Short circuit risk assessment method, system and equipment of energy storage system and medium

Abstract

The invention provides a short-circuit risk assessment method, system and device for an energy storage system and a medium, and the method comprises the steps: obtaining historical temperature data, fitting a temperature change curve according to the historical temperature data, and dividing the temperature change curve into a plurality of temperature detection curves; acquiring historical load data, and fitting a load change curve according to the historical load data; setting a plurality of detection SOC nodes and a plurality of detection times, and matching the temperature detection curve, the load change curve, the detection SOC nodes and the detection times to generate a plurality of test conditions; and performing short-circuit test on the battery according to the test condition, and performing short-circuit risk assessment on the battery according to the test result. According to the invention, the problem that the state change of the battery in the use process cannot be comprehensively reflected because the short-circuit test of the battery in the prior art is a no-load static working condition and the constraint conditions are few is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage system safety diagnosis, and in particular to a short-circuit risk assessment method, system, equipment and medium for an energy storage system. Background Art

[0002] With the diversified development of new energy vehicles, power requirements are getting higher and higher, the performance requirements of power batteries are becoming more and more diversified, and the charge and discharge rate requirements are becoming higher and higher, which in turn causes the difference in battery internal resistance under different power states to become larger and larger, and the difference in short-circuit current under different power states to become larger and larger.

[0003] As the car's power supply is used, the battery itself will age, be damaged by improper use, or suffer short circuits due to physical damage. If the battery management system fails to promptly and effectively sense the battery short circuit fault and implement effective isolation measures, it may cause a short circuit within the battery system, irreversible thermal runaway of the battery cell, and even induce heat diffusion throughout the vehicle, leading to a fire.

[0004] The existing external short-circuit protection test conditions only stipulate that the short-circuit resistance does not exceed 5mΩ and the temperature is 20℃±10℃, which has few constraints. At the same time, most tests are performed when the battery system is not charging or discharging. However, this battery testing method simulates no-load static conditions. Actual usage scenarios are all dynamic conditions with load. It cannot fully reflect the changes in the battery state during use and cannot accurately judge the short-circuit risk of the battery. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a short-circuit risk assessment method for an energy storage system, which solves the problem in the existing technology that when performing a short-circuit test on a battery, it is in a no-load static condition with few constraints, and cannot fully reflect the state changes of the battery during use.

[0006] According to an embodiment of the present invention, a short circuit risk assessment method for an energy storage system includes:

[0007] Obtain historical temperature data, fit a temperature change curve based on the historical temperature data, and then divide the temperature change curve into multiple temperature detection curves;

[0008] Obtain historical load data and fit the load change curve based on the historical load data;

[0009] Set multiple detection SOC nodes and multiple detection times, match the temperature detection curve, load change curve, detection SOC node and detection time, and generate multiple test conditions;

[0010] Perform a short-circuit test on the battery according to the test conditions, and conduct a short-circuit risk assessment on the battery based on the test results.

[0011] Preferably, the method of fitting a temperature change curve according to historical temperature data includes:

[0012] Calculate the monthly temperature average for each month in each year in the historical temperature data;

[0013] The historical temperature data are divided into odd-numbered year temperature classes and even-numbered year temperature classes according to their years, and then the average temperature of the same month in the odd-numbered year temperature class and the average temperature of the same month in the even-numbered year temperature class are calculated to obtain the odd-numbered year temperature data and the even-numbered year temperature data respectively;

[0014] The temperature data of odd years and even years are spliced together and smooth curve fitting is performed to obtain the temperature change curve.

[0015] Preferably, the method of dividing the temperature change curve into a plurality of temperature detection curves includes:

[0016] The temperature values corresponding to the first 12 months in the temperature change curve are set as the dividing nodes;

[0017] Taking each division node as the starting point and counting forward to the temperature corresponding to the 12th month as the end point, the temperature change curve is intercepted into multiple temperature detection curves.

[0018] Preferably, the load variation curve includes a discharge load curve and a charge load curve, and the detection SOC nodes include 20%, 40%, 60%, 80% and 99%. When the load variation curve in the test condition is a charge load curve, the detection SOC node cannot be 99%.

[0019] Preferably, the detection time is selected in the range of 10 minutes to 60 minutes and is randomly selected according to a normal distribution.

[0020] Preferably, when the load change curve in the test condition is a discharge load curve, the time required for complete discharge needs to be estimated based on the discharge load curve and the detection SOC node. If the time required for complete discharge is greater than the detection time, the new detection time is re-matched.

[0021] Preferably, after each test according to the test conditions, the energy storage system is scored according to the test results, and then the energy storage system is recharged / discharged to restore the state of the entire energy storage system to the state before the test before the next test;

[0022] If the energy storage system cannot be charged / discharged when restoring the state of the energy storage system, the test is stopped and the corresponding score of the unimplemented test condition is directly set to 0.

[0023] On the other hand, an embodiment of the present invention further provides a short-circuit risk assessment system for an energy storage system. The system uses the above-mentioned short-circuit risk assessment method for an energy storage system, including:

[0024] A data processing module, the data processing module is used to fit a temperature change curve and a load change curve according to historical temperature data and historical load data, and to divide the temperature change curve;

[0025] A working condition matching module is used to set multiple detection SOC nodes and multiple detection times, and randomly match the temperature detection curve, the load change curve, the detection SOC node and the detection time to generate multiple test conditions;

[0026] A test module is used to perform a short-circuit test on the energy storage system according to a test condition and to assess the short-circuit risk.

[0027] On the other hand, an embodiment of the present invention further provides a computer, comprising at least one processor and a memory, wherein the memory stores a computer program, and the computer program is configured to be executed by the processor to implement the above-mentioned short-circuit risk assessment method for an energy storage system.

[0028] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and the computer program can be executed by one or more processors to implement the above-mentioned short-circuit risk assessment method for an energy storage system.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention simulates the ambient temperature changes of the energy storage system during actual application by randomly selecting temperature detection curves with different temperature changes. At the same time, the energy storage system is equipped with the actual application load of the energy storage system under charging / discharging conditions. In this way, the actual use conditions of the energy storage system are simulated, and the performance of the energy storage system under actual dynamic working conditions is simulated, which comprehensively reflects the state changes of the battery during use and more accurately determines the short-circuit risk of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a short circuit risk assessment method according to an embodiment of the present invention.

[0032] Figure 2 2 is a temperature change curve diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0034] like Figure 1 As shown, an embodiment of the present invention provides a short circuit risk assessment method for an energy storage system, comprising:

[0035] Obtain historical temperature data, fit a temperature change curve based on the historical temperature data, and then divide the temperature change curve into multiple temperature detection curves;

[0036] The historical temperature data of each day in recent years can be obtained from the official website of the Meteorological Bureau. The historical temperature data in the present invention is obtained over a span of more than or equal to 2 years and is an even number, for example, 6 years. The present invention takes the local temperature of the previous 6 years as an example, calculates the monthly temperature average of each month in each year of the historical temperature data of the previous 6 years, and each year has a corresponding 12-month monthly temperature average. Then, the historical temperature data of 1 year, 3 years, and 5 years from the present moment are divided into odd-numbered year temperature categories, and the historical temperature data of 2 years, 4 years, 5 years, and 6 years from the present moment are divided into even-numbered year temperature categories.

[0037] Calculate the average temperature of the same month in the odd-numbered year temperature class for the three years to obtain the odd-numbered year temperature data, and the average temperature of the same month in the even-numbered year temperature class for the three years to obtain the even-numbered year temperature data. Then, splice the odd-numbered year temperature data with the even-numbered year temperature data and perform smooth curve fitting to obtain the temperature change curve, as shown below: Figure 2 As shown in the figure, L1-L12 are temperature data for odd years, and S1-S12 are temperature data for even years. The figure shows a total of 24 months of temperature change. The temperature values corresponding to the first 12 months in the temperature change curve are set as division nodes. Each division node is used as the starting point, and the temperature corresponding to the 12th month is used as the end point (for example, March of the first year is the starting point, and February of the second year is the end point). The temperature change curve is then segmented into multiple temperature detection curves, resulting in 12 temperature detection curves, as shown in Table 1.

[0038] Table 1: Temperature detection curve classification table

[0039] Temperature detection curve serial number Temperature selection starting point Temperature selection end point 1 L1 L12 2 L2 S1 3 L3 S2 4 L4 S3 5 L5 S4 6 L6 S5 7 L7 S6 8 L8 S7 9 L9 S8 10 L10 S9 11 L11 S10 12 L12 S11

[0040] Then, based on the load change data of the same model of energy storage system produced in the previous six years during each actual use, the energy storage system includes a discharge process and a charging process, so the load change data includes discharge load data and charging load data. The discharge load data and charging load data are then fitted separately to generate multiple discharge load curves and charging load curves.

[0041] Set multiple detection SOC nodes and multiple detection times, match the temperature detection curve, load change curve, detection SOC node and detection time, and generate multiple test conditions;

[0042] In this embodiment, the detection SOC nodes are set to 5, including 20%, 40%, 60%, 80% and 99%. In the present invention, the total number of test conditions is set to 100 (which can be set according to needs), and 20 are set for each of the 5 detection SOC nodes.

[0043] The detection time range is 10 minutes to 60 minutes and is randomly selected according to the normal distribution.

[0044] Temperature detection curve selection: Statistics are collected on the production time of the energy storage system in the previous six years. The proportion of the production quantity in each month of the six years to the total production quantity is calculated, and the proportion is used as the selection probability of the temperature detection curve.

[0045] Selection of load change curves: Calculate the ratio of the number of times each load curve appears in the first six years to the total number of times the load curve appears, and use this ratio as the selection probability of the corresponding discharge load curve or charging load curve.

[0046] Afterwards, the temperature detection curve, load change curve, detection SOC node and detection time are randomly matched to generate 100 test conditions.

[0047] In addition, when the load change curve in the test condition is a charging load curve, the detection SOC node cannot be 99% to avoid overcharging and damage to the energy storage system. When the load change curve in the test condition is a discharging load curve, the time required for complete discharge must be estimated based on the discharge load curve and the detection SOC node. If the time required for complete discharge is longer than the detection time, a new detection time must be re-matched.

[0048] Perform a short-circuit test on the battery according to the test conditions, and conduct a short-circuit risk assessment on the battery based on the test results.

[0049] Before testing the energy storage system, a battery charging test is first performed on the energy storage system. If it can be charged normally, the test is started according to the detection sequence. If it cannot be charged normally, it is directly judged as damaged.

[0050] There are 100 test conditions, so 100 consecutive tests are required. After each test, the energy storage system is recharged / discharged to restore the entire energy storage system to the state before the test before the next test. If the energy storage system cannot be charged / discharged during the state recovery, the test is stopped and the corresponding scores of the unimplemented test conditions are directly set to 0.

[0051] After each test is completed, the short-circuit test equipment is removed from the circuit to test the energy storage system status at this time, and the test is scored according to Table 2.

[0052] Table 2 External short circuit test scoring table

[0053]

[0054] If the vehicle can charge / discharge normally after the Nth test, the test will continue until 100 external short-circuit tests are completed. If the vehicle cannot charge normally after the Nth test, the test will be stopped, and the score of the unfinished 100-N test will be 0. In addition, after the discharge / charge is completed, the SOC value of the energy storage system at this time must be re-estimated to determine whether the charge / discharge of the energy storage system is within the expected range. If the charge / discharge is not within the expected range, it indicates that the internal materials of the energy storage system have aged, etc.

[0055] Then the scores of the vehicle's N short-circuit tests are summarized. Where yn is the score at the nth test. If the total score is greater than the preset threshold, the energy storage system is judged to be qualified.

[0056] On the other hand, an embodiment of the present invention further provides a short-circuit risk assessment system for an energy storage system. The system uses the above-mentioned short-circuit risk assessment method for an energy storage system, including:

[0057] A data processing module, the data processing module is used to fit a temperature change curve and a load change curve according to historical temperature data and historical load data, and to divide the temperature change curve;

[0058] A working condition matching module is used to set multiple detection SOC nodes and multiple detection times, and randomly match the temperature detection curve, the load change curve, the detection SOC node and the detection time to generate multiple test conditions;

[0059] A test module is used to perform a short-circuit test on the energy storage system according to a test condition and to assess the short-circuit risk.

[0060] On the other hand, an embodiment of the present invention further provides a computer, comprising at least one processor and a memory, wherein the memory stores a computer program, and the computer program is configured to be executed by the processor to implement the above-mentioned short-circuit risk assessment method for an energy storage system.

[0061] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and the computer program can be executed by one or more processors to implement the above-mentioned short-circuit risk assessment method for an energy storage system.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A short circuit risk assessment method for an energy storage system, characterized by: include: Obtain historical temperature data, fit a temperature change curve based on the historical temperature data, and then divide the temperature change curve into multiple temperature detection curves; Obtain historical load data and fit the load change curve based on the historical load data; Set multiple detection SOC nodes and multiple detection times, match the temperature detection curve, load change curve, detection SOC node and detection time, and generate multiple test conditions; Perform a short-circuit test on the battery according to the test conditions, and conduct a short-circuit risk assessment on the battery based on the test results.

2. The short-circuit risk assessment method for an energy storage system according to claim 1, wherein: Methods for fitting temperature change curves based on historical temperature data include: Calculate the monthly temperature average for each month in each year in the historical temperature data; The historical temperature data are divided into odd-numbered year temperature classes and even-numbered year temperature classes according to their years, and then the average temperature of the same month in the odd-numbered year temperature class and the average temperature of the same month in the even-numbered year temperature class are calculated to obtain the odd-numbered year temperature data and the even-numbered year temperature data respectively; The temperature data of odd years and even years are spliced together and smooth curve fitting is performed to obtain the temperature change curve.

3. The short-circuit risk assessment method for an energy storage system according to claim 2, wherein: The method of dividing the temperature change curve into multiple temperature detection curves includes: The temperature values corresponding to the first 12 months in the temperature change curve are set as the dividing nodes; Taking each division node as the starting point and counting forward to the temperature corresponding to the 12th month as the end point, the temperature change curve is intercepted into multiple temperature detection curves.

4. The short-circuit risk assessment method for an energy storage system according to claim 1, wherein: The load variation curve includes a discharge load curve and a charge load curve. The detection SOC nodes include 20%, 40%, 60%, 80% and 99%. When the load variation curve in the test condition is a charge load curve, the detection SOC node cannot be 99%.

5. The short-circuit risk assessment method for an energy storage system according to claim 1, wherein: The detection time range is 10 minutes to 60 minutes and is randomly selected according to the normal distribution.

6. The short-circuit risk assessment method for an energy storage system according to claim 5, wherein: When the load change curve in the test condition is a discharge load curve, the time required for complete discharge needs to be estimated based on the discharge load curve and the detection SOC node. If the time required for complete discharge is longer than the detection time, the new detection time is re-matched.

7. The short-circuit risk assessment method for an energy storage system according to claim 1, wherein: After each test according to the test conditions, the energy storage system is scored according to the test results. The energy storage system is then recharged / discharged to restore the entire energy storage system to its pre-test state before the next test. If the energy storage system cannot be charged / discharged when restoring the state of the energy storage system, the test is stopped and the corresponding score of the unimplemented test condition is directly set to 0.

8. A short circuit risk assessment system for an energy storage system, characterized by: The system uses a short-circuit risk assessment method for an energy storage system according to any one of claims 1 to 7, comprising: A data processing module, the data processing module is used to fit a temperature change curve and a load change curve according to historical temperature data and historical load data, and to divide the temperature change curve; A working condition matching module is used to set multiple detection SOC nodes and multiple detection times, and randomly match the temperature detection curve, the load change curve, the detection SOC node and the detection time to generate multiple test conditions; A test module is used to perform a short-circuit test on the energy storage system according to a test condition and to assess the short-circuit risk.

9. A computer, characterized in that: The method comprises at least one processor and a memory, wherein the memory stores a computer program, and the computer program is configured to be executed by the processor to implement the short-circuit risk assessment method for an energy storage system according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. The computer program can be executed by one or more processors to implement a short-circuit risk assessment method for an energy storage system as described in any one of claims 1 to 7.

Images