Method and device for calculating capacity attenuation of power battery, electronic equipment and storage medium

Abstract

The application provides a capacity attenuation calculation method and device of a power battery, electronic equipment and a storage medium, wherein the capacity attenuation calculation method of the power battery comprises the following steps: solving parameters of a fitting function based on least square linear fitting of a plurality of groups of first data, and determining a first SOHC value based on the parameters of the fitting function and a battery rated capacity; and correcting a stored SOHC value based on the first SOHC value, a second SOHC value and a third SOHC value to obtain an SOHC value of the power battery. The application can calculate the capacity attenuation of the power battery. On the other hand, compared with the prior art, the application can ensure the capacity recognition robustness and strong tracking performance under dynamic working conditions and complex degradation environments, that is, the capacity recognition stability and real-time performance under dynamic working conditions and complex degradation environments can be ensured.

Description

Technical Field

[0001] This application relates to the field of electric vehicle battery management systems, and more specifically, to a method, apparatus, electronic device, and storage medium for calculating the capacity decay of a power battery. Background Technology

[0002] Currently, there are two main methods for estimating battery pack capacity degradation: one is based on the capacity increment method to estimate battery pack capacity, and the other is based on a neural network algorithm using an empirical model to estimate battery pack capacity.

[0003] However, regarding the first method, although it can estimate the battery pack capacity based on the actual vehicle operating environment and conditions, this method requires the battery consistency to meet preset conditions. However, the thermal field distribution of the battery pack in the actual vehicle operating environment is quite complex, and the temperature difference distribution will lead to a large difference in battery consistency, which will eventually make the battery consistency unable to meet the preset conditions. Therefore, this method cannot be used in operating conditions with large differences in battery consistency. On the other hand, factors such as battery polarization will cause the estimation results of this method to be unstable.

[0004] The second method requires extensive preliminary tests of the basic characteristics and operating conditions of the power battery to obtain a large amount of offline test data. Then, by using offline test data and empirical prediction, the battery pack capacity can be stably estimated under the premise that the battery consistency cannot meet the preset conditions. However, this method relies on a large amount of offline test data, which is historical test data. Historical test data cannot cover the real vehicle operating environment and conditions, especially since the real vehicle operating environment and conditions are extremely complex. Therefore, because historical test data cannot cover the real vehicle operating environment and conditions, the estimation results of this method are not real-time. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for calculating the capacity degradation of a power battery. Furthermore, compared with existing technologies, this application ensures robustness and strong tracking performance of capacity identification under dynamic operating conditions and complex degradation environments, i.e., it ensures the stability and real-time performance of capacity identification under dynamic operating conditions and complex degradation environments.

[0006] In a first aspect, the present invention provides a method for calculating the capacity decay of a power battery, the method comprising:

[0007] Multiple sets of parameter sampling data of the power battery in charging mode are acquired. The parameter sampling data includes first data, which includes cell SOC change and cumulative charge change. The multiple sets of parameter sampling data are stored in a filterable data buffer.

[0008] The parameters of the fitting function are obtained by linearly fitting multiple sets of the first data using the least squares method, and the first SOHC value is determined based on the parameters of the fitting function and the rated capacity of the battery. The fitting function is Y = AX, where Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function.

[0009] Select a sub-storage interval of preset length from the filterable data buffer, and calculate the sum of the cell SOC change and the sum of the cumulative power change based on the first data stored in the sub-storage interval. Calculate the second SOHC value based on the sum of the cell SOC change, the sum of the cumulative power change, and the battery rated capacity.

[0010] The third SOHC value was obtained by querying the battery operating condition cycle degradation relationship table.

[0011] The SOHC value is corrected and stored based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0012] In its first aspect, this application acquires multiple sets of parameter sampling data of a power battery in charging mode, and then uses the least squares method to linearly fit multiple sets of the first data to solve for the parameters of the fitting function. Based on the parameters of the fitting function and the rated capacity of the battery, a first SOHC value is determined. Then, a sub-storage interval of a preset length can be selected from the filterable data buffer, and based on the first data stored in the sub-storage interval, the sum of the changes in cell SOC and the sum of the changes in cumulative charge are calculated. Based on the sum of the changes in cell SOC and the sum of the changes in cumulative charge, and the rated capacity of the battery, a second SOHC value is calculated. Then, a third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0013] Compared with the prior art, this application uses the least squares method to linearly fit multiple sets of the first data, and can dynamically estimate the battery capacity degradation based on the first data corresponding to the real-time operating conditions. That is, it can obtain an estimation result based on the real-time operating conditions. At the same time, it can obtain another estimation result by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, and the battery capacity degradation can be calculated stably in real time.

[0014] In an optional implementation, the step of correcting and storing the SOHC value based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery includes:

[0015] Calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value.

[0016] The storage SOHC value is corrected based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0017] This optional implementation can calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value. Then, the storage SOHC value can be corrected based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0018] In an optional implementation, the step of correcting the storage SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery includes:

[0019] When the confidence level of the estimated SOHC value meets the preset condition, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3 * SOHC2 + 0.7 * SOHC3, and the second calculation formula is: SOHC1 = 0.25 * SOHC4 + 0.75 * SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the storage SOHC value, and SOHC4 represents the third SOHC value.

[0020] This optional implementation can correct the stored SOHC value based on the first calculation formula and the second calculation formula.

[0021] In an optional implementation, the method further includes:

[0022] Based on multiple sets of the first data, a preprocessing result is calculated, wherein the preprocessing result includes at least the number of samples, variance, and covariance;

[0023] Based on at least one of the sample size, variance, and covariance, it is determined whether the confidence level of the estimated SOHC value meets the preset condition, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

[0024] This optional implementation can calculate a preprocessing result based on multiple sets of the first data, wherein the preprocessing result includes at least the number of samples, variance, and covariance, and can then determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the number of samples, the variance, and the covariance, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

[0025] In a second aspect, the present invention provides a capacity decay calculation device for a power battery, the device comprising:

[0026] The acquisition module is used to acquire multiple sets of parameter sampling data of the power battery in charging mode. The parameter sampling data includes first data, which includes cell SOC change and cumulative charge change. The multiple sets of parameter sampling data are stored in a filterable data buffer.

[0027] The fitting module is used to solve the parameters of the fitting function by linearly fitting multiple sets of the first data based on the least squares method, and to determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery, wherein the fitting function is Y = AX, Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function.

[0028] The first calculation module selects a sub-storage interval of a preset length from the filterable data buffer, and calculates the sum of the cell SOC changes and the sum of the cumulative power changes based on the first data stored in the sub-storage interval. Based on the sum of the cell SOC changes, the sum of the cumulative power changes, and the battery rated capacity, it calculates the second SOHC value.

[0029] The query module is used to retrieve the third SOHC value based on the battery operating condition cycle degradation relationship table;

[0030] The second calculation module is used to correct and store the SOHC value based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0031] In its first aspect, this application acquires multiple sets of parameter sampling data of a power battery in charging mode, and then uses the least squares method to linearly fit multiple sets of the first data to solve for the parameters of the fitting function. Based on the parameters of the fitting function and the rated capacity of the battery, a first SOHC value is determined. Then, a sub-storage interval of a preset length can be selected from the filterable data buffer, and based on the first data stored in the sub-storage interval, the sum of the changes in cell SOC and the sum of the changes in cumulative charge are calculated. Based on the sum of the changes in cell SOC and the sum of the changes in cumulative charge, and the rated capacity of the battery, a second SOHC value is calculated. Then, a third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0032] Compared with the prior art, this application uses the least squares method to linearly fit multiple sets of the first data, and can dynamically estimate the battery capacity degradation based on the first data corresponding to the real-time operating conditions. That is, it can obtain an estimation result based on the real-time operating conditions. At the same time, it can obtain another estimation result by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, and the battery capacity degradation can be calculated stably in real time.

[0033] In an optional implementation, the second computing module includes:

[0034] The summation and average calculation submodule is used to calculate the average value of the first SOHC value and the second SOHC value, and use the average value of the first SOHC value and the second SOHC value as the estimated SOHC value.

[0035] The correction submodule is used to correct the storage SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0036] This optional implementation can calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value. Then, the storage SOHC value can be corrected based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0037] In an optional implementation, the correction submodule performs the correction of the stored SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery in the following specific manner:

[0038] When the confidence level of the estimated SOHC value meets the preset condition, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3*SOHC2 + 0.7*SOHC3, and the second calculation formula is: SOHC1 = 0.25*SOHC4 + 0.75*SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the storage SOHC value, and SOHC4 represents the third SOHC value.

[0039] This optional implementation can correct the stored SOHC value based on the first calculation formula and the second calculation formula.

[0040] In an optional embodiment, the apparatus further includes:

[0041] The third calculation module is used to calculate the preprocessing result based on multiple sets of the first data, wherein the preprocessing result includes at least the number of samples, variance, and covariance.

[0042] The judgment module is used to determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the sample number, the variance, and the covariance, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

[0043] This optional implementation can calculate a preprocessing result based on multiple sets of the first data, wherein the preprocessing result includes at least the number of samples, variance, and covariance, and can then determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the number of samples, the variance, and the covariance, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

[0044] Thirdly, the present invention provides an electronic device, comprising:

[0045] Processor; and

[0046] The memory is configured to store machine-readable instructions that, when executed by the processor, perform the capacity decay calculation method for the power battery as described in any of the foregoing embodiments.

[0047] The electronic device of the third aspect of this application, by executing a power battery capacity decay calculation method, can acquire multiple sets of parameter sampling data of the power battery in charging mode, and then solve the parameters of the fitting function by linearly fitting multiple sets of the first data using the least squares method, and determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery, and then select a sub-storage interval of a preset length from the filterable data buffer, and calculate the sum of the cell SOC changes and the sum of the cumulative charge changes based on the first data stored in the sub-storage interval, and calculate the second SOHC value based on the sum of the cell SOC changes and the sum of the cumulative charge changes and the rated capacity of the battery, and then query the third SOHC value based on the battery operating condition cycle decay relationship table, so as to correct the stored SOHC value based on the first SOHC value, the second SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0048] Compared with the prior art, this application uses the least squares method to linearly fit multiple sets of the first data, and can dynamically estimate the battery capacity degradation based on the first data corresponding to the real-time operating conditions. That is, it can obtain an estimation result based on the real-time operating conditions. At the same time, it can obtain another estimation result by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, and the battery capacity degradation can be calculated stably in real time.

[0049] Fourthly, the present invention provides a storage medium storing a computer program, the computer program being executed by a processor using the power battery capacity decay calculation method as described in any of the foregoing embodiments.

[0050] The storage medium of the fourth aspect of this application, by executing a power battery capacity decay calculation method, can acquire multiple sets of parameter sampling data of the power battery in charging mode, and then solve the parameters of the fitting function based on the least squares method linearly fitting multiple sets of the first data. Based on the parameters of the fitting function and the rated capacity of the battery, a first SOHC value is determined. Then, a sub-storage interval of a preset length can be selected from the filterable data buffer, and based on the first data stored in the sub-storage interval, the sum of the cell SOC changes and the sum of the cumulative charge changes are calculated. Based on the sum of the cell SOC changes, the sum of the cumulative charge changes, and the rated capacity of the battery, a second SOHC value is calculated. Then, a third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0051] Compared with the prior art, this application uses the least squares method to linearly fit multiple sets of the first data, and can dynamically estimate the battery capacity degradation based on the first data corresponding to the real-time operating conditions. That is, it can obtain an estimation result based on the real-time operating conditions. At the same time, it can obtain another estimation result by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, and the battery capacity degradation can be calculated stably in real time. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating a method for calculating the capacity decay of a power battery disclosed in an embodiment of this application.

[0054] Figure 2 This is a schematic diagram of the structure of a power battery capacity decay calculation device disclosed in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. Detailed Implementation

[0056] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0057] Example 1

[0058] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for calculating the capacity decay of a power battery disclosed in an embodiment of this application. Figure 1 As shown, the method in this application embodiment includes the following steps:

[0059] 101. Obtain multiple sets of parameter sampling data of the power battery in charging mode, wherein the parameter sampling data includes first data, which includes cell SOC change and cumulative charge change, and the multiple sets of parameter sampling data are stored in a filterable data buffer.

[0060] 102. Solve the parameters of the fitting function by linearly fitting multiple sets of first data using the least squares method, and determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery. The fitting function is Y = AX, where Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function.

[0061] 103. Select a sub-storage interval of preset length from the filterable data buffer, and calculate the sum of cell SOC changes and the sum of cumulative power changes based on the first data stored in the sub-storage interval. Calculate the second SOHC value based on the sum of cell SOC changes, the sum of cumulative power changes, and the battery rated capacity.

[0062] 104. The third SOHC value is obtained by querying the battery operating condition cycle degradation relationship table;

[0063] 105. Based on the first SOHC value, the second SOHC value and the third SOHC value, the stored SOHC value is corrected to obtain the SOHC value of the power battery.

[0064] This application embodiment acquires multiple sets of parameter sampling data of the power battery in charging mode, and then solves the parameters of the fitting function based on the least squares method linear fitting of multiple sets of first data. Based on the parameters of the fitting function and the rated capacity of the battery, the first SOHC value is determined. Then, a sub-storage interval of a preset length can be selected from the filterable data buffer. Based on the first data stored in the sub-storage interval, the sum of cell SOC changes and the sum of cumulative charge changes are calculated. Based on the sum of cell SOC changes, the sum of cumulative charge changes, and the rated capacity of the battery, the second SOHC value is calculated. Then, the third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0065] Compared with the prior art, the embodiments of this application can determine the real-time change law of battery capacity decay by linearly fitting multiple sets of first data using the least squares method, thereby calculating the battery capacity decay in real time. Based on the first data corresponding to the real-time operating conditions, the real-time change law of battery capacity decay can be determined, thereby dynamically estimating the battery capacity decay. That is, an estimation result based on the real-time operating conditions can be obtained. At the same time, another estimation result can be obtained by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, which can calculate the battery capacity decay in real time and stably.

[0066] In this embodiment of the application, for step 101, the charging mode refers to the power battery being in a charging state. The power battery being in a charging state can be determined by detecting the status of the charging gun.

[0067] In this embodiment of the application, for step 101, the sampling data can be collected by the battery management system (BMS). The BMS periodically collects the temperature of each cell module, the voltage of each cell module, and the total current of the power battery at a preset first cycle. For example, the BMS periodically collects the temperature of each cell module, the voltage of each cell module, and the total current of the power battery at a cycle of 0.1 seconds.

[0068] In this embodiment, regarding step 101, the Battery Management System (BMS) periodically calculates the cell SOC change and cumulative charge change within each cycle using a preset first sampling period and a preset second period. The BMS collects these values ​​into a data buffer. For example, assuming the BMS periodically collects the temperature, voltage, and total current of each cell module at 0.1 seconds within 30 minutes, and then periodically calculates the cell SOC change and cumulative charge change within each cycle at a fixed period of 3 minutes, 10 pairs of first data (cell SOC change and cumulative charge change) can be generated within 30 minutes. It should be noted that the first and second preset periods are calibrable parameters, and their specific values ​​are not limited in this embodiment.

[0069] In this embodiment of the application, for step 101, the filterable data buffer can refer to a numbered data buffer. For example, the data buffer includes 100 storage blocks, and the 100 storage blocks are numbered 0-99. In this way, data within a specified range of storage blocks can be obtained by filtering through the number.

[0070] In this embodiment, for step 101, the multiple sets of parameter sampling data can refer to 50 sets of parameter sampling data or 100 sets of parameter sampling data. This embodiment does not limit the specific number of sets of parameter sampling data. Further, some of the multiple sets of parameter sampling data do not include changes in cell SOC or cumulative charge, while others do include changes in cell SOC and cumulative charge. The reason some parameter sampling data do not include changes in cell SOC and cumulative charge is that they are not collected at a preset second cycle node. As a preferred embodiment, the multiple sets of parameter sampling data obtained in step 101 can be parameter sampling data that, after filtering, all include changes in cell SOC and cumulative charge.

[0071] In this embodiment of the application, for step 101, the data stored in the filterable data buffer corresponds to a charging process, that is, it is used to store the parameter data corresponding to a charging process. If the current charging process is settled in advance, the data stored in the filterable data buffer will be cleared to abandon the estimation of battery capacity degradation in this case.

[0072] In this embodiment of the application, for step 101, the change in SOC of the battery cell refers to the change in the state of charge of the battery cell, which is used to reflect the remaining capacity of the battery cell, where SOC refers to State of Charge.

[0073] In this embodiment of the application, for step 101, the cumulative change in charge refers to the total charge from a certain metering start point to a certain metering end point, which includes the battery's discharge and charge amounts. Further, the cumulative change in charge can be obtained based on the cell's current. For example, at a preset second cycle sampling node, assuming that during a 3-minute charging period, the cell's current and voltage are 0.1A and 3.5V respectively, then the cumulative change in charge is 0.1A * 3min.

[0074] In this embodiment of the application, a specific method for determining the first SOHC value based on the parameters of the fitting function and the rated battery capacity in step 102 is as follows:

[0075] The ratio of the parameters of the fitted function to the rated capacity of the battery is determined as the first SOHC value.

[0076] In the above-mentioned optional implementation method, the parameter A in the fitting function Y=AX reflects the actual capacity of the battery. For example, whenever 1Ah of electricity is charged, the actual SOC change is 1.05%, and thus Y / X=A=0.9523. Based on this calculation result, it can be considered that when the battery is fully charged, the actual SOC is 95.23%, not the displayed 100%. That is, when the battery is fully charged, the actual battery capacity is 0.9523 units. Correspondingly, if the rated capacity of the battery is 1 unit, then 0.9523 / 1 indicates that the battery capacity is only 95% of the rated capacity, that is, the battery capacity has decreased by 5%. It should be noted that the above calculation process is dimensionless.

[0077] In this embodiment of the application, for step 104, the battery operating condition cycle degradation relationship table can be as shown in Table 1.

[0078]

[0079] Table 1

[0080] In an optional implementation, the step of correcting the stored SOHC value based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery includes the following sub-steps:

[0081] Calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value;

[0082] The SOHC value of the power battery is obtained by correcting the stored SOHC value based on the estimated SOHC value and the third SOHC value.

[0083] This optional implementation can calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value. Then, based on the estimated SOHC value and the third SOHC value, the stored SOHC value can be corrected to obtain the SOHC value of the power battery.

[0084] Regarding the above optional implementation methods, the first SOHC value is calculated based on real-time parameters throughout the entire charging process, thus it can estimate battery capacity degradation from the perspective of the entire charging process. As for the second SOHC value, it estimates battery capacity degradation from the perspective of a sub-region throughout the entire charging process. In this way, the first SOHC value can be optimized based on the estimation results of a specific sub-region, so that the optimized first SOHC value is compatible with both overall and local variations. For example, assuming the first SOHC value is 96% and the second SOHC value corresponding to the sub-region is 98%, the average of the two, 97%, is taken, which is compatible with both overall and local variations.

[0085] In an optional implementation, the step of correcting the stored SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery includes the following sub-steps:

[0086] When the confidence level of the estimated SOHC value meets the preset conditions, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3 * SOHC2 + 0.7 * SOHC3, and the second calculation formula is: SOHC1 = 0.25 * SOHC4 + 0.75 * SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the stored SOHC value, and SOHC4 represents the third SOHC value.

[0087] This optional implementation can correct the stored SOHC value based on the first calculation formula and the second calculation formula.

[0088] In optional implementations, the method of this application embodiment further includes the following sub-steps:

[0089] Based on multiple sets of first data, the preprocessing results are calculated, which include at least the number of samples, variance, and covariance.

[0090] The confidence level of the estimated SOHC value is determined based on at least one of the following data: sample size, variance, and covariance. Specifically, the confidence level of the estimated SOHC value is considered to meet the preset condition when the variance is less than or equal to 2.

[0091] This optional implementation can calculate a preprocessing result based on multiple sets of first data. The preprocessing result includes at least the number of samples, variance, and covariance. Then, it can determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the data: the number of samples, variance, and covariance. When the variance is less than or equal to 2, the confidence level of the estimated SOHC value meets the preset condition.

[0092] Example 2

[0093] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a power battery capacity decay calculation device disclosed in an embodiment of this application, as shown below. Figure 2 As shown, the apparatus in this embodiment includes the following functional modules:

[0094] The acquisition module 201 is used to acquire multiple sets of parameter sampling data of the power battery in charging mode. The parameter sampling data includes first data, which includes cell SOC change and cumulative charge change. The multiple sets of parameter sampling data are stored in a filterable data buffer.

[0095] The fitting module 202 is used to solve the parameters of the fitting function by linearly fitting multiple sets of first data based on the least squares method, and to determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery. The fitting function is Y = AX, where Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function.

[0096] The first calculation module 203 selects a sub-storage interval of a preset length from the filterable data buffer, and calculates the sum of cell SOC changes and the sum of cumulative power changes based on the first data stored in the sub-storage interval. Based on the sum of cell SOC changes, the sum of cumulative power changes, and the battery rated capacity, it calculates the second SOHC value.

[0097] Query module 204 is used to query the third SOHC value based on the battery operating condition cycle degradation relationship table;

[0098] The second calculation module 205 is used to correct the stored SOHC value based on the first SOHC value, the second SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0099] This application embodiment acquires multiple sets of parameter sampling data of the power battery in charging mode, and then solves the parameters of the fitting function based on the least squares method linear fitting of multiple sets of first data. Based on the parameters of the fitting function and the rated capacity of the battery, the first SOHC value is determined. Then, a sub-storage interval of a preset length can be selected from the filterable data buffer. Based on the first data stored in the sub-storage interval, the sum of cell SOC changes and the sum of cumulative charge changes are calculated. Based on the sum of cell SOC changes, the sum of cumulative charge changes, and the rated capacity of the battery, the second SOHC value is calculated. Then, the third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0100] Compared with the prior art, the embodiments of this application use the least squares method to linearly fit multiple sets of first data, which can dynamically estimate the battery capacity decay based on the first data corresponding to the real-time operating conditions. That is, an estimation result based on the real-time operating conditions can be obtained. At the same time, another estimation result can be obtained by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, which can calculate the battery capacity decay in real time and stably.

[0101] In an optional implementation, the second calculation module includes the following sub-functional modules:

[0102] The summation and average calculation submodule is used to calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value.

[0103] The correction submodule is used to correct the stored SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery.

[0104] This optional implementation can calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value. Then, based on the estimated SOHC value and the third SOHC value, the stored SOHC value can be corrected to obtain the SOHC value of the power battery.

[0105] In an optional implementation, the correction submodule performs correction of the stored SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery in the following specific way:

[0106] When the confidence level of the estimated SOHC value meets the preset conditions, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3 * SOHC2 + 0.7 * SOHC3, and the second calculation formula is: SOHC1 = 0.25 * SOHC4 + 0.75 * SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the stored SOHC value, and SOHC4 represents the third SOHC value.

[0107] This optional implementation can correct the stored SOHC value based on the first calculation formula and the second calculation formula.

[0108] In optional embodiments, the apparatus of this application further includes the following functional modules:

[0109] The third calculation module is used to calculate the preprocessing results based on multiple sets of first data. The preprocessing results include at least the number of samples, variance, and covariance.

[0110] The judgment module is used to determine whether the confidence level of the estimated SOHC value meets the preset conditions based on at least one of the data of sample size, variance, and covariance. Specifically, when the variance is less than or equal to 2, the confidence level of the estimated SOHC value meets the preset conditions.

[0111] This optional implementation can calculate a preprocessing result based on multiple sets of first data. The preprocessing result includes at least the number of samples, variance, and covariance. Then, it can determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the data: the number of samples, variance, and covariance. When the variance is less than or equal to 2, the confidence level of the estimated SOHC value meets the preset condition.

[0112] It should be noted that for other detailed descriptions of the apparatus in the embodiments of this application, please refer to the relevant descriptions in Embodiment 1 of this application, which will not be repeated in the embodiments of this application.

[0113] Example 3

[0114] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application, such as... Figure 3 As shown, the electronic device in this application embodiment includes:

[0115] Processor 301; and

[0116] The memory 302 is configured to store machine-readable instructions, which, when executed by the processor 301, perform a power battery capacity decay calculation method as described in any of the foregoing embodiments.

[0117] The electronic device in this application embodiment can obtain multiple sets of parameter sampling data of the power battery in charging mode by executing the power battery capacity decay calculation method. Then, it can solve the parameters of the fitting function by linearly fitting multiple sets of first data using the least squares method, and determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery. Then, it can select a sub-storage interval of a preset length from the filterable data buffer, and calculate the sum of cell SOC changes and the sum of cumulative charge changes based on the first data stored in the sub-storage interval. Based on the sum of cell SOC changes, the sum of cumulative charge changes, and the rated capacity of the battery, it calculates the second SOHC value. Then, it can look up the third SOHC value based on the battery operating condition cycle decay relationship table. Thus, it can correct the stored SOHC value based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0118] Compared with the prior art, the embodiments of this application use the least squares method to linearly fit multiple sets of first data, which can dynamically estimate the battery capacity decay based on the first data corresponding to the real-time operating conditions. That is, an estimation result based on the real-time operating conditions can be obtained. At the same time, another estimation result can be obtained by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, which can calculate the battery capacity decay in real time and stably.

[0119] Fourthly, the present invention provides a storage medium storing a computer program, the computer program being executed by a processor as a method for calculating the capacity decay of a power battery as described in any of the foregoing embodiments.

[0120] Example 4

[0121] The storage medium in this embodiment of the application can obtain multiple sets of parameter sampling data of the power battery in charging mode by executing the capacity decay calculation method of the power battery. Then, it can solve the parameters of the fitting function by linearly fitting multiple sets of first data based on the least squares method, and determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery. Then, it can select a sub-storage interval of a preset length from the filterable data buffer, and calculate the sum of cell SOC changes and the sum of cumulative charge changes based on the first data stored in the sub-storage interval. Based on the sum of cell SOC changes, the sum of cumulative charge changes, and the rated capacity of the battery, the second SOHC value is calculated. Then, the third SOHC value can be obtained by querying the battery operating condition cycle decay relationship table. Thus, the stored SOHC value can be corrected based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery.

[0122] Compared with the prior art, the embodiments of this application use the least squares method to linearly fit multiple sets of first data, which can dynamically estimate the battery capacity decay based on the first data corresponding to the real-time operating conditions. That is, an estimation result based on the real-time operating conditions can be obtained. At the same time, another estimation result can be obtained by using the empirical prediction method of table lookup. Finally, the estimation result based on the table lookup is corrected by the estimation result based on the real-time operating conditions, which can calculate the battery capacity decay in real time and stably.

[0123] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0126] It should be noted that if a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0128] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for calculating the capacity decay of a power battery, characterized in that, The method includes: Multiple sets of parameter sampling data of the power battery in charging mode are acquired. The parameter sampling data includes first data, which includes cell SOC change and cumulative charge change. The multiple sets of parameter sampling data are stored in a filterable data buffer. The parameters of the fitting function are obtained by linearly fitting multiple sets of the first data using the least squares method, and the first SOHC value is determined based on the parameters of the fitting function and the rated capacity of the battery. The fitting function is Y=AX, where Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function. Select a sub-storage interval of preset length from the filterable data buffer, and calculate the sum of the cell SOC change and the sum of the cumulative power change based on the first data stored in the sub-storage interval. Calculate the second SOHC value based on the sum of the cell SOC change, the sum of the cumulative power change, and the battery rated capacity. The third SOHC value was obtained by querying the battery operating condition cycle degradation relationship table. The SOHC value is corrected and stored based on the first SOHC value, the second SOHC value and the third SOHC value to obtain the SOHC value of the power battery. And, the step of correcting and storing the SOHC value based on the first SOHC value, the second SOHC value, and the third SOHC value to obtain the SOHC value of the power battery includes: Calculate the average of the first SOHC value and the second SOHC value, and use the average of the first SOHC value and the second SOHC value as the estimated SOHC value. The storage SOHC value is corrected based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery. And, the step of correcting the storage SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery includes: When the confidence level of the estimated SOHC value meets the preset condition, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3 * SOHC2 + 0.7 * SOHC3, and the second calculation formula is: SOHC1 = 0.25 * SOHC4 + 0.75 * SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the storage SOHC value, and SOHC4 represents the third SOHC value.

2. The method as described in claim 1, characterized in that, The method further includes: Based on multiple sets of the first data, a preprocessing result is calculated, wherein the preprocessing result includes at least the number of samples, variance, and covariance; Based on at least one of the sample size, variance, and covariance, it is determined whether the confidence level of the estimated SOHC value meets the preset condition, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

3. A power battery capacity decay calculation device, characterized in that, The device includes: The acquisition module is used to acquire multiple sets of parameter sampling data of the power battery in charging mode. The parameter sampling data includes first data, which includes cell SOC change and cumulative charge change. The multiple sets of parameter sampling data are stored in a filterable data buffer. The fitting module is used to solve the parameters of the fitting function by linearly fitting multiple sets of the first data based on the least squares method, and to determine the first SOHC value based on the parameters of the fitting function and the rated capacity of the battery, wherein the fitting function is Y=AX, Y represents the cumulative change in charge, X represents the change in cell SOC, and A represents the parameters of the fitting function. The first calculation module selects a sub-storage interval of a preset length from the filterable data buffer, and calculates the sum of the cell SOC changes and the sum of the cumulative power changes based on the first data stored in the sub-storage interval. Based on the sum of the cell SOC changes, the sum of the cumulative power changes, and the battery rated capacity, it calculates the second SOHC value. The query module is used to retrieve the third SOHC value based on the battery operating condition cycle degradation relationship table; The second calculation module is used to correct the stored SOHC value based on the first SOHC value, the second SOHC value and the third SOHC value to obtain the SOHC value of the power battery. And, the second computing module includes: The summation and average calculation submodule is used to calculate the average value of the first SOHC value and the second SOHC value, and use the average value of the first SOHC value and the second SOHC value as the estimated SOHC value. The correction submodule is used to correct the storage SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery. Furthermore, the specific method by which the correction submodule performs the correction of the stored SOHC value based on the estimated SOHC value and the third SOHC value to obtain the SOHC value of the power battery is as follows: When the confidence level of the estimated SOHC value meets the preset condition, the SOHC value of the power battery is calculated based on the first calculation formula; otherwise, the SOHC value of the power battery is calculated based on the second calculation formula. The first calculation formula is: SOHC1 = 0.3 * SOHC2 + 0.7 * SOHC3, and the second calculation formula is: SOHC1 = 0.25 * SOHC4 + 0.75 * SOHC3, where SOHC1 represents the SOHC value of the power battery, SOHC2 represents the estimated SOHC value, SOHC3 represents the storage SOHC value, and SOHC4 represents the third SOHC value.

4. The apparatus as described in claim 3, characterized in that, The device further includes: The third calculation module is used to calculate the preprocessing result based on multiple sets of the first data, wherein the preprocessing result includes at least the number of samples, variance, and covariance. The judgment module is used to determine whether the confidence level of the estimated SOHC value meets the preset condition based on at least one of the sample number, the variance, and the covariance, wherein the confidence level of the estimated SOHC value meets the preset condition when the variance is less than or equal to 2.

5. An electronic device, characterized in that, include: processor; as well as The memory is configured to store machine-readable instructions that, when executed by the processor, perform the capacity decay calculation method for the power battery as described in any one of claims 1-2.

6. A storage medium, characterized in that, The storage medium stores a computer program, which is executed by a processor using the power battery capacity decay calculation method as described in any one of claims 1-2.

Images