Battery capacity fitting method and application

Abstract

The invention relates to a battery capacity fitting method and application, and relates to the field of new energy batteries, and the method comprises the following steps: (1) dividing a temperature range P-Q DEG C into N temperature gradients, the value of each temperature gradient being Tj; selecting batteries of M gears, performing capacity grading test on the batteries of each gear under different temperature gradients, and collecting the battery capacity Cij and the average temperature of the batteries under the temperature gradient of each battery in the test; (2) obtaining correction capacities C0-i of the batteries at different gears through data processing; and (3) a scatter diagram is drawn, a calibrated capacity fitting curve can be obtained by adding a trend line, a fitting formula Y = AX2 + BX + C is adopted, X =-, the calibration capacity C < quasi > with the temperature deviation eliminated is C < quasi > = C < production > / Y, C < production > is the capacity actually measured by a production line, and the fitting method can be applied to fitting of the capacities of the ternary lithium battery and the lithium iron phosphate battery. According to the capacity fitting method and application disclosed by the invention, the influence of environment temperature can be eliminated, and the accuracy of capacity calibration is improved.

Description

Technical Field

[0001] This invention relates to a method and application for fitting battery capacity, and more particularly to a method and application for fitting battery capacity that can eliminate the influence of ambient temperature. Background Technology

[0002] In the industry, the capacity of battery cells (such as ternary lithium cells and lithium iron phosphate cells) is usually determined by the capacity testing process. For power / energy storage battery customers, a temperature of 25℃ is typically required as the standard for determining cell capacity. For ternary lithium cells, their capacity testing is easily affected by temperature fluctuations in the capacity testing cabinet. The large space of the battery capacity testing environment makes it difficult to ensure a consistent temperature, leading to distortion of the actual capacity determination and significant data discrepancies. This inaccurate capacity testing shortens the battery pack's lifespan and increases the risk of combustion and explosion during module use. For lithium iron phosphate cells, their capacity is affected by the test environment temperature. During batch testing of cell capacity in the capacity testing workshop, inconsistent heat generation and dissipation rates at different locations and points, as well as uneven temperature distribution in the test environment, cause significant interference to capacity testing, making it difficult to accurately test the capacity of lithium-ion cells.

[0003] To eliminate the influence of test temperature on capacity, existing technologies typically employ temperature-compensated capacity methods. This involves obtaining a corresponding mathematical model through linear fitting using data from different battery temperatures and capacity grading. The temperature in these mathematical models is generally selected from the initial temperature, which has certain limitations. Specifically, the temperature field of the grading equipment is difficult to maintain consistently, and the battery itself releases heat during charging and discharging. Therefore, the battery temperature fluctuates significantly during charging and discharging, resulting in substantial capacity fluctuations. Consequently, using a mathematical model that compensates for capacity based on the initial temperature will lead to inaccuracies.

[0004] Therefore, the capacity allocation needs to be calculated more accurately. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a battery capacity fitting method and its application.

[0006] One objective of this invention is to provide a method for fitting battery capacity, comprising the following steps: (S1) Divide the temperature range P~Q℃ into N temperature gradients, and denote the value of each temperature gradient as T. j M battery grades were selected, and capacity tests were performed on each grade of battery under different temperature gradients. The battery capacity C of each battery under the temperature gradient was collected during the tests. ij and the average temperature of the battery With i=1,2……M and j=1,2……N, different test temperatures of batteries of the same grade are grouped into the same experimental group, thus obtaining M groups of data; (S2) Obtain the corrected capacity C of batteries at different levels through data processing. 0-i ; (S3) with C ij / C 0-i Using the vertical axis as the ordinate, with - Using the x-axis as the x-axis, plot a scatter plot. By adding a trend line, the capacity fitting formula Y = AX can be obtained. 2 +BX+C, where X= - The calibration capacity C for eliminating temperature deviation is... 拟 For: C 拟 =C 产 / Y,C 产 This refers to the actual capacity measured on the production line.

[0007] Preferably, during the capacity test, the battery needs to be placed in a constant temperature chamber with a corresponding temperature gradient for t hours, where t > 0.

[0008] Preferably, the standard temperature is 25°C.

[0009] The second objective of this invention is to apply the aforementioned capacity fitting method to the field of ternary lithium batteries. In this field, the data processing method is as follows: a scatter plot is drawn with the temperature gradient as the abscissa and the corresponding capacity at that temperature gradient as the ordinate. By adding trend lines, the experimental temperature-capacity relationship curves for batteries of different grades can be obtained, and the fitting formulas are y1=a1x1 respectively. 2 +b1x1+c1、y2=a2x2 2 +b²x² + c²……y M =a M x M 2 +b M x M +c M y1, y2...y M For the capacity, x1, x2... x M For temperature, coefficients a1, a2...a M Let b1, b2, ..., b be the quadratic coefficients of the volumetric capacity with respect to temperature. M Here are the first-order coefficients of capacity and temperature, c1, c2, ..., c M The constant coefficients for capacity and temperature are used; the average temperature of each battery level is taken at the standard experimental temperature. Substituting the corresponding fitting formula, we obtain the corrected capacity C of batteries at different price levels. 0-i .

[0010] A third objective of this invention is to apply the aforementioned capacity fitting method to the field of lithium iron phosphate batteries. In this field, the data processing method is as follows: the capacity obtained from the capacity testing of the battery at a standard temperature is denoted as C. 0-i .

[0011] The present invention has the following technical effects: (1) The capacity of the cells calibrated by this method has high consistency, which reduces the screening difficulty during battery pack assembly and significantly improves production efficiency.

[0012] (2) Using this method to calibrate the capacity of the battery cell can avoid repeated charging and discharging, reduce energy consumption, and further reduce production costs.

[0013] (3) The capacity calibration process provided by this method provides a more accurate basis for capacity calibration in the lithium battery industry and helps to regulate the chaos of false capacity labeling in the market.

[0014] (4) In the field of new energy vehicles, the power output of the battery pack after capacity calibration is more stable, avoiding the risk of power interruption caused by differences in individual cells.

[0015] (5) In energy storage devices, battery packs with strong consistency have more balanced charge and discharge cycles, extending the overall service life of the energy storage system and reducing the replacement and maintenance costs for users. Attached Figure Description

[0016] Figure 1 The experimental temperature-capacity relationship curves of batteries at different levels in Example 1 are shown. Figure 2 This is the calibrated capacity fitting curve from Example 1; Figure 3 This is the capacity fitting curve after calibration in Example 2. Detailed Implementation

[0017] The principles and features of the present invention are described below with reference to embodiments; the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0018] Example 1

[0019] This embodiment is used for fitting the capacity of ternary lithium batteries, and specifically includes the following steps: (S1) Divide the temperature range P~Q℃ into N temperature gradients, and denote the value of each temperature gradient as T. j M battery grades were selected, and capacity tests were performed on each grade of battery under different temperature gradients. The battery capacity C of each battery under the temperature gradient was collected during the tests. ij and the average temperature of the battery The different test temperatures of batteries of the same grade are grouped into the same experimental group, i=1,2……M, j=1,2……N, thus obtaining M groups of data. In this embodiment, P=19, Q=39, M=4, N=11.

[0020] Specifically, based on the different capacity cells of the production line, four grades of EA batteries were selected and tested under laboratory conditions (constant temperature chamber) using the same capacity grading process as the production line. Different temperature gradients were set for the tests, ranging from 19 to 39°C, with each gradient in 2°C increments: 19°C, 21°C, 23°C, 25°C, 27°C, 29°C, 31°C, 33°C, 35°C, 37°C, and 39°C. Before each capacity grading test, the experimental batteries were placed in the constant temperature chamber at that temperature for 4 hours, and the battery capacity C was collected for each battery under the specified temperature gradient during the test. ij and the average temperature of the battery That is, the capacity test of each battery grade is carried out at 19℃, 21℃, 23℃, 25℃, 27℃, 29℃, 31℃, 33℃, 35℃, 37℃ and 39℃, and then the capacity test data obtained from different temperature gradients of the same grade are grouped together.

[0021] Table 1 Actual test capacity at different experimental temperatures

[0022] (S2) Plot a scatter plot with the temperature gradient on the x-axis and the corresponding capacity at that temperature gradient on the y-axis. By adding a trend line, the experimental temperature-capacity relationship curves for batteries at different price levels can be obtained. The fitting formulas are y1 = -0.0082x1. 2 +0.632x1+14.266, y2=-0.0085x2 2 +0.6358x2+14.46, y3=-0.0062x3 2 +0.4733x3+17.219, y4=-0.0068x4 2 +0.5461x4+15.971, where y1, y2, y3, and y4 represent the individual capacity, and x1, x2, x3, and x4 represent the temperature; the average temperatures of each battery level at the standard experimental temperature of 25℃ (26.46℃, 26.38℃, 26.58℃, and 26.78℃) are substituted into the corresponding fitting formula to obtain the corrected capacity C for different battery levels. 0-1 =25.27861、C 0-2 =25.33974、C 0-3 =25.44685、C 0-4 =25.66754.

[0023] (S3) with C ij / C 0-iUsing the vertical axis as the ordinate, with - Plot a scatter plot with C as the x-axis, where C 0-i C 0-1 C 0-2 C 0-3 C 0-4 C ij This indicates the test capacity of each battery during capacity testing. The average temperature of each battery is used to obtain a calibrated capacity fitting curve by adding a trend line. The fitting formula is Y = -0.003X. 2 +0.0075X+0.9913, where X= - The calibration capacity C for eliminating temperature deviation is... 拟 For: C 拟 =C 产 / Y,C 产 This refers to the actual capacity measured on the production line.

[0024] Example 2 This embodiment is used for fitting the capacity of lithium iron phosphate batteries.

[0025] The difference between this embodiment and Embodiment 1 lies in the data processing method. Lithium iron phosphate (LFP) cells have an ordered olivine structure, and the initial lithium-ion insertion / extraction may trigger slight adjustments and relaxations in the microcrystalline lattice. After the first complete 'training,' the material's lattice structure tends to stabilize, and the kinetics of lithium-ion insertion / extraction are improved, resulting in higher reversible capacity and greater stability in the second cycle. Sorting and grouping cells according to this capacity leads to poor consistency in the cell groups, thus affecting the performance of systems using these cell groups. Therefore, compared to ternary lithium batteries, the method for fitting the capacity of lithium iron phosphate batteries specifically includes the following steps: (S1) Divide the temperature range P~Q℃ into N temperature gradients, and denote the value of each temperature gradient as T. j M battery grades were selected, and capacity tests were performed on each grade of battery under different temperature gradients. The battery capacity C of each battery under the temperature gradient was collected during the tests. ij and the average temperature of the battery , i=1,2...M, j=1,2...N.

[0026] Specifically, in this embodiment, 40EA (40 EA) of unprocessed, high-quality battery cells from the same grade on the production line were selected. These cells were grouped into 10 groups of 4 EA cells each. The temperature range of 21~39℃ was divided into 10 experimental groups at 2℃ increments: 21℃, 23℃, 25℃, 27℃, 29℃, 31℃, 33℃, 35℃, 37℃, and 39℃. Each experimental group corresponded to 4 EA cells. The battery capacity C under each temperature gradient during the test was collected. ij and the average temperature of the battery As shown in Table 2. In this embodiment, P=21℃, Q=39℃, M=1, N=10.

[0027] Table 2 Battery capacity and average temperature at various temperature gradients

[0028] (S2) Record the capacity obtained from the capacity test of the battery at the standard temperature of 25°C as C. 0-i .

[0029]

[0030] (S3) with C ij / C 0-i Using the vertical axis as the ordinate, with - Using the x-axis as the horizontal axis, a scatter plot is drawn. By adding a trend line, the calibrated capacity fitting curve can be obtained. The fitting formula is Y = -0.003X. 2 +0.0077X+0.966, where X= - The calibration capacity C for eliminating temperature deviation is... 拟 For: C 拟 =C 产 / Y,C 产 This refers to the actual capacity measured on the production line.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for fitting battery capacity, characterized in that, Includes the following steps: (S1) Divide the temperature range P~Q℃ into N temperature gradients, and denote the value of each temperature gradient as T. j M battery grades were selected, and capacity tests were performed on each grade of battery under different temperature gradients. The battery capacity C of each battery under the temperature gradient was collected during the tests. ij and the average temperature of the battery With i=1,2……M and j=1,2……N, different test temperatures of batteries of the same grade are grouped into the same experimental group, thus obtaining M groups of data; (S2) Obtain the corrected capacity C of batteries at different levels through data processing. 0-i ; (S3) with C ij / C 0-i Using the vertical axis as the ordinate, with - Plot a scatter plot with the x-axis as the horizontal axis. By adding a trend line, you can obtain the calibrated capacity fitting curve. The fitting formula is Y = AX. 2 +BX+C, where X= - The calibration capacity C for eliminating temperature deviation is... 拟 For: C 拟 =C 产 / Y,C 产 This refers to the actual capacity measured on the production line.

2. The battery capacity fitting method according to claim 1, characterized in that, When performing capacity testing, the battery needs to be placed in a constant temperature chamber with a corresponding temperature gradient for t hours, where t > 0.

3. The battery capacity fitting method according to claim 2, characterized in that, The data processing method in step (S2) is as follows: A scatter plot is drawn with the temperature gradient as the abscissa and the corresponding capacity at that temperature gradient as the ordinate. By adding trend lines, the experimental temperature-capacity relationship curves for batteries of different grades can be obtained. The fitting formulas are y1=a1x1 and y1=a1x1 respectively. 2 +b1x1+c1、y2=a2x2 2 +b²x² + c²……y M =a M x M 2 +b M x M +c M y1, y2...y M For the capacity, x1, x2... x M For temperature, coefficients a1, a2...a M Let b1, b2, ..., b be the quadratic coefficients of the volumetric capacity with respect to temperature. M Here are the first-order coefficients of capacity and temperature, c1, c2, ..., c M The constant coefficients for capacity and temperature are used; the average temperature of each battery level is taken at the standard experimental temperature. Substituting the corresponding fitting formula, we obtain the corrected capacity C of batteries at different price levels. 0-i .

4. The battery capacity fitting method according to claim 2, characterized in that, The data processing method in step (S2) is as follows: the capacity obtained from the capacity test of the battery at standard temperature is recorded as C. 0-i .

5. The battery capacity fitting method according to claim 3, characterized in that, The standard temperature is 25°C.

6. The battery capacity fitting method according to claim 4, characterized in that, The standard temperature is 25°C.

7. A method for fitting the battery capacity based on claim 5, applicable to fitting the capacity of ternary lithium batteries.

8. A method for fitting the battery capacity based on claim 6, applicable to fitting the capacity of lithium iron phosphate batteries.

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