CN114137421A - Battery abnormality detection method, apparatus, device and storage medium - Google Patents
Battery abnormality detection method, apparatus, device and storage medium Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
The application relates to a battery abnormity detection method, a device, equipment and a storage medium, in particular to the field of new energy. The method comprises the following steps: acquiring a target battery data set; the target battery data set comprises battery message data in a target time period; determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal level in at least two dimensions according to the relation between the difference degree between the battery message data and the level threshold; and weighting the basic abnormal levels of the at least two dimensions according to the abnormal weight of the target battery in the at least two dimensions to obtain the comprehensive abnormal level of the target battery so as to indicate the abnormal condition of the target battery. According to the scheme, the data of multiple dimensions obtained by the target battery in the operation process are considered, the data abnormity is judged according to the difference degree between the data, the operation state of the target battery is represented accurately and comprehensively, and the accuracy of the target battery abnormity detection is improved.
Description
Technical Field
The invention relates to the field of new energy, in particular to a battery abnormity detection method, device, equipment and storage medium.
Background
The demand of new energy electric vehicles is rapidly rising, and rechargeable batteries have an irreplaceable status as an energy supply component for electric vehicles.
At present, under the influence of various factors such as manufacturing process, materials and use habits of users of battery packs of rechargeable batteries of electric vehicles, potential dangers exist in the battery packs in the use process, so that the dangers can be avoided by identifying the abnormity of the battery packs in advance or the harm caused by the abnormity is reduced to the minimum by the conventional battery pack abnormity detection method.
In the above scheme, it is difficult to accurately identify the abnormal condition of the battery pack only by detecting the discharge voltage, which results in a low identification accuracy of the battery abnormality detection.
Disclosure of Invention
The application provides a battery abnormity detection method, a device, computer equipment and a storage medium, which improve the accuracy of target battery abnormity detection.
In one aspect, a battery abnormality detection method is provided, the method including:
acquiring a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprises data of at least two dimensions of voltage, temperature and insulation resistance of a target battery;
according to the relation between the difference degree between the battery message data and the grade threshold value; determining an abnormality weight of the target battery in at least two dimensions and a base abnormality level in the at least two dimensions;
and weighting the basic abnormal levels of the at least two dimensions through the abnormal weight of the target battery in the at least two dimensions to obtain the comprehensive abnormal level of the target battery so as to indicate the abnormal condition of the target battery.
In still another aspect, there is provided a battery abnormality detection apparatus applied to a cloud server, the apparatus including:
the data set acquisition module is used for acquiring a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprises data of at least two dimensions of voltage, temperature and insulation resistance of a target battery;
the difference judging module is used for determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the target battery in the at least two dimensions according to the relation between the difference degree between the battery message data and the level threshold;
and the abnormality determining module is used for weighting the basic abnormality levels of at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality level of the target battery so as to indicate the abnormal condition of the target battery.
In one possible implementation, the apparatus further includes:
the first judgment module is used for determining the state of the target battery as no abnormity when the comprehensive abnormity grade of the target battery is smaller than a first abnormity threshold value;
a second determination module, configured to determine that the state of the target battery is slightly abnormal when the comprehensive abnormality level of the target battery is greater than the first abnormality threshold and less than a second abnormality threshold;
a third judging module, configured to determine that the state of the target battery is a medium abnormality when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and smaller than a third abnormality threshold;
and the fourth judging module is used for determining the state of the target battery as serious abnormity when the comprehensive abnormity grade of the target battery is greater than the third abnormity threshold value.
In one possible implementation, the apparatus further includes:
the battery cell parameter acquisition module is used for acquiring battery cell parameters of the target battery, wherein the battery cell parameters comprise at least one of a battery cell structure, a battery cell material, a battery cell capacity and a battery cell resistance;
the abnormal threshold acquisition module is used for inquiring in a cell threshold table according to the cell parameters of the target battery to obtain an abnormal threshold set corresponding to the cell parameters; the anomaly threshold set includes at least one of a first anomaly threshold, a second anomaly threshold, and a third anomaly threshold.
In a possible implementation manner, the difference between the battery message data includes a dimension difference of the battery message data in a target dimension; the dimension difference value is the difference between the maximum value and the minimum value in the data of the same dimension;
the difference judging module comprises:
a first base level determining unit, configured to determine a base anomaly level of the target dimension as a first base anomaly level when the dimension difference value is between a first level threshold and a second level threshold;
a first anomaly weight determining unit, configured to obtain a difference between the dimension difference value and the first level threshold and a ratio between differences between the first level threshold and the second level threshold, and determine a sum of the ratio and the first level threshold as an anomaly weight of the target dimension.
In a possible implementation manner, the difference determining module further includes:
a second base level determining unit, configured to determine, when the dimension difference value is between the second level threshold and a third level threshold, that a base anomaly level of the target dimension is a second base anomaly level;
and the second anomaly weight determining unit is used for acquiring the difference between the dimension difference value and the second level threshold value and the ratio between the difference between the second level threshold value and the third level threshold value, and determining the sum of the ratio and the second level threshold value as the anomaly weight of the target dimension.
In a possible implementation manner, the difference determining module further includes:
a third base level determining unit, configured to determine, when the dimension difference is greater than the third level threshold, that a base anomaly level of the target dimension is a third base anomaly level;
a third anomaly weight determination unit, configured to determine the third level threshold as an anomaly weight of the target dimension.
In one possible implementation, the anomaly determination module is further configured to,
performing first weighting processing on the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the at least two dimensions to obtain the weighted abnormal level of the at least two dimensions;
acquiring comprehensive abnormal weight of at least two dimensions based on the difference between the abnormal weight of at least two dimensions and the sequence number of the basic abnormal grade;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weight of the at least two dimensions to obtain the comprehensive abnormal grade of the target battery.
In still another aspect, a computer device is provided, where the computer device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above battery abnormality detection method.
In yet another aspect, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the battery abnormality detection method described above.
In yet another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and executes the computer instructions, so that the computer device executes the battery abnormality detection method.
The technical scheme provided by the application can comprise the following beneficial effects:
when the battery abnormity of the target battery is detected, data of at least two dimensions of the target battery in a target time period can be acquired firstly; and determining the basic abnormal levels and the abnormal weights of the target battery in two dimensions according to the relationship between the difference degree of the data of the two dimensions and the level threshold, and weighting the basic abnormal levels of the two dimensions according to the abnormal weights of the target battery in the two dimensions to obtain the final comprehensive abnormal level of the target battery. According to the comprehensive abnormal grade obtained by the scheme, the data of multiple dimensions obtained by the target battery in the operation process are comprehensively considered, whether the data are abnormal or not is judged according to the difference degree between the data, the operation state of the target battery can be represented accurately and comprehensively, whether the target battery is abnormal or not is determined, and the accuracy of detecting the abnormality of the target battery is improved.
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In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic configuration diagram illustrating a battery abnormality detection system according to an exemplary embodiment.
Fig. 2 is a method flow diagram illustrating a battery abnormality detection method according to an exemplary embodiment.
Fig. 3 is a method flow diagram illustrating a battery abnormality detection method according to an exemplary embodiment.
Fig. 4 is a flow chart diagram illustrating a method of battery pack anomaly detection according to an exemplary embodiment.
Fig. 5 is a block diagram showing the configuration of a battery abnormality detecting apparatus according to an exemplary embodiment.
Fig. 6 shows a block diagram of a computer device according to an exemplary embodiment of the present application.
Detailed Description
The technical solutions of the present application will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be understood that "indication" mentioned in the embodiments of the present application may be a direct indication, an indirect indication, or an indication of an association relationship. For example, a indicates B, which may mean that a directly indicates B, e.g., B may be obtained by a; it may also mean that a indicates B indirectly, for example, a indicates C, and B may be obtained by C; it can also mean that there is an association between a and B.
In the description of the embodiments of the present application, the term "correspond" may indicate that there is a direct correspondence or an indirect correspondence between the two, may also indicate that there is an association between the two, and may also indicate and be indicated, configure and configured, and so on.
In the embodiment of the present application, "predefining" may be implemented by saving a corresponding code, table, or other manners that may be used to indicate related information in advance in a device (for example, including a terminal device and a network device), and the present application is not limited to a specific implementation manner thereof.
Fig. 1 is a schematic configuration diagram illustrating a battery abnormality detection system according to an exemplary embodiment. The battery abnormality detection system includes a server 110 and a target vehicle 120.
The target vehicle 120 has a data processing device loaded with a BMS (Battery Management System) and a data storage module, and the Battery Management System can detect various parameters in the target vehicle, such as a Battery state (e.g., output voltage), a vehicle body insulation resistance value, and the like, according to a specified period, and store the parameters, such as the Battery state and the vehicle body insulation resistance value, in the data storage module of the target vehicle.
Optionally, when the target vehicle 120 is connected to the charging pile to realize the charging process of the battery, the BMS still detects the body insulation resistance value of the target vehicle according to a designated period, and stores the body insulation resistance value of the target vehicle in the charging process in the data storage module.
Alternatively, the BMS may still detect the body insulation resistance value in the target vehicle according to a designated period during the operation of the target vehicle 120 (i.e., during the discharge of the rechargeable battery), and store the body insulation resistance value of the target vehicle during the operation in the data storage module.
Optionally, the target vehicle 120 is communicatively connected to the server 110 through a transmission network (e.g., a wireless communication network), and the target vehicle 120 may upload the respective data (e.g., the body insulation resistance value) stored in the data storage module to the server 110 through the wireless communication network, so that the server 110 may analyze the safety state of the rechargeable battery of the target vehicle.
Optionally, the data processing device in the target vehicle 120 may process each data stored in the data storage module, so as to analyze the safety state of the rechargeable battery of the target vehicle.
Optionally, the server 110 may also perform wireless communication connection to each new energy vehicle including the target vehicle 120 and establishing communication connection with the server 110 through a wireless communication network, and send corresponding indication information, such as safety prompt information, to each new energy vehicle.
Optionally, the server may be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server providing technical computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and a big data and artificial intelligence platform.
Optionally, the system may further include a management device, where the management device is configured to manage the system (e.g., manage connection states between the modules and the server, and the management device is connected to the server through a communication network. Optionally, the communication network is a wired network or a wireless network.
Optionally, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the internet, but may be any other network including, but not limited to, a local area network, a metropolitan area network, a wide area network, a mobile, a limited or wireless network, a private network, or any combination of virtual private networks. In some embodiments, data exchanged over the network is represented using techniques and/or formats including hypertext markup language, extensible markup language, and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure sockets layer, transport layer security, virtual private network, internet protocol security, and the like. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of, or in addition to, the data communication techniques described above.
Fig. 2 is a method flow diagram illustrating a battery abnormality detection method according to an exemplary embodiment. The method is performed by a computer device, which may be a data processing device in a target vehicle as shown in fig. 1. As shown in fig. 2, the battery abnormality detecting method may include the steps of:
Wherein, the target battery data set comprises battery message data in a target time period.
Optionally, the battery message data may be message data obtained by detecting the operating state of the target battery through each sensor device on the target vehicle, and after the sensor device detects the operating state of the target battery and obtains real-time message data, the message data is transmitted to a data storage device in the target vehicle to generate a target battery data set.
Optionally, the target battery data set stored in the data storage device is battery message data in a target time period with the current time as the end of the interval. That is, in the data storage device, the stored battery message data are all the battery message data in the latest time period.
The battery message data includes data of at least two dimensions of voltage, temperature, and insulation resistance of the target battery.
In the embodiment of the present application, in order to improve the detection accuracy of the abnormal state of the target battery, the sensor devices in the target vehicle may detect the voltage, the temperature, and the insulation resistance value of the target battery in real time, respectively, and generate data stored in the data storage device in chronological order, generating a target battery data set.
After the target battery data set is obtained, the computer device may compare the battery message data in the target battery data set according to the dimension, for example, according to the difference of the battery message data in the temperature dimension, so as to determine the temperature difference of the target battery in the target time period.
After the difference degree of the battery message data in at least two dimensions is determined, the basic abnormal grade and the abnormal weight corresponding to the battery message data in at least two dimensions can be determined according to the relation between the difference degree and the grade threshold.
For example, for the temperature dimension, when the difference between the temperature difference between the battery message data and the temperature difference is greater than the first temperature level threshold, the basic abnormality level of the temperature dimension is determined as the first temperature level, and at this time, the abnormality weight of the temperature dimension may be determined according to the difference between the temperature difference and the first temperature level threshold, and when the difference between the temperature difference and the first temperature level threshold is larger, it indicates that the abnormality in the temperature dimension that can be represented by the temperature difference at this time is more serious, and a larger abnormality weight should be assigned to the temperature dimension.
For other dimensions, the exception weight and the basic exception level should also be obtained through the above logic, and are not described herein again.
After the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the at least two dimensions are obtained, the comprehensive abnormal level of the target battery is obtained in a weighting mode, so that the abnormal condition of the target battery is indicated, the comprehensive abnormal level is obtained by synthesizing the abnormal conditions of the data of all the dimensions, the working state of the target battery in a target time period can be reflected really, and the accuracy of judging the abnormal condition of the battery is improved.
In summary, when the battery abnormality detection is performed on the target battery, data of at least two dimensions of the target battery in the target time period may be obtained first; and determining the basic abnormal levels and the abnormal weights of the target battery in two dimensions according to the relationship between the difference degree of the data of the two dimensions and the level threshold, and weighting the basic abnormal levels of the two dimensions according to the abnormal weights of the target battery in the two dimensions to obtain the final comprehensive abnormal level of the target battery. According to the comprehensive abnormal grade obtained by the scheme, the data of multiple dimensions obtained by the target battery in the operation process are comprehensively considered, whether the data are abnormal or not is judged according to the difference degree between the data, the operation state of the target battery can be represented accurately and comprehensively, whether the target battery is abnormal or not is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Fig. 3 is a method flow diagram illustrating a battery abnormality detection method according to an exemplary embodiment. The method is performed by a computer device, which may be a data processing device in a target vehicle as shown in fig. 1. As shown in fig. 3, the battery abnormality detecting method may include the steps of:
In a possible implementation manner of the embodiment of the application, the target battery data set includes battery message data in a target time period, and the battery message data includes data of a target battery in a voltage dimension, data in a temperature dimension, and data in an insulation resistance dimension.
The target battery data set comprises battery message data in a target time period; the battery message data includes data of at least two dimensions of voltage, temperature, and insulation resistance of the target battery.
In a possible implementation manner, the battery message data in the target time period is obtained by setting a sliding window threshold in the target battery data set.
In the running process of the target battery, a sensor in the target vehicle detects the dimension data of the target battery according to a certain period, and sends the generated data to a data storage device in the target vehicle in a message form.
At this time, the data processor in the target vehicle may set a sliding window with an assigned threshold value N in a target battery data set stored in the data memory, and may discard the earliest packet data in the sliding window when a new packet data is added in real time after the packet data in the sliding window reaches the threshold value, so as to obtain the packet data of the battery generated in the latest target time period, and analyze the state of the target battery in real time.
Optionally, the difference between the battery message data includes a dimension difference of the battery message data in a target dimension; the dimension difference is the difference between the maximum value and the minimum value in the data of the same dimension.
The target dimension may be any one of a temperature dimension, a voltage dimension, and an insulation resistance dimension. Taking the target dimension as an example of a temperature dimension, the difference between the battery message data includes a difference between a maximum value and a minimum value in the data of each temperature dimension in the sliding window.
When the difference between the maximum value and the minimum value in the data of the temperature dimension is larger, the larger the fluctuation situation of the temperature in the target time period indicated by the sliding window is, the unstable operation of the battery is and the abnormal situation is more likely to occur.
For example, the voltage base abnormality level BVRank is divided into three levels, where 1 represents a slight abnormality of voltage, 2 represents a moderate abnormality of voltage, and 3 represents a severe abnormality of voltage, and BVRank is different and the voltage abnormality weight is also different, and the higher BVRank is, the larger the voltage abnormality weight is. Finally, the voltage abnormality level VRank is BVRank voltage abnormality weight.
The temperature basic abnormal grade BTRank is divided into three grades, wherein 1 represents that a slight abnormal condition occurs in temperature, 2 represents that a moderate abnormal condition occurs in temperature, 3 represents that a serious abnormal condition occurs in temperature, the BTRank is different, the temperature abnormal weight is also different, and the higher the BTRank is, the larger the temperature abnormal weight is. Finally, the temperature anomaly level TRank is BTRank temperature anomaly weight.
The basic abnormal grade BRRank is divided into three grades, 1 represents slight abnormal conditions of insulation, 2 represents medium abnormal conditions of insulation, 3 represents serious abnormal conditions of insulation, the BRRank is different, the weight of the insulation abnormal conditions is different, and the higher the BRRank is, the larger the weight of the insulation abnormal conditions is. Finally, the insulation anomaly level RRank is BRRank insulation anomaly weight.
In one possible implementation, when the dimension difference is between a first level threshold and a second level threshold, determining a base anomaly level of the target dimension as a first base anomaly level; and acquiring the difference between the dimension difference value and a first level threshold value and the ratio between the difference between the first level threshold value and a second level threshold value, and determining the sum of the ratio and the first level threshold value as the abnormal weight of the target dimension.
Also by way of example of the dimensional difference of the temperature dimensions, after the dimensional difference of the temperature dimensions within the target time period is calculated, the dimensional difference may be compared with each level threshold. When the dimension difference is less than the first level threshold, the base anomaly level and anomaly weight for the temperature dimension may be determined to be 0. And when the dimension difference value is larger than the first grade threshold and smaller than the second grade threshold, determining the basic abnormal grade of the target dimension as a first basic abnormal grade.
Moreover, since there is a certain degree of error when evaluating the dimension difference of the temperature dimension only through the first basic abnormal level, and it is difficult to further describe the specific size of the dimension difference only by considering that the dimension difference is between the first level threshold and the second level threshold, in a possible implementation manner of the embodiment of the present application, the difference between the dimension difference value and the first level threshold value and the difference between the first level threshold value and the second level threshold value may be divided to obtain the difference between the dimension difference value and the first level threshold value and the ratio between the difference between the first level threshold value and the second level threshold value, thereby determining the position of the dimension difference value in the interval formed by the first level threshold value and the second level threshold value, and acquiring the abnormal weight of the target dimension according to the ratio, the first basic abnormal level of the target dimension is weighted, so that the accurate extraction of the abnormal features of the target dimension is realized.
In one possible implementation, when the dimension difference is between the second level threshold and the third level threshold, determining the base anomaly level of the target dimension as a second base anomaly level; and acquiring the difference between the dimension difference value and a second level threshold value and the ratio between the difference between the second level threshold value and a third level threshold value, and determining the sum of the ratio and the second level threshold value as the abnormal weight of the target dimension.
Also taking the dimension difference of the temperature dimension as an example, when the dimension difference is greater than the second level threshold and less than the third level threshold, the base anomaly level of the target dimension is determined as the second base anomaly level.
In addition, also for more accurately extracting the abnormal feature of the temperature dimension, in a possible implementation manner of the embodiment of the present application, the difference between the temperature dimension and the second-level threshold and the difference between the second-level threshold and the third-level threshold may be divided, and the ratio between the difference between the dimension difference and the second-level threshold and the difference between the second-level threshold and the third-level threshold is obtained, so as to determine the position of the dimension difference in an interval formed by the second-level threshold and the third-level threshold, and obtain the abnormal weight of the target dimension according to the ratio, so as to weight the second basic abnormal level of the target dimension, thereby accurately obtaining the abnormal feature of the target dimension.
In one possible implementation, when the dimension difference is greater than a third level threshold, determining that the base anomaly level of the target dimension is a third base anomaly level; and determining the dimension difference value as the abnormal weight of the target dimension.
Similarly, taking the dimension difference value of the temperature dimension as an example, when the dimension difference value is greater than a third-level threshold, directly determining the basic abnormal level of the target dimension as a third basic abnormal level, and taking the size of the dimension difference value as the abnormal weight of the target dimension, wherein although the third basic abnormal level is a fixed value, the larger the dimension difference value is, the larger the abnormal weight is, and the abnormal condition of the temperature dimension can be reflected when the dimension difference value is larger.
For example, the anomaly level TRank of the temperature dimension is a specific calculation method. And calculating the difference value delta T between the maximum monomer temperature and the minimum monomer temperature in the current sliding window, wherein the threshold value delta T1 with BTrank of 1, the threshold value delta T2 with BTrank of 2 and the threshold value delta T3 with BTrank of 3 are recorded.
When the temperature difference Δ T is equal to or greater than Δ T1 and less than Δ T2, BTRank is 1, and the abnormality weight of the temperature dimension is as follows.
When the temperature difference Δ T is equal to or greater than Δ T2 and less than Δ T3, BTRank is 2, and the abnormality weight of the temperature dimension is as follows.
When the temperature difference Δ T is equal to or greater than Δ T3, BTRank is 3, and the abnormality weight of the temperature dimension is constantly 3.
Finally, TRank ═ BTRank ═ WT。
The basic anomaly level and the anomaly weight are both exemplified by a temperature dimension, and it should be noted that the above-described scheme of the embodiment of the present application may be implemented for data of each dimension, and a specific implementation process is similar to the above-described scheme, and is not described here again.
For example, the voltage abnormality level VRank is calculated as follows.
And calculating a difference value delta V between the maximum cell voltage and the minimum cell voltage in the current sliding window, wherein a threshold value delta V1 with the BVrank of 1 is recorded, a threshold value delta V2 with the BVrank of 2 is recorded, and a threshold value delta V3 with the BVrank of 3 is recorded.
When the differential pressure Δ V is equal to or greater than Δ V1 and less than Δ V2, BVRank is 1, and the voltage anomaly weight is as follows.
When the differential pressure Δ V is equal to or greater than Δ V2 and less than Δ V3, BVRank is 2, and the voltage anomaly weight is as follows.
When the differential pressure Δ V is equal to or greater than Δ V3, BVRank is 3, and the voltage anomaly weight is constantly 3.
Finally, VRank ═ BVRank Wv。
For another example, the insulation abnormality level RRank is specifically calculated as follows.
And calculating the difference value delta R between the maximum insulation resistance value and the minimum insulation resistance value in the current sliding window, wherein the threshold value delta R1 with BRRank of 1, the threshold value delta R2 with BRRank of 2 and the threshold value delta R3 with BRRank of 3 are recorded.
When the insulation resistance difference Δ R is equal to or greater than Δ R1 and less than Δ R2, BRRank is 1, and the insulation abnormality weight is as follows.
When the insulation resistance difference Δ R is equal to or greater than Δ R2 and less than Δ R3, the absolute BRRank is 2, and the insulation abnormality weight is as follows.
When the insulation resistance difference Δ R is equal to or greater than Δ R3, BRRank is 3, and the insulation abnormality weight is constantly 3. Finally, RRank ═ BRRank ═ WR。
That is, the data of each dimension is determined by the above scheme, so that the basic abnormality level and the abnormality weight of the target battery in each dimension can be obtained. Further, the actual abnormality level of each dimension can be calculated by multiplying the basic abnormality level of each dimension by the abnormality weight.
In a possible implementation manner, in each dimension, the basic abnormality level in the dimension is weighted by the abnormality weight to obtain the actual abnormality weight of each dimension, and at this time, the actual abnormality weights of each dimension are added to obtain the comprehensive abnormality level of the target battery.
In another possible implementation manner, for the abnormal weight of the target battery in at least two dimensions, performing first weighting processing on the basic abnormal levels of the at least two dimensions to obtain weighted abnormal levels of the at least two dimensions; acquiring comprehensive abnormal weight of at least two dimensions based on the difference between the abnormal weight of the at least two dimensions and the sequence number of the basic abnormal grade; and performing second weighting processing on the weighted abnormal levels of the at least two dimensions according to the comprehensive abnormal weight of the at least two dimensions to obtain the comprehensive abnormal level of the target battery.
For example, the abnormal condition of the battery pack is comprehensively determined by VRank, TRank, RRank, the voltage abnormality comprehensive weight CVW, the temperature abnormality comprehensive weight CTW, and the insulation abnormality comprehensive weight CRW. The basic weight of voltage, temperature and insulation is 1, and the comprehensive weight calculation logic is as follows: the specific calculation method of the difference between the basic weight + the respective weight and the corresponding basic level is as follows: CVW 1+ (W)v-BVRank)，CTW＝1+(WT-BTRank)，CRW＝1+(WR-BRRank). The integrated abnormality level Δ of the secondary battery is CVW VRank + CTW TRank + CRW RRank.
And step 304, judging the abnormal condition of the target battery according to the comprehensive abnormal level of the target battery.
In one possible implementation manner, when the comprehensive abnormality level of the target battery is smaller than a first abnormality threshold, determining the state of the target battery as a no-abnormality condition;
and when the comprehensive abnormal level of the target battery is greater than the first threshold and less than the second abnormal threshold, determining the state of the target battery as a slight abnormal condition.
And when the comprehensive abnormal grade of the target battery is larger than the second abnormal threshold and smaller than the third abnormal threshold, determining the state of the target battery as a medium abnormal condition.
When the comprehensive abnormality level of the target battery is greater than the third abnormality threshold, the state of the target battery is determined as a serious abnormality.
Setting a comprehensive slight abnormal threshold (and a first abnormal threshold) of the battery pack as delta 1, a comprehensive moderate abnormal threshold (namely a second abnormal threshold) of the battery pack as delta 2, and a comprehensive serious abnormal threshold (namely a third abnormal threshold) of the battery pack as delta 3, wherein when the comprehensive abnormal grade delta of the battery pack is smaller than delta 1, the battery pack has no abnormal condition; when the comprehensive abnormal grade delta of the battery pack is greater than or equal to delta 1 and smaller than delta 2, the battery pack has a slight abnormal condition; when the comprehensive abnormal grade delta of the battery pack is greater than or equal to delta 2 and smaller than delta 3, the battery pack has a moderate abnormal condition; when the comprehensive abnormal level delta of the battery pack is larger than or equal to delta 3, the battery pack has serious abnormal conditions.
In a possible implementation manner, the first abnormal threshold, the second abnormal threshold, and the third abnormal threshold are obtained according to the cell parameter of the target battery.
In one possible implementation, acquiring a cell parameter of the target battery; the cell parameters comprise at least one of cell structure, cell material, cell capacity and cell internal resistance; inquiring in a cell threshold table according to the cell parameters of the target battery to obtain an abnormal threshold value set corresponding to the cell parameters; the anomaly threshold set includes at least one of a first anomaly threshold, a second anomaly threshold, and a third anomaly threshold.
Optionally, the anomaly threshold set further includes a first level threshold, a second level threshold, and a third level threshold for each dimension.
Before the computer equipment analyzes the operation data of the target battery, the computer equipment also can firstly acquire the electric core parameters of the target battery, including the electric core structure (such as the size of the electric core), the material quality of the electric core, the capacity of the electric core and the internal resistance of the electric core, and inquires in the electric core threshold value table according to the electric core parameters of the target battery, so that the abnormal threshold value set matched with the target battery is determined, and the accuracy of judging the battery abnormity is improved.
When the abnormal threshold value set matched with the target battery is determined, the abnormal threshold value set can be obtained from a battery cell threshold value table preset by a developer, or corresponding parameters can be generated according to the input battery cell structure, the battery cell material, the battery cell capacity and the battery cell internal resistance, so that each threshold value can be obtained according to a certain calculation flow. The calculation process can be flexibly set according to the running environment of the vehicle, the overall situation of the vehicle and the like, and the method is not limited in this application.
In summary, when the battery abnormality detection is performed on the target battery, data of at least two dimensions of the target battery in the target time period may be obtained first; and determining the basic abnormal levels and the abnormal weights of the target battery in two dimensions according to the relationship between the difference degree of the data of the two dimensions and the level threshold, and weighting the basic abnormal levels of the two dimensions according to the abnormal weights of the target battery in the two dimensions to obtain the final comprehensive abnormal level of the target battery. According to the comprehensive abnormal grade obtained by the scheme, the data of multiple dimensions obtained by the target battery in the operation process are comprehensively considered, whether the data are abnormal or not is judged according to the difference degree between the data, the operation state of the target battery can be represented accurately and comprehensively, whether the target battery is abnormal or not is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Fig. 4 is a flow chart diagram illustrating a method of battery pack anomaly detection according to an exemplary embodiment. As shown in fig. 4, the battery pack abnormality detection method may include the steps of:
1) and setting various threshold values for judging the abnormal conditions of the battery pack by combining the comprehensive influence factors such as the battery cell structure, the battery cell material, the battery cell voltage, the battery cell capacity, the battery cell internal resistance and the data uploading frequency.
2) And (6) performing data cleaning. And eliminating repeated data and invalid data in the battery pack operation data in real time (the invalid data comprises data without sampling time, data without frame number, non-real-time data and complementary data).
3) And setting a real-time sliding window threshold value. The battery pack abnormity detection is required to be carried out according to the real-time message data in a period of time, and the number N of messages in the period of time is set as a sliding window threshold value. When a message data is newly added in real time, the earliest message data in the sliding window is discarded in sequence, the length of the sliding window is ensured to be unchanged, and the length is always kept to be N.
4) And calculating the voltage abnormity level. The voltage basic abnormal level BVrank is divided into three levels, wherein 1 represents a slight abnormal condition of voltage, 2 represents a moderate abnormal condition of voltage, and 3 represents a serious abnormal condition of voltage, and BVrank is different and the voltage abnormal weight is also different, and the higher BVrank is, the higher the voltage abnormal weight is. Finally, the voltage abnormality level VRank is BVRank voltage abnormality weight.
5) And calculating the temperature anomaly level. The temperature basic abnormal grade BTRank is divided into three grades, wherein 1 represents that a slight abnormal condition occurs in temperature, 2 represents that a moderate abnormal condition occurs in temperature, 3 represents that a serious abnormal condition occurs in temperature, the BTRank is different, the temperature abnormal weight is also different, and the higher the BTRank is, the larger the temperature abnormal weight is. Finally, the temperature anomaly level TRank is BTRank temperature anomaly weight.
6) And calculating the insulation abnormity grade. The insulating base abnormal grade BRRank is divided into three grades, wherein 1 represents a slight abnormal condition of insulation, 2 represents a medium abnormal condition of insulation, and 3 represents a serious abnormal condition of insulation, and the BRRank is different and has different insulating abnormal weights, and the higher the BRRank is, the larger the insulating abnormal weight is. Finally, the insulation anomaly level RRank is BRRank insulation anomaly weight.
7) And judging the comprehensive abnormal grade of the battery pack according to the abnormal grade of dimensions such as voltage, temperature, insulation resistance value and the like.
8) And judging the abnormal condition of the battery pack according to the comprehensive abnormal grade of the battery pack.
Fig. 5 is a block diagram showing the configuration of a battery abnormality detecting apparatus according to an exemplary embodiment. The battery abnormality detection apparatus is applied to a computer device, which may be the data processing device 110 in the target vehicle shown in fig. 1, and includes:
a data set obtaining module 510, configured to obtain a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprises data of at least two dimensions of voltage, temperature and insulation resistance of a target battery;
a difference determining module 520, configured to determine, according to a relationship between a difference degree between the battery message data and a level threshold, an abnormal weight of the target battery in at least two dimensions and a basic abnormal level in the at least two dimensions;
the anomaly determination module 530 is configured to perform weighting processing on the basic anomaly levels of the at least two dimensions according to the anomaly weights of the target battery in the at least two dimensions, so as to obtain a comprehensive anomaly level of the target battery, so as to indicate an anomaly condition of the target battery.
In one possible implementation, the apparatus further includes:
the first judgment module is used for determining the state of the target battery as no abnormity when the comprehensive abnormity grade of the target battery is smaller than a first abnormity threshold value;
a second determination module, configured to determine that the state of the target battery is slightly abnormal when the comprehensive abnormality level of the target battery is greater than the first abnormality threshold and less than a second abnormality threshold;
a third judging module, configured to determine that the state of the target battery is a medium abnormality when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and smaller than a third abnormality threshold;
and the fourth judging module is used for determining the state of the target battery as serious abnormity when the comprehensive abnormity grade of the target battery is greater than the third abnormity threshold value.
In one possible implementation, the apparatus further includes:
the battery cell parameter acquisition module is used for acquiring battery cell parameters of the target battery, wherein the battery cell parameters comprise at least one of a battery cell structure, a battery cell material, a battery cell capacity and a battery cell resistance;
the abnormal threshold acquisition module is used for inquiring in a cell threshold table according to the cell parameters of the target battery to obtain an abnormal threshold set corresponding to the cell parameters; the anomaly threshold set includes at least one of a first anomaly threshold, a second anomaly threshold, and a third anomaly threshold.
In a possible implementation manner, the difference between the battery message data includes a dimension difference of the battery message data in a target dimension; the dimension difference value is the difference between the maximum value and the minimum value in the data of the same dimension;
the difference judging module comprises:
a first base level determining unit, configured to determine a base anomaly level of the target dimension as a first base anomaly level when the dimension difference value is between a first level threshold and a second level threshold;
a first anomaly weight determining unit, configured to obtain a difference between the dimension difference value and the first level threshold and a ratio between differences between the first level threshold and the second level threshold, and determine a sum of the ratio and the first level threshold as an anomaly weight of the target dimension.
In a possible implementation manner, the difference determining module further includes:
a second base level determining unit, configured to determine, when the dimension difference value is between the second level threshold and a third level threshold, that a base anomaly level of the target dimension is a second base anomaly level;
and the second anomaly weight determining unit is used for acquiring the difference between the dimension difference value and the second level threshold value and the ratio between the difference between the second level threshold value and the third level threshold value, and determining the sum of the ratio and the second level threshold value as the anomaly weight of the target dimension.
In a possible implementation manner, the difference determining module further includes:
a third base level determining unit, configured to determine, when the dimension difference is greater than the third level threshold, that a base anomaly level of the target dimension is a third base anomaly level;
a third anomaly weight determination unit, configured to determine the third level threshold as an anomaly weight of the target dimension.
In one possible implementation, the anomaly determination module is further configured to,
performing first weighting processing on the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the at least two dimensions to obtain the weighted abnormal level of the at least two dimensions;
acquiring comprehensive abnormal weight of at least two dimensions based on the difference between the abnormal weight of at least two dimensions and the sequence number of the basic abnormal grade;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weight of the at least two dimensions to obtain the comprehensive abnormal grade of the target battery.
In summary, when the battery abnormality detection is performed on the target battery, data of at least two dimensions of the target battery in the target time period may be obtained first; and determining the basic abnormal levels and the abnormal weights of the target battery in two dimensions according to the relationship between the difference degree of the data of the two dimensions and the level threshold, and weighting the basic abnormal levels of the two dimensions according to the abnormal weights of the target battery in the two dimensions to obtain the final comprehensive abnormal level of the target battery. According to the comprehensive abnormal grade obtained by the scheme, the data of multiple dimensions obtained by the target battery in the operation process are comprehensively considered, whether the data are abnormal or not is judged according to the difference degree between the data, the operation state of the target battery can be represented accurately and comprehensively, whether the target battery is abnormal or not is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Fig. 6 shows a block diagram of a computer device 600 according to an exemplary embodiment of the present application. The computer device may be implemented as a server in the above-mentioned aspects of the present application. The computer apparatus 600 includes a Central Processing Unit (CPU) 611, a system Memory 604 including a Random Access Memory (RAM) 602 and a Read-Only Memory (ROM) 603, and a system bus 605 connecting the system Memory 604 and the CPU 611. The computer device 600 also includes a mass storage device 606 for storing an operating system 609, application programs 160, and other program modules 611.
The mass storage device 606 is connected to the central processing unit 611 through a mass storage controller (not shown) connected to the system bus 605. The mass storage device 606 and its associated computer-readable media provide non-volatile storage for the computer device 600. That is, the mass storage device 606 may include a computer-readable medium (not shown) such as a hard disk or Compact Disc-Only Memory (CD-ROM) drive.
Without loss of generality, the computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 604 and mass storage device 606 described above may be collectively referred to as memory.
The computer device 600 may also operate as a remote computer connected to a network via a network, such as the internet, in accordance with various embodiments of the present disclosure. That is, the computer device 600 may be connected to the network 608 through the network interface unit 607 coupled to the system bus 605, or may be connected to other types of networks or remote computer systems (not shown) using the network interface unit 607.
The memory further includes at least one computer program, which is stored in the memory, and the central processing unit 611 implements all or part of the steps of the methods shown in the above embodiments by executing the at least one computer program.
In an exemplary embodiment, a computer readable storage medium is also provided for storing at least one computer program, which is loaded and executed by a processor to implement all or part of the steps of the above method. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program product or a computer program is also provided, which comprises computer instructions, which are stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform all or part of the steps of the method described in any of the embodiments of fig. 2 or fig. 3.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A battery abnormality detection method, characterized by comprising:
acquiring a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprises data of at least two dimensions of voltage, temperature and insulation resistance of a target battery;
determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the target battery in the at least two dimensions according to the relation between the difference degree between the battery message data and the level threshold;
and weighting the basic abnormal levels of the at least two dimensions through the abnormal weight of the target battery in the at least two dimensions to obtain the comprehensive abnormal level of the target battery so as to indicate the abnormal condition of the target battery.
2. The method of claim 1, further comprising:
when the comprehensive abnormity grade of the target battery is smaller than a first abnormity threshold value, determining the state of the target battery as no abnormity;
when the comprehensive abnormity grade of the target battery is larger than the first abnormity threshold and smaller than a second abnormity threshold, determining the state of the target battery as slight abnormity;
when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and less than a third abnormality threshold, determining the state of the target battery as a medium abnormality;
when the comprehensive abnormality level of the target battery is greater than the third abnormality threshold, determining the state of the target battery as a serious abnormality.
3. The method of claim 2, further comprising:
acquiring the cell parameters of the target battery; the cell parameters comprise at least one of cell structure, cell material, cell capacity and cell internal resistance;
inquiring in a cell threshold table according to the cell parameters of the target battery to obtain an abnormal threshold value set corresponding to the cell parameters; the anomaly threshold set includes at least one of a first anomaly threshold, a second anomaly threshold, and a third anomaly threshold.
4. The method of any of claims 1 to 3, wherein the degree of difference between the battery message data comprises a dimensional difference of the battery message data in a target dimension; the dimension difference value is the difference between the maximum value and the minimum value in the data of the same dimension;
determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the target battery in at least two dimensions according to the relation between the difference degree between the battery message data and the level threshold, wherein the method comprises the following steps:
when the dimension difference value is between a first level threshold and a second level threshold, determining a base anomaly level of the target dimension as a first base anomaly level;
and acquiring the difference between the dimension difference value and the first level threshold value and the ratio between the difference between the first level threshold value and the second level threshold value, and determining the sum of the ratio and the first level threshold value as the abnormal weight of the target dimension.
5. The method of claim 4, further comprising:
when the dimension difference value is between the second level threshold and a third level threshold, determining a base anomaly level of the target dimension as a second base anomaly level;
and acquiring the difference between the dimension difference value and the second level threshold value and the ratio between the difference between the second level threshold value and the third level threshold value, and determining the sum of the ratio and the second level threshold value as the abnormal weight of the target dimension.
6. The method of claim 5, further comprising:
when the dimension difference value is larger than the third level threshold value, determining that the basic abnormity level of the target dimension is a third basic abnormity level;
determining the third level threshold as an anomaly weight for the target dimension.
7. The method according to any one of claims 1 to 3, wherein the weighting the abnormality levels of the at least two dimensions to obtain a comprehensive abnormality level of the target battery comprises:
performing first weighting processing on the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the at least two dimensions to obtain the weighted abnormal level of the at least two dimensions;
obtaining comprehensive abnormal weight of at least two dimensions based on the difference between the abnormal weight of at least two dimensions and the value corresponding to the basic abnormal level;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weight of the at least two dimensions to obtain the comprehensive abnormal grade of the target battery.
8. A battery abnormality detection apparatus, characterized in that the apparatus comprises:
the data set acquisition module is used for acquiring a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprises data of at least two dimensions of voltage, temperature and insulation resistance of a target battery;
the difference judging module is used for determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal level of the target battery in the at least two dimensions according to the relation between the difference degree between the battery message data and the level threshold;
and the abnormality determining module is used for weighting the basic abnormality levels of at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality level of the target battery so as to indicate the abnormal condition of the target battery.
9. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction that is loaded and executed by the processor to implement the battery abnormality detection method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored therein at least one instruction, which is loaded and executed by a processor, to implement the battery abnormality detection method according to any one of claims 1 to 7.
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| CN117192314B (en) * | 2023-11-03 | 2024-04-02 | 广州疆海科技有限公司 | Insulation detection method and device based on insulation detection circuit and computer equipment |
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