test and cell calendar degradation test. Based on the cell cycle degradation test, a cycle life degradation rate parameter table is established to represent the cycle life degradation rate per unit energy flux of the cell under different combinations of SOH, temperature, and rate. Without loss of generality, cell capacity degradation curves are measured at 1C rated rate and temperatures of 10℃, 20℃, 25℃, 35℃, and 45℃. The corresponding rate is calculated using the derivative at each data point on this cell capacity degradation curve. ,temperature , Below, capacity decay per unit energy flux The corresponding cyclic decay test results are obtained, and the calculation expression is as follows:

[0070]

[0071] In the formula, This represents the capacity decay per unit of energy flux. This indicates the change in SOH. Indicates the depth of charge and discharge. For the increment of the cycle number, Indicates the first Without loss of generality, the capacity decay curves of each battery cell were measured at 20℃ at rates of 0.5, 1, and 2 times the rated power. Cyclic decay test results were obtained at different temperatures at 1C rate and at different rates at 20℃.

[0072] Furthermore, in step S1, the method for conducting experimental tests on specific battery models for both cyclic and static usage scenarios, obtaining experimental test results, and acquiring real-time operational data of the energy storage power station is as follows:

[0073] A calendar life degradation rate parameter table was established based on cell calendar degradation testing. This table is used to calculate the total life degradation of the battery energy storage system within a statistical period, representing the calendar life degradation rate of the battery cell per unit resting time under different SOH, temperature, and SOC conditions. Without loss of generality, cell capacity degradation curves were measured at 1C rated rate and temperatures of 10℃, 15℃, 20℃, 25℃, and 30℃. The derivative at each data point on the cell capacity degradation curve was used to calculate the corresponding rate. ,temperature , Below, capacity decay per unit energy flux The calculation expression is as follows:

[0074]

[0075] In the formula, To measure the calendar time increments without loss of generality, the cell capacity decay curves were measured at 20℃ with SOCs of 20%, 35%, 50%, 65%, 80%, 90%, and 100%. Calendar decay test results were obtained for different temperatures at 80% SOC and for different SOCs at 20℃.

[0076] In this embodiment, the method consists of two parts: experimental testing and on-site data acquisition. The experimental testing primarily establishes the basic degradation parameter model and acquires the battery system's operational data input, providing experimental basis and data support for subsequent SOH evolution analysis. On the other hand, to improve the model's real-world adaptability, the system simultaneously collects real-time operational data from the energy storage power station. In the actually deployed energy storage system, the BMS, EMS, and PCS continuously collect operational parameters such as charge / discharge capacity, temperature, SOC, voltage, and current. This data is stored in a local database in real time and simultaneously uploaded to a cloud platform for model invocation and historical backtracking. It supports multi-variable inputs such as rate, temperature, and SOC, covering the battery system's operating status under complex conditions and improving the accuracy of capacity prediction.

[0077] S2. Based on the actual operating data, obtain statistical indicators of daily operating conditions through parallel analysis;

[0078] Furthermore, in step S2, the method for obtaining statistical indicators of daily operating conditions through parallel analysis based on the actual operating data is as follows:

[0079] The actual operating data generated by the battery energy storage system during operation is stored locally and simultaneously uploaded and stored in the cloud. When it is necessary to retrieve actual operating data from a point in time prior, it is obtained from both local records and the cloud. The statistical indicators of daily operating conditions obtained through parallel analysis include the cumulative charge / discharge amount. The average charge / discharge temperature is Average charge / discharge rate The resting time after charging / discharging is The SOC after charging / discharging and resting is The average temperature after charging / discharging and resting is .

[0080] In this embodiment, the method constructs a set of statistical indicators for daily operating conditions, providing high-quality feature support for subsequent SOH evolution modeling. Based on the collected real battery operating data, the method efficiently extracts the daily operating feature indicators of the battery within the statistical period through a parallel analysis mechanism, forming a daily operating condition profile. The statistical indicators cover core dimensions such as battery capacity, rate of return, temperature, and resting status, which can reflect the battery's usage status on different operating days in a fine-grained manner, making it easier to improve the comprehensiveness and accuracy of feature information. Through the parallel analysis mechanism, the method effectively reduces data processing latency and adapts to large-scale data scenarios.

[0081] S3. During the period when any of the operational data mentioned in the statistical indicators is missing, the missing value completion method is used to fill in the missing values ​​to obtain the statistical indicators of the daily operating conditions within the statistical period.

[0082] Furthermore, in step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values, and the method for obtaining the statistical indicators of continuous daily operating conditions within the statistical period is as follows:

[0083] The missing value completion methods include the nearest neighbor method and the linear interpolation method, for time series. Set any position involving statistical indicators For periods of missing runtime data, the nearest neighbor interpolation method will replace the missing values ​​with the nearest known data point, as shown in the following formula:

[0084]

[0085] In the formula, Indicates separation The most recent known data points, Indicates position Missing values ​​at the specified location.

[0086] Furthermore, in step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values, and the method for obtaining the statistical indicators of continuous daily operating conditions within the statistical period is as follows:

[0087] The linear interpolation method is a method for estimating missing data based on the linear relationship between adjacent known data points. It sets... and These are two known data points. These are the missing values ​​between them. The formula for the linear interpolation method is as follows:

[0088]

[0089] In the formula, Indicates a point in time Missing values, It is the time point where the missing value is located. and These represent the time intervals for the known data points to the left and right of the missing value, respectively. and These are the known data values ​​at these two points in time.

[0090] In this embodiment, the method addresses the issue of missing operational data during battery operation by introducing a missing value completion mechanism. This ensures the completeness and continuity of daily operational statistical indicators within the statistical period, laying a data foundation for subsequent SOH evolution modeling. Specifically, when statistical operational indicators, including but not limited to charge / discharge rate, temperature, SOC fluctuation, and resting time, are missing on certain days, the system automatically calls the missing value completion algorithm for interpolation and filling, ensuring that the indicator vector for each day remains complete. This effectively solves the problem of incomplete data, improves the stability and robustness of SOH estimation, and flexibly selects nearest neighbor or linear interpolation for different data types and missing scenarios to improve completion accuracy.

[0091] S4. Based on the statistical indicators and experimental test results, calculate in parallel the daily cycle decay under charge and discharge conditions, calendar decay under static conditions, and daily total capacity decay for all SOH starting segments within the statistical period, and record them in the daily result vector.

[0092] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0093] According to the statistical indicators And a list of initial SOH intervals under all SOH starting segments for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. :

[0094]

[0095] In the formula, This represents the total daily cycle decay under the nth SOH range, obtained by adding the charging cycle decay component and the discharging cycle decay component, where charging decay is the primary factor. This represents the cyclic decay component generated during the charging process in the nth SOH range, and the discharge decay. This represents the cyclic decay component generated during the discharge process in the nth SOH range. and They are respectively:

[0096] ,

[0097] In the formula, , Indicates the first Under the SOH range, the cyclic decay components generated during the charging and discharging processes are as follows: This indicates the charging temperature under the nth SOH range. Charging rate The basic coefficient of degradation due to charging cycle effects, similarly, on the discharging side... It is the basic attenuation coefficient under discharge conditions.

[0098] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0099] Based on the statistically obtained working condition indicators , , And the initial SOH range list for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. :

[0100]

[0101] In the formula, It is the first The total attenuation under static conditions in the SOH range, the first After charging in the SOH range, the static decay occurs. and the After discharge in the SOH range, the temperature is allowed to drop and then allowed to decay. They are respectively:

[0102]

[0103] In the formula, , No. The attenuation components generated after charging and resting, and after discharging and resting, under the SOH range. It is the initial static condition after charging. The attenuation baseline coefficient affected by discharge SOH factor, and the static side after discharge. Similarly, it is the basic attenuation coefficient after being left to stand still after discharge.

[0104] Further, in step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the corresponding daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay in all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows:

[0105] The initial SOH range list for each day is calculated based on the daily operating conditions. The total possible attenuation is shown in the following formula:

[0106]

[0107] In the formula, This indicates that the nth type of total decay is the sum of charge-discharge cycle decay and rest calendar decay. This represents the daily cycle degradation under charge-discharge conditions in the nth case. It is the first The total attenuation under static conditions in the SOH range.

[0108] In this embodiment, the method combines statistical operating indicators with experimental test degradation model parameters to model the daily operation of the battery under all possible SOH initial states within a statistical period. By parallel calculation of the daily cyclic degradation under charge-discharge conditions and the calendar degradation under rest conditions, the total daily capacity degradation vector is summarized. By linking daily operating conditions with experimental parameters, the method refines the multi-factor degradation estimation to the SOH section, which helps improve the accuracy of the method. The parallel modeling scheme for multiple SOH initial states facilitates parallel deployment and is suitable for large-scale battery cluster state assessment. Separating the cyclic degradation and calendar degradation models enhances the interpretability and engineering control capabilities of the model. It is suitable for real-time deployment in edge BMS and batch modeling in cloud big data platforms, combining real-time performance and accuracy.

[0109] S5. Based on the initial SOH of the battery, the daily result vectors are aggregated within the statistical period to obtain the final SOH result;

[0110] Further, in step S5, based on the initial SOH of the battery, the method for aggregating the daily result vectors within the statistical period to obtain the final SOH result is as follows:

[0111] The initial SOH for the first day of the statistical period is set to 100% or any other specified value. The initial SOH for each day is derived from the SOH calculation result of the previous day. This SOH is set to be the third interval in the SOH interval list. Obtained from the total decay vector of that day As the decay value for that day, the final SOH value for that day is:

[0112]

[0113] In the formula, This represents the decay value obtained from the total decay vector on that day, corresponding to the interval where SOH is located. It is used to calculate the final SOH value for that day. The process is iterated from the first day to the last day of the statistical period to obtain the final SOH result, which is the estimated SOH value for the statistical period.

[0114] In this embodiment, the method dynamically tracks and calculates the battery's health status by aggregating the daily result vector obtained in the preceding step S4 over the entire statistical period based on the battery's initial SOH value. This method supports two initial SOH setting modes: the SOH for day 1 can be set to 100%, or it can be set to any user-specified value. Subsequently, the initial SOH value for each day is derived from the final calculated value of the previous day, forming a time-continuous health status sequence. This complete SOH evolution path from the initial to the final day improves the temporal resolution of SOH estimation. The method employs segmented SOH modeling and a distributed decay vector extraction mechanism to reduce the accumulation of local errors, adapting to different initial SOH conditions and supporting flexible estimation for both new and old batteries.

[0115] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the lifespan of a battery energy storage system, characterized in that, Includes the following steps: S1. Conduct experimental tests on specific battery models for both cyclic and static usage scenarios, obtain experimental test results, and acquire real-time operational data of the energy storage power station. S2. Based on the actual operating data, obtain statistical indicators of daily operating conditions through parallel analysis; S3. During the period when any of the operational data mentioned in the statistical indicators is missing, the missing value completion method is used to fill in the missing values ​​to obtain the statistical indicators of the daily operating conditions for the continuous period. S4. Based on the statistical indicators and experimental test results, calculate in parallel the daily cycle decay under charge and discharge conditions, calendar decay under static conditions, and daily total capacity decay for all SOH starting segments within the statistical period, and record them in the daily result vector. S5. Based on the initial SOH of the battery, the daily result vector is aggregated within the statistical period to obtain the final SOH result.

2. The battery energy storage system lifetime prediction method according to claim 1, characterized in that, In step S1, the method for conducting experimental tests on a specific battery model under two usage scenarios—cyclic operation and static storage—to obtain the experimental test results and acquire real-time operational data of the energy storage power station is as follows: The experimental tests include two aspects: cell cycle degradation test and cell calendar degradation test. Based on the cell cycle degradation test, a cycle life degradation rate parameter table is established to represent the cycle life degradation rate per unit energy flux of the cell under different combinations of SOH, temperature, and rate. Without loss of generality, cell capacity degradation curves are measured at 1C rated rate and temperatures of 10℃, 20℃, 25℃, 35℃, and 45℃. The corresponding rate is calculated using the derivative at each data point on this cell capacity degradation curve. ,temperature , Below, capacity decay per unit energy flux The corresponding cyclic decay test results are obtained, and the calculation expression is as follows: ， In the formula, This represents the capacity decay per unit of energy flux. This indicates the change in SOH. Indicates the depth of charge and discharge. For the increment of the cycle number, Indicates the first Without loss of generality, the capacity decay curves of each battery cell were measured at 20℃ at rates of 0.5, 1, and 2 times the rated power. Cyclic decay test results were obtained at different temperatures at 1C rate and at different rates at 20℃.

3. The battery energy storage system lifetime prediction method according to claim 2, characterized in that, In step S1, the method for conducting experimental tests on a specific battery model under two usage scenarios—cyclic operation and static storage—to obtain the experimental test results and acquire real-time operational data of the energy storage power station is as follows: A calendar life degradation rate parameter table was established based on cell calendar degradation testing. This table is used to calculate the total life degradation of the battery energy storage system within a statistical period, representing the calendar life degradation rate of the battery cell per unit resting time under different SOH, temperature, and SOC conditions. Without loss of generality, cell capacity degradation curves were measured at 1C rated rate and temperatures of 10℃, 15℃, 20℃, 25℃, and 30℃. The derivative at each data point on the cell capacity degradation curve was used to calculate the corresponding rate. ,temperature , Below, capacity decay per unit energy flux The calculation expression is as follows: ， In the formula, To measure the calendar time increments without loss of generality, the cell capacity decay curves were measured at 20℃ with SOCs of 20%, 35%, 50%, 65%, 80%, 90%, and 100%. Calendar decay test results were obtained for different temperatures at 80% SOC and for different SOCs at 20℃.

4. The battery energy storage system lifetime prediction method according to claim 3, characterized in that, In step S2, the method for obtaining statistical indicators of daily operating conditions through parallel analysis based on the actual operating data is as follows: The actual operating data generated by the battery energy storage system during operation is stored locally and simultaneously uploaded and stored in the cloud. When it is necessary to retrieve actual operating data from a point in time prior, it is obtained from both local records and the cloud. The statistical indicators of daily operating conditions obtained through parallel analysis include the cumulative charge / discharge amount. The average charge / discharge temperature is Average charge / discharge rate The resting time after charging / discharging is The SOC after charging / discharging and resting is The average temperature after charging / discharging and resting is .

5. The battery energy storage system lifetime prediction method according to claim 4, characterized in that, In step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values. The method for obtaining the statistical indicators of continuous daily operational conditions within the statistical period is as follows: The missing value completion methods include the nearest neighbor method and the linear interpolation method, for time series. Set any position involving statistical indicators For periods of missing runtime data, the nearest neighbor interpolation method will replace the missing values ​​with the nearest known data point, as shown in the following formula: ， In the formula, Indicates separation The most recent known data points, Indicates position Missing values ​​at the specified location.

6. The battery energy storage system lifetime prediction method according to claim 5, characterized in that, In step S3, during periods when any of the operational data related to statistical indicators is missing, a missing value completion method is used to fill in the missing values. The method for obtaining the statistical indicators of continuous daily operational conditions within the statistical period is as follows: The linear interpolation method is a method for estimating missing data based on the linear relationship between adjacent known data points. It sets... and These are two known data points. These are the missing values ​​between them. The formula for the linear interpolation method is as follows: ， In the formula, Indicates a point in time Missing values, It is the time point where the missing value is located. and These represent the time intervals for the known data points to the left and right of the missing value, respectively. and These are the known data values ​​at these two points in time.

7. The battery energy storage system lifetime prediction method according to claim 6, characterized in that, In step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay under all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows: According to the statistical indicators And a list of initial SOH intervals under all SOH starting segments for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. : ， In the formula, This represents the total daily cycle decay under the nth SOH range, obtained by adding the charging cycle decay component and the discharging cycle decay component, where charging decay is the primary factor. This represents the cyclic decay component generated during the charging process in the nth SOH range, and the discharge decay. This represents the cyclic decay component generated during the discharge process in the nth SOH range. and They are respectively: ， In the formula, , Indicates the first Under the SOH range, the cyclic decay components generated during the charging and discharging processes are as follows: This indicates the charging temperature under the nth SOH range. Charging rate The basic coefficient of degradation due to charging cycle effects, similarly, on the discharging side... It is the basic attenuation coefficient under discharge conditions.

8. The battery energy storage system lifetime prediction method according to claim 7, characterized in that, In step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay under all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows: Based on the statistically obtained working condition indicators , , And the initial SOH range list for that day. The following formula can be used to calculate all possible cyclic decay amounts for that day. : ， In the formula, It is the first The total attenuation under static conditions in the SOH range, the first After charging in the SOH range, the static decay occurs. and the After discharge in the SOH range, the temperature is allowed to drop and then allowed to decay. They are respectively: ， In the formula, , No. The attenuation components generated after charging and resting, and after discharging and resting, under the SOH range. It is the initial static condition after charging. The attenuation baseline coefficient affected by discharge SOH factor, and the static side after discharge. Similarly, it is the basic attenuation coefficient after being left to stand still after discharge.

9. The battery energy storage system lifetime prediction method according to claim 8, characterized in that, In step S4, based on the statistical indicators and experimental test results, the method for parallel calculation of the daily cycle decay under charge / discharge conditions, calendar decay under static conditions, and daily total capacity decay under all SOH starting segments within the statistical period, and recording them in the daily result vector, is as follows: The initial SOH range list for each day is calculated based on the daily operating conditions. The total possible attenuation is shown in the following formula: ， In the formula, This indicates that the nth type of total decay is the sum of charge-discharge cycle decay and rest calendar decay. This represents the daily cycle degradation under charge-discharge conditions in the nth case. It is the first The total attenuation under static conditions in the SOH range.

10. The battery energy storage system lifetime prediction method according to claim 8, characterized in that, In step S5, based on the initial SOH of the battery, the method for aggregating the daily result vectors within the statistical period to obtain the final SOH result is as follows: The initial SOH for the first day of the statistical period is set to 100% or any other specified value. The initial SOH for each day is derived from the SOH calculation result of the previous day. This SOH is set to be the third interval in the SOH interval list. Obtained from the total decay vector of that day As the decay value for that day, the final SOH value for that day is: ， In the formula, This represents the decay value obtained from the total decay vector on that day, corresponding to the interval where SOH is located. It is used to calculate the final SOH value for that day. The process is iterated from the first day to the last day of the statistical period to obtain the final SOH result, which is the estimated SOH value for the statistical period.

Images