A method for warning of thermal runaway of a battery pack for distributed temperature acquisition

Abstract

The present invention proposes a method for warning of thermal runaway of a battery pack by distributed temperature acquisition. It belongs to the technical field of batteries. Facing the structure and grouping mode of the target battery pack, characteristic temperature monitoring points are selected, fiber optic sensors are designed according to the number and spacing of the measuring points, and the integration of the sensors with the battery pack and the initialization of the sensor parameters are completed. Subsequently, tests under normal conditions and fault conditions are carried out to collect the distributed temperature of the battery pack during the test. Based on the distributed temperature collected under normal conditions, the temperature measuring points in the battery pack are grouped. Subsequently, the Shannon entropy of each group of temperatures is calculated by using the Shannon entropy method, and the weighted Shannon entropy of the battery pack is obtained through weighted average. Based on the weighted Shannon entropy calculated under different test conditions and different battery pack states, a mapping relationship between the weighted Shannon entropy, temperature and battery pack state is constructed, and this mapping relationship is used as the dynamic warning edge for thermal runaway of the battery pack.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and relates to a method for early warning of thermal runaway of a battery pack with distributed temperature acquisition. Background Art

[0002] The fire and explosion of new energy vehicles and energy storage power stations caused by thermal runaway of the battery pack pose a serious threat to the lives of users and bring immeasurable economic losses to society. Therefore, how to achieve early warning of battery pack thermal runaway is a major challenge faced by the current academic and industrial circles.

[0003] From the currently disclosed information, it can be learned that the existing early warning strategies for battery pack thermal runaway mainly use current, voltage, and a small amount of temperature measurement point data in the battery pack as variables, and judge by comparing the variable information collected by the sensor with the preset variable threshold or variable change rate threshold in the battery management system, so as to achieve early warning of battery pack thermal runaway. However, this method has high requirements for the accuracy and stability of the sensor, otherwise it is easy to form false alarms. In addition, the battery packs used in new energy vehicles and energy storage power stations usually consist of hundreds of battery cells, and the voltage or a small amount of temperature point information collected by the sensor cannot accurately reflect the voltage or temperature of each battery cell in the battery pack. Therefore, for some minor faults in the battery pack, this method cannot detect and give early warning in time.

[0004] Based on this situation, the present invention intends to utilize the characteristics of single-fiber multi-point measurement of fiber optic sensors to achieve distributed temperature measurement in the battery pack. Based on the distributed temperature information collected by the fiber optic sensors, on the basis of the previously developed threshold determination method, a battery pack thermal runaway early warning algorithm is developed in combination with Shannon entropy. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for early warning of thermal runaway of a battery pack with distributed temperature acquisition.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for early warning of thermal runaway of a battery pack with distributed temperature acquisition, the method comprising the following steps:

[0008] S1: Based on the grouping structure of the actual battery pack, select positions such as the sides and bottom of the battery as temperature monitoring points to obtain the actual surface temperature of the battery pack during operation. At the same time, to reduce the influence of the strain generated by the bending of the fiber optic sensor on temperature measurement, based on the selected characteristic temperature monitoring points, select a laying method with fewer bending points of the fiber optic sensor. Based on the determined sensor laying scheme, determine the number of grating points to be inscribed on the fiber optic sensor and the distance between adjacent grating points. Subsequently, lay the fiber optic sensor according to the preset laying scheme and complete parameter initialization. The main parameters include the reference wavelength of the fiber optic sensor grating points at the reference temperature and the temperature sensitivity coefficient.

[0009] S2: For the battery pack with the sensor integrated in step S1, conduct conventional working condition tests and fault working condition tests, and collect the distributed temperature within the battery pack during the tests.

[0010] S3: Based on the distributed temperature of the battery pack under the conventional working condition tests collected in step S2, divide the temperature measurement points arranged in the battery pack into N groups, and use the Shannon entropy method to calculate the Shannon entropy of the temperature data of each group. Subsequently, perform weighted averaging on the N groups of Shannon entropy, and based on the weighted Shannon entropy calculated under different test environments and different battery pack states, construct a mapping relationship as the dynamic early warning boundary for the thermal runaway of this battery pack.

[0011] S4: Based on the distributed temperature of the battery pack under the conventional working condition and fault working condition tests collected in step S2, calculate the weighted Shannon entropy SE of the battery pack. According to the calculation results, combined with the test working condition of the current data, verify the accuracy and generalization of the constructed dynamic early warning boundary for the thermal runaway of the battery pack.

[0012] Optionally, in step S2, the conventional working condition tests are to conduct constant current charge and discharge cycle tests at different rates (including 0.1C, 0.5C, 1C, and 2C) and conduct vehicle-like working condition tests (including NEDC and FUDS) at different ambient temperatures (including 0°C, 5°C, 10°C, 15°C, 25°C, and 40°C); the fault working condition tests are to select micro-short circuit, overcharge, and overheat tests, but are not limited to the above tests.

[0013] In step S3, based on the collected distributed temperature of the battery pack, complete the grouping of the temperature measurement points within the battery pack. Subsequently, use the Shannon entropy method to calculate the weighted Shannon entropy SE of the battery pack under different test working conditions, and construct a dynamic early warning boundary for the thermal runaway of the battery pack according to the calculation results. The specific steps are as follows:

[0014] S31: Based on the distributed temperature of the battery pack collected during the conventional working condition test, by analyzing the temperature distribution on the surface of the battery pack, and adopting the principle of grouping the measurement points with similar temperatures during the actual operation process, the temperature measurement points in the battery pack are divided into N groups;

[0015] S32: Based on the grouping results of the temperature measurement points in step S31, first use the Shannon entropy method to calculate the Shannon entropy of each group of temperatures, and then perform a weighted average on the calculated N groups of Shannon entropies. The specific implementation steps are as follows:

[0016]

[0017]

[0018] where, S j represents the calculation result of the Shannon entropy of the temperature data of the j-th group in the battery pack, and SE represents the result after weighted average of the N groups of Shannon entropies.

[0019] S33: Based on the test data of the battery pack under different test environments and different battery pack states, calculate the weighted Shannon entropy SE, and construct the mapping relationship between SE and the ambient temperature, the aging state of the battery pack, etc., and use it as the dynamic warning boundary of the battery pack thermal runaway.

[0020] In step S4, based on the distributed temperature of the battery pack collected under the conventional working condition and the fault working condition in step S2, the specific steps to verify the accuracy and generalization of the algorithm are as follows:

[0021] S41: For the same type of battery pack as the one for constructing the dynamic warning boundary of thermal runaway, based on the distributed temperature data collected under the conventional test working condition and the abuse test working condition, calculate the change in the weighted Shannon entropy SE of the battery pack to verify the accuracy of the constructed dynamic thermal runaway warning boundary.

[0022] S42: Based on the distributed temperature data collected under the conventional test working condition and the abuse test working condition of other types of battery packs, calculate the change in the weighted Shannon entropy SE of the battery pack to verify the generalization of the constructed dynamic thermal runaway warning boundary;

[0023] S43: Further optimize the dynamic warning boundary of the battery pack thermal runaway according to the verification results.

[0024] The beneficial effects of the present invention are as follows:

[0025] (1) Make full use of the advantage of single-fiber multi-point measurement of the fiber optic sensor, integrate the optical fiber inside the battery pack, and can accurately obtain the quasi-distributed temperature data on the surface of the battery pack in real time;

[0026] (2) The constructed dynamic early warning boundary for the thermal runaway of the battery pack fully considers the temperature inconsistency existing in the battery pack itself, and can accurately evaluate the safety status of the battery pack without increasing the computing resources and hardware costs of the vehicle-mounted terminal.

[0027] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent description, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0029] Figure 1 is a schematic diagram for the acquisition of thermal runaway early warning of the present invention;

[0030] Figure 2 is a flow chart for the development of the method for early warning of thermal runaway of the battery pack of the present invention;

[0031] Figure 3 is a flow chart for the construction of the dynamic early warning boundary for the thermal runaway of the battery pack of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following uses specific specific examples to illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention schematically, and the following embodiments and the features in the embodiments can be combined with each other without conflict.

[0033] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0034] Please refer to Figure 1 、 Figure 2 , a method for early warning of thermal runaway of a battery pack with distributed temperature acquisition, including the following steps:

[0035] S1: Based on the grouping structure of the actual battery pack, select positions such as the sides and bottom of the battery as temperature monitoring points to obtain the actual surface temperature of the battery pack during operation. At the same time, to reduce the influence of strain caused by the bending of the fiber optic sensor on temperature measurement, based on the selected characteristic temperature monitoring points, select a laying method with fewer bending points of the fiber optic sensor. Based on the determined sensor laying scheme, determine the number of grating points to be inscribed on the fiber optic sensor and the distance between adjacent grating points. Subsequently, lay the fiber optic sensor according to the preset laying scheme and complete parameter initialization. The main parameters include the reference wavelength of the fiber optic sensor grating points at the reference temperature and the temperature sensitivity coefficient.

[0036] S2: For the battery pack with sensor integration completed in step S1, conduct conventional working condition tests and fault working condition tests, and collect the distributed temperature inside the battery pack during the tests.

[0037] S3: Based on the distributed temperature of the battery pack under the conventional working condition tests collected in step S2, divide the temperature measurement points arranged in the battery pack into N groups, and use the Shannon entropy method to calculate the Shannon entropy of the temperature data of each group. Subsequently, perform weighted averaging on the N groups of Shannon entropy, and based on the weighted Shannon entropy calculated under different test environments and different battery pack states, construct a mapping relationship as the dynamic early warning boundary for thermal runaway of the battery pack.

[0038] S4: Based on the distributed temperature of the battery pack under the conventional working condition and fault working condition tests collected in step S2, calculate the weighted Shannon entropy SE of the battery pack. According to the calculation results, combined with the test working condition of the current data, verify the accuracy and generalization of the constructed dynamic early warning boundary for thermal runaway of the battery pack.

[0039] In step S2, the conventional working condition tests are selected to conduct constant current charge and discharge cycle tests at different rates (including 0.1C, 0.5C, 1C, and 2C) and conduct vehicle-like working condition tests (including NEDC and FUDS) at different ambient temperatures (including 0°C, 5°C, 10°C, 15°C, 25°C, and 40°C); the fault working condition tests are selected as micro-short circuit, overcharge, and overheat tests, but are not limited to the above tests.

[0040] Please refer to Figure 3 , based on the collected distributed temperature of the battery pack, complete the grouping of temperature measurement points inside the battery pack, and then use the Shannon entropy method to calculate the weighted Shannon entropy SE of the battery pack under different test working conditions, and construct a dynamic early warning boundary for thermal runaway of the battery pack according to the calculation results. The specific steps are as follows:

[0041] S31: Based on the distributed temperatures of the battery pack collected during the conventional working condition test, by analyzing the temperature distribution on the surface of the battery pack, and following the principle of grouping the measuring points with similar temperatures during the actual operation process, divide the temperature measuring points in the battery pack into N groups;

[0042] S32: Based on the grouping results of the temperature measuring points in step S31, first calculate the Shannon entropy of each group of temperatures using the Shannon entropy method, and then perform a weighted average on the N calculated Shannon entropies. The specific implementation steps are as follows:

[0043]

[0044]

[0045] where, S j represents the calculation result of the Shannon entropy of the temperature data of the j-th group in the battery pack, and SE represents the result after performing a weighted average on the N Shannon entropies.

[0046] S33: Based on the test data of the battery pack under different test environments and different battery pack states, calculate the weighted Shannon entropy SE, and construct the mapping relationship between SE and the environmental temperature, the aging state of the battery pack, etc., and use it as the dynamic early warning boundary for the thermal runaway of the battery pack.

[0047] In the said step S4, based on the distributed temperatures of the battery pack collected under the conventional working condition and the fault working condition in step S2, the specific steps to verify the accuracy and generalization of this algorithm are:

[0048] S41: For the same type of battery pack as the one for constructing the dynamic early warning boundary of thermal runaway, based on the distributed temperature data collected under the conventional test working condition and the abuse test working condition, calculate the change in the weighted Shannon entropy SE of the battery pack, and verify the accuracy of the constructed dynamic thermal runaway early warning boundary.

[0049] S42: Based on the distributed temperature data of other types of battery packs collected under the conventional test working condition and the abuse test working condition, calculate the change in the weighted Shannon entropy SE of the battery pack, and verify the generalization of the constructed dynamic thermal runaway early warning boundary;

[0050] S43: Further optimize the dynamic early warning boundary for the thermal runaway of the battery pack according to the verification results.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A battery pack thermal runaway early warning method with distributed temperature acquisition, characterized in that: The method comprises the following steps: S1: Based on the grouping structure of the actual battery pack, the battery side and bottom are selected as temperature monitoring points to obtain the actual surface temperature of the battery pack during operation; at the same time, in order to reduce the influence of the strain caused by the bending of the optical fiber sensor on the temperature measurement, based on the selected characteristic temperature monitoring points, a laying method with fewer bending points of the optical fiber sensor is selected; based on the determined sensor laying scheme, the number of grating points required to be written on the optical fiber sensor and the distance between adjacent grating points are determined; then, the optical fiber sensor is laid according to the preset laying scheme and the parameters are initialized, including the reference wavelength of the optical fiber sensor grating point at the reference temperature, and the temperature sensitivity coefficient; S2: Carry out a normal working condition test and a fault working condition test for the battery pack in which the sensor integration is completed in step S1, and collect the distributed temperature in the battery pack during the test; S3: Based on the distributed temperature of the battery pack under the normal working condition test collected in step S2, the temperature measurement points arranged in the battery pack are divided into N groups, and the Shannon entropy method is used to calculate the temperature data of each group; in order to improve the accuracy of thermal runaway warning, the N Shannon entropies are weighted and calculated, and the thermal runaway threshold is selected according to the normal working condition; S4: Based on the distributed temperature of the battery pack under normal operating conditions and fault condition tests collected in step S2, verify the accuracy and versatility of the algorithm.

2. The method for early warning of thermal runaway of a battery pack using distributed temperature collection according to claim 1, characterized in that: In step S2, the conventional operating condition test selects constant current charge-discharge cycle tests of different rates at different ambient temperatures and performs real vehicle operating condition tests; the fault operating condition test selects micro-short circuit, overcharge and overheating tests, but is not limited to the above tests.

3. According to the distributed temperature acquisition method for thermal runaway early warning of battery packs as described in claim 1, it is characterized in that: In step S3, the steps of developing a battery pack thermal runaway warning algorithm based on the collected distributed temperature of the battery pack and in combination with the Shannon entropy method are as follows: S31: Based on the distributed temperature of the battery pack collected during the conventional working condition test, by analyzing the temperature distribution on the surface of the battery pack, the temperature measurement points in the battery pack are divided into N groups according to the principle of grouping measurement points with similar temperatures during actual operation; S32: Based on the grouping results of the temperature measurement points in step S31, the Shannon entropy of each temperature group is calculated in combination with the Shannon entropy method, and the N Shannon entropies are weighted. The weighted formula is as follows: S33: Based on some normal operating condition and fault operating condition test data, the weighted Shannon entropy is obtained using the calculation formula in step S32, and a suitable battery pack thermal runaway warning threshold is determined according to the calculation result.

4. The method for early warning of thermal runaway of a battery pack using distributed temperature collection according to claim 1, characterized in that: In step S4, based on the distributed temperature of the battery pack under the normal working condition and the fault working condition test collected in step S2, the specific steps of verifying the accuracy and generalization of the algorithm are: S41: By inputting distributed temperature data of the same type of module as the one on which the algorithm is developed, including normal operating conditions and fault conditions, the developed early warning algorithm can be verified without false alarms, thus verifying the accuracy of the algorithm; S42: By inputting the distributed temperature data output by different types of modules or battery packs under normal operating conditions and fault conditions, the developed early warning algorithm can accurately warn and verify the generalization of the algorithm.

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