A method for generating large-scale battery data

Abstract

The present invention discloses a method for generating large-scale battery data, belonging to the technical field of new energy batteries, which includes testing battery cells to obtain battery cell test data, where the battery cell test data includes current, voltage, and temperature; establishing a battery simulation model; using the battery cell test data to extract and identify the parameters of the battery simulation model to obtain the performance parameters of the battery cells; setting different combinations of battery cell performance parameters and operating conditions, and using the battery simulation model to generate simulation data; establishing a data storage repository to store the generated simulation data for the development of battery fault warning and detection algorithms. The present invention can simulate and generate a sufficient amount of battery data for battery algorithm development, reducing the dependence on battery operation data.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy batteries, and in particular to a method for generating large-scale battery data. Background Art

[0002] Currently, the new energy industry is booming, and electrochemical products represented by lithium-ion batteries have been widely used, and have been vigorously developed in the fields of new energy vehicle industry, energy storage industry, household appliances, etc.

[0003] Currently, due to the process problems in the manufacturing process of battery cells and the inadequate quality control of assembly and integration, it is easy to cause inconsistencies and serious failures of battery cells during subsequent use, affecting the safe use of equipment and reducing the economic benefits of users. In order to achieve the detection and early warning of battery failures, a large amount of battery cell operation data is required for the establishment and debugging of algorithm models. This requires us to first collect the data generated by the battery before we can develop the algorithm. As a result, the development of algorithms for fault early warning and detection will lag behind the actual operation of the battery cells. In order to be able to develop the fault early warning and detection algorithms in advance, it is necessary to be able to use sufficient battery data for algorithm modeling at an early stage, so that the algorithms can be deployed and applied simultaneously when the equipment is running.

[0004] In summary, the present invention proposes a method for generating large-scale data, which can realize the early development, deployment and application of battery fault early warning and detection algorithms. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a method for generating large-scale battery data.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] A method for generating large-scale battery data includes:

[0008] Testing the battery cells to obtain battery cell test data, where the battery cell test data includes current, voltage, and temperature;

[0009] Establishing a battery simulation model;

[0010] Using the test data of the battery cells to extract and identify the parameters of the battery simulation model to obtain the performance parameters of the battery cells;

[0011] Setting different combinations of battery cell performance parameters and operating conditions, and using the battery simulation model to generate simulation data;

[0012] Establishing a data storage repository to store the generated simulation data for the development of battery fault early warning and detection algorithms.

[0013] Further, obtain the cell test data, specifically including: performing tests on the cell, including but not limited to capacity test, rate performance test, EIS test, and temperature performance test. For each test, record the time series of current, voltage, and temperature changing with time, as shown in the following formula:

[0014] ;

[0015] In the formula, is the set of all test data, is the th test data set, is the number of test data sets, is time, is the cell voltage, is the test current, is the cell temperature.

[0016] Further, establishing a battery simulation model includes: using the cell performance parameters obtained by extraction and identification, establishing and improving models including but not limited to battery equivalent circuit model, battery heat generation model, and electrochemical mechanism simulation model. The model combination is:

[0017] ;

[0018] In the formula, M is the battery model combination, is the hth battery model, and h is the number of battery models.

[0019] Further, the performance parameters of the cell include: using the cell test data for extraction and identification, obtaining multiple cell performance parameters including the capacity of the cell changing with temperature and rate, the open circuit voltage, ohmic resistance, polarization resistance, time constant, heat transfer coefficient, and specific heat capacity of the cell at various temperatures, as shown in the following formula:

[0020] ;

[0021] In the formula, P is the combination of cell performance parameters, is the nth cell performance parameter, and n is the number of cell performance parameters.

[0022] Further, set different combinations of cell performance parameters and operating conditions, and use the battery simulation model to generate simulation data, including:

[0023] The battery operating conditions can preset multiple operating conditions, including charge and discharge direction, charge and discharge rate, and data sampling period conditions, as shown in the following formula:

[0024] ;

[0025] In the formula, C is the battery operating condition combination, is the m-th battery operating condition, where m is the number of categories of battery operating conditions. is the number of variable parameters of the m-th battery operating condition. is the k-th variable parameter of the m-th battery operating condition.

[0026] Since the settings among the above-mentioned various battery operating condition categories are independent of each other, the number of setting combinations of battery operating conditions is:

[0027] ;

[0028] The combination of cell performance parameters can also be set in multiple combinations, including battery capacity, open-circuit voltage, ohmic resistance, polarization resistance, time constant, heat transfer coefficient, and specific heat capacity parameter. The settings of the above parameters are independent of each other.

[0029] ;

[0030] In the formula, P is the combination of cell performance parameters. is the n-th cell performance parameter, where n is the number of cell performance parameters. is the number of variable parameters of the n-th cell performance parameter. is the q-th variable parameter of the n-th cell performance parameter.

[0031] Since the settings among the above-mentioned various battery operating condition categories are independent of each other, the number of setting combinations of cell performance parameters is:

[0032] .

[0033] Furthermore, by setting different combinations of cell performance parameters and operating conditions, simulation data is generated using the battery simulation model, including:

[0034] After setting the battery model, battery operating conditions, and cell performance parameters, the total number of simulation data generated using the battery simulation model is:

[0035] .

[0036] A computer device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned large-scale battery data generation method.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned large-scale battery data generation method.

[0038] In summary, compared with the prior art, the beneficial effects of the above technical solution are:

[0039] At present, the development of battery fault warning and detection algorithms requires a large amount of battery operation data. However, in the initial stage of battery operation, insufficient data is generated to support algorithm development. Therefore, the present invention proposes a large-scale battery data generation method, which can simulate and generate a sufficient amount of battery data based on battery test data for battery algorithm development, reducing the dependence on battery operation data. Brief Description of the Drawings

[0040] Figure 1 It is a flowchart of a large-scale battery data generation method;

[0041] Figure 2 It is a schematic diagram of a battery equivalent circuit model;

[0042] Figure 3 It is a schematic diagram of an HPPC test curve;

[0043] Figure 4 It is a relationship curve between open circuit voltage OCV and state of charge SOC of the battery;

[0044] Figure 5 It is the ohmic internal resistance of the battery And the relationship curve between the state of charge SOC of the battery;

[0045] Figure 6 It is the polarization internal resistance of the battery And the relationship curve between the state of charge SOC of the battery;

[0046] Figure 7 It is the polarization capacitance of the battery And the relationship curve between the state of charge SOC of the battery;

[0047] Figure 8 It is a schematic diagram of battery working conditions and cell performance parameter settings;

[0048] Figure 9 It is a schematic diagram of the generated data curve. Detailed Embodiments

[0049] The principles and features of the present invention will be described below in conjunction with all the drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0050] An embodiment of the present invention discloses a large-scale battery data generation method.

[0051] Refer to Figures 1 to 9, A large-scale battery data generation method, including: testing the battery cells to obtain battery cell test data, where the battery cell test data includes current, voltage, and temperature; establishing a battery simulation model; using the battery cell test data to extract and identify the parameters of the battery simulation model to obtain the battery cell performance parameters; setting different combinations of battery cell performance parameters and operating conditions, and using the battery simulation model to generate simulation data; establishing a data repository to store the generated simulation data for the development of battery fault warning and detection algorithms.

[0052] Currently, the development of battery fault warning and detection algorithms requires a large amount of battery operation data. However, in the initial stage of battery operation, insufficient data will be generated to support algorithm development. Therefore, the present invention proposes a large-scale battery data generation method. Based on battery test data, it can simulate and generate a sufficient amount of battery data for battery algorithm development, reducing the dependence on battery operation data.

[0053] The following elaborates on each step.

[0054] S1: Test the battery cells to obtain current, voltage, and temperature parameters.

[0055] Specifically, it is necessary to test the battery cells including but not limited to capacity test, rate performance test, EIS test, temperature performance test, etc. For each test, time series of current, voltage, and temperature changing with time are recorded.

[0056] ;

[0057] In the formula, is the set of all test data, is the i-th test data set, is the number of test data sets, is time, is the battery cell voltage, is the test current, is the battery cell temperature.

[0058] S2: Establish a battery simulation model according to the performance parameters of the battery cells.

[0059] Using the extracted and identified battery parameters, establish and improve simulation models such as battery equivalent circuit models, battery heat generation models, and electrochemical mechanism models. The model combination is:

[0060] ;

[0061] In the formula, M is the battery model combination, is the h-th battery model, and h is the number of battery models.

[0062] S3: Use the cell test data to perform parameter extraction and identification to obtain the performance parameters of the cell.

[0063] Specifically, use the cell test data for extraction and identification to obtain multiple cell performance parameters, including the capacity of the cell varying with temperature and rate, the open circuit voltage (OCV), ohmic internal resistance, polarization internal resistance, time constant, heat transfer coefficient, specific heat capacity, etc. of the cell at various temperatures.

[0064] ;

[0065] In the formula, P is the combination of cell performance parameters, is the nth cell performance parameter, and n is the number of cell performance parameters.

[0066] In this embodiment, establish a battery equivalent circuit model, Figure 2 is the equivalent circuit model of the battery. The cell performance parameters included in this model are the battery open circuit voltage OCV, the battery ohmic internal resistance , the battery polarization internal resistance , the battery polarization capacitance . Then the cell performance parameter matrix of this equivalent circuit model is:

[0067] .

[0068] In practical applications, the Hybrid Pulse Power Characterization (HPPC) is a characteristic used to reflect the pulse charge and discharge performance of power batteries. HPPC data can be used for battery model parameter identification. At an ambient temperature of 25°C, the discharge HPPC test curve is as Figure 3 shown.

[0069] Use the discharge HPPC data for parameter identification. The parameter results of the equivalent circuit model are as Figures 4 to 7 shown.

[0070] S4: Set different battery parameter combinations and operating conditions, and use the battery simulation model to generate simulation data.

[0071] The following explains the battery operating conditions:

[0072] The battery operating conditions can preset multiple operating conditions, including operating condition parameters such as charge and discharge direction, charge and discharge rate, and data sampling period;

[0073] ;

[0074] In the formula, C is the combination of battery operating conditions, is the mth battery operating condition, and m is the number of categories of battery operating conditions, is the number of change parameters of the m-th battery operating condition, is the k-th change parameter of the m-th battery operating condition.

[0075] Since the settings among the above battery operating condition categories are independent of each other, the number of setting combinations of the battery operating conditions is:

[0076] .

[0077] Taking the equivalent circuit model as an example, different battery parameter groups and operating conditions are set, such as Figure 8 shown, Figure 8 in the figure of refers to the battery capacity, and N is the number of set capacity changes. The battery capacity change range is set to [100%~80%], and it decreases by 0.5% at each interval; it is divided into two directions of charging and discharging; the charge and discharge rates are 0.3P / 0.4P / 0.5P / 0.6P; the sampling interval can be set to 100ms / 200ms / 300ms / 400ms, then the above battery operating condition combinations are:

[0078] ;

[0079] In the formula, SOH (State of Health, SOH) is the battery health degree, that is, the ratio of the actual available capacity of the battery to the initial rated capacity.

[0080] Since the settings among the above battery operating condition categories are independent of each other, the number of setting combinations of the battery operating conditions is:

[0081] .

[0082] In the above formula, 41: is because the battery capacity change range is [100%~80%], and it decreases by 0.5% at each interval, with a total of 41 points.

[0083] 2: is because it is divided into two directions of battery charging and discharging, representing 2 operating conditions.

[0084] The first 4: represents that the set charge and discharge rates are 0.3P / 0.4P / 0.5P / 0.6P, with a total of 4 rates.

[0085] The second 4: represents 100ms / 200ms / 300ms / 400ms, with a total of 4 sampling intervals.

[0086] The battery parameter combinations are described below:

[0087] The battery parameter combinations can also be set in multiple ways, including parameters such as battery capacity (or battery life), OCV, ohmic internal resistance, polarization internal resistance, time constant, heat transfer coefficient, specific heat capacity, etc. The settings of the above parameters are independent of each other.

[0088] ;

[0089] Where P is the cell performance parameter combination, is the nth cell performance parameter, and n is the number of cell performance parameters, is the number of change parameters of the nth cell performance parameter, is the qth change parameter of the nth cell performance parameter.

[0090] Since the settings between the above various battery operating condition categories are independent of each other, the number of setting combinations of cell performance parameters is:

[0091] ;

[0092] After setting the battery model, battery operating conditions, and cell performance parameters, the total number of simulation data generated by the battery simulation model is:

[0093] .

[0094] Set the boundaries of the four parameters of the battery OCV / / / to 100% ± 5% of the initial value, and increase or decrease by 1% every time. OCV / / / The initial value is as Figures 4 to 7 shown. This initial value refers to the true value of the cell parameters provided by the cell manufacturer after the battery leaves the factory, and is used as the initial value. During the use of the cell, these four parameters will change due to factors such as temperature change, and will increase or decrease around the initial value. Then the setting combination of cell performance parameters is:

[0095] ;

[0096] is the initial value of the open-circuit voltage of the cell; is the initial value of the ohmic internal resistance; is the initial value of the polarization internal resistance; is the initial value of the polarization capacitance.

[0097] Since the settings between the above various battery operating condition categories are independent of each other, the number of setting combinations of cell performance parameters is:

[0098] ;

[0099] Because the setting combinations of OCV / / / are all in the range of [95%, 96%,..., 104%, 105%] of the initial value, changing every 1%, for a total of 11 points.

[0100] After setting the battery model, battery operating conditions, and cell performance parameters, the total number of simulated data generated by the battery simulation model is:

[0101] .

[0102] Due to the huge number of data combinations, in this embodiment, only the generated data of one case is shown, as Figure 9 shown.

[0103] Figure 9 In, SOH = 100%, discharging at a rate of 0.5P, data sampling interval of 100 ms, and the four parameters of OCV / / / are all 105% of the initial value.

[0104] S5: Establish a data repository to store the generated simulated data for the development of battery fault warning and detection algorithms.

[0105] Summarize the generated simulated data, establish a data repository, save the data, implement the read and write functions of the data, and use it for the development of battery fault warning and detection algorithms.

[0106] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A large-scale battery data generation method, characterized in that: include: Testing the battery cell to obtain battery cell test data, wherein the battery cell test data includes current, voltage and temperature; Establish a battery simulation model; Utilizing the battery cell test data, extracting and identifying parameters of a battery simulation model to obtain performance parameters of the battery cell; Set different battery cell performance parameter combinations and operating conditions, and use the battery simulation model to generate simulation data; Establish a data repository to store the generated simulation data for the development of battery failure warning and detection algorithms; The specific steps of setting different cell performance parameter combinations and operating conditions and using the battery simulation model to generate simulation data include: The battery operating conditions can preset multiple operating conditions, including charge and discharge direction, charge and discharge rate, and data sampling cycle conditions, as shown in the following formula: ； Where C is the battery operating condition combination, is the mth battery operating condition, m is the number of battery operating condition categories, is the number of changing parameters of the mth battery operating condition, is the kth variable parameter of the mth battery operating condition; Since the settings of the above battery operating condition categories are independent of each other, the number of setting combinations of battery operating conditions is: ； The cell performance parameter combinations also have multiple combinations, including battery capacity, open circuit voltage, ohmic internal resistance, polarization internal resistance, time constant, heat transfer coefficient and specific heat capacity parameters. The settings of the above parameters are independent of each other. ； Where P is the combination of battery performance parameters, is the nth battery cell performance parameter, n is the number of battery cell performance parameters, is the number of changing parameters of the nth battery cell performance parameter, is the qth variable of the nth battery cell performance parameter; Since the settings of the above battery operating condition categories are independent of each other, the number of setting combinations of battery cell performance parameters is: ； The specific steps of setting different cell performance parameter combinations and operating conditions and using the battery simulation model to generate simulation data include: After setting the battery model, battery operating conditions and battery cell performance parameters, the total amount of simulation data generated by the battery simulation model is: 。 2. A large-scale battery data generation method according to claim 1, characterized in that: The specific steps of obtaining the battery cell test data include: performing a capacity test, a rate performance test, an EIS test, and a temperature performance test on the battery cell, and for each test, recording the time series of current, voltage, and temperature changes over time, as shown in the following formula: ； In the formula, is the set of all test data, It is A test dataset, is the number of test data sets, It's time. is the cell voltage, is the test current, is the cell temperature array.

3. The method for generating large-scale battery data according to claim 1, characterized in that: The specific steps of establishing the battery simulation model include: using the extracted and identified battery cell performance parameters, establishing and improving a battery equivalent circuit model, a battery heat generation model, and an electrochemical mechanism simulation model, and the model combination is: ； Where M is the battery model combination, is the hth battery model, and h is the number of battery models.

4. A large-scale battery data generation method according to claim 1 or 3, characterized in that: The battery cell performance parameters include: using battery cell test data extraction and identification to obtain multiple battery cell performance parameters including the capacity of the battery cell that changes with temperature and rate, the open circuit voltage of the battery cell at various temperatures, ohmic internal resistance, polarization internal resistance, time constant, heat transfer coefficient, and specific heat capacity, as shown in the following formula: ； Where P is the combination of battery performance parameters, is the nth battery cell performance parameter, and n is the number of battery cell performance parameters.

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a large-scale battery data generation method as described in any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a large-scale battery data generation method according to any one of claims 1 to 4 is implemented.

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