Battery state prediction method, system and equipment based on interpolation standardization and medium

Abstract

The invention provides a battery state prediction method, system and device based on interpolation standardization and a medium, and relates to the technical field of batteries, and the method comprises the steps: obtaining initial voltage data and initial capacity data in the charging and discharging process of a target cell; performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset interpolation point number to obtain interpolation voltage data and interpolation capacity data; performing differential smoothing processing on the interpolation voltage data and the interpolation capacity data to obtain capacity increment data and differential voltage data; obtaining a capacity increment curve according to the interpolation voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolation capacity data and the differential voltage data; and obtaining a target interpolation capacity according to the curve characteristics of the differential voltage curve, and determining the battery health state of the target battery cell according to the target interpolation capacity. According to the invention, the test influence caused by different test conditions is normalized, so that the cell health state prediction based on the differential voltage curve is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery state prediction method, system, device and medium based on interpolation standardization. Background Art

[0002] The Incremental Capacity Curve (IC Curve) and the Differential Voltage Curve (DV Curve) are essential tools for the nondestructive analysis of lithium-ion batteries. Due to their significant advantages, such as nondestructiveness and ease of data acquisition, they have been widely used in a variety of fields, including lithium-ion battery research and development, production, and testing. The IC and DV curves, derived by differentiating the relationship between voltage and capacity, effectively reflect the internal electrochemical characteristics of the battery.

[0003] However, practical applications present numerous challenges. For one thing, battery charging and discharging processes exhibit voltage plateaus, particularly for lithium iron phosphate batteries. Furthermore, battery testing equipment suffers from accuracy and stability issues. Directly differentiating the raw data introduces significant noise into the curve, interfering with subsequent analysis, necessitating noise reduction. Furthermore, various noise reduction methods have their own advantages and disadvantages. Neural networks offer excellent smoothing but are prone to overfitting, while point-based statistical methods are simple but lack a standardized standard for selecting voltage intervals. Furthermore, the height and number of peaks and valleys in both the IC and DV curves are affected by test conditions, such as current level and sampling interval. Therefore, consistent test conditions must be maintained in practical analysis to ensure comparable results.

[0004] However, in the actual commercial application scenarios of battery state of health (SOH) prediction, it is often difficult to maintain consistent test conditions before and after battery samples. For example, battery tests of different batches may cause test conditions such as current size and sampling time interval to change due to factors such as equipment differences and environmental changes. In this case, if the existing technical methods are used, due to inconsistent test conditions, the reliability of the analysis results of SOH prediction based on the noise-reduced curve will be greatly reduced, and it will not be able to accurately reflect the true health status of the battery. Therefore, how to improve the accuracy and reliability of SOH prediction under inconsistent test conditions has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a battery state prediction method, system, device and medium based on interpolation standardization, which effectively solves the problem of low accuracy and reliability of battery health state prediction under inconsistent test conditions.

[0006] In a first aspect, the present invention provides a battery state prediction method based on interpolation normalization, the method comprising:

[0007] Obtain the initial voltage data and initial capacity data of the target battery cell during the charging and discharging process;

[0008] performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data;

[0009] performing differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data;

[0010] obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data;

[0011] A target interpolation capacity is obtained according to curve characteristics of the differential voltage curve, and a battery health state of the target battery cell is determined according to the target interpolation capacity.

[0012] In an optional implementation, the calculation formula for the preset number of interpolation points is:

[0013]

[0014] In the above formula, D represents the preset interpolation points, D0 represents the total number of points of the initial voltage data, N represents the sampling time interval of the initial voltage data, b represents the reference sampling time interval, n represents the sampling current magnification of the initial voltage data, and a represents the reference sampling current magnification.

[0015] In an optional embodiment, performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data includes:

[0016] Obtaining a voltage difference between the maximum voltage data and the minimum voltage data in the initial voltage data, and obtaining a voltage interpolation step length according to the voltage difference and the preset number of interpolation points;

[0017] Obtaining the interpolation voltage data according to the voltage interpolation step, the minimum voltage data, and the initial voltage data;

[0018] The interpolated capacity data is obtained according to the interpolated voltage data, the initial voltage data, and the initial capacity data.

[0019] In an optional embodiment, obtaining the interpolated capacity data according to the interpolated voltage data, the initial voltage data, and the initial capacity data includes:

[0020] determining first initial voltage data and second initial voltage data closest to target interpolation voltage data among the initial voltage data, wherein the first initial voltage data is less than or equal to the target interpolation voltage data, and the second initial voltage data is greater than or equal to the target interpolation voltage data;

[0021] determining corresponding first initial capacity data according to the first initial voltage data, and determining corresponding second initial capacity data according to the second initial voltage data;

[0022] calculating a target slope according to the first initial capacity data, the second initial capacity data, the first initial voltage data, and the second initial voltage data;

[0023] Target interpolation capacity data is determined according to the target interpolation voltage data, the first initial capacity data, the first initial voltage data, and the target slope.

[0024] In an optional embodiment, performing differential smoothing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data includes:

[0025] Taking the data interval as a microelement, performing differential calculation on the interpolated voltage data and the interpolated capacity data to obtain initial capacity increment data;

[0026] Using a filter to smooth the initial capacity increment data to obtain the capacity increment data;

[0027] The reciprocal of the capacity increment data is calculated to obtain the differential voltage data.

[0028] In an optional embodiment, obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data includes:

[0029] Using the interpolated voltage data as the abscissa and the capacity increment data as the ordinate, obtaining the capacity increment curve;

[0030] The differential voltage curve is obtained by taking the interpolation capacity data as the abscissa and the differential voltage data as the ordinate.

[0031] In an optional embodiment, obtaining a target interpolation capacity according to curve characteristics of the differential voltage curve, and determining the battery health state of the target battery cell according to the target interpolation capacity includes:

[0032] Obtaining a target interpolation capacity corresponding to a target trough according to the differential voltage curve;

[0033] The battery health state of the target battery cell is determined according to the target interpolation capacity and a preset capacity range.

[0034] In a second aspect, the present invention provides a battery state prediction system based on interpolation normalization, the system comprising:

[0035] A data acquisition module is used to obtain the initial voltage data and initial capacity data of the target battery cell during the charging and discharging process;

[0036] a linear interpolation module, configured to perform linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data;

[0037] a data processing module, configured to perform differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data;

[0038] a curve construction module, configured to obtain a capacity increment curve according to the interpolated voltage data and the capacity increment data, and to obtain a differential voltage curve according to the interpolated capacity data and the differential voltage data;

[0039] A state prediction module is used to obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine the battery health state of the target battery cell according to the target interpolation capacity.

[0040] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a battery status prediction method based on interpolation normalization as described in any one of the aforementioned embodiments.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the battery state prediction method based on interpolation normalization as described in any one of the aforementioned embodiments is implemented.

[0042] The battery state prediction method, system, device, and medium based on interpolation standardization provided by the present invention use interpolation standardization on the original voltage and capacity data to normalize the test effects caused by different sampling time intervals and test currents. On this basis, the generated interpolated data can be differentially smoothed. Interpolation standardization makes minimal changes to the original data, retains the authenticity of the data to the greatest extent, and provides a reliable basis for subsequent analysis and prediction. Through the standardized processing method, a unified test condition benchmark is established, and different test conditions are converted and processed to achieve the reliability of comparison between different test conditions, thereby improving the consistency and comparability of the data. The comparability and reliability of the capacity increment curve and differential voltage curve obtained for different test conditions are significantly improved, which can better cope with various complex working conditions in commercial applications and enhance practicality and adaptability. The accuracy of reading the peaks and troughs of the differential voltage curve is improved, making the prediction of the health state of the battery cell based on the differential voltage curve more accurate and significantly reducing the relative error. This provides a more reliable basis for battery health management, helps to timely detect potential battery problems, extend battery life, and reduce maintenance costs, which is of great significance to the commercial application and development of batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a first schematic diagram of the process of a battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0045] Figure 2 This is a second schematic diagram of the process of the battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0046] Figure 3 This is a third schematic diagram of the process of the battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0047] Figure 4 1 is a schematic diagram showing an overall comparison between the initial data and the interpolated data after linear interpolation processing in an embodiment of the present invention;

[0048] Figure 5 3 is a schematic diagram of a partial comparison between initial data and interpolated data after linear interpolation processing in an embodiment of the present invention;

[0049] Figure 6This is a fourth schematic diagram of the process of the battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0050] Figure 7 This is a fifth schematic diagram of the process of the battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0051] Figure 8 2 is a schematic diagram of a capacity increment curve under different sampling time intervals in an embodiment of the present invention;

[0052] Figure 9 Schematic diagram of capacity increment curve obtained by traditional interpolation processing under different sampling time intervals in an embodiment of the present invention;

[0053] Figure 10 1 is a schematic diagram of differential voltage curves under different sampling time intervals in an embodiment of the present invention;

[0054] Figure 11 1 is a schematic diagram of a differential voltage curve obtained by traditional interpolation processing under different sampling time intervals in an embodiment of the present invention;

[0055] Figure 12 This is a sixth schematic diagram of the process of the battery state prediction method based on interpolation normalization provided by an embodiment of the present invention;

[0056] Figure 13 is a schematic diagram of selecting a preset capacity range according to a differential voltage curve in an embodiment of the present invention;

[0057] Figure 14 Schematic diagram of relative errors of different processing methods in an embodiment of the present invention;

[0058] Figure 15 1 is a schematic diagram of the structure of a battery state prediction system based on interpolation standardization provided by an embodiment of the present invention;

[0059] Figure 16 It is a structural diagram of an electronic device provided by an embodiment of the present invention.

[0060] Description of main component symbols:

[0061] 200. Battery state prediction system based on interpolation standardization; 210. Data acquisition module; 220. Linear interpolation module; 230. Data processing module; 240. Curve construction module; 250. State prediction module; 300. Electronic device; 310. Processor; 320. Communication interface; 330. Memory; 340. Communication bus. DETAILED DESCRIPTION

[0062] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be further clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be noted that the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0065] The incremental capacity curve (IC curve) and the differential voltage curve (DV curve) are obtained by differentiating the relationship between voltage and capacity, and can effectively reflect the electrochemical characteristics of the battery. However, there are many problems in practical applications. On the one hand, there are voltage platforms during the battery charging and discharging process, especially for lithium iron phosphate batteries, and battery testing equipment has accuracy and stability issues. Directly differentiating the original data will cause the curve to produce large noise, which interferes with subsequent analysis, so noise reduction processing is required. On the other hand, each noise reduction method has its own advantages and disadvantages. The neural network has a good smoothing effect but is prone to overfitting. The point-by-point statistical method is simple but lacks a unified standard for the selection of voltage intervals. In addition, the height and number of peaks and valleys of the IC curve and DV curve are affected by test conditions, such as current size and sampling time interval. In actual analysis, the test conditions must be consistent to ensure comparability of the results.

[0066] However, in the actual commercial application scenarios of battery state of health (SOH) prediction, it is often difficult to maintain consistent test conditions before and after battery samples. For example, battery tests of different batches may cause test conditions such as current size and sampling time interval to change due to factors such as equipment differences and environmental changes. In this case, if the existing technical methods are used, due to inconsistent test conditions, the reliability of the analysis results of SOH prediction based on the noise-reduced curve will be greatly reduced, and it will not be able to accurately reflect the true health status of the battery. Therefore, how to improve the accuracy and reliability of SOH prediction under inconsistent test conditions has become a technical problem that needs to be solved urgently.

[0067] Example 1

[0068] The embodiment of the present invention provides a battery state prediction method based on interpolation normalization, which effectively solves the problem of low accuracy and reliability of battery health state prediction under inconsistent test conditions. Figure 1 FIG. 1 is a first schematic diagram of a method for predicting a battery state based on interpolation standardization according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0069] S100: Acquire initial voltage data and initial capacity data of a target battery cell during a charge and discharge process.

[0070] In the embodiment of the present invention, the target cell can be a lithium iron phosphate cell. The target cell is subjected to a charge and discharge test. The charging sampling current is set to X amperes and the sampling time interval is set to N seconds. After the target cell completes the charge or discharge test, all the sampled voltage data during the charge or discharge process are obtained as the initial voltage data V 初始电压 , obtain the capacity data corresponding to the sampling time point of all sampled voltage data during the charging or discharging process as the initial capacity data Q 初始容量 .

[0071] S200 , performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data.

[0072] In an embodiment of the present invention, taking into account the impact of different current sizes and different sampling time intervals on the change of the battery voltage platform, a benchmark test condition is set according to the performance of the battery's charge and discharge lithium insertion and delithiation phase reactions under different test conditions, and the data generated by other different test conditions are compared with the benchmark test strip. This comparison is achieved by adjusting the interpolation step size in the linear interpolation. After adjustment, the total number of initial data points can be completely eliminated and the fluctuation effect of the voltage platform can be partially eliminated. Optionally, the benchmark test condition can be set to a benchmark sampling current magnification of aC and a benchmark sampling time interval of b seconds.

[0073] In an embodiment of the present invention, the interpolation step size is adjusted by presetting the number of interpolation points and the interpolation voltage range, wherein the calculation formula for the presetting number of interpolation points is as follows:

[0074]

[0075] In the above formula, D represents the preset interpolation points, D0 represents the total number of points of the initial voltage data, N represents the sampling time interval of the initial voltage data, b represents the reference sampling time interval, n represents the sampling current magnification of the initial voltage data, and a represents the reference sampling current magnification.

[0076] The sampling current multiplier n of the initial voltage data can be calculated from the sampling current and the total capacity of the battery cell. The calculation formula is as follows:

[0077]

[0078] In the above formula, X represents the sampling current, and Q represents the total capacity of the target battery cell.

[0079] By setting a preset number of interpolation points, the initial voltage data and initial capacity data generated under different test conditions are subjected to standardized interpolation processing, and the number of interpolation data obtained is within the same horizontal range.

[0080] In the embodiment of the present invention, the interpolation voltage range is [V 初始电压 min, V 初始电压 max], where V 初始电压 max represents the maximum voltage data in the initial voltage data, V 初始电压 min represents the minimum voltage data among the initial voltage data.

[0081] Figure 2 This is a second schematic diagram of the process of the battery state prediction method based on interpolation standardization provided by an embodiment of the present invention, such as Figure 2 As shown, the linear interpolation process specifically includes the following steps:

[0082] S210 , obtaining a voltage difference between the maximum voltage data and the minimum voltage data in the initial voltage data, and obtaining a voltage interpolation step length according to the voltage difference and a preset number of interpolation points.

[0083] In an embodiment of the present invention, the calculation formula for the voltage interpolation step size is as follows:

[0084]

[0085] In the above formula, ΔV represents the voltage interpolation step, V 初始电压 max represents the maximum voltage data in the initial voltage data, V 初始电压min represents the minimum voltage data in the initial voltage data, and D represents the preset number of interpolation points.

[0086] S220 , obtaining interpolated voltage data according to the voltage interpolation step size, the minimum voltage data, and the initial voltage data.

[0087] In an embodiment of the present invention, the calculation formula for interpolation voltage data is as follows:

[0088] V 插值电压i =V 初始电压 min+(i-1)ΔV

[0089] In the above formula, i represents the serial number of the interpolated voltage data, V 插值电压i represents the i-th interpolation voltage data, ΔV represents the voltage interpolation step, V 初始电压 min represents the minimum voltage data among the initial voltage data.

[0090] S230 : Obtain interpolated capacity data according to the interpolated voltage data, the initial voltage data, and the initial capacity data.

[0091] Figure 3 FIG3 is a third schematic diagram of the process of the battery state prediction method based on interpolation standardization provided by an embodiment of the present invention, such as Figure 3 As shown, obtaining interpolation capacity data specifically includes the following steps:

[0092] S231 , determining first and second initial voltage data closest to target interpolation voltage data in the initial voltage data, wherein the first initial voltage data is smaller than or equal to the target interpolation voltage data, and the second initial voltage data is greater than or equal to the target interpolation voltage data.

[0093] For example, if the target interpolation voltage data is the i-th interpolation voltage data, then the i-th interpolation voltage data is compared with all the data in the initial voltage data to determine which two data points in the initial voltage data the i-th interpolation voltage data is between. These two data points are the first initial voltage data V in the initial voltage data that are closest in magnitude to the i-th interpolation voltage data. 初始电压j and the second initial voltage data V 初始电压j+1 , wherein the first initial voltage data V 初始电压j Less than or equal to the i-th interpolation voltage data, the second initial voltage data V 初始电压j+1 Greater than or equal to the i-th interpolation voltage data.

[0094] S232: Determine corresponding first initial capacity data according to the first initial voltage data, and determine corresponding second initial capacity data according to the second initial voltage data.

[0095] In the embodiment of the present invention, according to the first initial voltage data V初始电压j Determine the first initial capacity data Q corresponding to the same sampling time 初始容量j , and according to the second initial voltage data V 初始电压j+1 Determine the corresponding second initial capacity data Q at the same sampling time 初始容量j+1 .

[0096] S233 , calculating a target slope according to the first initial capacity data, the second initial capacity data, the first initial voltage data, and the second initial voltage data.

[0097] In an embodiment of the present invention, the target slope is calculated as follows:

[0098]

[0099] In the above formula, K i represents the target slope, Q 初始容量j Indicates the first initial capacity data, Q 初始容量j+1 Indicates the second initial capacity data, V 初始电压j Indicates the first initial voltage data, V 初始电压j+1 Indicates the second initial voltage data.

[0100] S234 : Determine target interpolation capacity data according to the target interpolation voltage data, the first initial capacity data, the first initial voltage data, and the target slope.

[0101] In an embodiment of the present invention, the target interpolation capacity data is calculated as follows:

[0102] Q 插值容量i =K i (V 插值电压i -V 初始电压j )+Q 初始容量j

[0103] In the above formula, Q 插值容量i represents the i-th interpolation capacity data, K i Indicates the target slope, V 插值电压i Represents the i-th interpolation voltage data, V 初始电压j Indicates the first initial voltage data, Q 初始容量j Indicates the first initial capacity data.

[0104] Similarly, the corresponding total interpolation capacity data can be obtained based on the total interpolation voltage data. Figure 4 : is a schematic diagram of the overall comparison between the initial data and the interpolated data after the linear interpolation processing in an embodiment of the present invention, Figure 5 FIG is a schematic diagram of a partial comparison between the initial data and the interpolated data after the linear interpolation processing in an embodiment of the present invention. Figure 4 and Figure 5As shown, compared with the initial data, the interpolated data after linear interpolation processing does not introduce too much noise, the overlap of the two data curves is very good, and the deviation value between the interpolated data and the initial data is extremely small, indicating that the linear interpolation processing in the embodiment of the present invention can retain the authenticity of the initial data.

[0105] S300 , performing differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data.

[0106] In the embodiment of the present invention, differential smoothing is performed on the interpolated voltage data and the interpolated capacity data. Figure 6 4 is a schematic diagram of a battery state prediction method based on interpolation standardization according to an embodiment of the present invention. Figure 6 As shown in FIG, the differential smoothing process specifically includes the following steps:

[0107] S310 , using the data interval as a microelement, performing differential calculation on the interpolated voltage data and the interpolated capacity data to obtain initial capacity increment data.

[0108] In the embodiment of the present invention, the formula for differential calculation is as follows:

[0109]

[0110] In the above formula, dQdV i ′ represents the i-th initial capacity increment data, that is, at point (V 插值电压i , Q 插值容量i ) differential, Q 插值容量i represents the i-th interpolation capacity data, Q 插值容量i-1 Represents the i-1th interpolation capacity data, V 插值电压i Represents the i-th interpolation voltage data, V 插值电压i-1 Represents the i-1th interpolated voltage data, where i is greater than or equal to 2.

[0111] S320: Use a filter to smooth the initial capacity increment data to obtain capacity increment data.

[0112] Optionally, a Savitzky-Golayl filter can be used to smooth the initial capacity increment data dQdV′. This filter can smooth the initial capacity increment data and retain the local characteristics of the data. The smoothing is achieved by fitting a polynomial to the data within the data window to obtain the capacity increment data dQdV. Optionally, the number of window points of the Savitzky-Golayl filter can be set to 21, that is, 21 data points around each initial capacity increment data point (10 points before and after) are used for fitting, and the polynomial order can be set to 2, that is, a quadratic polynomial is used to fit the initial capacity increment data within the window.

[0113] S330 , calculating the inverse of the capacity increment data to obtain differential voltage data.

[0114] In the embodiment of the present invention, the differential voltage data dVdQ can be obtained by calculating the inverse of the capacity increment data dQdV, that is, dVdQ=1 / dQdV.

[0115] S400 , obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data.

[0116] Figure 7 FIG5 is a fifth schematic diagram of a method for predicting a battery state based on interpolation standardization according to an embodiment of the present invention. Figure 7 As shown, obtaining the capacity increment curve and the differential voltage curve specifically includes the following steps:

[0117] S410 , using the interpolated voltage data as the abscissa and the capacity increment data as the ordinate to obtain a capacity increment curve.

[0118] In an embodiment of the present invention, test data from the same target cell under the same current conditions and different sampling time intervals is selected and subjected to linear interpolation processing in step S200 and differential smoothing processing in step S300 in an embodiment of the present invention to obtain interpolated voltage data and capacity increment data, thereby constructing a capacity increment curve. In contrast, test data from the same target cell under the same current conditions and different sampling time intervals is simultaneously selected and subjected to traditional interpolation to obtain interpolated voltage data and capacity increment data, and a capacity increment curve is also constructed. The voltage interpolation step size of this traditional interpolation is a fixed value, which can be set to 5mV or 10mV.

[0119] Figure 8 Schematic diagram of capacity increment curve under different sampling time intervals in the embodiment of the present invention. Figure 8 As shown, the capacity increment curves with a sampling time interval of 1 second and a sampling time interval of 5 seconds are basically completely overlapped. Figure 9 Schematic diagram of the capacity increment curve obtained by traditional interpolation processing under different sampling time intervals in the embodiment of the present invention, as shown in FIG. Figure 9 As shown, the two capacity increment curves exhibit significant differences in peak heights, as well as a certain degree of shift in the positions of the peaks and troughs. This phenomenon is more pronounced when the sampling interval differs further, i.e., when the sampling interval is 5 seconds. Therefore, the linear interpolation process in this embodiment of the present invention is more compatible with various test conditions.

[0120] S420 , using the interpolated capacity data as the abscissa and the differential voltage data as the ordinate to obtain a differential voltage curve.

[0121] In this embodiment of the present invention, the interpolated voltage data and capacity increment data obtained through the linear interpolation process in step S200 and the differential smoothing process in step S300 are selected in step S410 to calculate the interpolated capacity data and differential voltage data, thereby constructing a differential voltage curve. Similarly, the interpolated voltage data and capacity increment data obtained through traditional interpolation in step S410 are selected to calculate the interpolated capacity data and differential voltage data to construct a differential voltage curve.

[0122] Figure 10 Schematic diagram of differential voltage curves under different sampling time intervals in an embodiment of the present invention. Figure 10 As described above, the differential voltage curves when the sampling time interval is 1 second and the sampling time interval is 5 seconds are substantially completely overlapped. Figure 11 is a schematic diagram of a differential voltage curve obtained by traditional interpolation processing under different sampling time intervals in an embodiment of the present invention, Figure 11 As shown, similar to the capacity increment curve, the differential voltage curve obtained by traditional interpolation processing is easily affected by the test conditions, while the linear interpolation processing in the embodiment of the present invention can eliminate the influence of different numbers of initial data points caused by different currents and different sampling time intervals.

[0123] S500 : Obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine a battery health state of a target battery cell according to the target interpolation capacity.

[0124] In an embodiment of the present invention, the battery health status is predicted based on the differential voltage curve of the target battery cell. The smaller the sampling current rate, the more accurate the battery health status prediction. Optionally, the sampling current rate during the charge and discharge test of the target battery cell can be set to 0.1C, and the sampling time interval does not need to be controlled. The initial voltage data and initial capacity data obtained by sampling are subjected to linear interpolation and differential smoothing to obtain interpolated capacity data and differential voltage data, thereby constructing the differential voltage curve.

[0125] Figure 12 6 is a schematic diagram of a method for predicting battery status based on interpolation standardization according to an embodiment of the present invention. Figure 12 As shown, determining the battery health status of the target cell specifically includes the following steps:

[0126] S510 : Obtain a target interpolation capacity corresponding to a target valley according to the differential voltage curve.

[0127] In the embodiment of the present invention, the target trough is determined based on the cell aging experiment of different target cells. For example, the third trough of the lithium iron phosphate battery will fluctuate to a certain extent as the cell ages, so the third trough is selected as the target trough, and the interpolation capacity data corresponding to the third trough in the differential voltage curve is read as the target interpolation capacity Qm .

[0128] S520: Determine the battery health status of the target battery cell according to the target interpolated capacity and the preset capacity range.

[0129] Figure 13 is a schematic diagram of selecting a preset capacity range according to a differential voltage curve in an embodiment of the present invention, Figure 13 As shown in the figure, as the cycle progresses, the curve shifts to a certain extent, but the state of charge corresponding to the third trough remains basically unchanged. At a sampling current of 0.1C, the third trough varies between 41% and 42% of the total state of charge, so the preset capacity range is determined to be 41% to 42%. The value range of the battery health state of the target cell is (Q m / 42%,Q m / 41%).

[0130] As a further implementation method of the embodiment of the present invention, in order to illustrate the combined effect of linear interpolation processing and battery health status prediction in the implementation of the present invention, first use a sampling current of 0.1C and a sampling time interval of 1s to perform a charge and discharge test on the target battery cell, and then directly measure the actual capacity of the target battery cell through a battery testing instrument, and use the actual capacity result as the standard capacity result Q 标准 Then, the interpolation normalization method and the traditional interpolation method in the embodiment of the present invention are used to process the voltage data and capacitance data obtained during the charge and discharge test, and finally the differential voltage curve is obtained to predict the cell capacity. The predicted capacity results are Q 预测值，插值标准化 With Q 预测值，传统插值法 The relative error is calculated based on the predicted capacity results and standard capacity results obtained under different treatment methods. The calculation formula is as follows:

[0131]

[0132] In the above formula, Q 预测值 Indicates the predicted capacity result, which can be Q 预测值，插值标准化 With Q 预测值，传统插值法 , Q 标准 Indicates standard capacity results.

[0133] In the embodiment of the present invention, the predicted cell capacity is a range value. The differential voltage curves obtained by the interpolation normalization method and the traditional interpolation method in the embodiment of the present invention are used to determine Q m，插值标准化 and Q m，传统插值法 . Figure 14 is a schematic diagram of relative errors of different processing methods in the embodiment of the present invention, such as Figure 14 As shown, Figure 14 The blue part indicates that 42% is taken as the predicted capacity fraction. At this time, the predicted capacity result obtained by interpolation normalization in the embodiment of the present invention is Q预测值，插值标准化 =Q m，插值标准化 / 42%. After calculation, the minimum relative error of the interpolation standardization for cell capacity prediction in the embodiment of the present invention is:

[0134]

[0135] The predicted capacity obtained by the traditional interpolation method is Q 预测值，传统插值法 =Q m，传统插值法 / 42%. After calculation, the minimum relative error of the traditional interpolation method for cell capacity prediction is:

[0136]

[0137] The orange part indicates that 41% is taken as the predicted capacity fraction. At this time, the predicted capacity result obtained by interpolation normalization in the embodiment of the present invention is Q 预测值，插值标准化 =Q m，插值标准化 / 41%. After calculation, the maximum relative error of the interpolation standardization in the embodiment of the present invention for the cell capacity prediction is:

[0138]

[0139] The predicted capacity obtained by the traditional interpolation method is Q 预测值，传统插值法 =Q m，传统插值法 / 41%. After calculation, the maximum relative error of the traditional interpolation method for predicting battery cell capacity is:

[0140]

[0141] Therefore, the relative error range of predicting the battery cell capacity using the interpolation standardization processing in the embodiment of the present invention is 1.4% to 1.5%, and the relative error range of predicting the battery cell capacity using the traditional interpolation method is 13.7% to 17.1%. The relative error of the predicted battery cell capacity obtained by the interpolation standardization processing in the embodiment of the present invention is much smaller than the relative error of the predicted battery cell capacity obtained by the traditional interpolation method.

[0142] The battery state prediction method based on interpolation standardization provided by the present invention uses interpolation standardization on the original voltage and capacity data to normalize the test effects brought by different sampling time intervals and test currents, and on this basis, the generated interpolation data can be differentially smoothed. Interpolation standardization makes minimal changes to the original data, retains the authenticity of the data to the greatest extent, and provides a reliable basis for subsequent analysis and prediction. A unified test condition benchmark is established through a standardized processing method, and different test conditions are converted and processed to achieve the reliability of comparison between different test conditions, thereby improving the consistency and comparability of the data. The comparability and reliability of the capacity increment curve and differential voltage curve obtained for different test conditions are significantly improved, which can better cope with various complex working conditions for commercial applications and enhance practicality and adaptability. The accuracy of reading the peaks and troughs of the differential voltage curve is improved, making the prediction of the health state of the battery cell based on the differential voltage curve more accurate, and the relative error is greatly reduced, providing a more reliable basis for battery health management.

[0143] Example 2

[0144] Based on the same technical concept as the above-mentioned embodiment 1, the embodiment of the present invention provides a battery state prediction system based on interpolation normalization. Figure 15 FIG. 1 is a schematic diagram of a battery state prediction system based on interpolation standardization provided by an embodiment of the present invention. Figure 15 As shown, the battery state prediction system 200 based on interpolation normalization includes:

[0145] The data acquisition module 210 is used to acquire the initial voltage data and initial capacity data of the target battery cell during the charging and discharging process.

[0146] The linear interpolation module 220 is configured to perform linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data.

[0147] The data processing module 230 is used to perform differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data.

[0148] The curve construction module 240 is configured to obtain a capacity increment curve according to the interpolated voltage data and the capacity increment data, and to obtain a differential voltage curve according to the interpolated capacity data and the differential voltage data.

[0149] The state prediction module 250 is configured to obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine the battery health state of the target battery cell according to the target interpolation capacity.

[0150] The battery status prediction system based on interpolation standardization provided by the embodiment of the present invention helps to timely discover potential battery problems, extend battery service life, and reduce maintenance costs, which is of great significance to the commercial application and development of batteries.

[0151] It can be understood that the implementation of the battery state prediction method based on interpolation normalization described in the above embodiment 1 is also applicable to this embodiment and can achieve the same technical effect, so it will not be repeated here.

[0152] Example 3

[0153] Based on the same concept, an embodiment of the present invention further provides an electronic device, Figure 16 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, such as Figure 16 As shown, the electronic device 300 may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the steps of the battery state prediction method based on interpolation normalization as described in the above embodiments. For example, it includes:

[0154] S100, obtaining initial voltage data and initial capacity data of a target battery cell during a charge and discharge process;

[0155] S200, performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data;

[0156] S300, performing differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data;

[0157] S400, obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data;

[0158] S500 : Obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine a battery health state of a target battery cell according to the target interpolation capacity.

[0159] The processor 310 may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0160] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0161] The memory 330 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0162] Example 4

[0163] Based on the same concept, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program. The computer program includes at least one code segment that can be executed by a main control device to control the main control device to implement the steps of the battery state prediction method based on interpolation normalization as described in the above embodiments. For example, the steps include:

[0164] S100, obtaining initial voltage data and initial capacity data of a target battery cell during a charge and discharge process;

[0165] S200, performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data;

[0166] S300, performing differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data;

[0167] S400, obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data;

[0168] S500 : Obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine a battery health state of a target battery cell according to the target interpolation capacity.

[0169] Based on the same technical concept, an embodiment of the present invention further provides a computer program, which, when executed by a main control device, is used to implement the above method embodiment.

[0170] The computer program may be stored in whole or in part on a computer-readable storage medium packaged with the processor, or may be stored in whole or in part on a memory not packaged with the processor.

[0171] Based on the same technical concept, an embodiment of the present invention further provides a processor for implementing the above method embodiment. The above processor may be a chip.

[0172] In summary, the battery state prediction method, system, device and medium based on interpolation standardization provided by the present invention normalize the test effects caused by different sampling time intervals and test currents by using interpolation standardization on the original voltage and capacity data, and on this basis, the generated interpolation data can be differentially smoothed. Interpolation standardization makes minimal changes to the original data, retains the authenticity of the data to the greatest extent, and provides a reliable basis for subsequent analysis and prediction. A unified test condition benchmark is established through a standardized processing method, and different test conditions are converted and processed to achieve the reliability of comparison between different test conditions, thereby improving the consistency and comparability of the data. The comparability and reliability of the capacity increment curve and differential voltage curve obtained for different test conditions are significantly improved, which can better cope with various complex working conditions for commercial applications and enhance practicality and adaptability. The accuracy of reading the peaks and troughs of the differential voltage curve is improved, making the prediction of the health state of the battery cell based on the differential voltage curve more accurate, and the relative error is greatly reduced, providing a more reliable basis for battery health management, helping to timely discover potential battery problems, extend battery life, and reduce maintenance costs, which is of great significance to the commercial application and development of batteries.

[0173] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0174] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A battery state prediction method based on interpolation normalization, characterized in that: The method comprises: Obtain the initial voltage data and initial capacity data of the target battery cell during the charging and discharging process; performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data; performing differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data; obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data; A target interpolation capacity is obtained according to curve characteristics of the differential voltage curve, and a battery health state of the target battery cell is determined according to the target interpolation capacity.

2. The battery state prediction method based on interpolation normalization according to claim 1, characterized in that: The calculation formula for the preset interpolation points is: In the above formula, D represents the preset interpolation points, D0 represents the total number of points of the initial voltage data, N represents the sampling time interval of the initial voltage data, b represents the reference sampling time interval, n represents the sampling current magnification of the initial voltage data, and a represents the reference sampling current magnification.

3. The battery state prediction method based on interpolation normalization according to claim 2, characterized in that: The performing linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data includes: Obtaining a voltage difference between the maximum voltage data and the minimum voltage data in the initial voltage data, and obtaining a voltage interpolation step length according to the voltage difference and the preset number of interpolation points; Obtaining the interpolation voltage data according to the voltage interpolation step, the minimum voltage data and the initial voltage data; The interpolated capacity data is obtained according to the interpolated voltage data, the initial voltage data, and the initial capacity data.

4. The battery state prediction method based on interpolation normalization according to claim 3, characterized in that: The obtaining of the interpolated capacity data according to the interpolated voltage data, the initial voltage data, and the initial capacity data includes: determining first initial voltage data and second initial voltage data closest to target interpolation voltage data among the initial voltage data, wherein the first initial voltage data is less than or equal to the target interpolation voltage data, and the second initial voltage data is greater than or equal to the target interpolation voltage data; determining corresponding first initial capacity data according to the first initial voltage data, and determining corresponding second initial capacity data according to the second initial voltage data; calculating a target slope according to the first initial capacity data, the second initial capacity data, the first initial voltage data, and the second initial voltage data; Target interpolation capacity data is determined according to the target interpolation voltage data, the first initial capacity data, the first initial voltage data, and the target slope.

5. The battery state prediction method based on interpolation normalization according to claim 1, characterized in that: The performing differential smoothing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data includes: Taking the data interval as a microelement, performing differential calculation on the interpolated voltage data and the interpolated capacity data to obtain initial capacity increment data; Using a filter to smooth the initial capacity increment data to obtain the capacity increment data; The reciprocal of the capacity increment data is calculated to obtain the differential voltage data.

6. The battery state prediction method based on interpolation normalization according to claim 5, characterized in that: Obtaining a capacity increment curve according to the interpolated voltage data and the capacity increment data, and obtaining a differential voltage curve according to the interpolated capacity data and the differential voltage data, comprising: Using the interpolated voltage data as the abscissa and the capacity increment data as the ordinate, obtaining the capacity increment curve; The differential voltage curve is obtained by taking the interpolation capacity data as the abscissa and the differential voltage data as the ordinate.

7. The battery state prediction method based on interpolation normalization according to claim 6, characterized in that: The acquiring a target interpolation capacity according to the curve characteristics of the differential voltage curve, and determining the battery health state of the target battery cell according to the target interpolation capacity, includes: Obtaining a target interpolation capacity corresponding to a target trough according to the differential voltage curve; The battery health state of the target battery cell is determined according to the target interpolation capacity and a preset capacity range.

8. A battery state prediction system based on interpolation normalization, characterized in that: The system comprises: A data acquisition module is used to obtain the initial voltage data and initial capacity data of the target battery cell during the charging and discharging process; a linear interpolation module, configured to perform linear interpolation processing on the initial voltage data and the initial capacity data according to a preset number of interpolation points to obtain interpolated voltage data and interpolated capacity data; a data processing module, configured to perform differential smoothing processing on the interpolated voltage data and the interpolated capacity data to obtain capacity increment data and differential voltage data; a curve construction module, configured to obtain a capacity increment curve according to the interpolated voltage data and the capacity increment data, and to obtain a differential voltage curve according to the interpolated capacity data and the differential voltage data; A state prediction module is used to obtain a target interpolation capacity according to curve characteristics of the differential voltage curve, and determine the battery health state of the target battery cell according to the target interpolation capacity.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the computer program to implement the battery state prediction method based on interpolation normalization according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the battery state prediction method based on interpolation normalization according to any one of claims 1 to 7 is implemented.

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