Battery temperature change trend monitoring method, device, equipment and storage medium

Abstract

The present invention belongs to the technical field of battery detection, and discloses a method, device, equipment and storage medium for monitoring the temperature change trend of a battery. The method comprises: obtaining multiple impedance data of a battery cell of a battery pack at a preset frequency; determining the impedance change trend of the battery cell according to the multiple impedance data; determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend. By using the impedance value obtained by the electrochemical impedance spectrum test to determine whether there is an abnormality in the change trend of the battery impedance, and thus to determine whether there is an abnormality in the change trend of the battery temperature, the change of the internal temperature of the battery can be quickly obtained, the change of the internal temperature of the battery can be quickly monitored, the abnormality of the battery temperature can be discovered in time, and the safety of the battery during application can be ensured, while saving the testing cost and shortening the time period.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a method, device, equipment and storage medium for monitoring battery temperature change trend. Background Art

[0002] As an efficient electrochemical energy storage device, batteries have been widely used in various energy storage fields. Safety is an important performance of batteries. If the battery fails or loses control during operation, the energy in the battery will be converted into heat in a short time, causing fire or explosion. Therefore, battery temperature monitoring is very important for safety.

[0003] Since the internal temperature of the battery is difficult to collect by embedding thermistors, it is usually necessary to fit the electrochemical impedance spectroscopy (EIS) parameter values ​​with temperature to obtain the mapping relationship between the EI parameter values ​​(e.g., phase angle values, imaginary impedance values, and real impedance values) and temperature, so as to determine the change in the internal temperature of the battery and further judge the abnormality of the battery temperature. However, this method relies on a large number of calibration tests, which will consume a lot of testing costs and take a long time. Summary of the invention

[0004] In view of the above problems, the present application provides a battery temperature change trend monitoring method, device, equipment and storage medium, aiming to solve the technical problem that it takes a long time period when monitoring the changes in the internal temperature of the battery in the prior art.

[0005] In a first aspect, the present invention provides a method for monitoring a battery temperature change trend, comprising: acquiring multiple impedance data of a battery cell of a battery pack at a preset frequency; determining an impedance change trend of the battery cell based on the multiple impedance data; and determining a trend state of a battery temperature change trend of the battery pack based on the trend state of the impedance change trend.

[0006] In the technical solution of the embodiment of the present application, the impedance value obtained by the EIS test is used to monitor the impedance change trend of the battery during the static process, and it is determined whether there is an abnormality in the change trend of the battery impedance, thereby determining whether there is an abnormality in the change trend of the battery temperature. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be quickly monitored, and the abnormality of the battery temperature can be discovered in time, thereby ensuring the safety of battery application while saving testing costs and shortening the time period.

[0007] In some embodiments, determining the impedance change trend of the battery cell based on the multiple impedance data includes: determining an impedance median corresponding to the multiple impedance data based on the multiple impedance data; normalizing the multiple impedance data based on the impedance median to obtain multiple normalized impedance data corresponding to the multiple impedance data; and determining the impedance change trend based on the multiple normalized impedance data.

[0008] By taking the median of the collected impedance data as a benchmark, all impedance data are normalized, that is, taking the median as a benchmark, all impedance data are median-normalized to obtain normalized impedance data, thereby obtaining the impedance change. It is possible to determine whether there is an abnormality in the impedance change trend to determine whether there is an abnormality in the internal temperature trend of the battery. The internal temperature change of the battery can be quickly obtained, and the internal temperature change of the battery can be quickly monitored to shorten the time period. Moreover, by normalizing the impedance data of the battery cell by the median, it can be ensured that each comparison is based on the same battery pack and will not be limited to a specific battery cell type, thereby reducing the impact of different types of battery cells on the data, improving the versatility of application, and ensuring the reliability and accuracy of monitoring. When the battery changes, no additional calibration test is required, which saves test costs and shortens the overall monitoring time period.

[0009] In some embodiments, determining the impedance median corresponding to the multiple impedance data based on the multiple impedance data includes: dividing the multiple impedance data into at least one first dimensional data set based on time information corresponding to the multiple impedance data, the time information corresponding to the impedance data in the first dimensional data set being the same; and determining the impedance median corresponding to the first dimensional data set based on the impedance data in the first dimensional data set.

[0010] By taking the impedance data of all battery cells at the same time as a group and calculating the corresponding median, the median at this time can be considered as the data in this group with a high probability of being data at normal temperature. Normalization is performed based on the battery cell corresponding to the median to ensure that each comparison is based on the same battery pack and is not limited to a specific battery cell type. This reduces the impact of different types of battery cells on the data, improves the versatility of application, ensures the reliability and accuracy of monitoring, and when the battery changes, no additional calibration test is required, saving test costs while shortening the overall monitoring period.

[0011] In some embodiments, before dividing the multiple impedance data into at least one first dimensional data set based on the time information corresponding to the multiple impedance data, it also includes: dividing the multiple impedance data into at least one second dimensional data set based on the numbering information corresponding to the impedance data, the numbering information corresponding to the impedance data in the second dimensional data set is the same; screening out impedance abnormal data in the second dimensional data set, and eliminating the impedance abnormal data.

[0012] By taking the impedance data of all time dimensions of the same battery cell as a group, filtering out the outliers in the impedance data in each group, removing the outliers, and normalizing the data after removing the outliers, the accuracy of the normalization can be guaranteed, thereby ensuring the accuracy of the obtained change trend, and then ensuring the accuracy of the trend status judgment, thereby improving the reliability and accuracy of monitoring.

[0013] In some embodiments, screening out impedance abnormality data in the second dimensional data set and eliminating the impedance abnormality data includes: determining an impedance abnormality threshold corresponding to the second dimensional data set based on the impedance data in the second dimensional data set; determining the impedance abnormality data in the second dimensional data set based on the impedance abnormality threshold corresponding to the second dimensional data set, the impedance abnormality data in the second dimensional data set being greater than the impedance abnormality threshold of the second dimensional data set.

[0014] By determining the limit value of the normal range for each group of data, those exceeding the limit value are considered to be outliers, identifying all outliers in each group of data, removing the outliers, and normalizing the data after removing the outliers, the accuracy of the normalization can be guaranteed, thereby ensuring the accuracy of the obtained change trend, and then ensuring the accuracy of the trend status judgment, thereby improving the reliability and accuracy of monitoring.

[0015] In some embodiments, determining the impedance anomaly threshold corresponding to the second dimensional data set based on the impedance data in the second dimensional data set includes: sorting the impedance data in the second dimensional data set in ascending order to obtain an impedance data sequence corresponding to the second dimensional data set; determining a quantile in the impedance data sequence; and determining the impedance anomaly threshold of the second dimensional data set based on the quantile corresponding to the second dimensional data set.

[0016] By estimating the normal range limit value of each group of data through quantiles, taking it as the threshold, identifying the outliers in each group of data, and removing the outliers. Normalizing the data after removing the outliers can ensure the accuracy of normalization, thereby ensuring the accuracy of the obtained changing trend, and then ensuring the accuracy of the trend status judgment, improving the reliability and accuracy of monitoring, and using quantiles to identify outliers can ensure the accuracy of outlier identification, further improve the accuracy of normalization, and further improve the reliability and accuracy of monitoring.

[0017] In some embodiments, the quantile includes an upper quartile and a lower quartile, and determining the impedance anomaly threshold of the second dimensional data set based on the quantile corresponding to the second dimensional data set includes: obtaining a first corresponding relationship between the lower quartile, the upper quartile, the anomaly coefficient and the impedance anomaly threshold; obtaining the impedance anomaly threshold of the second dimensional data set based on the anomaly coefficient, the first corresponding relationship, and the upper quartile and the lower quartile corresponding to the second dimensional data set.

[0018] The lower quartile and upper quartile are used to estimate the limit of the normal range of each group of data, so as to identify the outliers in each group of data and eliminate them. The use of quantiles to identify outliers can ensure the accuracy of outlier identification. The data after eliminating outliers can be normalized to improve the accuracy of normalization and the reliability and accuracy of monitoring.

[0019] In some embodiments, before dividing the multiple impedance data into at least one second dimensional data set according to the numbering information corresponding to the impedance data, it also includes: combining the multiple impedance data according to the time information and numbering information corresponding to the multiple impedance data to obtain an impedance array, the first dimension of the impedance array represents the time information, and the first dimension of the impedance array represents the numbering information.

[0020] By combining all impedance data into a two-dimensional array according to time information and number information, the first dimension in the two-dimensional array represents time information, and the second dimension represents number information. When the impedance data is divided, according to the two dimensions of the array, the data sets corresponding to different time information can be quickly and accurately divided, and the data sets corresponding to different number information can also be quickly and accurately divided, thereby ensuring the efficiency and accuracy of data division, improving the accuracy of normalization, thereby improving the accuracy of the obtained change trend, and then improving the accuracy of trend status judgment, improving the reliability and accuracy of monitoring, and shortening the normalization time period, thereby shortening the overall monitoring time period.

[0021] In some embodiments, the multiple impedance data are normalized according to the impedance median to obtain multiple normalized impedance data corresponding to the multiple impedance data, including: determining the median difference corresponding to each impedance data in the first dimensional data set according to the impedance data and the impedance median of the first dimensional data set; obtaining a second corresponding relationship between the impedance median, the median difference and the normalized impedance data; and obtaining the normalized impedance data corresponding to each impedance data in the first dimensional data set according to the two corresponding relationships and the median difference and the impedance median corresponding to each impedance data in the first dimensional data set.

[0022] By obtaining the median impedance, the difference between the impedance data and the median impedance in each group of data is calculated, and all the impedance data are normalized using the difference between the impedance data and the median impedance. When normalizing, the median of each group is used as the benchmark, that is, the battery cell corresponding to the median is used as the benchmark to normalize the impedance data of all battery cells, ensuring that each comparison is based on the same battery pack and will not be limited to a specific battery cell type, reducing the impact of different types of battery cells on the data, improving the versatility of application, ensuring the reliability and accuracy of monitoring, and when the battery changes, no additional calibration test is required, saving testing costs while shortening the overall monitoring time period.

[0023] In some embodiments, determining the impedance change trend based on the multiple normalized impedance data includes: dividing the multiple normalized impedance data into at least one normalized data set according to the numbering information corresponding to the multiple normalized impedance data, the numbering information corresponding to the normalized impedance data in the normalized data set is the same; dividing the normalized data set into multiple normalized subsets according to a preset number of equal divisions, and determining the slope of each normalized subset; combining the slopes of the multiple normalized subsets corresponding to the normalized data set to obtain a slope array corresponding to the normalized data set, and the slope array reflects the impedance change trend corresponding to the normalized data set.

[0024] The normalized impedance data of all time dimensions of the same battery cell are divided into the same set, and each set is further divided into multiple subsets, so that the number of data in the subsets obtained by dividing each set is the same, and a linear slope calculation is performed on the data of each subset, and the slopes calculated from the subsets are combined into an array, that is, the slope array of each set. The multiple slopes in the slope array reflect the changing trend of the data in the set, which is equivalent to the changing trend of the impedance data of each battery cell, so as to judge the trend state of the changing trend, and then obtain the trend state of the changing trend of the internal temperature of the battery. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between EIS parameter values ​​and temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, so as to ensure the safety of battery application while saving testing costs and shortening the time cycle.

[0025] In some embodiments, before determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend, it also includes: dividing the slope array into a first slope data set and a second slope data set according to a preset data quantity; determining the slope average according to the first slope data set, and determining the slope median according to the second slope data set; determining the trend state of the impedance change trend according to the slope average and the slope median.

[0026] By dividing the slope data in the slope array into two data sets, the first data set calculates the average of the data as the basis for judgment, and the second data set calculates the median of the data as the data used for judgment, so as to judge the trend state of the impedance change trend, and then obtain the trend state of the change trend of the internal temperature of the battery. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, ensuring the safety of battery application while saving testing costs and shortening the time period.

[0027] In some embodiments, determining the trend state of the impedance change trend based on the slope average and the slope median includes: determining a trend abnormality threshold based on the slope average and a preset multiple; when the slope median is greater than or equal to the trend abnormality threshold, determining that the trend state of the impedance change trend is an abnormal trend state; when the slope median is less than the trend abnormality threshold, determining that the trend state of the impedance change trend is a normal trend state.

[0028] The threshold for judging whether the trend state is abnormal is calculated by the obtained slope average of the slope array. If the median slope of the slope array exceeds the threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is abnormal. If the median slope of the slope array is lower than the threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is not abnormal. Then, according to the obtained trend state of the impedance change trend, it is determined whether the trend state of the battery temperature change trend is abnormal. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, which ensures the safety of battery application while saving testing costs and shortening the time period.

[0029] In some embodiments, determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend includes: when the trend state of the impedance change trend is an abnormal trend state, determining the trend state of the battery temperature change trend is an abnormal trend state; when the trend state of the impedance change trend is a normal trend state, determining the trend state of the battery temperature change trend is a normal trend state.

[0030] By judging the trend state of the impedance change trend and the trend state of the battery temperature change trend, the change of the internal temperature of the battery can be quickly judged, the internal temperature change of the battery can be quickly monitored, and the abnormal battery temperature can be discovered in time to ensure the safety of the battery during application, while saving testing costs and shortening the time period.

[0031] In some embodiments, the obtaining of multiple impedance data of battery cells in a battery pack at a preset frequency includes: determining a selection time range based on a current time and a preset selection requirement, wherein the preset selection requirement is any one of a preset selection time and a preset selection quantity; and obtaining multiple impedance data of battery cells in the battery pack based on the preset frequency and the selection time range.

[0032] By acquiring the impedance data of all battery cells in the entire battery pack within a recent period of time and at a certain frequency, it is determined whether there is an abnormality in the change trend of the battery impedance based on the collected impedance data, thereby determining whether there is an abnormality in the change trend of the battery temperature. The change of the internal temperature of the battery can be quickly obtained, and the internal temperature change of the battery can be quickly monitored. Abnormalities in the battery temperature can be discovered in time to ensure the safety of the battery during application, while saving testing costs and shortening the time period. In addition, data selection is equivalent to a sliding window selection method. Each time, the most recent data is selected, so that monitoring is not limited to specific battery cells and specific models, which improves the versatility during application and ensures the reliability and accuracy of monitoring.

[0033] In some embodiments, after obtaining multiple impedance data of battery cells of the battery pack at a preset frequency, the step further includes: obtaining multiple current data corresponding to the multiple impedance data; when current data greater than a threshold current exists among the multiple current data, returning to execute the step of obtaining multiple impedance data of battery cells of the battery pack at a preset frequency; when current data greater than a threshold current does not exist among the multiple current data, executing the step of determining the impedance change trend of the battery cell based on the multiple impedance data.

[0034] By collecting the current data corresponding to each impedance data, the fluctuation of these current data is determined, so as to determine whether the current interferes with the impedance data. If there is no current exceeding the threshold, it is considered that the current data is relatively stable, and the impedance data can be used to monitor the changes in the internal temperature of the battery. If there is a current exceeding the threshold, it is considered that the current data fluctuates greatly. At this time, the current interferes with the impedance data more greatly, and the impedance data will be greatly affected. The collected impedance data is not suitable for monitoring the changes in the internal temperature of the battery and needs to be re-acquired to improve the accuracy of the obtained change trend, thereby improving the accuracy of the trend status judgment and improving the reliability and accuracy of monitoring.

[0035] In some embodiments, the method further includes: when new impedance data of a battery cell in the battery pack exists, executing the step of acquiring a plurality of impedance data of the battery cell of the battery pack at a preset frequency.

[0036] By timely acquiring the latest impedance data, the latest impedance data can be used to determine whether there is any abnormality in the change trend of the battery impedance, thereby determining whether there is any abnormality in the change trend of the battery temperature, thereby improving the reliability and accuracy of monitoring.

[0037] In a second aspect, in order to achieve the above-mentioned object, the present invention further proposes a battery temperature change trend monitoring device, the battery temperature change trend monitoring device comprising:

[0038] An acquisition module, used to acquire multiple impedance data of battery cells of a battery pack at a preset frequency;

[0039] A processing module, used for determining the impedance change trend of the battery cell according to the plurality of impedance data;

[0040] A monitoring module is used to determine the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend.

[0041] In the third aspect, in order to achieve the above-mentioned purpose, the present invention also proposes a battery temperature change trend monitoring device, the battery temperature change trend monitoring device comprising: a memory, a processor and a battery temperature change trend monitoring program stored in the memory and executable on the processor, the battery temperature change trend monitoring program is configured to implement the steps of the battery temperature change trend monitoring method as described above.

[0042] In a fourth aspect, in order to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a battery temperature change trend monitoring program is stored, and when the battery temperature change trend monitoring program is executed by a processor, the steps of the battery temperature change trend monitoring method as described above are implemented.

[0043] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Moreover, the same figure numbers are used throughout the drawings to represent the same components. In the drawings:

[0045] Figure 1 A flowchart of a method for monitoring battery temperature change trend according to some embodiments of the present application;

[0046] Figure 2 A schematic diagram of the impedance median calculation process in some embodiments of the present application;

[0047] Figure 3 A schematic diagram of a process for screening impedance anomaly data in some embodiments of the present application;

[0048] Figure 4 A schematic diagram of the upper quartile and the lower quartile of some embodiments of the present application;

[0049] Figure 5 A schematic diagram of detailed flow charts of some embodiments of the present application;

[0050] Figure 6 A schematic diagram of the slope array calculation process of some embodiments of the present application;

[0051] Figure 7 A schematic diagram of a trend state determination process of an impedance change trend in some embodiments of the present application;

[0052] Figure 8 Schematic diagram of a battery temperature change trend monitoring device according to some embodiments of the present application.

[0053] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0056] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0059] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0060] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0061] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0062] As an efficient electrochemical energy storage device, batteries have been widely used in various energy storage fields. Safety is an important performance of batteries. If the battery fails or loses control during operation, the energy in the battery will be converted into heat in a short time, causing fire or explosion. Therefore, battery temperature monitoring is very important for safety.

[0063] Since the internal temperature of the battery is difficult to collect by embedding thermistors, it is usually necessary to fit the EIS parameter value to the temperature to obtain the mapping relationship between the EI parameter value and the temperature, so as to determine the change in the internal temperature of the battery, and then judge the abnormality of the battery temperature. For example, a phase angle-temperature fitting curve is established, and the change in the internal temperature of the battery is monitored through the functional relationship between the phase angle value and the internal temperature of the battery. However, this method relies on a large number of calibration tests, which will consume a lot of testing costs and has a long time cycle. In addition, the method of mapping EIS and the internal temperature of the battery based on calibration can usually only determine the mapping relationship for a specific battery cell model. When the battery cell material changes, the calibration test needs to be re-performed, which does not have the universality of the battery. When the battery changes, the calibration test needs to be re-performed, which will increase the testing cost and time cycle.

[0064] In actual application scenarios, the absolute value of the battery temperature is not the most important thing. In most scenarios, it is not necessary to pay attention to the absolute temperature of the battery. The change in the battery temperature trend is the focus that users need to pay most attention to.

[0065] Since the temperature inside the battery usually has a strong linear relationship with the EIS parameter value, by monitoring the changing trend of the EIS parameter value during the static process of the battery and observing whether the trend has stabilized, it is possible to indirectly determine whether the temperature change of the battery is stable. Therefore, the embodiment of the present application uses the impedance value obtained by the EIS test to monitor the changing trend of the impedance during the static process of the battery, and determine whether there is an abnormality in the changing trend of the battery impedance, thereby determining whether there is an abnormality in the changing trend of the battery temperature. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, thereby ensuring the safety of battery application while saving testing costs and shortening the time period.

[0066] It should be noted that the embodiments described in the present application are applied to scenarios of battery temperature change trend monitoring, for example: battery cell temperature thermal runaway monitoring in new energy vehicles or other energy storage scenarios. Thermal runaway is the biggest potential risk for new energy vehicles. Once it occurs, it will have serious consequences. Based on the battery temperature change trend monitoring described in the present application, it is possible to quickly monitor the battery temperature change trend and promptly judge abnormal situations without the need for a large number of calibration tests.

[0067] This application aims to solve the technical problem that it takes a long time to monitor the changes in the internal temperature of the battery. A battery temperature change trend monitoring method is proposed. Please refer to Figure 1 In this example, the battery temperature trend monitoring method includes:

[0068] Step S10, obtaining multiple impedance data of battery cells of the battery pack at a preset frequency.

[0069] It should be noted that the executor of this embodiment may be a battery temperature change trend monitoring device, on which a battery temperature change trend monitoring program runs, and the battery temperature change trend is monitored through the battery temperature change trend monitoring program. It may also be other terminal devices with computing capabilities. This embodiment does not impose any restrictions on this, and takes the battery temperature change trend monitoring device as an example for explanation.

[0070] Generally speaking, the battery pack can be any type of battery pack, and the number of battery cells in the battery pack can be any value greater than 1, which is not limited in this embodiment. The preset frequency refers to a fixed frequency selected according to demand, and its specific value can be flexibly adjusted according to actual demand, which is not limited in this embodiment.

[0071] EIS is a non-destructive parameter determination and effective method for determining battery kinetic behavior. When the battery is subjected to EIS test in a static state, the EI parameter value can be obtained, including at least the phase angle value, the imaginary impedance value and the real impedance value. The impedance data refers to the impedance value in the EI parameter value obtained by the EIS test, including the imaginary impedance value and the real impedance value. The real impedance value usually represents the ohmic impedance, and the imaginary impedance value needs to be converted to the representation of capacitance.

[0072] When selecting impedance data, in addition to the preset frequency, other selection conditions can be set, such as time range, which can be flexibly set according to actual needs, and this embodiment does not limit this. After setting the selection conditions, all impedance data that meet the conditions are selected, and the specific number of impedance data is determined by the actual selection situation.

[0073] Step S20: determining the impedance variation trend of the battery cell according to the plurality of impedance data.

[0074] The impedance change trend of a battery cell refers to the change in the impedance data of the battery cell, that is, the impedance change of the battery cell. Through all the collected impedance data, the impedance change of all the battery cells in the battery pack is determined, and then the impedance change trend is judged to be abnormal according to the impedance change, and then the change in the internal temperature of the battery is determined, and the internal temperature change of the battery is quickly monitored.

[0075] Step S30, determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend.

[0076] The trend status of the impedance change trend refers to whether the impedance change trend is abnormal, that is, whether the overall change of the impedance is abnormal. The battery temperature change trend of the battery pack refers to the change in the temperature inside the battery in the battery pack, usually the overall temperature change of all battery cells. The trend status of the battery temperature change trend refers to whether the battery temperature change trend is abnormal, that is, whether the overall change in the battery internal temperature is abnormal.

[0077] Since the temperature inside the battery usually has a strong linear relationship with the EIS parameter value, by monitoring the changing trend of the EIS parameter value during the static process of the battery and observing whether the trend has stabilized, it is possible to indirectly determine whether the temperature change of the battery is stable. Therefore, it can be considered that the trend state of the battery temperature change trend of the battery pack is consistent with the trend state of the impedance change trend.

[0078] In the technical solution of the embodiment of the present application, the impedance value obtained by the EIS test is used to monitor the impedance change trend of the battery during the static process, and it is determined whether there is an abnormality in the change trend of the battery impedance, thereby determining whether there is an abnormality in the change trend of the battery temperature. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be quickly monitored, and the abnormality of the battery temperature can be discovered in time, thereby ensuring the safety of battery application while saving testing costs and shortening the time period.

[0079] According to some embodiments of the present application, the impedance change trend of a battery cell is determined based on multiple impedance data, including: determining an impedance median corresponding to the multiple impedance data based on the multiple impedance data; normalizing the multiple impedance data based on the impedance median to obtain multiple normalized impedance data corresponding to the multiple impedance data; determining the impedance change trend based on the multiple normalized impedance data.

[0080] The median generally refers to the value in the middle position after multiple data are arranged according to their numerical size. The impedance median refers to the median determined in all impedance data. Normalized impedance data refers to the data obtained after normalization, that is, normalized impedance data. Each normalized impedance data has corresponding normalized impedance data.

[0081] Normalization means taking a certain data as a benchmark, while maintaining the relative relationship between other data and this data, obtaining the comparative relationship between other data and this data, so that all data can be analyzed and processed under the same standard. Normalizing multiple impedance data according to the impedance median, that is, taking the impedance median as a benchmark, comparing all impedance data with the impedance median, normalizing all impedance data, and obtaining normalized impedance data. At this time, the impedance data has been normalized to the same standard, and the overall change of impedance data can be judged based on the normalized impedance data.

[0082] Since the impedance data consists of two parts: the imaginary impedance value and the real impedance value, when calculating the impedance median, the impedance median corresponding to the imaginary impedance value and the real impedance value will be calculated respectively.

[0083] By taking the median of the collected impedance data as a benchmark, all impedance data are normalized, that is, taking the median as a benchmark, all impedance data are median-normalized to obtain normalized impedance data, thereby obtaining the impedance change. It is possible to determine whether there is an abnormality in the impedance change trend to determine whether there is an abnormality in the internal temperature trend of the battery. The internal temperature change of the battery can be quickly obtained, and the internal temperature change of the battery can be quickly monitored to shorten the time period. Moreover, by normalizing the impedance data of the battery cell by the median, it can be ensured that each comparison is based on the same battery pack and will not be limited to a specific battery cell type, thereby reducing the impact of different types of battery cells on the data, improving the versatility of application, and ensuring the reliability and accuracy of monitoring. When the battery changes, no additional calibration test is required, which saves test costs and shortens the overall monitoring time period.

[0084] According to some embodiments of the present application, the impedance median corresponding to the multiple impedance data is determined based on multiple impedance data, including: dividing the multiple impedance data into at least one first dimensional data set according to time information corresponding to the multiple impedance data, the time information corresponding to the impedance data in the first dimensional data set is the same; based on the impedance data in the first dimensional data set, determining the impedance median corresponding to the first dimensional data set.

[0085] The time information of the impedance data refers to the time point when the impedance data was generated, for example: the current impedance data, the impedance data 5 seconds ago, determined according to the actual situation. The first dimensional data set refers to the data set obtained by dividing the impedance data according to the time point when the impedance data was generated. Since the acquired impedance data is multiple and has at least one generated time point, at least one first dimensional data set can be divided. The generation time point of all impedance data in the first dimensional data set is the same. If the multiple impedance data collected have different generation time points, they need to be divided into different first dimensional data sets.

[0086] After the first-dimensional data set is divided, it can be considered that the impedance data of all battery cells at the same time are divided into the same set. The median impedance at this time can be considered as the data in the set with a high probability of being under normal temperature conditions, and normalization is performed based on this. If there are multiple first-dimensional data sets obtained by division, the corresponding median impedance is determined separately for each first-dimensional data set, so that the impedance data can be normalized more accurately. Please refer to Figure 2 , j is the time dimension, usually used to represent the time point, i is another dimension, usually used to represent the number of the battery cell, which can be divided into j first dimension data sets, X 1,j ~X i,j is the data in the jth first dimension data set, a1~aj are the median impedances in the 1st to jth first dimension data sets respectively.

[0087] By taking the impedance data of all battery cells at the same time as a group, calculating the corresponding median, and normalizing it based on the battery cell corresponding to the median, we ensure that each comparison is based on the same battery pack and is not limited to a specific battery cell type. This reduces the impact of different types of battery cells on the data, improves the versatility of application, ensures the reliability and accuracy of monitoring, and when the battery changes, there is no need for additional calibration testing, which saves testing costs and shortens the overall monitoring period.

[0088] According to some embodiments of the present application, before dividing the multiple impedance data into at least one first dimensional data set according to time information corresponding to the multiple impedance data, it also includes: dividing the multiple impedance data into at least one second dimensional data set according to numbering information corresponding to the impedance data, the numbering information corresponding to the impedance data in the second dimensional data set is the same; screening out impedance abnormal data in the second dimensional data set, and eliminating the impedance abnormal data.

[0089] Each battery cell has its own corresponding number. The number information of the impedance data refers to the battery cell number of the impedance data. The impedance data of the same battery cell has the same battery cell number. The second dimensional data set refers to the data set obtained by dividing the impedance data according to the battery cell number of the impedance data. Since the acquired impedance data is multiple and has at least one battery cell number, at least one second dimensional data set can be divided. The battery cell numbers of all impedance data in the second dimensional data set are the same, that is, the impedance data of the same battery cell at different time points are divided into the same set. If the multiple impedance data collected have different battery cell numbers, they need to be divided into different second dimensional data sets.

[0090] Impedance abnormal data refers to abnormal data in impedance data, that is, abnormal values. The initially collected impedance data is not processed, and there may be some abnormal values ​​caused by interference during collection. If these abnormal values ​​are retained, it is very likely to affect the final monitoring results. Therefore, these abnormal values ​​need to be removed to ensure the accuracy of the final monitoring results obtained using impedance data and reduce the probability of misjudgment. Please refer to Figure 3 , i is the battery cell dimension, usually used to indicate the battery cell number, j is another dimension, usually used to indicate the time point, which can be divided into i second dimension data sets, Y i,1 ~Y i,j is the data in the i-th second dimension data set, b1~b iThey are the impedance abnormality data in the 1st to i-th second dimensional data sets respectively. The number of impedance abnormality data in each second dimensional data set is determined according to actual conditions, and this embodiment does not impose any limitation thereto.

[0091] Since the impedance data consists of two parts: the imaginary impedance value and the real impedance value, when outliers are removed, the imaginary impedance value and the real impedance value will be removed separately.

[0092] By taking the impedance data of the same battery cell at all time points as a group, filtering out the outliers in the impedance data in each group, removing the outliers, and normalizing the data after removing the outliers, the accuracy of the normalization can be guaranteed, thereby ensuring the accuracy of the obtained change trend, and then ensuring the accuracy of the trend status judgment, thereby improving the reliability and accuracy of monitoring.

[0093] According to some embodiments of the present application, impedance abnormality data is screened out in the second dimensional data set and the impedance abnormality data is eliminated, including: determining an impedance abnormality threshold corresponding to the second dimensional data set based on the impedance data in the second dimensional data set; determining the impedance abnormality data in the second dimensional data set based on the impedance abnormality threshold corresponding to the second dimensional data set, and the impedance abnormality data in the second dimensional data set is greater than the impedance abnormality threshold of the second dimensional data set.

[0094] The impedance abnormality threshold refers to the calculated normal range limit value, which is usually the upper limit value and can be determined by any abnormal value identification method, such as the quantile identification method, which is not limited in this embodiment. According to the calculated normal range limit value, data exceeding the normal range limit value can be found, which is the abnormal value that needs to be eliminated.

[0095] By determining the limit value of the normal range for each set of data, those exceeding the limit value are considered to be outliers, all outliers in each set of data are identified, outliers caused by interference during collection are eliminated, and the data after eliminating the outliers are normalized. This can ensure the accuracy of normalization, thereby ensuring the accuracy of the obtained change trend, and then ensuring the accuracy of the trend status judgment, thereby improving the reliability and accuracy of monitoring.

[0096] According to some embodiments of the present application, the impedance anomaly threshold corresponding to the second dimensional data set is determined based on the impedance data in the second dimensional data set, including: sorting the impedance data in the second dimensional data set in ascending order to obtain an impedance data sequence corresponding to the second dimensional data set; determining a quantile in the impedance data sequence; and determining the impedance anomaly threshold of the second dimensional data set based on the quantile corresponding to the second dimensional data set.

[0097] The impedance data sequence refers to the data sequence obtained by arranging the impedance data in the second dimension data set from small to large. Quantile refers to the numerical point that divides the probability distribution range of a data into several equal parts, such as median (i.e., quantile), quartile, percentile, etc. Quantiles can be used to identify outliers in the data.

[0098] The impedance data in the second dimensional data set are sorted to obtain the corresponding quantiles, and the outliers in each second dimensional data set are screened out according to the quantiles corresponding to each second dimensional data set, so as to be eliminated.

[0099] By estimating the normal range limit value of each group of data through quantiles, taking it as the threshold, identifying the outliers in each group of data, and removing the outliers. Normalizing the data after removing the outliers can ensure the accuracy of normalization, thereby ensuring the accuracy of the obtained changing trend, and then ensuring the accuracy of the trend status judgment, improving the reliability and accuracy of monitoring, and using quantiles to identify outliers can ensure the accuracy of outlier identification, further improve the accuracy of normalization, and further improve the reliability and accuracy of monitoring.

[0100] According to some embodiments of the present application, the impedance anomaly threshold of the second dimensional data set is determined according to the quantile corresponding to the second dimensional data set, including: obtaining a first corresponding relationship between the lower quartile, the upper quartile, the anomaly coefficient and the impedance anomaly threshold; obtaining the impedance anomaly threshold of the second dimensional data set according to the anomaly coefficient, the first corresponding relationship, and the upper quartile and the lower quartile corresponding to the second dimensional data set.

[0101] In this embodiment, the quantiles used are the lower quartile and the upper quartile. The impedance data in the second dimension data set are arranged from small to large and divided into four equal parts. The values ​​at the three division points are the quartiles. Please refer to Figure 4 The lower quartile is the value at the one-quarter split point (Q1), and the upper quartile is the value at the three-quarter split point (Q3). The abnormal coefficient refers to the coefficient used when calculating the limit value of the normal range, and is usually 1.5 or 3.

[0102] The first corresponding relationship refers to the estimated relationship of the impedance abnormality threshold, as shown below:

[0103] Impedance abnormality threshold = Q3 + k * (Q3-Q1)

[0104] In the formula, Q1 represents the lower quartile, Q3 represents the upper quartile, and k represents the anomaly coefficient. When k = 3, the impedance anomaly threshold represents an extreme anomaly value, and when k = 1.5, the impedance anomaly threshold represents a moderate anomaly value. It can be seen that when k = 3 is used, only the anomalies with more serious anomalies in the impedance data can be screened out, and the anomalies with ordinary anomalies cannot be screened out. Therefore, k = 1.5 can be used to calculate the impedance anomaly threshold.

[0105] The lower quartile and upper quartile are used to estimate the limit of the normal range of each group of data, so as to identify the outliers in each group of data and eliminate them. The use of quantiles to identify outliers can ensure the accuracy of outlier identification. The data after eliminating outliers can be normalized to improve the accuracy of normalization and the reliability and accuracy of monitoring.

[0106] According to some embodiments of the present application, before dividing multiple impedance data into at least one second dimensional data set according to the numbering information corresponding to the impedance data, it also includes: combining the multiple impedance data according to the time information and numbering information corresponding to the multiple impedance data to obtain an impedance array, wherein the first dimension of the impedance array represents the time information, and the first dimension of the impedance array represents the numbering information.

[0107] The impedance array refers to a two-dimensional array that combines all impedance data according to time information and number information. The two-dimensional array has two dimensions, the first dimension represents time information, and the second dimension represents number information.

[0108] Since the impedance data consists of two parts: the imaginary impedance value and the real impedance value, the impedance array obtained by combining the impedance data has two, namely the imaginary impedance array and the real impedance array. The imaginary impedance array is composed of the imaginary impedance values, and the real impedance array is composed of the real impedance values. Please refer to Figure 5 The imaginary impedance value and the real impedance value are combined to obtain the imaginary impedance array and the real impedance array respectively. The two impedance arrays are subjected to outlier removal, impedance median determination and normalization respectively to obtain the corresponding impedance normalized data respectively, and the data change trend is obtained respectively, so as to judge the overall impedance change trend, and then judge the trend state of the temperature change trend.

[0109] By combining all impedance data into a two-dimensional array according to time information and number information, when dividing the impedance data, according to the two dimensions of the array, the data sets corresponding to different time information can be quickly and accurately divided, and the data sets corresponding to different number information can also be quickly and accurately divided, thereby ensuring the efficiency and accuracy of data division, improving the accuracy of normalization, thereby improving the accuracy of the obtained change trend, and then improving the accuracy of trend status judgment, improving the reliability and accuracy of monitoring, and shortening the normalization time period, thereby shortening the overall monitoring time period.

[0110] According to some embodiments of the present application, multiple impedance data are normalized according to the impedance median to obtain multiple normalized impedance data corresponding to the multiple impedance data, including: determining the median difference corresponding to each impedance data in the first dimensional data set according to the impedance data and the impedance median of the first dimensional data set; obtaining a second corresponding relationship between the impedance median, the median difference and the normalized impedance data; and obtaining the normalized impedance data corresponding to each impedance data in the first dimensional data set according to the two corresponding relationships and the median difference corresponding to each impedance data in the first dimensional data set and the impedance median.

[0111] The median difference corresponding to the impedance data refers to the difference between the impedance data and the corresponding impedance median, and each first-dimensional data set uses the impedance median of the first-dimensional data set to determine the difference.

[0112] The second corresponding relationship refers to the calculation relationship adopted by normalization in this embodiment, which is as follows:

[0113]

[0114] Where V represents the impedance data, V med Indicates the median impedance, V nor It represents normalized impedance data, and uses the calculation formula of the second corresponding relationship to calculate the normalized impedance data of all impedance data in each first dimensional data set to achieve normalization of all impedance data.

[0115] In this embodiment, the imaginary impedance value and the real impedance value respectively determine the corresponding impedance median, and the impedance median is used for normalization to obtain the corresponding normalized impedance data.

[0116] By obtaining the median impedance, the difference between the impedance data and the median impedance in each group of data is calculated, and all the impedance data are normalized using the difference between the impedance data and the median impedance. When normalizing, the median of each group is used as the benchmark, that is, the battery cell corresponding to the median is used as the benchmark to normalize the impedance data of all battery cells, ensuring that each comparison is based on the same battery pack and will not be limited to a specific battery cell type, reducing the impact of different types of battery cells on the data, improving the versatility of application, ensuring the reliability and accuracy of monitoring, and when the battery changes, no additional calibration test is required, saving testing costs while shortening the overall monitoring time period.

[0117] According to some embodiments of the present application, an impedance change trend is determined based on multiple normalized impedance data, including: dividing the multiple normalized impedance data into at least one normalized data set according to the numbering information corresponding to the multiple normalized impedance data, the numbering information corresponding to the normalized impedance data in the normalized data set is the same; dividing the normalized data set into multiple normalized subsets according to a preset number of equal divisions, and determining the slope of each normalized subset; combining the slopes of the multiple normalized subsets corresponding to the normalized data set to obtain a slope array corresponding to the normalized data set, the slope array reflecting the impedance change trend corresponding to the normalized data set.

[0118] Normalized impedance data is data obtained after impedance data is normalized. Impedance data has a corresponding battery cell number, and normalized impedance data also has the battery cell number, that is, the battery cell number of the normalized impedance data is the battery cell number of the impedance data corresponding to the normalized impedance data. A normalized data set refers to a data set obtained by dividing the normalized impedance data according to the battery cell number of the normalized impedance data. Since there are multiple impedance data, there are also multiple corresponding normalized impedance data, at least one battery cell number, so at least one normalized data set can be divided. The battery cell numbers of all normalized impedance data in the normalized data set are the same, that is, the normalized impedance data of the same battery cell at different time points are divided into the same set. If the normalized impedance data has different battery cell numbers, it needs to be divided into different normalized data sets.

[0119] The preset number of equal parts refers to the number of equal parts that the normalized data set is divided into, for example, 5 or 10. Usually, the preset number of equal parts needs to be greater than or equal to 5. If the preset number of equal parts is greater than or equal to 10, it is necessary to ensure that each equal part has at least 10 data after the division, so as to ensure that the final result is better. The normalized subset refers to the data set obtained by dividing the normalized data set into equal parts according to the preset number of equal parts. Since the normalized subset is obtained by equal division, the number of data in each normalized subset obtained by dividing the normalized data set is the same. Please refer to Figure 6 , Z i，j represents the normalized impedance data, i is the battery cell dimension, usually used to represent the battery cell number, j is the time dimension, usually used to represent the time point, the normalized impedance data can be divided into i normalized data sets, Z i represents the i-th normalized data set, each normalized data set is divided into n normalized subsets, z i，1 ~z i，n They respectively represent the 1st to nth normalized subsets of the ith normalized data set, and n represents the preset number of equal divisions.

[0120] Each normalized subset is linearly calculated to obtain the slope corresponding to each normalized subset. The slopes of the normalized subsets obtained by dividing each normalized data set are combined to form an array, namely the slope array. Each normalized data set can obtain the corresponding slope array. Please refer to Figure 6 , l i，1 ~l i，n They represent the 1st to nth slopes of the normalized subset corresponding to the ith normalized data set, L i Represents the slope array composed of all slopes in the i-th normalized data set in order. Since the normalized data set is divided according to the battery cell number, the slope arrays corresponding to different normalized data sets can be considered as the slope arrays corresponding to different battery cells. The multiple slopes in the slope array reflect the change trend of the data in the set, which is equivalent to the change trend of the impedance data of each battery cell.

[0121] In this embodiment, the changing trend of the impedance data includes the changing trend of the real impedance value and the changing trend of the imaginary impedance value. According to the changing trend of the real impedance value and the changing trend of the imaginary impedance value, the trend state of the changing trend of the real impedance value and the trend state of the changing trend of the imaginary impedance value can be determined respectively.

[0122] The normalized impedance data of all time dimensions of the same battery cell are divided into the same set, and each set is further divided into multiple subsets, so that the number of data in the subsets obtained by dividing each set is the same, and a linear slope calculation is performed on the data of each subset, and the slopes calculated from the subsets are combined into an array in chronological order, that is, the slope array of each set. The multiple slopes in the slope array reflect the changing trend of the data in the set, which is equivalent to the changing trend of the impedance data of each battery cell, so as to judge the trend state of the changing trend, and then obtain the trend state of the changing trend of the internal temperature of the battery. It does not need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between EIS parameter values ​​and temperature. It can quickly obtain the changes in the internal temperature of the battery, quickly realize the monitoring of the internal temperature changes of the battery, and promptly discover the abnormality of the battery temperature, thereby ensuring the safety of battery application while saving testing costs and shortening the time cycle.

[0123] According to some embodiments of the present application, before determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend, it also includes: dividing the slope array into a first slope data set and a second slope data set according to a preset data quantity; determining the slope average according to the first slope data set, and determining the slope median according to the second slope data set; determining the trend state of the impedance change trend according to the slope average and the slope median.

[0124] The preset data quantity refers to a set data quantity used to divide the data in the slope array into two sets, namely the first slope data set and the second slope data set. Usually, the first slope data set is divided from the first data according to the preset data quantity, and the remaining data is divided into the second slope data set. If the first slope data set is the first 70% in the slope array, the preset data quantity is the slope array quantity * 0.7. In practical applications, the preset data quantity can be expressed by a ratio, for example: 0.7. When dividing the first slope data set and the second slope data set, the array can be divided according to the ratio and the number of data in the current slope array.

[0125] The slope mean refers to the average of all data in the first slope data set, and the slope median refers to the median of all data in the second slope data set. Figure 7 , assuming that there are n slopes in the slope array, the slope array is divided according to the preset number of data m, the first m data are used to calculate the average slope, and the last nm data are used to calculate the median slope, where L i represents the i-th slope array, l i，1 ~l i，nThey represent the 1st to nth slopes in the i-th slope array respectively. The first slope data set calculates the average of the data as the basis for judgment, and the second slope data set calculates the median of the data as the data used for judgment, thereby judging the trend state of the impedance change trend.

[0126] By dividing the slope data in the slope array into two data sets, the first data set calculates the average of the data as the basis for judgment, and the second data set calculates the median of the data as the data used for judgment, so as to judge the trend state of the impedance change trend, and then obtain the trend state of the change trend of the internal temperature of the battery. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, ensuring the safety of battery application while saving testing costs and shortening the time period.

[0127] According to some embodiments of the present application, the trend state of the impedance change trend is determined according to the slope average and the slope median, including: determining the trend abnormality threshold according to the slope average and a preset multiple; when the slope median is greater than or equal to the trend abnormality threshold, determining that the trend state of the impedance change trend is an abnormal trend state; when the slope median is less than the trend abnormality threshold, determining that the trend state of the impedance change trend is a normal trend state.

[0128] The preset multiple refers to the set multiple value, and the trend abnormality threshold is the threshold used to determine whether the trend state is abnormal. The trend abnormality threshold is calculated using the preset multiple and the calculated slope average. Each slope array can calculate the corresponding trend abnormality threshold, that is, each battery cell has a corresponding trend abnormality threshold.

[0129] An abnormal trend state means that the trend state is abnormal, and an abnormal trend state means that the trend state is not abnormal. If the median slope of the slope array exceeds the trend abnormality threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is abnormal, that is, the trend state of the impedance change trend of the corresponding battery cell is abnormal. If the median slope of the slope array is lower than the trend abnormality threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is not abnormal, that is, the trend state of the impedance change trend of the corresponding battery cell is not abnormal. When the trend state of the impedance change trend is not abnormal, the specific situation can be further determined. Similarly, when the trend state of the impedance change trend is abnormal, the specific abnormal situation can be further determined. This embodiment does not limit this.

[0130] In this embodiment, the trend state of the change trend of the real impedance value and the change trend of the imaginary impedance value needs to be determined separately. When one of the change trends is in an abnormal trend state, the change trend of the battery cell is considered to be in an abnormal trend state. If the impedance change trend of all battery cells in the battery pack is in a normal trend state, the overall impedance change trend is considered to be in a normal trend state. If the impedance change trend of at least one battery cell in the battery pack is in an abnormal trend state, the overall impedance change trend is considered to be in an abnormal trend state.

[0131] The threshold for judging whether the trend state is abnormal is calculated by the obtained slope average of the slope array. If the median slope of the slope array exceeds the threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is abnormal. If the median slope of the slope array is lower than the threshold, it is considered that the trend state of the impedance change trend reflected by the slope array is not abnormal. Then, according to the obtained trend state of the impedance change trend, it is determined whether the trend state of the battery temperature change trend is abnormal. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be monitored quickly, and the abnormality of the battery temperature can be discovered in time, which ensures the safety of battery application while saving testing costs and shortening the time period.

[0132] According to some embodiments of the present application, the trend state of the battery temperature change trend of the battery pack is determined according to the trend state of the impedance change trend, including: when the trend state of the impedance change trend is an abnormal trend state, determining that the trend state of the battery temperature change trend is an abnormal trend state; when the trend state of the impedance change trend is a normal trend state, determining that the trend state of the battery temperature change trend is a normal trend state.

[0133] The trend state of the battery temperature change trend of the battery pack is consistent with the trend state of the impedance change trend. If the trend state of the overall impedance change trend is an abnormal trend state, then the trend state of the battery temperature change trend is an abnormal trend state. If the trend state of the overall impedance change trend is a normal trend state, then the trend state of the battery temperature change trend is a normal trend state.

[0134] By judging the trend state of the impedance change trend and the trend state of the battery temperature change trend, the change of the internal temperature of the battery can be quickly judged, the internal temperature change of the battery can be quickly monitored, and the abnormal battery temperature can be discovered in time to ensure the safety of the battery during application, while saving testing costs and shortening the time period.

[0135] According to some embodiments of the present application, multiple impedance data of battery cells in a battery pack at a preset frequency are obtained, including: determining a selection time range based on the current time and a preset selection requirement, the preset selection requirement being any one of a preset selection time and a preset selection quantity; and obtaining multiple impedance data of battery cells in the battery pack based on the preset frequency and the selection time range.

[0136] The preset selection requirement refers to the requirement for selecting impedance data, for example, selecting the latest data within 1 hour. The requirement can be in the form of a preset selection time and a preset selection quantity. The preset selection time refers to the set selection time, for example, 1 hour, 50 minutes, and the preset selection quantity refers to the set selection quantity, for example, 100. The current time is the current time point, and the selection time range is the time range used when selecting impedance data. According to the current time and the preset selection time, the selection time range can be determined. For example, if the preset selection time is 1 hour, the selection time range is within 1 hour based on the current time. Since the impedance data has a corresponding generation time point, the time point boundary can also be calculated according to the preset selection quantity, so as to define the selection time range according to the current time. For example, if the preset selection quantity is 100, the 100th time point is calculated forward according to the current time, and the selection time range is from the current time to the 100th time point. The acquired impedance data includes real impedance value and imaginary impedance value. Each battery cell in the battery pack needs to obtain the corresponding real impedance value and imaginary impedance value respectively.

[0137] By acquiring the impedance data of all battery cells in the entire battery pack within a recent period of time and at a certain frequency, it is determined whether there is an abnormality in the change trend of the battery impedance based on the collected impedance data, thereby determining whether there is an abnormality in the change trend of the battery temperature. The change of the internal temperature of the battery can be quickly obtained, and the internal temperature change of the battery can be quickly monitored. Abnormalities in the battery temperature can be discovered in time to ensure the safety of the battery during application, while saving testing costs and shortening the time period. In addition, data selection is equivalent to a sliding window selection method. Each time, the most recent data is selected, so that monitoring is not limited to specific battery cells and specific models, which improves the versatility during application and ensures the reliability and accuracy of monitoring.

[0138] According to some embodiments of the present application, after obtaining multiple impedance data of battery cells of a battery pack at a preset frequency, it also includes: obtaining multiple current data corresponding to the multiple impedance data; when current data greater than a threshold current exists in the multiple current data, returning to execute the step of obtaining multiple impedance data of battery cells of the battery pack at the preset frequency; when current data greater than the threshold current does not exist in the multiple current data, executing the step of determining the impedance change trend of the battery cell based on the multiple impedance data.

[0139] Since the current fluctuation will affect the impedance data of the battery cell, when using impedance data to monitor the internal temperature of the battery cell, it is necessary to judge the relevant current fluctuation. The threshold current is a set threshold value used to judge the current fluctuation. If there is no current exceeding the threshold, the current data is considered to be relatively stable. The impedance data can be used to monitor the changes in the internal temperature of the battery. If there is a current exceeding the threshold, it is considered that the current data fluctuates greatly. At this time, the current has a greater interference with the impedance data, and the impedance data will be greatly affected. The impedance data should be re-acquired.

[0140] By collecting the current data corresponding to each impedance data, the fluctuation of these current data is judged, so as to judge whether the current interferes with the impedance data. If there is a current exceeding the threshold, it is considered that the current data fluctuates greatly. At this time, the current interferes with the impedance data greatly, and the impedance data will be greatly affected. The collected impedance data is not suitable for monitoring the changes in the internal temperature of the battery and needs to be re-acquired to improve the accuracy of the obtained change trend, thereby improving the accuracy of the trend status judgment and improving the reliability and accuracy of monitoring.

[0141] According to some embodiments of the present application, when new impedance data of a battery cell in a battery pack exists, a step of acquiring a plurality of impedance data of the battery cell of the battery pack at a preset frequency is performed.

[0142] By timely acquiring the latest impedance data, the latest impedance data can be used to determine whether there is any abnormality in the change trend of the battery impedance, thereby determining whether there is any abnormality in the change trend of the battery temperature, thereby improving the reliability and accuracy of monitoring.

[0143] In addition, an embodiment of the present invention further proposes a storage medium, on which a battery temperature change trend monitoring program is stored. When the battery temperature change trend monitoring program is executed by a processor, the steps of the battery temperature change trend monitoring method described above are implemented.

[0144] To achieve the above purpose, the present invention also provides a battery temperature change trend monitoring device, please refer to Figure 8 In this example, the battery temperature change trend monitoring device includes:

[0145] An acquisition module 10 is used to acquire multiple impedance data of battery cells of a battery pack at a preset frequency;

[0146] A processing module 20, configured to determine an impedance change trend of the battery cell according to the plurality of impedance data;

[0147] The monitoring module 30 is used to determine the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend.

[0148] In the technical solution of the embodiment of the present application, the impedance value obtained by the EIS test is used to monitor the impedance change trend of the battery during the static process, and it is determined whether there is an abnormality in the change trend of the battery impedance, thereby determining whether there is an abnormality in the change trend of the battery temperature. There is no need to spend a lot of time and cost on calibration tests to obtain the mapping relationship between the EIS parameter value and the temperature. The change of the internal temperature of the battery can be quickly obtained, the internal temperature change of the battery can be quickly monitored, and the abnormality of the battery temperature can be discovered in time, thereby ensuring the safety of battery application while saving testing costs and shortening the time period.

[0149] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0150] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0151] In addition, for technical details not fully described in this embodiment, reference may be made to the battery temperature change trend monitoring method provided in any embodiment of the present invention, and will not be repeated here.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be included in the scope of the claims and specification of the present application. In particular, as long as there is no structural conflict, the various technical features mentioned in the various embodiments can be combined in any way. The present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions that fall within the scope of the claims.

Claims

1. A method for monitoring battery temperature change trend, characterized in that: The battery temperature change trend monitoring method comprises: Acquire multiple impedance data of battery cells of a battery pack at a preset frequency; Determining an impedance change trend of the battery cell according to the plurality of impedance data; The trend state of the battery temperature change trend of the battery pack is determined according to the trend state of the impedance change trend.

2. The method according to claim 1, characterized in that The step of determining the impedance change trend of the battery cell according to the plurality of impedance data includes: Determine, according to the plurality of impedance data, an impedance median corresponding to the plurality of impedance data; Normalizing the plurality of impedance data according to the impedance median to obtain a plurality of normalized impedance data corresponding to the plurality of impedance data; The impedance variation trend is determined according to the plurality of normalized impedance data.

3. The method according to claim 2, characterized in that Determining the impedance median corresponding to the plurality of impedance data according to the plurality of impedance data includes: According to the time information corresponding to the multiple impedance data, the multiple impedance data are divided into at least one first dimensional data set, and the time information corresponding to the impedance data in the first dimensional data set is the same; According to the impedance data in the first dimensional data set, an impedance median corresponding to the first dimensional data set is determined.

4. The method according to claim 3, characterized in that Before dividing the plurality of impedance data into at least one first dimensional data set according to the time information corresponding to the plurality of impedance data, the method further includes: According to the numbering information corresponding to the impedance data, the plurality of impedance data are divided into at least one second dimensional data set, wherein the numbering information corresponding to the impedance data in the second dimensional data set is the same; The impedance abnormal data is screened out in the second dimensional data set, and the impedance abnormal data is eliminated.

5. The method according to claim 4, characterized in that The step of screening out the impedance abnormal data in the second dimensional data set and removing the impedance abnormal data includes: Determine, according to the impedance data in the second dimensional data set, an impedance anomaly threshold corresponding to the second dimensional data set; According to the impedance abnormality threshold corresponding to the second dimensional data set, the impedance abnormality data in the second dimensional data set is determined, and the impedance abnormality data in the second dimensional data set is greater than the impedance abnormality threshold of the second dimensional data set.

6. The method according to claim 5, characterized in that The step of determining the impedance abnormality threshold value corresponding to the second dimensional data set according to the impedance data in the second dimensional data set includes: Sorting the impedance data in the second dimensional data set in ascending order to obtain an impedance data sequence corresponding to the second dimensional data set; determining quantiles in the impedance data series; An impedance anomaly threshold of the second dimensional data set is determined according to the quantile corresponding to the second dimensional data set.

7. The method according to claim 6, characterized in that The quantile includes an upper quartile and a lower quartile, and determining the impedance anomaly threshold of the second dimensional data set according to the quantile corresponding to the second dimensional data set includes: Obtaining the first correspondence between the lower quartile, the upper quartile, the anomaly coefficient and the impedance anomaly threshold; The impedance anomaly threshold of the second dimensional data set is obtained according to the anomaly coefficient, the first corresponding relationship, and the upper quartile and the lower quartile corresponding to the second dimensional data set.

8. The method according to claim 4, characterized in that Before dividing the plurality of impedance data into at least one second dimensional data set according to the number information corresponding to the impedance data, the method further includes: According to the time information and number information corresponding to the multiple impedance data, the multiple impedance data are combined to obtain an impedance array, wherein the first dimension of the impedance array represents the time information, and the first dimension of the impedance array represents the number information.

9. The method according to claim 3, characterized in that The step of normalizing the plurality of impedance data according to the impedance median to obtain a plurality of normalized impedance data corresponding to the plurality of impedance data includes: Determine, according to the impedance data and the impedance median of the first dimensional data set, a median difference corresponding to each impedance data in the first dimensional data set; Obtaining a second corresponding relationship between the impedance median, the median difference and the normalized impedance data; According to the two corresponding relationships and the median difference and the impedance median corresponding to each impedance data in the first dimensional data set, the normalized impedance data corresponding to each impedance data in the first dimensional data set is obtained.

10. The method according to claim 2, characterized in that Determining the impedance change trend according to the plurality of normalized impedance data includes: According to the numbering information corresponding to the multiple normalized impedance data, the multiple normalized impedance data are divided into at least one normalized data set, and the numbering information corresponding to the normalized impedance data in the normalized data set is the same; Dividing the normalized data set into a plurality of normalized subsets according to a preset number of equal divisions, and determining the slope of each of the normalized subsets; The slopes of the multiple normalized subsets corresponding to the normalized data set are combined to obtain a slope array corresponding to the normalized data set, and the slope array reflects the impedance change trend corresponding to the normalized data set.

11. The method according to claim 10, characterized in that Before determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend, the method further includes: According to a preset data quantity, the slope array is divided into a first slope data set and a second slope data set; Determining a slope mean based on the first slope data set, and determining a slope median based on the second slope data set; The trend state of the impedance change trend is determined according to the slope average and the slope median.

12. The method according to claim 11, characterized in that Determining the trend state of the impedance change trend according to the slope average and the slope median includes: Determine a trend abnormality threshold value according to the slope average and a preset multiple; When the slope median is greater than or equal to the trend abnormality threshold, determining that the trend state of the impedance change trend is an abnormal trend state; When the slope median is less than the trend abnormality threshold, it is determined that the trend state of the impedance change trend is a normal trend state.

13. The method according to any one of claims 1 to 12, characterized in that The determining the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend includes: When the trend state of the impedance change trend is an abnormal trend state, determining that the trend state of the battery temperature change trend is an abnormal trend state; When the trend state of the impedance change trend is a normal trend state, it is determined that the trend state of the battery temperature change trend is a normal trend state.

14. The method according to any one of claims 1 to 13, characterized in that The step of obtaining a plurality of impedance data of battery cells of the battery pack at a preset frequency includes: Determine the selection time range according to the current time and the preset selection requirement, where the preset selection requirement is any one of the preset selection time and the preset selection quantity; According to the preset frequency and the selected time range, a plurality of impedance data of battery cells in the battery pack are obtained.

15. The method according to any one of claims 1 to 14, characterized in that After obtaining a plurality of impedance data of battery cells of the battery pack at a preset frequency, the method further includes: Acquire a plurality of current data corresponding to the plurality of impedance data; When there is current data greater than a threshold current in the plurality of current data, returning to the step of acquiring a plurality of impedance data of battery cells of the battery pack at a preset frequency; When there is no current data greater than a threshold current among the plurality of current data, a step of determining an impedance change trend of the battery cell according to the plurality of impedance data is performed.

16. The method according to any one of claims 1 to 15, characterized in that The battery temperature change trend monitoring method further includes: When new impedance data of the battery cells in the battery pack exists, a step of acquiring a plurality of impedance data of the battery cells of the battery pack at a preset frequency is performed.

17. A battery temperature trend monitoring device, characterized in that: The battery temperature change trend monitoring includes: An acquisition module, used to acquire multiple impedance data of battery cells of a battery pack at a preset frequency; A processing module, used for determining the impedance change trend of the battery cell according to the plurality of impedance data; A monitoring module is used to determine the trend state of the battery temperature change trend of the battery pack according to the trend state of the impedance change trend.

18. A battery temperature trend monitoring device, characterized in that: The device comprises: a memory, a processor, and a battery temperature change trend monitoring program stored in the memory and executable on the processor, wherein the battery temperature change trend monitoring program is configured to implement the steps of the battery temperature change trend monitoring method as described in any one of claims 1 to 16.

19. A storage medium, characterized in that: The storage medium stores a battery temperature change trend monitoring program, and when the battery temperature change trend monitoring program is executed by the processor, the steps of the battery temperature change trend monitoring method according to any one of claims 1 to 16 are implemented.

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