Battery pack thermal runaway early warning method based on distributed temperature acquisition

Abstract

The invention provides a battery pack thermal runaway early warning method based on distributed temperature acquisition. Belongs to the technical field of batteries. For the structure and grouping mode of a target battery pack, characteristic temperature monitoring points are selected, optical fiber sensors are designed according to the number and spacing of the measuring points, integration of the sensors and the battery pack and initialization of sensor parameters are completed, then conventional working condition and fault working condition tests are carried out, and the distributed temperature of the battery pack in the testing process is collected; the method comprises the following steps: completing grouping of temperature measuring points in a battery pack based on distributed temperatures collected under conventional working conditions, then calculating the temperature Shannon entropy of each group by using a Shannon entropy method, and obtaining the weighted Shannon entropy of the battery pack through weighted average; and based on the weighted Shannon entropy calculated under different test conditions and different battery pack states, constructing a mapping relation between the weighted Shannon entropy and the temperature and the battery pack state, and taking the mapping relation as a dynamic early warning edge of the thermal runaway of the battery pack.

Description

Technical Field

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

[0002] Fires and explosions in new energy vehicles and energy storage power stations caused by thermal runaway of battery packs seriously threaten the safety of users and bring immeasurable economic losses to society. Therefore, how to achieve early warning of thermal runaway of battery packs is a major challenge currently faced by academia and industry.

[0003] From the currently disclosed information, it can be understood that the existing battery pack thermal runaway warning strategy mainly uses current, voltage and a small amount of temperature measurement point data in the battery pack as variables, and compares and judges the variable information collected by the sensor with the variable threshold or variable change rate threshold preset in the battery management system, thereby realizing the battery pack thermal runaway warning. However, this method has high requirements on the accuracy and stability of the sensor, otherwise it is easy to form false alarms. In addition, battery packs used in new energy vehicles and energy storage power stations are usually composed 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, this method cannot detect and warn some minor faults in the battery pack in time.

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

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

[0006] To achieve the above object, the present invention provides the following technical solution: a battery pack thermal runaway early warning method with distributed temperature acquisition, the method comprising the following steps: S1: Based on the actual battery pack structure, select the battery side and bottom 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 strain caused by the bending of the optical fiber sensor on temperature measurement, based on the selected characteristic temperature monitoring points, select a laying method with fewer bending points of the optical fiber sensor; based on the determined sensor laying scheme, determine the number of grating points required to be written on the optical fiber sensor and the distance between adjacent grating points. Subsequently, the optical fiber sensor is laid according to the preset laying scheme and the parameters are initialized. The main parameters include the reference wavelength of the optical fiber sensor grating point at the reference temperature and the temperature sensitivity coefficient.

[0007] S2: For the battery pack that has completed sensor integration in step S1, perform routine operating condition tests and fault operating condition tests, and collect the distributed temperature in the battery pack during the test.

[0008] 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 Shannon entropy of the temperature data of each group. Then, the N groups of Shannon entropies are weighted averaged, and according to the weighted Shannon entropies calculated under different test environments and different battery pack states, a mapping relationship is constructed as the dynamic warning boundary of thermal runaway of the battery pack.

[0009] S4: Based on the distributed temperature of the battery pack under normal working conditions and fault working conditions collected in step S2, calculate the weighted Shannon entropy of the battery pack SE According to the calculation results and combined with the test conditions of the current data, the accuracy and generalization of the constructed dynamic warning boundary of thermal runaway of the battery pack are verified.

[0010] Optionally, in step S2, the conventional operating condition test selects to carry out constant current charge and discharge cycle tests at different rates (including 0.1C, 0.5C, 1C and 2C) at different ambient temperatures (including 0°C, 5°C, 10°C, 15°C, 25°C and 40°C) and to carry out real vehicle operating condition tests (including NEDC and FUDS); the fault operating condition test selects micro-short circuit, overcharge and overheating tests, but is not limited to the above tests.

[0011] In step S3, based on the collected distributed temperature of the battery pack, the temperature measurement points in the battery pack are grouped, and then the weighted Shannon entropy of the battery pack under different test conditions is calculated using the Shannon entropy method. SE , and construct the dynamic warning boundary of battery pack thermal runaway based on the calculation results. The specific steps are: 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 first calculated using the Shannon entropy method, and then the calculated N groups of Shannon entropies are weighted averaged. The specific implementation steps are as follows: (1) (2) in, Sj Indicates the battery pack j The Shannon entropy calculation results of the group temperature data are: SEIt represents the result of weighted averaging of N groups of Shannon entropy.

[0012] S33: Calculate the weighted Shannon based on the test data of the battery pack in different test environments and different battery pack states SE , and build SE The mapping relationship between the ambient temperature and the aging state of the battery pack is used as the dynamic warning boundary of thermal runaway of the battery pack. In step S4, based on the distributed temperature of the battery pack under normal working conditions and fault working conditions collected in step S2, the specific steps for verifying the accuracy and generalization of the algorithm are: S41: For the same type of battery pack as the one for which the thermal runaway warning dynamic boundary is constructed, the weighted Shannon entropy of the battery pack is calculated based on the distributed temperature data collected under normal test conditions and abuse test conditions. SE The changes in the thermal runaway warning boundary are used to verify the accuracy of the constructed dynamic thermal runaway warning boundary.

[0013] S42: Calculate the weighted Shannon entropy of the battery pack based on the distributed temperature data collected from other types of battery packs under normal test conditions and abuse test conditions SE , verifying the generalization of the constructed dynamic thermal runaway warning boundary; S43: further optimizing the battery pack thermal runaway dynamic warning boundary according to the verification results.

[0014] The beneficial effects of the present invention are: (1) The advantages of single-fiber multi-point measurement of optical fiber sensors are fully utilized, and the optical fiber is integrated inside the battery pack, which can accurately obtain quasi-distributed temperature data on the surface of the battery pack in real time; (2) The constructed dynamic warning boundary for battery pack thermal runaway fully considers the temperature inconsistency of the battery pack itself, and can accurately assess the safety status of the battery pack without increasing the computing resources and hardware costs of the on-board terminal.

[0015] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein: Picture 1 This is a schematic diagram of thermal runaway early warning collection of the present invention; Picture 2 A flow chart developed for the battery pack thermal runaway early warning method of the present invention; Picture 3 A flow chart for constructing a dynamic warning boundary for thermal runaway of a battery pack according to the present invention. DETAILED DESCRIPTION

[0017] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0018] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0019] Please refer to Figures 1 and 2. A distributed temperature acquisition battery pack thermal runaway warning method includes the following steps: S1: Based on the grouping structure of the actual battery pack, select the battery side and bottom 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, select a laying method with fewer bending points of the optical fiber sensor; based on the determined sensor laying scheme, determine the number of grating points required to be written on the optical fiber sensor and the distance between adjacent grating points. Subsequently, the optical fiber sensor is laid according to the preset laying scheme and the parameter initialization is completed. The main parameters include the reference wavelength of the optical fiber sensor grating point at the reference temperature, and the temperature sensitivity coefficient.

[0020] S2: For the battery pack that has completed sensor integration in step S1, conduct routine operating condition tests and fault operating condition tests, and collect the distributed temperature in the battery pack during the test; S3: Based on the distributed temperature of the battery pack under the routine operating condition test 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. Then, the N groups of Shannon entropies are weighted averaged, and according to the weighted Shannon entropies calculated in different test environments and different battery pack states, a mapping relationship is constructed as the dynamic warning boundary of thermal runaway of the battery pack. S4: Based on the distributed temperature of the battery pack under the routine operating condition and fault operating condition test collected in step S2, the weighted Shannon entropy of the battery pack is calculated. SE According to the calculation results and combined with the test conditions of the current data, the accuracy and generalization of the constructed dynamic warning boundary of thermal runaway of the battery pack are verified.

[0021] In step S2, the conventional operating condition test selects to carry out constant current charge and discharge cycle tests at different rates (including 0.1C, 0.5C, 1C and 2C) at different ambient temperatures (including 0°C, 5°C, 10°C, 15°C, 25°C and 40°C) and to carry out real vehicle operating condition tests (including NEDC and FUDS); the fault operating condition test selects micro-short circuit, overcharge and overheating tests, but is not limited to the above tests.

[0022] See also Picture 3 Based on the collected distributed temperature of the battery pack, the temperature measurement points in the battery pack are grouped, and then the Shannon entropy method is used to calculate the weighted Shannon entropy of the battery pack under different test conditions. SE , and construct the dynamic warning boundary of battery pack thermal runaway based on the calculation results. The specific steps are: 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 first calculated using the Shannon entropy method, and then the calculated N groups of Shannon entropies are weighted averaged. The specific implementation steps are as follows: (1) (2) Among them, Sj Indicates the battery pack j The Shannon entropy calculation results of the group temperature data are: SE It represents the result of weighted averaging of N groups of Shannon entropy.

[0023] S33: Calculate the weighted Shannon entropy based on the test data of the battery pack in different test environments and different battery pack states SE , and build SE The mapping relationship between the ambient temperature and the aging state of the battery pack is used as the dynamic warning boundary of thermal runaway of the battery pack. In step S4, based on the distributed temperature of the battery pack under normal working conditions and fault working conditions collected in step S2, the specific steps for verifying the accuracy and generalization of the algorithm are: S41: For the same type of battery pack as the one for which the thermal runaway warning dynamic boundary is constructed, the weighted Shannon entropy of the battery pack is calculated based on the distributed temperature data collected under normal test conditions and abuse test conditions. SE The changes in the thermal runaway warning boundary are used to verify the accuracy of the constructed dynamic thermal runaway warning boundary.

[0024] S42: Calculate the weighted Shannon entropy of the battery pack based on the distributed temperature data collected from other types of battery packs under normal test conditions and abuse test conditions SE The changes of , verify the generalization of the constructed dynamic thermal runaway warning boundary; S43: Further optimize the dynamic warning boundary of thermal runaway of the battery pack based on the verification results.

[0025] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in 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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