A method, apparatus, device, medium, and procedure for predicting battery life.

Abstract

This invention relates to the field of lithium-ion battery cell optimization technology, and discloses a method, apparatus, equipment, medium, and program product for predicting battery life. The method includes: acquiring multiple battery samples from the same batch; performing charge-discharge cycles on different battery samples under multiple different preset temperature conditions; extracting discharge capacity data corresponding to different numbers of cycles under each preset temperature condition from the battery samples during the charge-discharge cycle; fitting and generating capacity loss curves under each preset temperature condition based on the extracted discharge capacity data; using the capacity loss curves, calculating a standard equation for capacity loss prediction with temperature and number of cycles as independent variables and capacity loss rate as dependent variable; and substituting the target temperature into the standard equation for capacity loss prediction to obtain a capacity loss curve used to predict battery life. This invention improves the efficiency of battery life prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium ion battery optimization cell, and particularly relates to a battery life prediction method, device, equipment, medium and program product. BACKGROUND

[0002] With the popularization of new energy vehicles, consumers' demand for electric vehicles with battery fast charging is also increasing. However, different charging strategies have different degrees of expansion on the battery aging process. When the battery expands to a certain threshold, it will squeeze the battery module, thereby causing a series of safety problems such as capacity diving, battery lithium precipitation and battery deformation. Therefore, battery life prediction is particularly important. One method of battery life prediction is to obtain the relationship between battery capacity and cycle number through battery charge and discharge cycles. However, testing the capacity decay of the battery during the cycle process under different environmental temperatures requires a large amount of manpower and resource resources. For example, a user wants to obtain the relationship between battery capacity and cycle number under the conditions of 10℃, 12℃, 14℃, 16℃, 18℃ and 20℃. The related technology needs to separately perform battery charge and discharge cycles under each temperature condition, and then statistically analyze the capacity change of the battery under each temperature condition, so as to measure the relationship between battery capacity and cycle number. This method significantly increases the testing cost and time. Therefore, how to reduce the testing cost and time while ensuring the prediction accuracy and improving the efficiency of battery life prediction remains a problem to be solved. SUMMARY

[0003] Therefore, the present application provides a battery life prediction method, device, equipment, medium and program product to solve the problem of low battery life prediction efficiency.

[0004] In a first aspect, the present application provides a battery life prediction method, which comprises: obtaining a plurality of same batch battery samples; performing charge and discharge cycles on different battery samples under a plurality of different preset temperature conditions; extracting discharge capacity data corresponding to different cycle numbers under each preset temperature condition from the battery samples during the charge and discharge cycles; fitting a capacity loss curve under each preset temperature condition according to the extracted discharge capacity data, wherein the capacity loss curve is used to represent the capacity loss rate varying with the cycle number; using the capacity loss curve under each preset temperature condition to fit a capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable; obtaining a target temperature of a to-be-predicted scene, and substituting the target temperature into the capacity loss prediction standard equation to obtain a capacity loss curve for predicting the battery life under the to-be-predicted scene, and predicting the battery life through the capacity loss curve.

[0005] In a second aspect, the present application provides a battery life prediction device, the device comprising: a sample acquisition module configured to acquire a plurality of battery samples of the same batch; a cycle test module configured to perform charge-discharge cycling on different battery samples under a plurality of different preset temperature conditions respectively; a capacity data acquisition module configured to extract, during the charge-discharge cycling, discharge capacity data corresponding to different cycle numbers under each preset temperature condition for the battery samples; a capacity loss curve generation module configured to generate a capacity loss curve under each preset temperature condition according to the extracted discharge capacity data, the capacity loss curve being configured to represent a capacity loss rate varying with the cycle number; a first equation generation module configured to generate a capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable by using the capacity loss curves under each preset temperature condition; and a capacity loss prediction module configured to acquire a target temperature of a to-be-predicted scenario and substitute the target temperature into the capacity loss prediction standard equation to obtain a capacity loss curve for predicting the battery life under the to-be-predicted scenario.

[0006] In a third aspect, the present application provides a computer device, comprising: a memory and a processor, which are communicatively connected with each other, and the memory stores computer instructions; the processor executes the computer instructions to perform the method of the first aspect.

[0007] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are configured to make a computer execute the method of the first aspect.

[0008] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions are configured to make a computer execute the method of the first aspect.

[0009] The technical solution provided by the present application has the following advantages:

[0010] The application utilizes a plurality of same batch battery samples to perform charge-discharge cycles under a plurality of different preset temperature conditions. During the charge-discharge cycle, the discharge capacity data corresponding to different cycle numbers under each preset temperature condition is extracted from the battery samples, so as to generate a capacity loss curve under each preset temperature condition, which is used to represent the capacity loss rate varying with the cycle number; then a capacity loss prediction standard equation with two independent variable parameters is created by taking the temperature and cycle number as the independent variables and the capacity loss rate as the dependent variable. In the subsequent life prediction engineering, only the target temperature of the predicted scene needs to be obtained by the researchers, and the target temperature is substituted into the capacity loss prediction standard equation, so as to obtain the capacity loss curve used for predicting the battery life under the predicted scene, without the need to repeatedly perform the charge-discharge cycle test according to the target temperature, and the capacity loss curve is obtained through the capacity loss prediction standard equation, which significantly improves the battery life prediction efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0012] Figure 1 is a flowchart of a battery life prediction method according to an embodiment of the present application;

[0013] Figure 2 is a fitting process diagram of a capacity loss prediction standard equation according to an embodiment of the present application;

[0014] Figure 3 is a comparison effect diagram of a calculated capacity retention rate curve and an experimental capacity retention rate curve according to an embodiment of the present application;

[0015] Figure 4 is a relationship diagram of a capacity loss rate and a battery swelling thickness according to an embodiment of the present application;

[0016] Figure 5 is a comparison effect diagram of a calculated thickness growth curve and experimental thickness growth data according to an embodiment of the present application;

[0017] Figure 6 is an effect diagram of a capacity attenuation curve and a capacity retention rate curve according to an embodiment of the present application;

[0018] Figure 7 is a comparison effect diagram of a capacity loss rate curve and a battery thickness growth curve according to an embodiment of the present application;

[0019] Figure 8 is a structural schematic diagram of a battery life prediction device according to an embodiment of the present application;

[0020] Figure 9 is a hardware structural schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] According to an embodiment of the present application, a battery life prediction method embodiment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0023] In the present embodiment, a battery life prediction method is provided, Figure 1 is a flowchart of a battery life prediction method according to an embodiment of the present application, which includes the following steps:

[0024] Step S101, obtaining a plurality of battery samples of the same batch.

[0025] Step S102, performing charge-discharge cycles on different battery samples under a plurality of different preset temperature conditions, respectively.

[0026] Step S103, during the charge-discharge cycle, extracting the discharge capacity data corresponding to different cycle numbers under each preset temperature condition for the battery sample.

[0027] Specifically, the present application provides a mathematical model of high-temperature capacity loss rate, cycle number and temperature established by using historical data, which can effectively calculate the capacity loss rate curve of the target battery at different temperatures and predict the battery life (excluding the capacity diving case). Compared with the prior art, the method of the present application is simple, efficient and highly practical. To calculate the above mathematical model, the first preparation work required is to obtain a plurality of battery samples of the same batch, to ensure good consistency of each battery sample, so that the initial state of each battery sample is the same, and the experimental data collected subsequently has high reliability in the model creation process.

[0028] Afterwards, the embodiment respectively carries out the charge-discharge cycle to different battery samples under different preset temperature conditions, in order to ensure the accuracy of the model, the cycle test needs at least 3 groups of different temperature conditions, one group is the benchmark temperature, and the rest is the comparison temperature. The cycle test temperature selection range of the embodiment of the application is 0-80℃, wherein the standard temperature is preferably 25℃, the more the comparison temperature group is set, the more accurate the prediction result is. For example, the first battery sample is subjected to charge-discharge cycle under the condition of 25℃, the second battery sample is subjected to charge-discharge cycle under the condition of 35℃, the third battery sample is subjected to charge-discharge cycle under the condition of 45℃, and the fourth battery sample is subjected to charge-discharge cycle under the condition of 60℃, which is only an example, not limited thereto.

[0029] Afterwards, in the process of charge-discharge cycle, the discharge capacity data corresponding to different cycle numbers of each battery sample is extracted, the cycle test number is set to be between 50-1000 in the embodiment of the application, and under the premise of not analyzing lithium, the more the cycle number is, the more accurate the prediction result is. For example, the discharge capacity data of 0 cycles, 50 cycles, 100 cycles and 150 cycles is recorded. The above parameter selection is only used as an example, and other values not listed in the value range are also applicable.

[0030] Step S104, the capacity loss curve under each preset temperature condition is generated according to the extracted discharge capacity data, and the capacity loss curve is used to represent the capacity loss rate changing with the cycle number.

[0031] Specifically, the discharge capacity data extracted by the above steps S101-S103 can be calculated for different preset temperature conditions, and the capacity loss caused by the increase of the cycle number is calculated, the capacity loss rate changing with the cycle number is calculated in the embodiment of the application, and the capacity loss curve is obtained. For example, the capacity loss curves under the conditions of 25℃ / 35℃ / 45℃ / 60℃ are calculated respectively. The specific calculation method of the capacity loss rate is the prior art, and the embodiment of the application will not be repeated.

[0032] Step S105, using the capacity loss curve under each preset temperature condition, fitting the capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable.

[0033] Step S106, obtaining the target temperature of the to-be-predicted scene, and substituting the target temperature into the capacity loss prediction standard equation to obtain the capacity loss curve for predicting the battery life under the to-be-predicted scene, and predicting the battery life through the capacity loss curve.

[0034] Specifically, after the capacity loss curves under several typical temperature conditions are obtained, the variation law of the capacity loss curves as a whole with temperature change is found, and on the basis of taking the cycle number as the independent variable, the temperature is also taken as the independent variable, a capacity loss prediction standard equation including two independent variable parameters of temperature and cycle number and one dependent variable parameter of capacity loss rate is created. Based on the equation, when researchers want to predict the capacity loss of the battery with the cycle number under any temperature condition, the target temperature can be directly substituted into the capacity loss prediction standard equation, and the new equation obtained can represent the capacity loss curve under the target temperature condition, and the battery life can be predicted through the capacity loss curve.

[0035] Through the technical scheme provided by the embodiment of the application, it is not necessary to repeatedly perform the charge-discharge cycle test on the battery sample according to the target temperature of the user demand every time, the capacity loss curve under any temperature condition is obtained through the capacity loss prediction standard equation provided by the embodiment of the application, and then the life prediction is performed, and the battery life prediction efficiency is significantly improved.

[0036] In some optional embodiments, the above step S105 comprises:

[0037] Step a1, creating a capacity loss prediction standard equation before coefficient solving based on the Arrhenius formula;

[0038] Step a2, taking logarithm of the capacity loss prediction standard equation before solving;

[0039] Step a3, drawing a solving curve of the capacity loss prediction standard equation with the cycle number after taking logarithm as the X-axis and the capacity loss rate after taking logarithm as the Y-axis;

[0040] Step a4, substituting the values from the capacity loss curves under each preset temperature condition into the solving curve, and solving the coefficients of the capacity loss prediction standard equation according to the slope and intercept of the solving curve to obtain the capacity loss prediction standard equation after solving.

[0041] Specifically, lithium batteries, as a kind of commonly used rechargeable batteries, have the advantages of high energy density, long cycle life, etc., and are widely used in mobile devices. However, with the increase of use time, the capacity of lithium batteries gradually decreases, eventually leading to the end of the battery life, therefore, accurately predicting the life of lithium batteries is of great significance for the design and application of batteries. The Arrhenius formula is a mathematical model that can be used to predict the life of lithium batteries, which is based on the relationship between the cycle life and capacity loss of lithium batteries, and the life of the battery can be predicted by measuring the capacity decay rate of the battery. However, the related technology omits the temperature parameter in the Arrhenius formula, and often creates a battery life prediction equation based on the relationship between the cycle life and capacity loss of the battery, without applying the relationship between the chemical reaction rate constant and temperature to the capacity loss prediction. For example, the Arrhenius formula commonly used in related technologies to predict battery life is:

[0042] N=A′*e B′*Q

[0043] In the formula, N represents the number of cycles, A' and B' are coefficients of the Arrhenius formula, and Q represents the amount of capacity loss.

[0044] It can be seen that the above formula cannot determine the change rule of capacity loss with the number of cycles under any temperature condition. The present embodiment improves and adjusts the Arrhenius formula, thereby creating a capacity loss rate prediction equation with temperature and cycle number as independent variables, i.e. a capacity loss prediction standard equation, the specific formula is as follows:

[0045]

[0046] In the formula, x represents the number of cycles, y represents the capacity loss rate, T represents the temperature, and A, B and C are equation coefficients.

[0047] In the above formula, if A, B and C can be solved, then any temperature T can be brought into the solved equation, and the capacity loss curve under any temperature condition can be determined.

[0048] Based on this, the embodiment of the present application solves the coefficients of the capacity loss prediction standard equation by using the capacity loss curve under each preset temperature condition by means of an expensive solution, so as to solve the equation coefficients A, B and C. The solving principle of the expensive solution is to take the logarithm of both sides of the capacity loss prediction standard equation, and the formula can be changed into ln(y) = C*ln(x) + ln(A) + B*1 / T. The solving curve is drawn by taking ln(x) as the X axis and ln(y) as the Y axis, at this time the slope of the solving curve is C, and the intercept is ln(A) + B*1 / T. The values are taken from the capacity loss curves under different temperatures obtained from the foregoing experiment, substituted into the solving curve ln(y) = C*ln(x) + lnA + B*1 / T, and an image is drawn, so as to determine the specific values of C and lnA + B*1 / T under different temperatures, wherein the coefficient C can be obtained by averaging the slopes under each temperature condition. Then, the intercept Z = ln(A) + B*1 / T is graphed according to the same method, the Z values and 1 / T under each temperature condition determined from the foregoing are plotted with 1 / T as the horizontal axis and Z as the vertical axis, and the slope of the curve obtained by plotting is the coefficient B, and the intercept of the curve is the logarithm ln(A) of the coefficient A, so as to fit the three empirical parameters A, B and C.

[0049] The specific solving process is illustrated by way of example.

[0050] In this embodiment, the lithium ion battery 2.5 Ah soft battery is taken as an example, the charge and discharge cycle test temperature conditions are 25℃ / 35℃ / 45℃ / 60℃, wherein 25℃ is the reference temperature; the cycle test rate is 1C, the charge and discharge mode is static-constant current constant voltage charging-static-constant current discharging cycle, the charge cut-off voltage is 3.65V, the discharge cut-off voltage is 2.5V, the static time is 30min, and the cycle cell is cased at 0 / 250 / 700 cycles under room temperature, and the thickness is measured by using a micrometer under room temperature, wherein the cell is discharged at 0.05C to 0% SOC before the test.

[0051] In the foregoing charge and discharge cycle test process, the capacity loss curve under each temperature condition is obtained by the processing of the foregoing step S104, for example, four curves under four temperature conditions of 25℃ / 35℃ / 45℃ / 60℃, and then the capacity loss rate and the cycle number are respectively taken as the logarithm, and the logarithmic form of the foregoing capacity loss curve is plotted with ln(x) as the X axis and ln(y) as the Y axis. Figure 2 As shown in (a), since the first 10 cycles (ln value is less than 2) belong to the initial stage of film formation, and the film layer is stable after 100 cycles (ln value is greater than 4) and more in line with the linear model, the curve after 100 cycles is only fitted in the embodiment of the present application. The average of the slopes of the curves in (a) is C = 0.79, and then according to Figure 2 Figure 2 ​(a) the vertical intercept of the different temperature curves with the Y axis can be determined, so that the vertical intercepts at four temperature conditions are plotted against the four temperature parameters 1 / T, and a straight line is obtained after connection, as shown in Figure 2 (b) and Figure 2 (b) the slope of the curve, i.e. the coefficient B, Figure 2 (b) the new intercept, i.e. ln(A), can be obtained, and the fitted curve satisfies the function relationship y = -1249.6x + 0.9422, so that A = e 0.94 , and B = -1250. Finally, the solved capacity loss prediction standard equation is:

[0052]

[0053] If different temperatures (25℃ / 35℃ / 45℃ / 60℃) are substituted into the above fitted capacity loss prediction standard equation , the capacity loss curves of the lithium ion soft pack battery at the corresponding temperatures can be calculated, and the calculated capacity loss curves are compared with the experimentally obtained capacity loss curves (as shown in Figure 3 ), and the comparison is made by converting the capacity loss curves into capacity retention rates, wherein groups G, H, I and J are experimental data), and it is found that the calculated curves are in good agreement with the actual test curves.

[0054] Further, when a user wants to perform battery life prediction at an arbitrary target temperature condition (for example, 55℃, which is only an example and is not limited thereto), the target temperature can be directly substituted into the above solved formula to obtain the capacity loss curve at the target temperature, and then the battery life prediction is performed according to the change rule of the capacity loss curve. Through the technical scheme provided in the embodiments of the present application, it is no longer necessary to repeatedly perform charge-discharge cycle tests according to the target temperature, and the capacity loss curve is obtained through the capacity loss prediction standard equation, which significantly improves the battery life prediction efficiency.

[0055] In some optional embodiments, the battery life prediction method provided by the present application further comprises:

[0056] Step b1, collecting the thickness expansion data of the battery sample at each preset temperature condition in a preset period during the charge-discharge cycle;

[0057] Step b2, extracting a plurality of expansion sub-data at different temperatures and under the same cycle number from the thickness expansion data;

[0058] Step b3, determining the thickness capacity relationship of the battery sample according to the expansion sub-data and the corresponding discharge capacity data under the same condition, and obtaining a thickness capacity relationship equation, the thickness capacity relationship being used to represent the relationship between the thickness growth rate and the capacity loss rate of the battery;

[0059] Step b4, the thickness capacity relationship equation is substituted into the capacity loss prediction standard equation to obtain a thickness prediction standard equation with temperature and cycle number as independent variables and thickness growth rate as dependent variable;

[0060] Step b5, the target temperature is substituted into the thickness prediction standard equation to obtain a thickness growth curve for predicting battery life in the to-be-predicted scenario, and battery life prediction is performed through the thickness growth curve.

[0061] Specifically, in addition to the way of predicting battery life through the relationship between capacity loss rate and cycle number, the change relationship between cycle number and battery thickness is also one of the commonly used ways to predict battery life, but the related technology also faces the problem of different change characteristics at different temperatures, resulting in the need to test the battery expansion degree at different environmental temperatures, which wastes a lot of human resources.

[0062] The embodiment of the present application considers that if a mathematical model similar to the above capacity prediction can be created, and any temperature data is substituted, the battery thickness-cycle number change relationship at any temperature can be obtained, the prediction efficiency of the battery life can be further improved.

[0063] To achieve the above scheme, in the charging and discharging cycle process of the above step S102, the thickness expansion data of the battery sample under each preset temperature condition is collected at a preset period. For example, the cycle test number is between 50-1000, and under the premise of not analyzing lithium, the more the cycle number is, the more accurate the prediction result is; the thickness test collection cycle interval is 50-500 cycles during the cycle test process, and the more the collected data is, the more accurate the prediction result is. The cell thickness collection tool includes but is not limited to a caliper, a thickness gauge, a force distance sensor, a displacement sensor, etc.

[0064] After that, the embodiment of the present application finds the rule between the battery thickness expansion and the capacity change according to the measured data, and the specific steps include: 1, first extract several expansion sub-data with the same cycle number at different temperatures from the thickness expansion data, for example, extract the expansion sub-data of 50 cycles at 25℃, the expansion sub-data of 50 cycles at 35℃, the expansion sub-data of 50 cycles at 45℃ and the expansion sub-data of 50 cycles at 60℃ from the thickness expansion data obtained in the whole experiment process. 2, then compare and analyze the above expansion sub-data and the discharge capacity data at four temperature conditions of 50 cycles to determine the relationship between the battery thickness and the capacity at a fixed cycle number, so as to obtain the thickness capacity relationship equation.

[0065] As Figure 4As shown, taking 250 cycles and 700 cycles as examples, the embodiment of the application determines that there is a linear relationship between the thickness growth rate and the capacity loss rate of the battery through analysis, and the physical meaning of this law is that the active lithium causes most of the capacity loss to be used for film formation, and the film formation thickness growth amount has a linear relationship with the active lithium consumption amount. Therefore, the thickness capacity relationship equation determined by the embodiment of the application is:

[0066] y=E*d+F

[0067] In the formula, y represents the capacity loss rate, d represents the thickness growth rate, and E and F are coefficients of the thickness capacity relationship equation.

[0068] After the relationship between the capacity loss rate and the thickness growth rate is found, the thickness capacity relationship equation is substituted into the aforementioned capacity loss prediction standard equation, and after transformation and arrangement of the equation, the thickness prediction standard equation with temperature and cycle number as independent variables and thickness growth rate as dependent variable can be calculated, and the specific formula is as follows:

[0069]

[0070] In the formula, x represents cycle number, T represents temperature, A, B, C and E are equation coefficients, d represents thickness growth rate, T0 represents reference cycle temperature, and d0 represents thickness growth rate at reference temperature.

[0071] Similarly, as shown in Figure 5 The thickness growth curve of the lithium ion soft package battery at different temperatures (25℃ / 35℃ / 45℃ / 60℃) can be calculated by substituting the different temperatures (25℃ / 35℃ / 45℃ / 60℃) into the aforementioned thickness prediction standard equation, and the actual test cell thickness change data at different temperatures (25℃ / 35℃ / 45℃ / 60℃) are marked in Figure 5 After comparison, it is found that the actual test data well conform to the model, so that the method provided by the embodiment of the application can be more accurate for the cycle thickness growth result of the battery at different temperatures, and has universal applicability.

[0072] In the subsequent battery life prediction process, the user can input any target temperature into the thickness prediction standard equation to obtain the thickness growth curve at the target temperature, and analyze the thickness growth rule of the battery with cycle number through the thickness growth curve, so as to accurately predict the life of the battery.

[0073] The technical scheme provided by the embodiment of the application associates the capacity loss, active lithium film formation consumption reaction and film formation thickness, thereby integrating the three parameters of temperature / cycle number / thickness, after modeling by a small amount of testing, not only can the capacity attenuation and thickness change at all temperatures be deduced, but also the modeling does not need to be re-established every time a temperature is changed, and the battery life prediction efficiency is significantly improved.

[0074] In some optional embodiments, before step b1, further comprising:

[0075] Step c1, adjusting each battery sample to the same SOC at a preset current ratio under the same temperature condition.

[0076] Specifically, the embodiment of the present application adjusts each battery sample to the same SOC at a preset current ratio before collecting thickness data, for example, the battery can be adjusted to the same SOC at 0.05C ratio under 25±2℃, including but not limited to 0%, 50% and 100% SOC, etc., which is only an example and is not limited thereto. Because the battery has a reversible thickness change at different SOC (this phenomenon is caused by different lithium intercalation amounts), and because it is a reversible thickness change, it does not pose a safety risk in the later stage. However, the embodiment needs to ensure that the reversible thickness is consistent among the battery samples when collecting thickness data, so as to ensure that the battery is adjusted to the same state, thereby ensuring that the thickness data collected is the irreversible thickness growth, avoiding the difference in reversible thickness change among the battery samples, thereby reducing data errors and improving the accuracy of subsequent model creation.

[0077] In a specific application embodiment, the technical solutions provided by the embodiment of the present application are described as follows, so that the implementation process of how to apply technical means to solve technical problems and achieve technical effects can be fully understood and implemented.

[0078] 1. Prepare a plurality of battery samples of the same batch with good consistency, and the embodiment takes a 2.5 Ah lithium ion battery as an example. Record the initial thickness data of each battery sample, and adjust the battery to the same SOC at a small current ratio under the same temperature, including but not limited to 0%, 50% and 100% SOC, etc., and preferably 0.05C ratio under 25℃ to 0% SOC.

[0079] 2. Cycle test the test battery samples at different temperatures, for example, one battery sample corresponds to one temperature for cycle test. In the embodiment, four battery samples are cycled tested at 25℃ / 35℃ / 45℃ / 60℃, the cycle test ratio is 1C, the charge and discharge mode is static-constant current and constant voltage charging-static-constant current discharging cycle, the charge cut-off voltage is 3.65V, the discharge cut-off voltage is 2.5V, the static time is 30min, and the cycle battery is cycled at 0 / 250 / 700 cycles.

[0080] 3. During the cycle test, the thickness expansion data after a certain number of cycles at different temperatures is measured periodically. The thickness collection tools include, but are not limited to, a caliper, a thickness gauge, a force distance sensor, and a displacement sensor, etc. The interval of the collection number of thickness tests during the cycle test is 50-500 cycles, i.e., the thickness is collected once every 50 cycles, and then the battery expansion thickness is calculated by using the collected thickness expansion data.

[0081] 4. After the cycle, the discharge capacity data at different cycle numbers at each temperature is extracted, and the capacity loss rate of each battery sample is calculated. The discharge capacity data of the cycle test in this example is shown in Figure 6 (a). It can be seen that the higher the cycle temperature, the higher the initial capacity. This is because the higher the temperature, the higher the kinetic performance of the battery cell. The capacity retention rate curve of the battery cell can be calculated according to the initial capacity, as shown in Figure 6 (b). The capacity retention rate curve is shown in (b). The capacity retention rate is inversely proportional to the capacity loss rate. The higher the temperature, the faster the capacity decay of the battery cell. This is because the higher the temperature, the more side reactions of SEI (Solid Electrolyte Interface) film formation or repair of the battery cell, the more active lithium consumption, and the faster the capacity decay. As shown in Figure 7 (a) and Figure 7 (b). Comparing the capacity loss curve obtained by the test with the thickness growth curve, both of them have the same growth trend. This is because the active lithium loss of capacity is mostly used for the growth of SEI. Therefore, both the capacity loss curve and the thickness growth curve can be used for battery life prediction.

[0082] 5. Using the data collected by the above steps, the capacity loss rate and the cycle number during the cycle at different temperatures are fitted and calculated by the improved Arrhenius formula. The fitting relationship is:

[0083]

[0084] In the formula, x represents the cycle number, y represents the capacity loss rate, T represents the temperature, and A, B, and C are equation coefficients.

[0085] The fitting process is as follows: first, the capacity loss rate and the cycle number obtained by the cycle test are taken as logarithms, and a graph is drawn with ln(x) as the X-axis and ln(y) as the Y-axis. As shown in Figure 2 a. Since the first 10 cycles of the cycle (ln value less than 2) belong to the initial film formation period, and the film layer is stable after 100 cycles (ln value greater than 4), which is more consistent with the linear model, only the curve after 100 cycles is fitted, and the average slope of each curve in Figure 2 a is calculated as C=0.79. Then, according to Figure 2a can determine the different temperature curve and the vertical intercept of Y axis, so that the vertical intercept of four temperature conditions and four temperature parameters 1 / T are plotted, and a straight line is connected as shown in Figure 2 b, Figure 2 b The slope of the curve is the coefficient B, Figure 2 b The new intercept in is ln(A), and the fitted curve satisfies the function relationship y=-1249.6x+0.9422, and A=e 0.94 , B=-1250. Finally, the solved capacity loss prediction standard equation is:

[0086]

[0087] 6, the capacity loss rate and thickness data of different temperatures under the same cycle are fitted and calculated, and the fitted thickness capacity relationship equation is:

[0088] y=E*d+F

[0089] In the formula, y represents the capacity loss rate, d represents the thickness growth rate, E and F are the coefficients of the thickness capacity relationship equation.

[0090] As shown in Figure 4 , the average of the fitting slope can be calculated as E=1.25.

[0091] 7, the thickness capacity relationship equation is substituted into the aforementioned capacity loss prediction standard equation, and the equation is transformed and arranged, so that the thickness prediction standard equation with temperature and cycle number as independent variables and thickness growth rate as dependent variable can be calculated:

[0092]

[0093] In the formula, x represents the cycle number, T represents the temperature, A, B, C and E are the equation coefficients, the values obtained by fitting in steps 5 and 6 are substituted, d represents the thickness growth rate, T0 represents the reference cycle temperature, and d0 represents the thickness growth rate at the reference temperature.

[0094] 8, any target temperature is substituted into the aforementioned thickness prediction standard equation and capacity loss prediction standard equation, so that the thickness growth curve and capacity loss curve of the lithium ion soft package battery at the target temperature can be calculated, and the battery life prediction can be carried out. It can be seen that the method of the present application is more accurate for the cycle thickness growth of the battery under different temperatures, and has universal applicability.

[0095] By the technical scheme provided in the embodiments of the present application, batch experiments do not need to consume a large amount of manpower and material resources, and expensive and precise test equipment and complex theoretical calculations are not needed, and only short-term cycle tests are needed to quickly predict the thickness growth of the battery cell under different temperatures, greatly reducing the time and resource waste generated by conventional tests. In addition, the higher the temperature, the faster the reaction speed, and the active lithium film formation consumption reaction under high-temperature aging will accelerate, so that a model can be quickly established through high-temperature rapid aging data to evaluate the capacity attenuation and thickness change under normal-temperature cycles, reduce test cost and time, and also evaluate the degree of uneven expansion. In addition, according to the use scenario, the technical scheme provided by the present application can also provide a reference for battery cell group margin design, and the thickness growth curve calculated by the thickness prediction standard equation can provide a capacity diving warning signal for the battery in the cycle. In addition, the technical scheme provided by the present application can be applied to new research and development battery life prediction, obtain a linear model at different temperatures to evaluate the limit life of different charging strategies, so as to know the matching of the charging strategy and the battery performance and whether it can meet the customer's requirements, and optimize it.

[0096] In the embodiments, a battery life prediction device is also provided, which is used to implement the above embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware, or a combination of software and hardware is also possible and contemplated.

[0097] The present embodiment provides a battery life prediction device, as shown in Figure 8 , comprising:

[0098] The sample acquisition module 801 is configured to acquire a plurality of battery samples of the same batch.

[0099] The cycle test module 802 is configured to perform charge-discharge cycles on different battery samples under a plurality of different preset temperature conditions, respectively.

[0100] The capacity data acquisition module 803 is configured to extract, during the charge-discharge cycle, discharge capacity data corresponding to different cycle numbers under each preset temperature condition for the battery sample.

[0101] The capacity loss curve generation module 804 is configured to generate a capacity loss curve under each preset temperature condition by fitting the extracted discharge capacity data, the capacity loss curve being configured to represent a capacity loss rate varying with the cycle number.

[0102] The first equation generation module 805 is configured to generate a capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable by using the capacity loss curve under each preset temperature condition.

[0103] The capacity loss prediction module 806 is configured to obtain a target temperature of a to-be-predicted scenario, and substitute the target temperature into the capacity loss prediction standard equation to obtain a capacity loss curve for predicting the battery life under the to-be-predicted scenario.

[0104] In some optional embodiments, the first equation generation module 805 comprises:

[0105] An equation creation module is configured to create the capacity loss prediction standard equation before coefficient solving based on the Arrhenius formula.

[0106] A coefficient solving module is configured to solve the coefficients of the capacity loss prediction standard equation by using the capacity loss curves under the preset temperature conditions through an expensive solution method, to obtain the capacity loss prediction standard equation after coefficient solving.

[0107] In some optional embodiments, the device further comprises:

[0108] A thickness data acquisition module is configured to acquire thickness expansion data of the battery sample under each preset temperature condition in a preset period during the charge-discharge cycle.

[0109] An expansion sub-data extraction module is configured to extract expansion sub-data of the same cycle number at different temperatures from the thickness expansion data.

[0110] A thickness capacity relationship determination module is configured to determine a thickness capacity relationship of the battery sample according to the expansion sub-data and corresponding discharge capacity data under the same condition, and obtain a thickness capacity relationship equation, the thickness capacity relationship being used to represent a relationship between a thickness growth rate and a capacity loss rate of the battery.

[0111] A second equation generation module is configured to substitute the thickness capacity relationship equation into the capacity loss prediction standard equation to obtain a thickness prediction standard equation with temperature and cycle number as independent variables and thickness growth rate as dependent variable.

[0112] A thickness growth prediction module is configured to substitute a target temperature into the thickness prediction standard equation to obtain a thickness growth curve for predicting the battery life under the to-be-predicted scenario, and perform the battery life prediction through the thickness growth curve.

[0113] The further function descriptions of the above modules and units are the same as those of the corresponding embodiments, and will not be described here.

[0114] The device in the embodiment is presented in the form of functional units. The units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories executing one or more software or fixed programs, and / or other devices that can provide the above functions.

[0115] This invention also provides a computer device having the above-described features. Figure 8 The battery life prediction device shown.

[0116] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 9 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0117] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0118] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0119] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] The memory 20 can include a volatile memory, such as a random access memory, and / or a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 20 can also include a combination of the above-mentioned types of memories.

[0121] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0122] The embodiments of the present application also provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or be implemented as computer code stored in a remote storage medium or a non-transitory machine readable storage medium and stored in a local storage medium to be downloaded through a network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method shown in the above embodiments.

[0123] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of computer program instructions executed by a computer includes but is not limited to: the computer directly executes the instructions, or the computer executes the corresponding compiled program after compiling the instructions, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0124] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A battery life prediction method, characterized by, The method comprises: obtaining a plurality of battery samples of the same batch; respectively performing charge-discharge cycles on different battery samples under a plurality of different preset temperature conditions; during the charge-discharge cycles, extracting discharge capacity data corresponding to different cycle numbers under each preset temperature condition for the battery samples; generating a capacity loss curve under each preset temperature condition according to the extracted discharge capacity data, the capacity loss curve being used to represent a capacity loss rate varying with cycle numbers; using the capacity loss curves under each preset temperature condition to fit a capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable; obtaining a target temperature of a to-be-predicted scenario, and substituting the target temperature into the capacity loss prediction standard equation to obtain a capacity loss curve for predicting battery life under the to-be-predicted scenario, and predicting battery life through the capacity loss curve; the method further comprises: during the charge-discharge cycles, collecting thickness expansion data of the battery samples under each preset temperature condition at a preset period; extracting expansion sub-data of the same cycle number at different temperatures from the thickness expansion data; determining a thickness-capacity relationship of the battery samples according to the expansion sub-data and corresponding discharge capacity data under the same condition, and obtaining a thickness-capacity relationship equation, the thickness-capacity relationship being used to represent the relationship between the thickness growth rate and the capacity loss rate of the battery; substituting the thickness-capacity relationship equation into the capacity loss prediction standard equation to obtain a thickness prediction standard equation with temperature and cycle number as independent variables and thickness growth rate as dependent variable; substituting the target temperature into the thickness prediction standard equation to obtain a thickness growth curve for predicting battery life under the to-be-predicted scenario, and predicting battery life through the thickness growth curve; the thickness-capacity relationship equation is: wherein represents a capacity loss rate, represents a thickness growth rate, and is a coefficient of the thickness capacity relationship equation; the thickness prediction standard equation is: wherein represents the number of cycles, represents the temperature, , , and is the equation coefficient, represents the thickness growth rate, 0 represents the reference cycle temperature, represents the thickness growth rate at the reference temperature.

2. The method of claim 1, wherein, the using of the capacity loss curves under each preset temperature condition to fit the capacity loss prediction standard equation with temperature and cycle number as independent variables and capacity loss rate as dependent variable comprises: creating the capacity loss prediction standard equation before solving the coefficients based on the Arrhenius formula; taking logarithm of the capacity loss prediction standard equation before solving; plotting a solving curve of the capacity loss prediction standard equation with cycle number after taking logarithm as X-axis and capacity loss rate after taking logarithm as Y-axis; substituting values from the capacity loss curves under each preset temperature condition into the solving curve, and solving the coefficients of the capacity loss prediction standard equation according to the slope and intercept of the solving curve to obtain the capacity loss prediction standard equation after solving.

3. The method of claim 2, wherein, the capacity loss prediction standard equation is: wherein represents the number of cycles, represents the capacity loss rate, represents the temperature, , and is an equation coefficient.

4. The method of claim 1, wherein, before the collecting of the thickness expansion data of the battery samples under each preset temperature condition at a preset period, the method further comprises: adjusting each battery sample to the same SOC under the same temperature condition at a preset current rate.

5. A battery life prediction apparatus characterized by comprising: the device comprises: a sample acquisition module for obtaining a plurality of battery samples of the same batch; a cycle test module for respectively performing charge-discharge cycles on different battery samples under a plurality of different preset temperature conditions; The capacity data collection module is configured to collect discharge capacity data corresponding to different cycle numbers under each preset temperature condition during the charge-discharge cycle process. The capacity loss curve generation module is configured to generate a capacity loss curve under each preset temperature condition by fitting the collected discharge capacity data, and the capacity loss curve is configured to represent a capacity loss rate varying with cycle numbers. The first equation generation module is configured to generate a capacity loss prediction standard equation with temperature and cycle numbers as independent variables and the capacity loss rate as a dependent variable by fitting the capacity loss curves under each preset temperature condition. The capacity loss prediction module is configured to obtain a target temperature of a to-be-predicted scenario, and substitute the target temperature into the capacity loss prediction standard equation to obtain a capacity loss curve for predicting the battery life under the to-be-predicted scenario. The thickness capacity relationship equation is as follows: wherein represents a capacity loss rate, represents a thickness growth rate, and is a coefficient of the thickness capacity relationship equation; The thickness prediction standard equation is as follows: wherein represents the number of cycles, represents the temperature, , , and is the equation coefficient, represents the thickness growth rate, 0 represents the reference cycle temperature, represents the thickness growth rate at the reference temperature.

6. A computer device, comprising: The computer readable storage medium stores computer instructions, and the computer instructions are configured to cause the computer to execute the method in any one of claims 1 to 4. The computer readable storage medium stores computer instructions, and the computer instructions are configured to cause the computer to execute the method in any one of claims 1 to 4.

7. A computer readable storage medium characterized by, The computer readable storage medium stores computer instructions, and the computer instructions are configured to cause the computer to execute the method in any one of claims 1 to 4.

8. A computer program product, characterised in that, ​

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