Method and System for Predicting Cycle Life and Operating Temperature of Lithium-Ion Batteries

Abstract

The present invention relates to a method and system for predicting the cycle life and operating temperature of a lithium-ion battery. The above prediction method includes: determining the driving condition temperature of a vehicle that can use the lithium-ion battery to be tested, obtaining the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and inputting it into a pre-constructed cycle capacity attenuation model of the lithium-ion battery to obtain the cycle attenuation rate of the lithium-ion battery to be tested under different driving condition conditions, and then predicting the cycle life of the lithium-ion battery to be tested under the corresponding driving condition conditions; determining the cycle temperature corresponding to the driving condition condition with the minimum cycle attenuation rate, and this cycle temperature is the operating temperature of the lithium-ion battery to be tested. The present invention can cover predictions from low temperature to high temperature, broaden the temperature range of use, and can be widely applied to the technical field of lithium-ion battery life prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery life prediction, and particularly to a method and system for predicting the cycle life and operating temperature of a lithium-ion battery. Background Art

[0002] Electric vehicles are very efficient in using electrical energy, and electrical energy can basically be converted from new energy sources such as solar energy, wind energy, water energy, and biomass energy, with basically zero emissions. Therefore, various countries are currently actively promoting the development of new energy vehicles. During the operation of a whole vehicle, lithium-ion batteries are inevitably degraded in performance due to various aging stresses, and people generally pay attention to their service life and driving range. Temperature is the main factor affecting the attenuation of lithium-ion batteries. During the driving process of the whole vehicle system, due to the heat generated by the battery itself, the temperature rises rapidly. To extend the service life, when the temperature reaches the critical value, liquid cooling or air cooling is activated to control the battery within a specific operating temperature range. Currently, electric vehicles all have requirements for long driving ranges, and operating vehicles even require more than 300,000 kilometers. However, the conventional cycle attenuation of batteries is relatively slow, which affects the progress of product development. In many cases, cyclic accelerated life tests are the only way to evaluate whether a battery meets the requirements of high reliability and long life.

[0003] Cyclic accelerated life tests include acceleration factors such as temperature, rate, and DOD (DOD refers to the depth of discharge of the battery, which represents the percentage of the battery discharge capacity to the battery rated capacity), and there are many studies on cyclic aging models. There are mainly two performance degradation modes in the cycle of lithium-ion batteries: lithium plating and material aging. The former is likely to occur at low temperatures and high rates, and the latter occurs at high temperatures and high rates. Therefore, for a specific rate cycle, there must be an optimal temperature that minimizes the comprehensive attenuation rate of lithium plating and material aging. However, the currently widely used temperature acceleration is the Arrhenius model, where the lower the temperature, the smaller the battery attenuation rate, which does not match the results of some accelerated cycle tests.

[0004] In addition, the thermal management strategy of the whole vehicle also needs to determine the optimal operating temperature of the battery in terms of performance. Therefore, a reasonable and accurate temperature acceleration model for lithium-ion batteries and the optimal operating temperature of lithium-ion batteries are still one of the key technical problems that are widely concerned and urgently need to be solved in the fields of lithium-ion battery manufacturing and electric vehicle whole vehicles. Summary of the Invention

[0005] In view of the above problems, the object of the present invention is to provide a method and system for predicting the cycle life and operating temperature of a lithium-ion battery considering the temperature operating range.

[0006] To achieve the above object, the present invention adopts the following technical solutions: In the first aspect, a method for predicting the cycle life and operating temperature of a lithium-ion battery is provided, including:

[0007] Determine the driving condition temperature of the vehicle that can use the lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input it into the pre-constructed cyclic capacity attenuation model of the lithium-ion battery to obtain the cyclic attenuation rate of the lithium-ion battery to be tested under different driving condition conditions, and then predict the cyclic life of the lithium-ion battery to be tested under the corresponding driving condition conditions;

[0008] Determine the cyclic temperature corresponding to the driving condition condition with the minimum cyclic attenuation rate, and this cyclic temperature is the operating temperature of the lithium-ion battery to be tested.

[0009] Furthermore, the construction process of the cyclic capacity attenuation model of the lithium-ion battery is as follows:

[0010] Construct a cyclic capacity attenuation model of the lithium-ion battery;

[0011] Based on the set cyclic accelerated life test, determine the cyclic accelerated life test data of the test samples;

[0012] According to the cyclic accelerated life test data of the test samples, determine the model parameters of the cyclic capacity attenuation model of the lithium-ion battery.

[0013] Furthermore, the cyclic capacity attenuation model of the lithium-ion battery is:

[0014]

[0015] where Q Reten represents the capacity retention rate; k represents the cyclic attenuation rate; T and En respectively represent the cyclic temperature and the cyclic cumulative discharge energy; z, β 0 、β 1 、β 2 are the parameters to be solved in the model.

[0016] Furthermore, the steps of determining the cyclic accelerated life test data of the test samples based on the set cyclic accelerated life test include:

[0017] According to the driving condition conditions of the vehicle that can use the lithium-ion battery to be tested, set the cyclic accelerated life test and its cyclic test conditions, and set the test time and the number of cycles;

[0018] Select at least two from the lithium-ion batteries produced in the same batch as the test samples, and conduct a cyclic accelerated life test on the selected test samples based on the set cyclic test conditions, test time and number of cycles to obtain the cyclic accelerated life test data of each test sample.

[0019] Furthermore, the cyclic test conditions include the cyclic temperature, the depth of discharge of the battery, and the charge-discharge rate.

[0020] Further, the step of selecting at least two lithium-ion batteries produced in the same batch as test samples, and performing a cyclic accelerated life test on the selected test samples based on set cyclic test conditions, test time, and number of cycles to obtain cyclic accelerated life test data of each test sample includes:

[0021] Select lithium-ion batteries with deviations of performance indicators within a preset range from the lithium-ion batteries produced in the same batch as test samples;

[0022] Randomly place a number of test samples in each set cyclic test condition for cyclic accelerated life test to obtain cyclic accelerated life test data of each test sample.

[0023] Further, the step of determining the model parameters of the cyclic capacity attenuation model of the lithium-ion battery according to the cyclic accelerated life test data of the test samples includes:

[0024] Determine the capacity retention rate of each cyclic test according to the cyclic accelerated life test data of the test samples;

[0025] If the capacity retention rate determined in a certain cyclic test deviates from the capacity retention rates determined in the two cyclic tests before and after it by a preset threshold, or the determined capacity retention rate is greater than the threshold, then regard it as an abnormal point and exclude it;

[0026] According to the capacity retention rates of each cyclic test after excluding abnormal points, perform power function fitting on the cyclic accelerated life test data of all test samples respectively to obtain the cell fitting power value z of each test sample lithium-ion battery;

[0027] Use a box plot to exclude abnormal points of the cell fitting power value, and calculate the average value of the cell fitting power value after excluding abnormal points as the fixed power value;

[0028] According to the fixed power value, refit the cyclic accelerated life test data of all test samples, and take the average value of the cyclic attenuation rates fitted by the test samples under the same cyclic test conditions. This average cyclic attenuation rate is the cyclic attenuation rate k of this cyclic test condition;

[0029] Take the logarithm ln k of the cyclic attenuation rates k under different cyclic test conditions, and perform multiple linear fitting on ln k, 1 / T, and T to determine the model parameters β 0 、β 1 and β 2 .

[0030] In a second aspect, a cyclic life and service temperature prediction system for a lithium-ion battery is provided, including:

[0031] A cycle life determination module is used to determine the driving condition temperature of a vehicle that can use a lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input it into a pre-constructed cycle capacity attenuation model of the lithium-ion battery to obtain the cycle attenuation rate of the lithium-ion battery to be tested under different driving condition conditions, and then predict the cycle life of the lithium-ion battery to be tested under the corresponding driving condition conditions;

[0032] A use temperature determination module is used to determine the cycle temperature corresponding to the driving condition condition with the minimum cycle attenuation rate, and this cycle temperature is the use temperature of the lithium-ion battery to be tested.

[0033] In a third aspect, a processing device is provided, including computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the above-mentioned method for predicting the cycle life and use temperature of a lithium-ion battery.

[0034] In a fourth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the above-mentioned method for predicting the cycle life and use temperature of a lithium-ion battery.

[0035] Due to the above technical solutions adopted by the present invention, it has the following advantages:

[0036] 1. Starting from two main attenuation factors of lithium battery cycle lithium plating and material aging, which are negatively and positively correlated with temperature respectively, the present invention constructs a cycle capacity attenuation model of a lithium-ion battery, can predict the cycle life and use temperature of the lithium-ion battery to be tested under corresponding conditions, can cover the prediction from low temperature to high temperature, broaden the temperature use range, and has high prediction accuracy.

[0037] 2. Through the parabolic relationship between cycle capacity attenuation and temperature, the present invention can determine the optimal use temperature, and the vehicle system is based on this optimal temperature as a reference basis for the temperature critical value to turn on the cooling system.

[0038] 3. For a vehicle system using low-temperature heating, the use temperature determined by the present invention can be used as a reference basis for the temperature critical value to turn on or off low-temperature heating.

[0039] In summary, the present invention can be widely applied to the technical field of lithium-ion battery life prediction. Description of the Drawings

[0040] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiments. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0041] Figure 1 is a schematic flow chart of a method provided by an embodiment of the present invention;

[0042] Figure 2 is a schematic diagram of the original data of the capacity retention rate and the cumulative discharge energy at different temperatures provided by an embodiment of the present invention;

[0043] Figure 3 is a schematic diagram of the free fitting of partial test data provided by an embodiment of the present invention, including the fitting diagrams of the capacity retention rate and the cumulative discharge energy at 10°C, 25°C, 45°C, and 60°C;

[0044] Figure 4 is a box plot of all battery power values under free fitting provided by an embodiment of the present invention;

[0045] Figure 5 is a schematic diagram of the comparison between the measured value of Lnk-1 / T and the predicted values of different models provided by an embodiment of the present invention;

[0046] Figure 6 is a schematic diagram of the cycle decay rate at different temperatures of 10C / 10C provided by an embodiment of the present invention;

[0047] Figure 7 is a schematic diagram of the cycle decay rate at different temperatures of 15C / 15C provided by an embodiment of the present invention. Detailed Embodiments

[0048] The exemplary embodiments of the present invention will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0049] It should be understood that the terms used herein are for the purpose of describing particular example embodiments only and are not intended to be limiting. Unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" as used herein may also include the plural forms. The terms "comprising", "including", "containing", and "having" are inclusive and thus specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless explicitly indicated as an order of performance. It should also be understood that additional or alternative steps may be used.

[0050] Although the terms first, second, third, etc. may be used herein to describe multiple elements, components, regions, layers, and / or sections, these elements, components, regions, layers, and / or sections should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or section from another. Unless the context clearly indicates otherwise, terms such as "first", "second", and other numerical terms when used herein do not imply an order or sequence. Thus, the first element, component, region, layer, or section discussed below may be referred to as a second element, component, region, layer, or section without departing from the teachings of the example embodiments.

[0051] Currently, there is much research on the cycle accelerated life model of lithium-ion batteries. The mechanism model therein is based on the microscopic electrochemical reaction process, depends on a large number of physical and chemical parameters, and has a large computational amount for solving partial differential equations, resulting in difficulties in practical applications. The data-driven model does not require the attenuation mechanism of lithium-ion batteries, but a large amount of historical or state data is needed to train the model in the early stage. In contrast, the cycle semi-empirical model is easy to implement and is widely used in the long-term performance evaluation of lithium batteries. However, there is no universal battery life model yet, especially ignoring the temperature usage range. Temperature is the main attenuation factor for the cycle of lithium-ion batteries. Usually, the Arrhenius model is used to handle temperature acceleration: the higher the temperature, the greater the attenuation rate; the lower the temperature, the smaller the attenuation rate. The cycle test data at wider temperatures and different charge-discharge rates show that for some high-rate cycles, the attenuation rate is relatively large at low temperatures. If the traditional model is still used for prediction, the attenuation at low temperatures will be greatly underestimated. Therefore, the current cycle model needs to be corrected. The cycle life and usage temperature prediction method and system for lithium-ion batteries provided by the embodiments of the present invention can predict the cycle life and usage temperature of the lithium-ion battery to be tested under corresponding conditions by constructing a cycle capacity attenuation model of the lithium-ion battery.

[0052] Example 1

[0053] As shown in Figure 1 the figure, this embodiment provides a method for predicting the cycle life and operating temperature of a lithium-ion battery, including the following steps:

[0054] 1) Construct a cycle capacity attenuation model for the lithium-ion battery.

[0055] 2) Based on the set cycle accelerated life test, determine the cycle accelerated life test data of the test sample.

[0056] 3) According to the cycle accelerated life test data of the test sample, determine the model parameters of the cycle capacity attenuation model of the lithium-ion battery.

[0057] 4) Determine the driving condition temperature of the vehicle that can use the lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input it into the cycle capacity attenuation model obtained in step 3) to obtain the cycle attenuation rate of the lithium-ion battery to be tested under different driving condition conditions, and then predict the cycle life of the lithium-ion battery to be tested under the corresponding driving condition conditions.

[0058] 5) Determine the cycle temperature corresponding to the driving condition with the minimum cycle attenuation rate, and this cycle temperature is the operating temperature of the lithium-ion battery to be tested.

[0059] Through the constructed cycle capacity attenuation model, the present invention can determine the cycle life and operating temperature of the lithium-ion battery to be tested, can cover the prediction from low temperature to high temperature, broaden the temperature usage range, and has high prediction accuracy.

[0060] In the above step 1), the cycle capacity attenuation model of the lithium-ion battery is:

[0061]

[0062] where Q Reten represents the capacity retention rate; k represents the cycle attenuation rate, which is an intermediate variable; T and En respectively represent the cycle temperature and the cycle cumulative discharge energy; z, β 0 β 1 β 2 are the parameters to be solved in the model.

[0063] In the above step 2), based on the set cycle accelerated life test, determine the cycle accelerated life test data of the test sample. The specific process is as follows:

[0064] 2.1) According to the driving condition of the vehicle that can use the lithium-ion battery to be tested, set the cycle accelerated life test and its cycle test conditions, and set the test time and the number of cycles. Among them, each cycle test condition includes the cycle temperature, DOD, and charge-discharge rate.

[0065] 2.2) Select at least two lithium-ion batteries from the same batch of production as test samples, and conduct a cyclic accelerated life test on the selected test samples based on the set cyclic test conditions, test time, and number of cycles to obtain the cyclic accelerated life test data of each test sample, including the cyclic temperature, the cumulative discharge energy corresponding to different numbers of cycles, and the capacity retention rate.

[0066] The specific process of the above step 2.1) is as follows:

[0067] 2.1.1) Determine the test time and number of cycles of the cyclic accelerated life test.

[0068] For example, the test time of the lithium-ion battery in a pure electric vehicle is 3 to 6 months, and the test time of the lithium-ion battery in a hybrid vehicle is 6 to 12 months.

[0069] 2.1.2) Determine the cyclic temperature of the cyclic accelerated life test, including the lowest temperature, the highest temperature, and one to three temperature gradients between the lowest temperature and the highest temperature.

[0070] Specifically, according to the driving temperature range of the vehicle where the lithium-ion battery to be tested is located, determine the lowest temperature and the highest temperature of the cyclic accelerated life test. More specifically, the temperature range between the determined lowest temperature and the highest temperature should cover more than 95% of the working temperature of the vehicle where the lithium-ion battery to be tested is located. Then, set one to three temperature gradients between the determined temperature ranges, and the set temperature gradients should match the commonly used test channels in practice.

[0071] 2.1.3) Determine the DOD range (depth of discharge) of the cyclic accelerated life test according to the usage range of the lithium-ion battery SOC (state of charge, charge state).

[0072] Specifically, according to the charge and discharge power requirements and available energy requirements of the lithium-ion battery to be tested at different life stages of the vehicle, determine the usage range of the lithium-ion battery SOC, and according to the usage range of the lithium-ion battery SOC, determine the upper and lower limits of the cyclic SOC, and then determine the DOD range of the cyclic accelerated life test. The determined DOD range should cover the common usage intervals of the SOC of the vehicle where it is located.

[0073] 2.1.4) Determine the charge and discharge rate of the cyclic accelerated life test.

[0074] Specifically, the driving conditions of the vehicle where the lithium-ion battery to be tested is located are different from the constant-rate cycling test. The instantaneous large-current impact and the frequent switching between charging and discharging during this process can more accurately verify the performance of the lithium-ion battery. The dynamic condition cycling test is of great significance for evaluating the dynamic performance of power batteries. However, this test requires high-precision equipment and high test costs. The constant-rate cycling test has simple steps and low requirements for equipment accuracy, so it is widely used. However, it is necessary to determine the reasonable magnitude of the charge and discharge rate, and calculate the maximum, average, and root mean square magnitudes of the charge and discharge rate. The latter can represent the severity of the driving conditions to a greater extent, is more physically meaningful than the average value, and can consider the influence of large currents more. Therefore, the root mean square of the charge and discharge rate is used as the determined charge and discharge rate.

[0075] The specific process of step 2.2) above is as follows:

[0076] 2.2.1) Select at least two lithium-ion batteries with no obvious deviation in performance indicators such as initial capacity, impedance, thickness, and self-discharge rate from the lithium-ion batteries produced in the same batch. Here, no obvious deviation means that the mean value is within the preset range.

[0077] Specifically, the lithium-ion batteries produced in the same batch can be determined based on the batch of raw materials or the lithium-ion batteries produced within a certain period of time, etc., and can be determined based on the actual situation.

[0078] Specifically, the number of selected lithium-ion batteries can be determined according to the set cycling test conditions. For example, two, three, four, five lithium-ion batteries can be selected for one cycling test condition. The more test samples are selected, the more experimental data will be obtained, thus improving the referenceability of the experimental data.

[0079] 2.2.2) Randomly place at least 2 test samples in each set cycling test condition for cyclic accelerated life test to obtain the cyclic accelerated life test data of each test sample:

[0080] ① Conduct capacity test and DC impedance test on the test samples at room temperature (25°C) to obtain the initial capacity and initial DC impedance of the test samples at room temperature.

[0081] ② Adjust the test samples to the set SOC range.

[0082] ③ Transfer the test samples to a thermostat with a set temperature for cyclic test.

[0083] ④ When the set number of cycles is reached, take out the test samples and let them stand still at room temperature for sufficient time.

[0084] ⑤ Conduct capacity test on the test samples after standing still to obtain the capacity of the test samples at room temperature after cyclic test.

[0085] ⑥ Readjust the test sample to the set SOC range and go to step ③ until the set test time is reached or a specific capacity retention rate (such as 80% - 60%) is achieved, and finally obtain the cycle temperature of the test sample, the cumulative discharge energy corresponding to different cycle numbers, and the capacity retention rate.

[0086] In the above step 3), according to the cycle accelerated life test data of the test sample, determine the model parameters of the cycle capacity attenuation model of the lithium-ion battery to obtain the final cycle capacity attenuation model. The specific process is as follows:

[0087] 3.1) According to the cycle accelerated life test data of the test sample, determine the capacity retention rate Q of each cycle test Reten :

[0088]

[0089] where Q 0 represents the initial normal temperature capacity of the lithium-ion battery, and Q i represents the normal temperature capacity of the lithium-ion battery after the i-th test cycle.

[0090] 3.2) If the capacity retention rate determined in a certain cycle test deviates by more than 5% from the capacity retention rates determined in the two adjacent cycle tests, or the determined capacity retention rate is greater than 100%, then it is regarded as an abnormal point and excluded from the subsequent calculation.

[0091] 3.3) According to the capacity retention rates of each cycle test after excluding the abnormal points, perform power function fitting of the following formula (3) on the cycle accelerated life test data of all test samples respectively to obtain the cell fitting power value z of each test sample:

[0092] Q Reten = 1 - k(En) z (3)

[0093] where the initial fitting value of z usually ranges from 0.2 to 0.9. For example, 0.5 can be selected.

[0094] 3.4) Use a box plot to exclude the abnormal points of the cell fitting power value and calculate the average value of the cell fitting power value after excluding the abnormal points as the fixed power value.

[0095] 3.5) According to the fixed power value, refit the cycle accelerated life test data of all test samples, and take the average value of the cycle decay rates obtained by fitting the test samples under the same cycle test conditions. This average cycle decay rate is the cycle decay rate k under this cycle test condition.

[0096] 3.6) Take the logarithm ln k of the cycle decay rate k under different cycle test conditions, and perform multiple linear fitting on ln k, 1 / T, and T to determine the model parameters β 0 , β 1 and β 2 .

[0097] In step 4) above, according to the cycle decay rate of the lithium-ion battery to be tested, the cycle life of the lithium-ion battery to be tested is predicted. The specific process is as follows:

[0098] In lithium battery tests, usually when the capacity retention rate of the battery decays to a certain specific value, the number of cycles, time, or cumulative discharge energy required is defined as the life. For example, in the cycle test of this embodiment, generally, a capacity retention rate of 80% is set as the life cut-off point, and the cumulative discharge energy En at this time is the cycle life:

[0099] En = ((1 - Q Reten ) / k)^(1 / z) = ((1 - 0.8) / k)^(1 / z) (4)

[0100] Since the cycle decay rate k in the constructed cycle capacity decay model is related to temperature, once the driving condition temperature of the vehicle using the lithium-ion battery to be tested is determined, the cycle decay rate k at this temperature can be obtained, and then the cycle life can be determined.

[0101] The following takes a ternary lithium-ion battery applied to a mild hybrid as a specific embodiment to detail the method for predicting the cycle life and operating temperature of the lithium-ion battery of the present invention:

[0102] 1) Construct a cycle capacity decay model for the lithium-ion battery.

[0103] 2) Based on the set cycle accelerated life test, determine the cycle accelerated life test data of the test sample:

[0104] 2.1) According to the driving condition of the vehicle using the lithium-ion battery to be tested, set the cycle accelerated life test and its cycle test conditions:

[0105] Specifically, according to the driving condition provided by a certain vehicle customer, the operating range of the cycle temperature is 3 - 57°C. Therefore, the minimum and maximum temperatures of the cycle accelerated life test are set to 10°C and 60°C. The temperature range of 10 - 60°C covers more than 95% of the temperature range, and 25°C and 45°C are selected as temperature gradients in the middle.

[0106] According to the charging and discharging power demand and available energy demand of the whole vehicle, the usable range of the lithium-ion battery SOC is determined to be 35-76%, which can meet the customer's needs. Furthermore, the upper and lower limits of the cycle are determined to be 30% SOC and 80% SOC respectively. The lithium-ion battery is charged and discharged within the range of 30-80% SOC. Therefore, the DOD range of the cycle accelerated life test is 30-80% SOC.

[0107] According to the charging and discharging analysis in the driving conditions of the whole vehicle, the root mean square charging and discharging rate is close to 10C. For the convenience of cycle testing, the charging and discharging rate is determined to be 10C / 10C.

[0108] 2.2) Select at least two lithium-ion batteries from the same batch of production as test samples, and based on the set cycle test conditions, conduct cycle accelerated life tests on the selected test samples to obtain the cycle accelerated life test data of each test sample:

[0109] Specifically, the accelerated life test plan is shown in Table 1, and 3 or more lithium-ion batteries are put into each cycle test condition. In the early stage of the cycle test, the batteries are taken out of the cabinet every 2000 cycle turns, and in the middle and later stages, every 4000 cycle turns. The normal temperature capacity is tested to quantify the attenuation of the lithium-ion battery.

[0110] Table 1: Cycle Accelerated Life Test Plan

[0111]

[0112] Select 12 lithium-ion batteries from the same batch of production whose initial capacity, impedance, thickness, self-discharge rate and other indicators do not deviate from the overall distribution as test samples. Randomly put 3 test samples into each cycle test condition for cycle accelerated life test to obtain the cycle accelerated life test data of each test sample: First, at 25°C, charge at a rate of 1C to the upper limit voltage (4.1V); then switch to constant voltage charging until the current is less than 0.05C, stand still for 30 minutes, and discharge at a rate of 1C to the lower limit voltage (3.0V), and record the discharge capacity as the initial capacity; adjust to 30% SOC at normal temperature, and finally transfer to the set constant temperature box for charge and discharge cycle; when reaching the sampling frequency set in Table 1 above, take the test sample out of the constant temperature box and let it stand at 25°C for at least 2 hours; conduct capacity test according to the above process, and record the discharge capacity as the recoverable capacity; readjust the test sample to the set SOC range and transfer it to the set constant temperature box again for cycle until the set test time is reached or a certain specific capacity retention rate is reached. Finally, obtain the cycle temperature, the cumulative discharge energy corresponding to different cycle turns and the capacity retention rate of the test sample. The calculation method of the capacity retention rate is shown in the above formula (2), and the original cycle test data is as Figure 2As shown (at 10°C, 25°C, 45°C, 60°C). It can be clearly seen that the attenuation rate at 10°C is much larger than that at 25°C and slightly larger than that at 45°C, and the traditional temperature acceleration model is no longer applicable.

[0113] 3) Determine the model parameters of the cycle capacity attenuation model of the lithium-ion battery according to the cycle accelerated life test data of the test samples:

[0114] Specifically, for the cycle accelerated life test data of each test sample, if the deviation of the capacity retention rate determined in a certain cycle test from the capacity retention rates determined in the previous and subsequent two cycle tests is more than 5%, it is regarded as an abnormal point and does not participate in the subsequent calculation. If the capacity retention rate exceeds 100%, it is also regarded as an abnormal point and does not participate in the subsequent calculation.

[0115] Use the above formula (3) to perform power function fitting on the cycle accelerated life test data of each test sample respectively. The initial fitting value of the cell fitting power value z is selected as 0.5, and the cell fitting power values and cycle attenuation rates of all test samples are obtained, as Figure 3 shown. Use a box plot to exclude the abnormal points of the cell fitting power value, as Figure 4 shown, and calculate the average value of 0.605 of the cell fitting power value after excluding the abnormal points as the fixed power value.

[0116] According to the fixed power value, refit the cycle accelerated life test data of all test samples, take the average value of the cycle attenuation rate k under the same cycle test conditions, and take the logarithm lnk of the cycle attenuation rate k under different cycle test conditions, as shown in Table 2 below:

[0117] Table 2: Summary of cycle attenuation rates at different cycle temperatures

[0118] Temperature (°C) DOD (%) k z T 1 / T Lnk 10 30-80 0.00464424 0.605 283.15 0.003532 -5.36182 25 30-80 0.00396943 0.605 298.15 0.003354 -5.51662 45 30-80 0.00461006 0.605 318.15 0.003143 -5.36686 60 30-80 0.00612717 0.605 333.15 0.003002 -5.08172

[0119] Perform multiple linear fitting on lnk, 1 / T, and T to obtain the model parameters β 0 、β 1 and β 2 , as shown in Table 3 below. Figure 5 Taking 1 / as the independent variable and lnk as the dependent variable, the cycle attenuation coefficients predicted by different models and the measured values are compared. Among them, the fitting degree of the optimized model of the present invention is very high. Using the traditional Arrhenius temperature acceleration model, lnk and 1 / T show a linear relationship, but Figure 5 shows that the prediction line seriously deviates from the actual value in the range of 10 - 60°C. Even if only considering the cycle test data of 25 - 60°C, the linear fitting goodness of fit of the traditional model is only 0.92. Therefore, the model of the present invention can be applied to the low-temperature and medium-high-temperature ranges, and the fitting degree is greatly improved:

[0120] Table 3: Optimized fitting parameters of the cycling model

[0121] <![CDATA[β 0 > <![CDATA[β 1 > <![CDATA[β 2 > z -87.0090 0.135543 12247.3 0.605

[0122] 4) Obtain the temperature of the driving condition of the vehicle that can use the lithium-ion battery to be tested and the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input them into the cycling capacity attenuation model obtained in step 3) to obtain the cycling attenuation rate of the lithium-ion battery to be tested under different driving conditions, and then predict the cycling life of the lithium-ion battery to be tested under the corresponding driving conditions. In this embodiment, the driving temperature of the vehicle that can use the lithium-ion battery to be tested is 31 °C, and the capacity retention rate at the end of the life of the lithium-ion battery to be tested is 80%. Therefore, the cycling life of the lithium-ion battery to be tested is 617 kWh.

[0123] Specifically, according to the above-fitted model parameters, predict the cycling attenuation coefficient under each cycling test condition, as Figure 6 shown, which is generally parabolic.

[0124] 5) Determine the cycling temperature corresponding to the driving condition with the minimum cycling attenuation rate, and this cycling temperature is the operating temperature of the lithium-ion battery to be tested.

[0125] Specifically, as the temperature gradually increases, the cycling attenuation rate first decreases and then increases, and there is a minimum value at 26 °C, so the temperature with the best performance is 26 °C. In fact, the cycling attenuation rates at 25 - 35 °C differ very little, and the vehicle can design a thermal management strategy to keep the cycling temperature within the range of 25 - 35 °C to minimize the cycling attenuation and improve the service life of the battery system. For other cycling rates or duty cycles, the present invention is also implemented. As Figure 7 shown, it shows the predicted values of different attenuation coefficients for the 15C / 15C cycle, and the attenuation rate is the smallest at 33 °C. It can be predicted that when the cycling rate and DOD interval of the lithium-ion battery are larger, the temperature range with the best performance shifts towards the high-temperature region.

[0126] Example 2

[0127] This embodiment provides a system for predicting the cycling life and operating temperature of a lithium-ion battery, including:

[0128] A cycling attenuation rate determination module, configured to determine the driving condition temperature of the vehicle that can use the lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input them into a pre-constructed cycling capacity attenuation model to obtain the cycling attenuation rate of the lithium-ion battery to be tested under different driving conditions, and then predict the cycling life of the lithium-ion battery to be tested under the corresponding driving conditions.

[0129] Use a temperature determination module to determine the cycle temperature corresponding to the driving condition with the minimum cycle decay rate, and this cycle temperature is the use temperature of the lithium-ion battery to be tested.

[0130] In a preferred embodiment, the prediction system further includes: a model construction module for pre-constructing a cycle capacity decay model, including: constructing a cycle capacity decay model of the lithium-ion battery; determining the cycle accelerated life test data of the test samples based on the set cycle accelerated life test; and determining the model parameters of the cycle capacity decay model of the lithium-ion battery according to the cycle accelerated life test data of the test samples.

[0131] In a preferred embodiment, determining the cycle accelerated life test data of the test samples based on the set cycle accelerated life test includes: setting the cycle accelerated life test and its cycle test conditions according to the driving condition of the vehicle that can use the lithium-ion battery to be tested, and setting the test time and the number of cycles; selecting at least two lithium-ion batteries from the same batch of production as test samples, and performing a cycle accelerated life test on the selected test samples based on the set cycle test conditions, test time, and number of cycles to obtain the cycle accelerated life test data of each test sample.

[0132] Specifically, selecting at least two lithium-ion batteries from the same batch of production as test samples, and performing a cycle accelerated life test on the selected test samples based on the set cycle test conditions, test time, and number of cycles to obtain the cycle accelerated life test data of each test sample includes: selecting lithium-ion batteries with performance index deviations within a preset range from the same batch of production as test samples; randomly placing at least 2 test samples in each set cycle test condition for a cycle accelerated life test to obtain the cycle accelerated life test data of each test sample.

[0133] In a preferred embodiment, based on the cyclic accelerated life test data of test samples, the model parameters of the cyclic capacity attenuation model of the lithium-ion battery are determined to obtain the final cyclic capacity attenuation model, including: determining the capacity retention rate of each cyclic test according to the cyclic accelerated life test data of the test samples; if the capacity retention rate determined in a certain cyclic test deviates from the capacity retention rates determined in the two adjacent cyclic tests by a preset threshold, or the determined capacity retention rate is greater than the threshold, it is regarded as an abnormal point and excluded; according to the capacity retention rates of each cyclic test after excluding the abnormal points, the cyclic accelerated life test data of all test samples are respectively fitted by a power function to obtain the cell fitting power value z of each test sample; using a box plot to exclude the abnormal points of the cell fitting power value, and calculating the average value of the cell fitting power value after excluding the abnormal points as the fixed power value; according to the fixed power value, refitting the cyclic accelerated life test data of all test samples, and taking the average value of the cyclic attenuation rates fitted by the test samples under the same cyclic test conditions, and this average value of the cyclic attenuation rate is the cyclic attenuation rate k under this cyclic test condition; taking the logarithm ln k of the cyclic attenuation rates k under different cyclic test conditions, and performing multiple linear fitting on ln k, 1 / T and T to determine the model parameters β 0 , β 1 and β 2 .

[0134] The system provided in this embodiment is used to execute the above method embodiment. For the specific process and detailed content, please refer to the above embodiment and will not be elaborated here.

[0135] Embodiment 3

[0136] This embodiment provides a processing device corresponding to the method for predicting the cycle life and operating temperature of the lithium-ion battery provided in Embodiment 1. The processing device can be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of Embodiment 1.

[0137] The processing device includes a processor, a memory, a communication interface and a bus. The processor, the memory and the communication interface are connected through the bus to complete communication with each other. The memory stores a computer program that can run on the processing device. When the processing device runs the computer program, it executes the method for predicting the cycle life and operating temperature of the lithium-ion battery provided in Embodiment 1.

[0138] In some implementations, the memory can be a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory.

[0139] In some other implementations, the processor may be various types of general-purpose processors such as a central processing unit (CPU) or a digital signal processor (DSP), which are not limited herein.

[0140] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0141] Those skilled in the art can understand that the structure of the above-mentioned computing device is only a part of the structure related to the solution of the present application, and does not constitute a limitation on the computing device to which the solution of the present application is applied. The specific computing device may include more or fewer components, or combine certain components, or have different component arrangements.

[0142] Embodiment 4

[0143] This embodiment provides a computer program product corresponding to the method for predicting the cycle life and operating temperature of the lithium-ion battery provided in Embodiment 1. The computer program product may include a computer-readable storage medium, on which computer-readable program instructions for executing the method for predicting the cycle life and operating temperature of the lithium-ion battery described in Embodiment 1 are loaded.

[0144] A computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above.

[0145] For the computer-readable storage medium provided in the above embodiment, its implementation principle and technical effects are similar to those of the above method embodiment, and will not be elaborated herein.

[0146] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0147] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0149] The above embodiments are only used to illustrate the present invention, and the structures, connection methods, manufacturing processes, etc. of the components can all be changed. Any equivalent transformation and improvement based on the technical solutions of the present invention should not be excluded from the protection scope of the present invention.

Claims

1. A method for predicting the cycle life and operating temperature of a lithium-ion battery, characterized in that, it includes: Determine the operating condition temperature of the vehicle that can use the lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input it into the pre-constructed cycle capacity attenuation model of the lithium-ion battery to obtain the cycle attenuation rate of the lithium-ion battery to be tested under different operating condition conditions, and then predict the cycle life of the lithium-ion battery to be tested under the corresponding operating condition conditions; Determine the cycle temperature corresponding to the operating condition condition with the minimum cycle attenuation rate, and this cycle temperature is the operating temperature of the lithium-ion battery to be tested; The construction process of the cycle capacity attenuation model is: Construct a cycle capacity attenuation model of the lithium-ion battery, and the cycle capacity attenuation model of the lithium-ion battery is: Among them, represents the capacity retention rate; represents the cycle attenuation rate; and respectively represent the cycle temperature and the cumulative discharge energy of the cycle; 、 、 are the parameters to be solved by the model; Based on the set cycle accelerated life test, determine the cycle accelerated life test data of the test samples; According to the cycle accelerated life test data of the test samples, determine the model parameters of the cycle capacity attenuation model of the lithium-ion battery.

2. The method for predicting the cycle life and operating temperature of a lithium-ion battery according to claim 1, characterized in that, The step of determining the cycle accelerated life test data of the test samples based on the set cycle accelerated life test includes: According to the operating condition conditions of the vehicle that can use the lithium-ion battery to be tested, set the cycle accelerated life test and its cycle test conditions, and set the test time and the number of cycles; Select at least two lithium-ion batteries produced in the same batch as test samples, and conduct a cycle accelerated life test on the selected test samples based on the set cycle test conditions, test time and number of cycles to obtain the cycle accelerated life test data of each test sample.

3. The method for predicting the cycle life and operating temperature of a lithium-ion battery according to claim 2, characterized in that, The cycle test conditions include cycle temperature, battery discharge depth and charge-discharge rate.

4. The method for predicting the cycle life and operating temperature of a lithium-ion battery according to claim 3, characterized in that, The step of selecting at least two lithium-ion batteries produced in the same batch as test samples and conducting a cycle accelerated life test on the selected test samples based on the set cycle test conditions, test time and number of cycles to obtain the cycle accelerated life test data of each test sample includes: Select lithium-ion batteries with performance index deviations within a preset range from the lithium-ion batteries produced in the same batch as test samples; Randomly place a number of test samples in each set cycle test condition for cycle accelerated life test to obtain the cycle accelerated life test data of each test sample.

5. The method for predicting the cycle life and operating temperature of a lithium-ion battery according to claim 1, characterized in that, The step of determining the model parameters of the cycle capacity attenuation model of the lithium-ion battery according to the cycle accelerated life test data of the test samples includes: According to the cycle accelerated life test data of the test samples, determine the capacity retention rate of each cycle test; If the capacity retention rate determined in a certain cycle test deviates from the capacity retention rates determined in the two cycle tests before and after it by a preset threshold, or the determined capacity retention rate is greater than the threshold, it is regarded as an abnormal point and excluded. According to the capacity retention rate of each cycle test after excluding abnormal points, the cyclic accelerated life test data of all test samples are respectively fitted by a power function to obtain the fitted power value of the battery core of each test sample's lithium-ion battery ; Use a box plot to exclude the abnormal points of the fitted power value of the battery cell, and calculate the average value of the fitted power value of the battery cell after excluding the abnormal points as the fixed power value. According to the fixed power value, refit the cyclic accelerated life test data of all test samples, and take the average value of the cyclic decay rates fitted by the test samples under the same cyclic test conditions. This average value of the cyclic decay rate is the cyclic decay rate under this cyclic test condition. ; The cyclic decay rates under different cyclic test conditions Take the logarithm , and for Perform multiple linear fitting to determine the model parameters of the cyclic capacity decay model of the lithium-ion battery .

6. A cycle life and operating temperature prediction system for a lithium-ion battery Characterized in that It includes: A cycle life determination module, configured to determine the driving condition temperature of a vehicle that can use the lithium-ion battery to be tested, obtain the capacity retention rate at the end of the life of the lithium-ion battery to be tested, and input it into a pre-constructed cycle capacity attenuation model of the lithium-ion battery to obtain the cycle attenuation rate of the lithium-ion battery to be tested under different driving condition conditions, and then predict the cycle life of the lithium-ion battery to be tested under the corresponding driving condition conditions. An operating temperature determination module, configured to determine the cycle temperature corresponding to the driving condition condition with the minimum cycle attenuation rate, and this cycle temperature is the operating temperature of the lithium-ion battery to be tested. The construction process of the cycle capacity attenuation model is as follows: Construct a cycle capacity attenuation model of the lithium-ion battery, and the cycle capacity attenuation model of the lithium-ion battery is: Among them, represents the capacity retention rate; represents the cycle attenuation rate; and respectively represent the cycle temperature and the cumulative discharge energy of the cycle; 、 、 are the parameters to be solved by the model; Based on the set cycle accelerated life test, determine the cycle accelerated life test data of the test sample. According to the cycle accelerated life test data of the test sample, determine the model parameters of the cycle capacity attenuation model of the lithium-ion battery.

7. A processing device Characterized in that It includes computer program instructions, wherein when the computer program instructions are executed by the processing device, they are used to implement the steps corresponding to the method for predicting the cycle life and operating temperature of the lithium-ion battery according to any one of claims 1-5.

8. A computer-readable storage medium Characterized in that The computer-readable storage medium stores computer program instructions, wherein when the computer program instructions are executed by a processor, they are used to implement the steps corresponding to the method for predicting the cycle life and operating temperature of the lithium-ion battery according to any one of claims 1-5.

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