Lithium battery power supply module operation monitoring method and system

Abstract

The invention relates to the technical field of data processing, in particular to a lithium battery power module operation monitoring method and system, and the method comprises the steps: constructing a fault propagation chain of each fault type; obtaining an abnormal value sequence corresponding to the real-time data of each parameter, and monitoring the abnormal rising trend of each parameter in real time to construct a real-time propagation chain; and comparing the real-time propagation chain with the fault propagation chain of each fault type to obtain the probability of each fault type at any moment. Through the technical scheme of the invention, the successive change of each parameter before the fault occurs can be considered, and the operation condition of the lithium battery power supply module can be accurately obtained.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for monitoring the operation of a lithium battery power module. Background Art

[0002] Lithium batteries are rechargeable batteries favored by users for their relative lightness, high energy density, fast charging speed, and lack of memory effect. They are used as power modules in new energy vehicles and electric bicycles. However, due to their highly reactive chemical composition, lithium batteries are susceptible to thermal runaway and fire caused by electrical, thermal, and mechanical abuse. If early warning alarms are not promptly issued, these can pose a serious threat to human life and surrounding vehicles, necessitating operational monitoring of lithium battery power modules.

[0003] At present, the patent application document with application publication number CN115792629A discloses an alarm monitoring system and method for lithium battery energy storage, wherein the method includes: real-time collection of operating data of the lithium battery energy storage process, and uploading the leakage detection data, temperature detection data, environmental humidity detection data and charge and discharge detection data of the lithium battery energy storage process to an operation control platform; the operation control platform compares the leakage detection data, temperature detection data, environmental humidity detection data and charge and discharge detection data with their corresponding preset threshold parameters respectively, and when the value of any data parameter among the leakage detection data, temperature detection data, environmental humidity detection data and charge and discharge detection data exceeds its corresponding preset threshold parameter, the operation of the lithium battery is evaluated using a comprehensive evaluation model to obtain an evaluation score; the evaluation score is compared with a preset evaluation score threshold, and when the evaluation score is lower than the preset evaluation score threshold, it is determined that the current lithium battery operation is abnormal; and the lithium battery operation and alarm are remotely controlled based on the judgment result of whether the current lithium battery operation is normal.

[0004] The above method comprehensively evaluates the operation process of the lithium battery by comparing the detection results of various parameters during the lithium battery energy storage process with the threshold parameters, and then determines whether the current lithium battery operation is normal. However, when a lithium battery fails, not all parameter detection results will exceed the threshold parameters. Often, a certain parameter becomes abnormal first, which in turn leads to a series of parameter changes. The above method ignores the changes in various parameters before the failure and simply compares the detection results of various parameters with the threshold parameters. It is impossible to accurately obtain the operating status of the lithium battery power module. Summary of the Invention

[0005] In order to solve the technical problem of being unable to accurately obtain the operating status of the lithium battery power module, the present application provides a lithium battery power module operation monitoring method and system, which can take into account the successive changes of various parameters before the fault occurs and accurately obtain the operating status of the lithium battery power module.

[0006] In a first aspect, the present application provides a method for monitoring the operation of a lithium battery power module, the monitoring method comprising: constructing a fault propagation chain for each fault type, comprising: performing anomaly detection on the time series of each parameter in the historical data of any fault type to obtain an abnormal value sequence of each parameter; calculating the abnormal rising trend at each moment in any abnormal value sequence; connecting each parameter in order from small to large according to the abnormal moment of each parameter, the abnormal moment being the moment when the abnormal rising trend first exceeds a threshold; using the abnormal interval and trend increment as the edge weight of each parameter other than the first parameter to obtain the fault propagation chain of the fault type; the abnormal interval and trend increment are respectively the interval between the abnormal moments of any parameter and the previous adjacent parameter, and the difference between the abnormal rising trends at the abnormal moments; obtaining the abnormal value sequence corresponding to the real-time data of each parameter, and monitoring the abnormal rising trend of each parameter in real time to construct a real-time propagation chain; comparing the real-time propagation chain with the fault propagation chain of each fault type to obtain the probability of each fault type at any moment.

[0007] A fault propagation chain for each fault type is constructed using historical data. This fault propagation chain can accurately capture the evolution of abnormal trends in various parameters before a fault occurs. During the operation of the lithium battery power module, the abnormal rising trend of each parameter is monitored in real time to construct a real-time propagation chain. The real-time propagation chain can reflect the time sequence and intensity changes of abnormalities in various parameters during operation. The real-time propagation chain is matched with the fault propagation chain of each fault type to accurately determine the probability of each fault type at any time.

[0008] Preferably, the parameters include voltage, current, internal resistance, battery temperature and state of charge.

[0009] Covering core performance parameters of lithium batteries such as voltage, current, internal resistance, temperature, and SOC, it ensures that the transmission chain can fully reflect the characteristics of electrical and thermal runaway faults.

[0010] Preferably, the anomaly detection includes: dividing the time series of any parameter into multiple subsequences according to a preset window, obtaining the anomaly value of each subsequence using the LOF algorithm, and the anomaly value of each subsequence constitutes the anomaly value sequence of the parameter.

[0011] Preferably, the moment in the outlier sequence Abnormal upward trend for: ; For the moment Neighborhood interval of ; and is a set of value pairs, representing the time In the neighborhood interval of and time Parameter value of Indicates the acquisition time The neighborhood interval of All value pairs of is the mean value function.

[0012] In the neighborhood Calculate the average slope of all data pairs, suppress instantaneous noise interference, and accurately calculate the time abnormal upward trend.

[0013] Preferably, the construction of the fault propagation chain for each fault type also includes: counting the number of occurrences of each parameter sequence in the fault propagation chains corresponding to multiple historical data, determining the parameter sequence with the largest number of occurrences, and then calculating the average value of the edge weights of each parameter in all fault propagation chains corresponding to the parameter sequence to obtain the final fault propagation chain for the fault type.

[0014] The high-frequency parameter sequences of multiple historical data are counted to ensure the accuracy of fault propagation; edge weights are averaged to reduce the deviation of single samples and improve the generalization ability of the propagation chain.

[0015] Preferably, the real-time monitoring of the abnormal rising trend of each parameter to construct a real-time propagation chain includes: in response to the abnormal moment of any parameter at the current moment, constructing a real-time propagation chain with the parameter as the starting end at the current moment; after the current moment, if any other parameter other than the parameter has an abnormal moment without exceeding the maximum waiting time, connecting the other parameters to the terminal end of the real-time propagation chain, and calculating the edge weights of the other parameters, until all parameters are traversed to complete the construction of the real-time propagation chain.

[0016] The construction is started only when an anomaly is detected, saving computing power; the propagation chain is dynamically expanded according to the actual time sequence of the anomaly occurrence, realizing the efficient construction of the fault propagation chain.

[0017] Preferably, the time Fault type Probability The acquisition steps include: responding to the moment There is no established real-time propagation chain, the fault type Probability is 0, otherwise, calculate the time Real-time propagation chains and fault types The similarity between fault propagation chains, taking the maximum similarity as the moment Fault type Probability .

[0018] Get the real-time transmission chain built at any time, and only participate in the probability calculation when the real-time transmission chain is complete, to ensure that the time Fault type Probability accuracy to avoid false positives.

[0019] Preferably, real-time transmission chain and fault type Fault propagation chain The similarity between for: ; For real-time communication chain and fault propagation chains length; and Fault propagation chain and real-time communication chain Middle parameters, if , ,like , 0; and Fault propagation chain and real-time communication chain Middle The edge weight after parameter standardization, for The L1 norm of .

[0020] Preferably, before obtaining the abnormal value sequence of each parameter, the monitoring method further comprises: performing normalization processing on the time series of each parameter.

[0021] In the second aspect of the present application, a lithium battery power module operation monitoring system is also provided, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a lithium battery power module operation monitoring method according to the first aspect of the present application is implemented.

[0022] The technical solution of this application has the following beneficial technical effects: A fault propagation chain for each fault type is constructed through historical data. The fault propagation chain reflects the time sequence and intensity changes of abnormal parameters when a fault occurs, and can accurately capture the evolution process of abnormal trends of various parameters before the fault occurs. During the operation of the lithium battery power module, the abnormal rising trend of each parameter is monitored in real time to construct a real-time propagation chain. The real-time propagation chain can reflect the time sequence and intensity changes of abnormal parameters during the operation process. The real-time propagation chain is matched with the fault propagation chain of each fault type, and the evolution process of the abnormal trend of each parameter before the fault occurs is comprehensively considered to accurately judge the probability of each fault type at any time, thereby accurately obtaining the operation status of the lithium battery power module and reducing false detections. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for monitoring the operation of a lithium battery power module according to an embodiment of the present application.

[0024] Figure 2 1 is a schematic diagram of a fault propagation chain corresponding to the fault type in Table 1 according to an embodiment of the present application.

[0025] Figure 3 This is a structural block diagram of a lithium battery power module operation monitoring system according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0027] According to a first aspect of the present application, the present application provides a method for monitoring the operation of a lithium battery power module. Figure 1 This is a flow chart of a method for monitoring the operation of a lithium battery power module according to an embodiment of the present application. Figure 1 As shown, the lithium battery power module operation monitoring method includes steps S101 to S103, which are described in detail below.

[0028] S101: Construct a fault propagation chain for each fault type.

[0029] In one embodiment, the lithium battery power module performs multiple charge and discharge processes during operation, and various fault types such as short circuit, overcharging or loose connector may occur during the charge and discharge process. During any charge and discharge process, if the lithium battery power module fails, the time series of various parameters of the charge and discharge process are collected as historical data of the corresponding fault type.

[0030] For example, in a scenario where a new energy vehicle is started using a lithium battery power module, if a loose connector fault occurs in the lithium battery power module during a startup process, the time series of various parameters during this startup process will be used as historical data of the fault type "loose connector".

[0031] The parameters include voltage, current, internal resistance, battery temperature and state of charge (SOC). The state of charge (SOC) refers to the available state of the remaining charge in the battery, which is the ratio of the remaining charge in the battery to the rated charge capacity.

[0032] In one embodiment, after obtaining the historical data of each fault type, a fault propagation chain for each fault type can be constructed based on the historical data of each fault type. Specifically, constructing the fault propagation chain for each fault type includes: performing anomaly detection on the time series of each parameter in the historical data of any fault type to obtain an abnormal value sequence of each parameter; calculating the abnormal rising trend at each moment in any abnormal value sequence; connecting each parameter in order of the abnormal moment of each parameter from small to large, where the abnormal moment is the moment when the abnormal rising trend first exceeds a threshold, and using the abnormal interval and trend increment as the edge weight of each parameter other than the first parameter to obtain the fault propagation chain of the fault type, where the abnormal interval is the interval between the abnormal moments of any parameter and the previous adjacent parameter, and the trend increment is the difference between the abnormal rising trend at the abnormal moment of any parameter and the previous adjacent parameter.

[0033] The anomaly detection includes: dividing the time series of any parameter into multiple subsequences according to a preset window, obtaining the anomaly value of each subsequence using the LOF algorithm, and the anomaly value of each subsequence constitutes the anomaly value sequence of the parameter.

[0034] For example, the preset window size is 5×1, meaning that a subsequence contains five parameter values. The preset window slides across the time series with a step size of 1 until all values in the time series are traversed. The outlier values in each subsequence are then arranged in the order of the subsequences to obtain an outlier sequence. If the time series length is 50, and the preset window size is 5×1, the outlier sequence contains a total of 46 outliers.

[0035] Among them, the moment in the outlier sequence Abnormal upward trend for: ; For the moment Neighborhood interval of ; and is a set of value pairs, representing the time In the neighborhood interval of and time Parameter value of Indicates the acquisition time The neighborhood interval of All value pairs of is the mean value function. The value of is 5.

[0036] It should be noted that in order to avoid the influence of noise on the outlier sequence and make the outlier sequence truly reflect the operation status of the lithium battery power module, before obtaining the outlier sequence, the time series of each parameter in the historical data is preprocessed, and the preprocessing includes normalization and denoising; the normalization is used to eliminate the influence of the dimension on the abnormal rising trend of each parameter, and the denoising is used to eliminate the influence of noise on the outlier sequence.

[0037] It is understandable that when the lithium battery power module operates normally, the abnormal values at each moment should be consistent in the region, that is, at each moment Abnormal upward trend The value of is close to 0; when When it is greater than 0, it means that the abnormal value is at time There is an upward trend in the neighborhood of The larger it is, the more obvious the upward trend is, which means the more obvious the abnormality of the parameter is.

[0038] In one embodiment, the abnormal upward trend of a parameter at any moment is calculated. When the abnormal upward trend of a parameter exceeds a threshold, the parameter is considered abnormal at that moment. The moment when the parameter first becomes abnormal is used as the abnormal moment. The parameter at the first abnormal moment is used as the starting parameter, that is, the initial parameter is considered to be the trigger of the fault type. The threshold value is 0.5.

[0039] After connecting each parameter in ascending order of their anomaly moments, the anomaly interval and trend increment are calculated for each parameter. These are used as edge weights to construct the fault propagation chain for the corresponding fault type. The anomaly interval of a parameter characterizes the lag in the occurrence of an anomaly in that parameter after the occurrence of an anomaly in the previous adjacent parameter. For example, an anomaly interval of 3 indicates that the anomaly occurred three moments after the occurrence of an anomaly in the previous adjacent parameter. The trend increment is the difference in the anomaly upward trend between any parameter and the previous adjacent parameter at the time of the anomaly, reflecting the relative relationship between the anomaly upward trend of any parameter and the previous adjacent parameter in the fault propagation chain.

[0040] It can be understood that the edge weight of an edge includes two values; if there are two other parameters with the same lag time, the other parameter with a larger trend increment can be placed in front.

[0041] In an example, the number of parameters is 5, and the abnormal time of each parameter and the abnormal rising trend of the abnormal time are shown in Table 1; then the fault propagation chain of the corresponding fault type can be constructed based on Table 1, see Figure 2 , is a schematic diagram of the fault propagation chain corresponding to the fault type in Table 1 according to an embodiment of the present application.

[0042] Table 1. Lag times and relative trends of other parameters.

[0043]

[0044] In this way, the fault propagation chain of each fault type is constructed. According to the fault propagation chain of each fault type, the evolution process of all parameter abnormal values of each fault type can be intuitively understood, providing a data basis for the subsequent operation monitoring of the lithium battery power module.

[0045] It's understandable that a single piece of historical data for a single fault type can construct a single fault propagation chain; multiple pieces of historical data can construct multiple fault propagation chains. However, given the presence of noise in the historical data, randomly selecting a fault propagation chain inevitably leads to errors in the fault propagation chain. Therefore, constructing a fault propagation chain for each fault type also includes: counting the number of occurrences of various parameter sequences in the fault propagation chains corresponding to the multiple pieces of historical data, determining the parameter sequence with the highest occurrence, and then calculating the average value of the edge weights of each parameter in all fault propagation chains corresponding to that parameter sequence to obtain the final fault propagation chain for that fault type. This ensures the accuracy of the fault propagation chain for each fault type.

[0046] S102, obtain the abnormal value sequence corresponding to the real-time data of each parameter, and monitor the abnormal rising trend of each parameter in real time to build a real-time propagation chain.

[0047] In one embodiment, during the operation of the lithium battery power module, real-time data of each parameter is collected, and the real-time data is a time series; abnormal values at each moment in the real-time data are obtained to obtain an abnormal value sequence of the real-time data of each parameter; and the abnormal rising trend of the real-time data at each moment is calculated.

[0048] During the real-time operation of the lithium battery power module, the real-time monitoring of the abnormal rising trend of each parameter to construct a real-time propagation chain includes: in response to the abnormal moment of any parameter at the current moment, constructing a real-time propagation chain with the parameter as the starting end at the current moment; after the current moment, if any other parameter other than the parameter has an abnormal moment without exceeding the maximum waiting time, connecting the other parameters to the terminal end of the real-time propagation chain and calculating the edge weights of the other parameters; until all parameters are traversed and the construction of the real-time propagation chain is completed.

[0049] It is understandable that during the real-time operation of the lithium battery power module, if all parameters do not detect an abnormal moment, the construction of the real-time propagation chain will not be executed; whenever an abnormal moment is detected, the construction of the real-time propagation chain will be executed starting from the abnormal moment, with the parameters corresponding to the abnormal moment as the starting end; the maximum waiting time is a preset multiple of the maximum interval of abnormal moments in all fault propagation chains, and the preset multiple is greater than 1. In the embodiment of the present application, the value of the preset multiple is 1.5. For example, if the moment If the monitoring parameters 1 and 3 are abnormal, then at time Parameter 1 and parameter 3 are used as the starting points to build a real-time propagation chain, that is, at time There are 2 real-time transmission chains being built; if the moment If the parameter 2 is detected as abnormal at time The real-time transmission chain is constructed again with parameter 2 as the starting point, that is, at time There is 1 real-time transmission chain being built.

[0050] Among them, in a real-time propagation chain, other parameters except the starting segment correspond to a set of edge weights, which also include abnormal intervals and trend increments; the abnormal intervals of other parameters in the real-time propagation chain are the intervals between the abnormal moments of the other parameters and the previous adjacent parameters in the real-time propagation chain; the trend increments of other parameters in the real-time propagation chain are the differences in the abnormal rising trends between the other parameters and the previous adjacent parameters in the real-time propagation chain at the abnormal moments.

[0051] In this way, multiple real-time propagation chains will be constructed during the real-time operation of the lithium battery power module. It should be noted that there are incomplete real-time propagation chains. If a real-time propagation chain is incomplete, that is, the real-time propagation chain does not contain all parameters, the real-time propagation chain will be discarded and only the complete real-time propagation chain will be retained; each retained real-time propagation chain contains all parameters. At this time, the length of each real-time propagation chain is the same, and the length of the real-time propagation chain is equal to the fault propagation chain of each fault type.

[0052] S103 , comparing the real-time propagation chain with the fault propagation chain of each fault type to obtain the probability of each fault type at any time.

[0053] In one embodiment, during the real-time operation of the lithium battery power module, multiple real-time propagation chains may be constructed at a moment, or there may be no real-time propagation chain; when there is no real-time propagation chain at a moment, it means that the probability of each fault type at that moment is 0, and the lithium battery power module is in a normal operating state.

[0054] Specific, moment Fault type Probability The acquisition steps include: responding to the moment There is no established real-time propagation chain, the fault type Probability is 0, otherwise, calculate the time Each real-time propagation chain and fault type The similarity between fault propagation chains, taking the maximum similarity as the moment Fault type Probability .

[0055] Among them, the real-time transmission chain and fault type Fault propagation chain The similarity between for: ; For real-time communication chain and fault propagation chains length; and Fault propagation chain and real-time communication chain Middle parameters, if , ,like , 0; and Fault propagation chain and real-time communication chain Middle The edge weight after parameter standardization, for The L1 norm of .

[0056] Among them, the normalization process is used to eliminate the dimension of abnormal intervals and trend increments in edge weights.

[0057] In this way, the probability of each fault type at any time is obtained. In response to the probability of any fault type at any time being greater than the probability threshold, it means that the lithium battery power module corresponds to the corresponding fault type and an early warning is issued; the threshold is 0.5.

[0058] According to the second aspect of the present application, the present application also provides a lithium battery power module operation monitoring system. Figure 3 This is a structural block diagram of a lithium battery power module operation monitoring system according to an embodiment of the present application. Figure 3As shown, the system 50 includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a method for monitoring the operation of a lithium battery power module according to the first aspect of the present application is implemented. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are well known in the art and are therefore not described in detail here.

[0059] It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all fall within the scope of protection of the present application.

Claims

1. A lithium battery power module operation monitoring method, characterized in that: The monitoring method includes: constructing a fault propagation chain for each fault type, including: performing anomaly detection on the time series of each parameter in the historical data of any fault type to obtain an abnormal value sequence of each parameter; calculating the abnormal rising trend at each moment in the abnormal value sequence; connecting each parameter in ascending order of abnormal moments, where the abnormal moment is the moment when the abnormal rising trend first exceeds a threshold; using the abnormal interval and trend increment as the edge weight of each parameter other than the first parameter to obtain the fault propagation chain of the fault type; the abnormal interval and trend increment are respectively the interval between the abnormal moments of any parameter and the previous adjacent parameter, and the difference in the abnormal rising trend at the abnormal moment; The abnormal value sequence corresponding to the real-time data of each parameter is obtained, and the abnormal rising trend of each parameter is monitored in real time to build a real-time propagation chain. The real-time propagation chain is compared with the fault propagation chain of each fault type to obtain the probability of each fault type at any time.

2. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: The parameters include voltage, current, internal resistance, battery temperature and state of charge.

3. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: The anomaly detection includes: The time series of any parameter is divided into multiple subsequences according to a preset window, and the outlier value of each subsequence is obtained using the LOF algorithm. The outlier value of each subsequence constitutes the outlier value sequence of the parameter.

4. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: Moments in the outlier sequence Abnormal upward trend for: ; For the moment Neighborhood interval of ; and is a set of value pairs, representing the time In the neighborhood interval of and time Parameter value of Indicates the acquisition time The neighborhood interval of All value pairs of is the mean value function.

5. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: The construction of the fault propagation chain for each fault type further includes: In the fault propagation chains corresponding to multiple historical data, the number of occurrences of each parameter sequence is counted. After determining the parameter sequence with the largest number of occurrences, the average value of the edge weights of each parameter in all fault propagation chains corresponding to this parameter sequence is calculated to obtain the final fault propagation chain for this fault type.

6. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: The real-time monitoring of abnormal rising trends of various parameters to build a real-time transmission chain includes: In response to an abnormal moment of any parameter at the current moment, a real-time propagation chain with the parameter as the starting end is constructed at the current moment; after the current moment, if an abnormal moment occurs for any other parameter other than the parameter without exceeding the maximum waiting time, the other parameter is connected to the terminal end of the real-time propagation chain, and the edge weight of the other parameter is calculated until all parameters are traversed and the construction of the real-time propagation chain is completed.

7. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: time Fault type Probability The steps to obtain include: Respond to the moment There is no established real-time propagation chain, the fault type Probability is 0, otherwise, calculate the time Real-time propagation chains and fault types The similarity between fault propagation chains, taking the maximum similarity as the moment Fault type Probability .

8. The method for monitoring the operation of a lithium battery power module according to claim 7, wherein: Real-time communication chain and fault type Fault propagation chain The similarity between for: ; For real-time communication chain and fault propagation chains length; and Fault propagation chain and real-time communication chain Middle parameters, if , ,like , 0; and Fault propagation chain and real-time communication chain Middle The edge weight after parameter standardization, for The L1 norm of .

9. The method for monitoring the operation of a lithium battery power module according to claim 1, wherein: Before obtaining the abnormal value sequence of each parameter, the monitoring method further includes: performing normalization processing on the time series of each parameter.

10. A lithium battery power module operation monitoring system, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a lithium battery power module operation monitoring method according to any one of claims 1 to 9 is implemented.

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