CN114137421B - Battery abnormality detection method, device, equipment and storage medium - Google Patents
Battery abnormality detection method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN114137421B CN114137421B CN202111442794.3A CN202111442794A CN114137421B CN 114137421 B CN114137421 B CN 114137421B CN 202111442794 A CN202111442794 A CN 202111442794A CN 114137421 B CN114137421 B CN 114137421B
- Authority
- CN
- China
- Prior art keywords
- battery
- target
- abnormality
- dimension
- level
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application relates to a battery abnormality detection method, device, equipment and 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 grade in at least two dimensions according to the relationship between the difference degree and the grade threshold value of the battery message data; and weighting the basic abnormality grades of the at least two dimensions by the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality condition of the target battery. According to the scheme, the data of multiple dimensions obtained in the operation process of the target battery are considered, the abnormality of the data is judged according to the difference degree between the data, the operation state of the target battery is more accurately and comprehensively represented, and the accuracy of detecting the abnormality of the target battery is improved.
Description
Technical Field
The application relates to the field of new energy, in particular to a battery abnormality detection method, device, equipment and storage medium.
Background
The demand for new energy electric vehicles has increased sharply, and a rechargeable battery has an irreplaceable position as an energy supply part for electric vehicles.
At present, the battery pack is affected by factors such as manufacturing process, materials, use habits of users and the like of the rechargeable battery of the electric automobile, and potential dangers exist in the use process of the battery pack, so that the occurrence of dangers can be avoided or the harm caused by the abnormality can be reduced to the minimum by identifying the abnormality of the battery pack in advance.
In the above scheme, the abnormal condition of the battery pack is difficult to accurately identify only by detecting the discharge voltage, so that the identification accuracy of the battery abnormality detection is low.
Disclosure of Invention
The application provides a battery abnormality detection method, a device, computer equipment and a storage medium, which improve the accuracy of detecting the abnormality of a target battery.
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 comprise data of at least two dimensions of voltage, temperature and insulation resistance of the target battery;
According to the relationship between the degree of difference between the battery message data and the grade threshold value; determining an anomaly weight of the target battery in at least two dimensions and a basic anomaly level in the at least two dimensions;
and weighting the basic abnormality grades of the at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality 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 comprise data of at least two dimensions of voltage, temperature and insulation resistance of the 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 grade in the at least two dimensions according to the relation between the difference degree and the grade threshold value of the battery message data;
and the abnormality determination module is used for weighting the basic abnormality grades of the at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality condition of the target battery.
In one possible implementation, the apparatus further includes:
the first judging module is used for determining the state of the target battery to be abnormal when the comprehensive abnormal level of the target battery is smaller than a first abnormal threshold value;
the second judging module is used for determining the state of the target battery as slight abnormality when the comprehensive abnormality level of the target battery is larger than the first abnormality threshold and smaller than the second abnormality threshold;
a third judging module, configured to determine a state of the target battery as a medium abnormality when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and less than a third abnormality threshold;
and the fourth judging module is used for determining the state of the target battery as serious abnormality when the comprehensive abnormality level of the target battery is greater than the third abnormality 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 battery cell threshold table according to the battery cell parameters of the target battery to obtain an abnormal threshold set corresponding to the battery cell parameters; the set of anomaly thresholds includes at least one of a first anomaly threshold value, a second anomaly threshold value, and a third anomaly threshold value.
In one possible implementation manner, the degree of difference between the battery message data includes a dimension difference value 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;
and the first abnormal weight determining unit is used for 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.
In one possible implementation manner, the difference judging module further includes:
a second base level determining unit configured to determine a base abnormality level of the target dimension as a second base abnormality level when the dimension difference value is between the second level threshold and a third level threshold;
and the second abnormal 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 abnormal weight of the target dimension.
In one possible implementation manner, the difference judging module further includes:
a third base level determining unit, configured to determine, when the dimension difference value is greater than the third level threshold, that the base anomaly level of the target dimension is a third base anomaly level;
and a third abnormal weight determining unit, configured to determine the third level threshold as an abnormal weight of the target dimension.
In one possible implementation, the anomaly determination module is further configured to,
performing first weighting processing on the basis anomaly grades of at least two dimensions on the anomaly weights of the target battery in the at least two dimensions to obtain weighted anomaly grades of the at least two dimensions;
obtaining comprehensive anomaly weights of at least two dimensions based on the difference between the anomaly weights of the at least two dimensions and the sequence numbers of the basic anomaly level;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weights of the at least two dimensions to obtain the comprehensive abnormal grade of the target battery.
In yet another aspect, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, the at least one instruction loaded and executed by the processor to implement the battery anomaly detection method described above.
In yet another aspect, a computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement the above-described battery anomaly detection method is provided.
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 the processor executes the computer instructions so that the computer device executes the battery abnormality detection method described above.
The technical scheme provided by the application can comprise the following beneficial effects:
when the battery abnormality detection is performed on the target battery, data of at least two dimensions of the target battery in a target time period can be acquired first; and determining the basic abnormality grade and the abnormality weight of the target battery in the two dimensions according to the relationship between the difference degree and the grade threshold value of the data in the two dimensions, and weighting the basic abnormality grade of the target battery in the two dimensions according to the abnormality weight of the target battery in the two dimensions to obtain the final comprehensive abnormality grade of the target battery. The comprehensive abnormality level obtained by the scheme comprehensively considers the data of multiple dimensions obtained by the target battery in the operation process, judges whether the data are abnormal according to the difference degree between the data, and can more accurately and comprehensively represent the operation state of the target battery, so that whether the target battery is abnormal is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic configuration diagram showing a battery abnormality detection system according to an exemplary embodiment.
Fig. 2 is a method flowchart illustrating a battery abnormality detection method according to an exemplary embodiment.
Fig. 3 is a method flowchart illustrating a battery abnormality detection method according to an exemplary embodiment.
Fig. 4 is a flow chart illustrating a method of detecting battery pack anomalies according to an exemplary embodiment.
Fig. 5 is a block diagram showing the structure of a battery abnormality detection 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 following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the "indication" mentioned in the embodiments of the present application may be a direct indication, an indirect indication, or an indication having an association relationship. For example, a indicates B, which may mean that a indicates B directly, e.g., B may be obtained by a; it may also indicate that a indicates B indirectly, e.g. a indicates C, B may be obtained by C; it may also be indicated that there is an association between a and B.
In the description of the embodiments of the present application, the term "corresponding" may indicate that there is a direct correspondence or an indirect correspondence between the two, or may indicate that there is an association between the two, or may indicate a relationship between the two and the indicated, configured, etc.
In the embodiment of the present application, the "predefining" may be implemented by pre-storing corresponding codes, tables or other manners that may be used to indicate relevant information in devices (including, for example, terminal devices and network devices), and the present application is not limited to the specific implementation manner thereof.
Fig. 1 is a schematic configuration diagram showing 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 and a data storage module, where the data processing device is loaded with a BMS (Battery Management System ), 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 implement the charging process of the battery, the BMS still detects the insulation resistance value of the vehicle body in the target vehicle according to a specified period, and stores the insulation resistance value of the vehicle body of the target vehicle in the data storage module during the charging process.
Optionally, the BMS still detects the body insulation resistance value of the target vehicle according to a designated period during the operation of the vehicle (i.e., during the discharging of the rechargeable battery) and stores the body insulation resistance value of the target vehicle during the operation in the data storage module.
Optionally, the target vehicle 120 is in communication connection with the server 110 through a transmission network (such as a wireless communication network), and the target vehicle 120 may upload each data (such as a vehicle body insulation resistance value) stored in the data storage module to the server 110 through the wireless communication network, so that the server 110 analyzes 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 respective corresponding indication information, such as a safety prompt message, 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 that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and technical computing services such as big data and artificial intelligence platforms.
Optionally, the system may further include a management device, where the management device is configured to manage the system (e.g., manage a connection state between each module and the server, etc.), where the management device is connected to the server through a communication network. Optionally, the communication network is a wired network or a wireless network.
Alternatively, 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, techniques and/or formats including hypertext markup language, extensible markup language, and the like are used to represent data exchanged over a network. All or some of the links may also be encrypted using conventional encryption techniques such as secure socket layer, transport layer security, virtual private network, internet protocol security, etc. 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 flowchart 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 detection method may include the steps of:
step 201, a target battery data set is acquired.
The target battery data set includes battery message data in a target time period.
Optionally, the battery message data may be message data obtained by detecting an operation state of the target battery through each sensor device on the target vehicle, and after the sensor device detects the operation state of the target battery, the message data is transmitted to a data storage device in the target vehicle to generate the 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 comprises data of at least two dimensions of voltage, temperature and insulation resistance of the target battery.
In the embodiment of the application, in order to improve the detection accuracy of the abnormal state of the target battery, the sensor equipment in the target vehicle can respectively detect the voltage, the temperature and the insulation resistance value of the target battery in real time, and generate and store the voltage, the temperature and the insulation resistance value in the data storage equipment according to the time sequence to generate the target battery data set.
Step 202, determining an abnormal weight of the target battery in at least two dimensions and a basic abnormal grade in the at least two dimensions according to a relationship between the degree of difference and the grade threshold value of the battery message data.
After the target battery data set is obtained, the computer device may compare the battery message data existing in the target battery data set according to dimensions, for example, according to a degree of difference of the battery message data in a temperature dimension, so as to determine a 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 the at least two dimensions respectively can be determined according to the relation between the difference degree and the grade threshold.
For example, for the temperature dimension, when 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, at this time, the abnormality weight of the temperature dimension can 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 greater, it is indicated that the greater the abnormality condition in the temperature dimension that can be represented by the temperature difference at this time, the greater the abnormality weight should be allocated to the temperature dimension.
For other dimensions, the anomaly weights and the base anomaly levels should also be obtained through the above logic, and will not be described herein.
And 203, weighting the basic abnormality grades of the at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality condition of the target battery.
When the abnormal weights of the target battery in at least two dimensions and the basic abnormal grades of the target battery are obtained, the comprehensive abnormal grade of the target battery is obtained in a weighting mode, so that the abnormal condition of the target battery is indicated, the comprehensive abnormal grade is obtained by integrating the abnormal conditions of the data of each dimension, the working state of the target battery in a target time period can be reflected more truly, and the accuracy of judging the battery abnormality 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 acquired first; and determining the basic abnormality grade and the abnormality weight of the target battery in the two dimensions according to the relationship between the difference degree and the grade threshold value of the data in the two dimensions, and weighting the basic abnormality grade of the target battery in the two dimensions according to the abnormality weight of the target battery in the two dimensions to obtain the final comprehensive abnormality grade of the target battery. The comprehensive abnormality level obtained by the scheme comprehensively considers the data of multiple dimensions obtained by the target battery in the operation process, judges whether the data are abnormal according to the difference degree between the data, and can more accurately and comprehensively represent the operation state of the target battery, so that whether the target battery is abnormal is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Fig. 3 is a method flowchart 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 detection method may include the steps of:
Step 301, a target battery data set is acquired.
In one possible implementation manner of the embodiment of the present application, the target battery data set includes battery message data in a target time period, where the battery message data includes data of the target battery in a voltage dimension, data of the target battery in a temperature dimension, and data of the target battery in an insulation resistance dimension.
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 the target battery.
In one possible implementation, the battery message data in the target time period is obtained by setting a sliding window threshold in the target battery data set.
During the operation of the target battery, the sensor in the target vehicle detects dimension data of the target battery according to a certain period, and sends the generated data to the data storage device in the target vehicle in the form of a message.
At this time, the data processor in the target vehicle may set a sliding window with a designated threshold value of N in the target battery data set stored in the data memory, and when the message data in the sliding window reaches the threshold value, each time one message data is newly added in real time, the earliest message data in the sliding window may be sequentially discarded, so as to obtain the battery message data generated in the latest target time period, so as to analyze the state of the target battery in real time.
Step 302, determining an abnormal weight of the target battery in at least two dimensions and a basic abnormal grade in the at least two dimensions according to a relationship between the degree of difference and the grade threshold value of the battery message data.
Optionally, the degree of difference between the battery message data includes a dimension difference value of the battery message data in a target dimension; the dimension difference is the difference between the maximum and minimum values 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 example that the target dimension is a temperature dimension, the difference degree between the battery message data includes the difference between the maximum value and the 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 condition of the temperature is in the target time period indicated by the sliding window, the unstable operation of the battery is more likely to occur.
For example, let the voltage base anomaly level BVRank be divided into three levels, 1 represents a slight anomaly condition of the voltage, 2 represents a moderate anomaly condition of the voltage, 3 represents a severe anomaly condition of the voltage, and BVRank is different in voltage anomaly weight, and the higher BVRank is, the greater voltage anomaly weight is. Finally, voltage anomaly class vrank=bvrank.
The temperature base abnormal grade BTRank is divided into three stages, wherein 1 represents a slight abnormal condition of temperature, 2 represents a moderate abnormal condition of temperature, 3 represents a serious abnormal condition of temperature, and the temperature abnormal weights are different from each other, and the higher the BTRank is, the larger the temperature abnormal weight is. Finally, temperature anomaly class trank=btrank.
The border base anomaly level BRRank is divided into three levels, wherein 1 represents that insulation is slightly abnormal, 2 represents that insulation is moderately abnormal, 3 represents that insulation is severely abnormal, and the insulation anomaly weights are different from each other and are larger as BRRank is higher. Finally, insulation anomaly level rrank=brrank.
In one possible implementation, when the dimension difference value is between the first level threshold and the second level threshold, determining the base anomaly level of the target dimension as the first base anomaly level; and obtaining 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.
Also exemplified by the dimension difference in the temperature dimension, after the dimension difference in the temperature dimension over the target time period is calculated, the dimension difference can be compared with the respective level thresholds. When the dimension difference is less than the first level threshold, the base anomaly level for the temperature dimension and anomaly weight may be determined to be 0. And when the dimension difference value is greater than the first level threshold and less than the second level threshold, determining the base anomaly level of the target dimension as the first base anomaly level.
In addition, as a certain error exists when the dimension difference value of the temperature dimension is evaluated only through the first basic abnormal level, the specific size of the dimension difference value is difficult to further describe only by considering that the dimension difference value is between the first level threshold and the second level threshold, in one possible implementation manner of the embodiment of the application, the difference between the dimension difference value and the first level threshold and the difference between the first level threshold and the second level threshold can be divided to obtain the difference between the dimension difference value and the first level threshold and the ratio between the difference between the first level threshold and the second level threshold, so that the position of the section formed by the dimension difference value between the first level threshold and the second level threshold is determined, and the abnormal weight of the target dimension is obtained according to the ratio, so that the first basic abnormal level of the target dimension is weighted, and the accurate extraction of the abnormal characteristics 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 for the target dimension as the second base anomaly level; and obtaining 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.
Also exemplified by the dimension difference of the temperature dimension, 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 to be the second base anomaly level.
In order to extract the abnormal characteristics of the temperature dimension more accurately, in a possible implementation manner of the embodiment of the 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 can be divided to obtain the ratio between the dimension difference and the second level threshold and the ratio between the second level threshold and the third level threshold, so that the position of the dimension difference between the second level threshold and the third level threshold is determined, the abnormal weight of the target dimension is obtained according to the ratio, and the second basic abnormal level of the target dimension is weighted, thereby realizing accurate acquisition of the abnormal characteristics of the target dimension.
In one possible implementation, when the dimension difference is greater than a third level threshold, determining the base anomaly level for the target dimension as a third base anomaly level; and determining the dimension difference value as an abnormal weight of the target dimension.
And when the dimension difference value is larger than a third level threshold, directly determining the basic abnormality level of the target dimension as a third basic abnormality level, and taking the size of the dimension difference value as the abnormality weight of the target dimension, wherein the abnormality weight is larger when the dimension difference value is larger, and the abnormality condition of the temperature dimension can be reflected when the dimension difference value is larger.
For example, an anomaly level TRank concrete calculation method of a temperature dimension. And calculating the difference delta T between the maximum monomer temperature and the minimum monomer temperature in the current sliding window, wherein the threshold value of BTRank 1 is delta T1, the threshold value of BTRank 2 is delta T2, and the threshold value of BTRank 3 is delta T3.
When the temperature difference Δt is greater than or equal to Δt1 and smaller than Δt2, BTRank is 1, and the abnormal weight of the temperature dimension is as follows.
When the temperature difference Δt is greater than or equal to Δt2 and smaller than Δt3, BTRank is 2, and the abnormal weight of the temperature dimension is as follows.
When the temperature difference deltat is greater than or equal to deltat 3, BTRank is 3, and the abnormal weight of the temperature dimension is constantly 3.
Finally, trank=btrank×w T 。
The above basic anomaly level and anomaly weight use temperature dimension as an example, and it should be noted that, in the above scheme of the embodiment of the present application, data of each dimension may be implemented, and a specific implementation process is similar to the above scheme and will not be repeated here.
For example, the voltage abnormality level VRank is specifically calculated as follows.
And calculating a difference value delta V between the maximum monomer voltage and the minimum monomer voltage in the current sliding window, wherein the threshold value of BVRank being 1 is delta V1, the threshold value of BVRank being 2 is delta V2, and the threshold value of BVRank being 3 is delta V3.
When the differential voltage Δv is equal to or greater than Δv1 and smaller than Δv2, BVRank is 1, and the voltage anomaly weight is as follows.
When the differential voltage Δv is equal to or greater than Δv2 and smaller than Δv3, BVRank is 2, and the voltage anomaly weight is as follows.
When the differential pressure Δv is equal to or greater than Δv3, BVRank is 3, and the voltage anomaly weight is constant 3.
Finally, vrank=bvrank×w v 。
For another example, the insulation abnormality level RRank is specifically calculated as follows.
And calculating a difference value delta R between the maximum insulation resistance and the minimum insulation resistance in the current sliding window, wherein the threshold value of BRRank being 1 is delta R1, the threshold value of BRRank being 2 is delta R2, and the threshold value of BRRank being 3 is delta R3.
When the insulation resistance difference Δr is equal to or greater than Δr1 and smaller than Δr2, BRRank is 1, and the insulation abnormality weight is as follows.
When the insulation resistance difference value Δr is greater than or equal to Δr2 and smaller than Δr3, the absolute BRRank is 2, and the insulation abnormality weight is as follows.
When the insulation resistance difference value delta R is more than or equal to delta R3, BRRank is 3, and the insulation abnormal weight is constant 3. Finally, rrank=brrank×w R 。
That is, the data of each dimension is judged by the scheme, so that the basic abnormality level and the abnormality weight of the target battery in each dimension can be obtained. The actual anomaly level of each dimension can be calculated by the product of the anomaly weight and the base anomaly level of each dimension.
And 303, weighting the basic abnormality grades of the at least two dimensions by using the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery.
In one possible implementation manner, in each dimension, the basic anomaly level in the dimension is weighted by the anomaly weight to obtain the actual anomaly weight of each dimension, and at this time, the actual anomaly weights of each dimension are added to obtain the comprehensive anomaly level of the target battery.
In another possible implementation manner, the anomaly weight of the target battery in at least two dimensions is subjected to first weighting processing on the basic anomaly level of the at least two dimensions, so as to obtain the weighted anomaly level of the at least two dimensions; obtaining comprehensive anomaly weights of at least two dimensions based on the difference between the anomaly weights of the at least two dimensions and the sequence numbers of the basic anomaly level; and carrying out second weighting processing on the weighted abnormality grades of the at least two dimensions according to the comprehensive abnormality weights of the at least two dimensions to obtain the comprehensive abnormality grade of the target battery.
For example, the voltage anomaly composite weight CVW, the temperature anomaly composite weight CTW and the absolute value are VRank, TRank, RRankAnd comprehensively judging the abnormal condition of the battery pack by using the edge abnormality comprehensive weight CRW. Setting the basic weights of voltage, temperature and insulation as 1, and the comprehensive weight calculation logic is as follows: the specific calculation mode of the difference value between the basic weight and the corresponding basic level is as follows: cvw=1+ (W v -BVRank)，CTW＝1+(W T -BTRank)，CRW＝1+(W R BRRank). The composite anomaly level of the secondary battery Δ=cvw vrank+ctw trank+crw RRank.
And step 304, judging the abnormal condition of the target battery according to the comprehensive abnormal grade of the target battery.
In one possible implementation, when the integrated anomaly level of the target battery is less than a first anomaly threshold value, then determining the state of the target battery as being anomaly-free;
when the comprehensive abnormality level of the target battery is greater than the first threshold and less than the second abnormality threshold, the state of the target battery is determined to be a slight abnormality.
And when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and less than the third abnormality threshold, determining the state of the target battery as a medium abnormality.
And when the comprehensive abnormality level of the target battery is greater than a third abnormality threshold, determining the state of the target battery as a serious abnormality.
Setting a battery pack comprehensive slight abnormal threshold (and a first abnormal threshold) as delta 1, setting a battery pack comprehensive medium abnormal threshold (namely a second abnormal threshold) as delta 2, setting a battery pack comprehensive serious abnormal threshold (namely a third abnormal threshold) as delta 3, and when the battery pack comprehensive abnormal grade delta is smaller than delta 1, enabling the battery pack to have no abnormal condition; when the comprehensive abnormal grade delta of the battery pack is more than or equal to delta 1 and less than delta 2, the battery pack has slight abnormal conditions; when the comprehensive abnormal grade delta of the battery pack is more than or equal to delta 2 and less than delta 3, the battery pack has a moderate abnormal condition; when the comprehensive abnormality grade delta of the battery pack is more than or equal to delta 3, the battery pack has serious abnormality.
In one possible implementation, the first anomaly threshold value, the second anomaly threshold value, and the third anomaly threshold value are obtained according to a cell parameter of the target battery.
In one possible implementation, obtaining the cell parameters of the target battery; the battery cell parameters comprise at least one of battery cell structure, battery cell material, battery cell capacity and battery cell internal resistance; inquiring in a battery cell threshold value table according to the battery cell parameters of the target battery to obtain an abnormal threshold value set corresponding to the battery cell parameters; the set of anomaly thresholds includes at least one of a first anomaly threshold value, a second anomaly threshold value, and a third anomaly threshold value.
Optionally, the anomaly threshold set further includes a first level threshold, a second level threshold, and a third level threshold for each dimension.
The computer equipment also obtains the battery core parameters of the target battery before analyzing the operation data of the target battery, including the battery core structure (such as the battery core size), the battery core material, the battery core capacity and the battery core internal resistance, and queries in a battery core threshold table according to the battery core parameters of the target battery, so as to determine an abnormal threshold set matched with the target battery, and improve the accuracy of judging battery abnormality.
When determining the abnormal threshold value set matched with the target battery, 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, battery cell material, battery cell capacity and battery cell internal resistance, and each threshold value can be obtained according to a certain calculation flow. The calculation flow can be flexibly set according to the running environment of the vehicle, the overall condition of the vehicle and the like, and the application is not limited to the above.
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 acquired first; and determining the basic abnormality grade and the abnormality weight of the target battery in the two dimensions according to the relationship between the difference degree and the grade threshold value of the data in the two dimensions, and weighting the basic abnormality grade of the target battery in the two dimensions according to the abnormality weight of the target battery in the two dimensions to obtain the final comprehensive abnormality grade of the target battery. The comprehensive abnormality level obtained by the scheme comprehensively considers the data of multiple dimensions obtained by the target battery in the operation process, judges whether the data are abnormal according to the difference degree between the data, and can more accurately and comprehensively represent the operation state of the target battery, so that whether the target battery is abnormal is determined, and the accuracy of detecting the abnormality of the target battery is improved.
Fig. 4 is a flow chart illustrating a method of detecting battery pack anomalies 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 thresholds for judging abnormal conditions of the battery pack by combining comprehensive influence factors such as a battery cell structure, a battery cell material, a battery cell voltage, a battery cell capacity, a battery cell internal resistance, a data uploading frequency and the like.
2) Data cleaning is performed. Duplicate data and invalid data (invalid data comprises data without sampling time, data without frame number, non-real-time and concurrent data) in the battery pack operation data are removed in real time.
3) A real-time sliding window threshold is set. The battery pack abnormality detection is required according to real-time message data within a period of time, and the number of messages N within the period of time is set as a sliding window threshold. And when one message data is newly added in real time, the earliest message data in the sliding window is sequentially discarded, so that the length of the sliding window is ensured to be unchanged, and the length of the sliding window is always kept to be N.
4) And calculating the voltage abnormality level. Let voltage base anomaly level BVRank be divided into three levels, 1 represents voltage occurrence slight anomaly, 2 represents voltage occurrence moderate anomaly, 3 represents voltage occurrence serious anomaly, and BVRank is different in voltage anomaly weight, and the higher BVRank is, the greater the voltage anomaly weight is. Finally, voltage anomaly class vrank=bvrank.
5) And calculating the temperature anomaly level. Setting the temperature basic anomaly level BTRank to be three-level, wherein 1 represents a slight anomaly condition of the temperature, 2 represents a moderate anomaly condition of the temperature, 3 represents a serious anomaly condition of the temperature, and the temperature anomaly weights are different from each other, and the higher the BTRank is, the larger the temperature anomaly weight is. Finally, temperature anomaly class trank=btrank.
6) And calculating the insulation abnormality grade. The insulation basic anomaly level BRRank is divided into three levels, wherein 1 represents that insulation is slightly abnormal, 2 represents that insulation is moderately abnormal, 3 represents that insulation is severely abnormal, and the insulation anomaly weights are different from each other, and the higher the BRRank is, the larger the insulation anomaly weight is. Finally, insulation anomaly level rrank=brrank.
7) And judging the comprehensive abnormal grade of the battery pack through the abnormal grade of the dimensions such as voltage, temperature, insulation resistance 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 structure of a battery abnormality detection apparatus according to an exemplary embodiment. The battery abnormality detection apparatus is applied to a computer device, which may be a data processing device 110 in a target vehicle as shown in fig. 1, and includes:
A data set acquisition module 510, configured to acquire a target battery data set; the target battery data set comprises battery message data in a target time period; the battery message data comprise data of at least two dimensions of voltage, temperature and insulation resistance of the target battery;
the difference judging module 520 is configured to determine an abnormal weight of the target battery in at least two dimensions and a basic abnormal level of the target battery in the at least two dimensions according to a relationship between the degree of difference and a level threshold value of the battery message data;
the anomaly determination module 530 is configured to weight the basic anomaly level of the at least two dimensions according to the anomaly weight 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 the anomaly condition of the target battery.
In one possible implementation, the apparatus further includes:
the first judging module is used for determining the state of the target battery to be abnormal when the comprehensive abnormal level of the target battery is smaller than a first abnormal threshold value;
the second judging module is used for determining the state of the target battery as slight abnormality when the comprehensive abnormality level of the target battery is larger than the first abnormality threshold and smaller than the second abnormality threshold;
A third judging module, configured to determine a state of the target battery as a medium abnormality when the comprehensive abnormality level of the target battery is greater than the second abnormality threshold and less than a third abnormality threshold;
and the fourth judging module is used for determining the state of the target battery as serious abnormality when the comprehensive abnormality level of the target battery is greater than the third abnormality 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 battery cell threshold table according to the battery cell parameters of the target battery to obtain an abnormal threshold set corresponding to the battery cell parameters; the set of anomaly thresholds includes at least one of a first anomaly threshold value, a second anomaly threshold value, and a third anomaly threshold value.
In one possible implementation manner, the degree of difference between the battery message data includes a dimension difference value 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;
and the first abnormal weight determining unit is used for 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.
In one possible implementation manner, the difference judging module further includes:
a second base level determining unit configured to determine a base abnormality level of the target dimension as a second base abnormality level when the dimension difference value is between the second level threshold and a third level threshold;
and the second abnormal 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 abnormal weight of the target dimension.
In one possible implementation manner, the difference judging module further includes:
a third base level determining unit, configured to determine, when the dimension difference value is greater than the third level threshold, that the base anomaly level of the target dimension is a third base anomaly level;
and a third abnormal weight determining unit, configured to determine the third level threshold as an abnormal weight of the target dimension.
In one possible implementation, the anomaly determination module is further configured to,
performing first weighting processing on the basis anomaly grades of at least two dimensions on the anomaly weights of the target battery in the at least two dimensions to obtain weighted anomaly grades of the at least two dimensions;
obtaining comprehensive anomaly weights of at least two dimensions based on the difference between the anomaly weights of the at least two dimensions and the sequence numbers of the basic anomaly level;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weights 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 acquired first; and determining the basic abnormality grade and the abnormality weight of the target battery in the two dimensions according to the relationship between the difference degree and the grade threshold value of the data in the two dimensions, and weighting the basic abnormality grade of the target battery in the two dimensions according to the abnormality weight of the target battery in the two dimensions to obtain the final comprehensive abnormality grade of the target battery. The comprehensive abnormality level obtained by the scheme comprehensively considers the data of multiple dimensions obtained by the target battery in the operation process, judges whether the data are abnormal according to the difference degree between the data, and can more accurately and comprehensively represent the operation state of the target battery, so that whether the target battery is abnormal 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 application. The computer device may be implemented as a server in the above-described aspects of the present application. The computer apparatus 600 includes a central processing unit (Central Processing Unit, CPU) 611, a system Memory 604 including a random access Memory (Random Access Memory, RAM) 602 and a Read-Only Memory (ROM) 603, and a system bus 605 connecting the system Memory 604 and the central processing unit 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 a compact disk-Only (CD-ROM) drive.
The computer readable medium may include computer storage media and communication media without loss of generality. 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 register (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 (Digital Versatile Disc, 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 recognize that the computer storage medium is not limited to the one described above. The system memory 604 and mass storage 606 described above may be collectively referred to as memory.
According to various embodiments of the present disclosure, the computer device 600 may also operate by being connected to a remote computer on a network, such as the Internet. I.e., the computer device 600 may be connected to the network 608 through a network interface unit 607 connected to the system bus 605, or alternatively, the network interface unit 607 may be used to connect to other types of networks or remote computer systems (not shown).
The memory further comprises at least one computer program stored in the memory, and the central processor 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 that 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 Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
In an exemplary embodiment, a computer program product or a computer program is also 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 to cause the computer device to perform all or part of the steps of the method shown in any of the embodiments of fig. 2 or 3 described above.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application 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 application 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 is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (9)
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 comprise data of at least two dimensions of voltage, temperature and insulation resistance of the target battery;
determining the abnormal weight of the target battery in at least two dimensions and the basic abnormal grade in the at least two dimensions according to the relationship between the degree of difference and the grade threshold value of the battery message data;
weighting the basic abnormality grades of the at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality condition of the target battery;
the difference degree between the battery message data comprises a dimension difference value 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 grade in at least two dimensions according to the relationship between the degree of difference and the grade threshold value of the battery message data, 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 basic abnormality level of the target dimension as a first basic abnormality level;
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;
determining the sum of the ratio and the first level threshold as an abnormal weight of the target dimension, including:
and determining the sum of the ratio and the value corresponding to the first level threshold as the abnormal weight of the target dimension.
2. The method according to claim 1, wherein the method further comprises:
when the comprehensive abnormality level of the target battery is smaller than a first abnormality threshold, determining the state of the target battery as abnormal-free;
when the comprehensive abnormality level of the target battery is greater than the first abnormality threshold and less than a second abnormality threshold, determining the state of the target battery as slightly abnormal;
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;
And when the comprehensive abnormality level of the target battery is greater than the third abnormality threshold, determining the state of the target battery as severely abnormal.
3. The method according to claim 2, wherein the method further comprises:
acquiring the cell parameters of the target battery; 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 internal resistance;
inquiring in a battery cell threshold value table according to the battery cell parameters of the target battery to obtain an abnormal threshold value set corresponding to the battery cell parameters; the set of anomaly thresholds includes at least one of a first anomaly threshold value, a second anomaly threshold value, and a third anomaly threshold value.
4. The method according to claim 1, wherein the method further comprises:
when the dimension difference value is between the second level threshold and a third level threshold, determining that the basic abnormality level of the target dimension is a second basic abnormality level;
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;
Determining the sum of the ratio and the second level threshold as an anomaly weight for the target dimension, comprising:
and determining the sum of the values of the ratio and the second level threshold as the abnormal weight of the target dimension.
5. The method according to claim 4, wherein the method further comprises:
when the dimension difference value is larger than the third level threshold, determining that the basic abnormality level of the target dimension is a third basic abnormality level;
determining the third level threshold as an abnormal weight of the target dimension;
determining the third level threshold as an outlier weight for the target dimension, comprising:
and determining a value corresponding to the third level threshold as an abnormal weight of the target dimension.
6. The method of claim 1, wherein weighting the anomaly level of the at least two dimensions to obtain a composite anomaly level for the target battery comprises:
performing first weighting processing on the basis anomaly grades of at least two dimensions on the anomaly weights of the target battery in the at least two dimensions to obtain weighted anomaly grades of the at least two dimensions;
Obtaining comprehensive abnormal weights of at least two dimensions based on the difference value between the abnormal weights of the at least two dimensions and the numerical values corresponding to the basic abnormal grades;
and carrying out second weighting processing on the weighted abnormal grades of the at least two dimensions according to the comprehensive abnormal weights of the at least two dimensions to obtain the comprehensive abnormal grade of the target battery.
7. A battery abnormality detection device, characterized by comprising:
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 comprise data of at least two dimensions of voltage, temperature and insulation resistance of the 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 grade in the at least two dimensions according to the relation between the difference degree and the grade threshold value of the battery message data;
the abnormality determining module is used for weighting the basic abnormality grades of the at least two dimensions through the abnormality weights of the target battery in the at least two dimensions to obtain the comprehensive abnormality grade of the target battery so as to indicate the abnormality condition of the target battery;
The difference degree between the battery message data comprises a dimension difference value 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 grade in at least two dimensions according to the relationship between the degree of difference and the grade threshold value of the battery message data, 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 basic abnormality level of the target dimension as a first basic abnormality level;
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;
determining the sum of the ratio and the first level threshold as an abnormal weight of the target dimension, including:
and determining the sum of the ratio and the value corresponding to the first level threshold as the abnormal weight of the target dimension.
8. A computer device comprising a processor and a memory having stored therein at least one instruction that is loaded and executed by the processor to implement the battery anomaly detection method of any one of claims 1 to 6.
9. A computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the battery anomaly detection method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111442794.3A CN114137421B (en) | 2021-11-30 | 2021-11-30 | Battery abnormality detection method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111442794.3A CN114137421B (en) | 2021-11-30 | 2021-11-30 | Battery abnormality detection method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114137421A CN114137421A (en) | 2022-03-04 |
| CN114137421B true CN114137421B (en) | 2023-09-19 |
Family
ID=80389897
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111442794.3A Active CN114137421B (en) | 2021-11-30 | 2021-11-30 | Battery abnormality detection method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114137421B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114705995A (en) * | 2022-03-25 | 2022-07-05 | 章鱼博士智能技术(上海)有限公司 | Electric connection state identification method, device, equipment and storage medium |
| CN115277491B (en) * | 2022-06-15 | 2023-06-06 | 中国联合网络通信集团有限公司 | Method and device for determining abnormal data and computer readable storage medium |
| CN115081552B (en) * | 2022-07-28 | 2022-11-11 | 一道新能源科技(衢州)有限公司 | Solar cell data exception handling method and system based on cloud platform |
| CN115237040B (en) * | 2022-09-23 | 2022-12-16 | 河北东来工程技术服务有限公司 | Ship equipment safety operation management method, system, device and medium |
| CN116030550B (en) * | 2023-03-24 | 2023-06-23 | 中国汽车技术研究中心有限公司 | Abnormality recognition and processing method, device and medium for vehicle state data |
| CN116719701B (en) * | 2023-08-10 | 2024-03-08 | 深圳海辰储能控制技术有限公司 | Method and device for determining running state of energy storage system and computer equipment |
| CN117096475B (en) * | 2023-10-20 | 2024-01-30 | 珠海中力新能源科技有限公司 | Battery pack management method and device, electronic equipment and storage medium |
| 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 |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105954615A (en) * | 2016-04-29 | 2016-09-21 | 国家电网公司 | State assessment method and assessment system after transformer short circuit |
| CN109323363A (en) * | 2018-09-30 | 2019-02-12 | 广东美的制冷设备有限公司 | Secondary refrigerant leakage fault detection method, detection system and the air conditioner of air conditioner |
| CN109669143A (en) * | 2019-01-30 | 2019-04-23 | 中航锂电（洛阳）有限公司 | A kind of battery capacity evaluating method |
| CN110579716A (en) * | 2019-10-22 | 2019-12-17 | 东软睿驰汽车技术（沈阳）有限公司 | Battery detection method and device |
| CN110687457A (en) * | 2019-11-13 | 2020-01-14 | 东软睿驰汽车技术（沈阳）有限公司 | Battery pack abnormity detection method and device, storage medium and electronic equipment |
| CN110715678A (en) * | 2019-10-22 | 2020-01-21 | 东软睿驰汽车技术（沈阳）有限公司 | Sensor abnormity detection method and device |
| CN111537939A (en) * | 2020-04-17 | 2020-08-14 | 武汉格蓝若智能技术有限公司 | Voltage transformer state evaluation method and device based on multi-index fusion |
| JP2020136247A (en) * | 2019-02-26 | 2020-08-31 | 株式会社豊田自動織機 | Abnormality detection device of parallel unit |
| CN112092675A (en) * | 2020-08-31 | 2020-12-18 | 长城汽车股份有限公司 | Battery thermal runaway early warning method, system and server |
| CN112289379A (en) * | 2020-10-15 | 2021-01-29 | 天津诺禾致源生物信息科技有限公司 | Method and device for determining cell type, storage medium and electronic device |
| CN112444748A (en) * | 2020-10-12 | 2021-03-05 | 武汉蔚来能源有限公司 | Battery abnormality detection method, battery abnormality detection device, electronic apparatus, and storage medium |
| CN112580961A (en) * | 2020-12-15 | 2021-03-30 | 国网电力科学研究院有限公司 | Power grid information system based operation risk early warning method and device |
| CN112766429A (en) * | 2021-04-09 | 2021-05-07 | 北京瑞莱智慧科技有限公司 | Method, device, computer equipment and medium for anomaly detection |
| CN112816885A (en) * | 2021-01-06 | 2021-05-18 | 北京嘀嘀无限科技发展有限公司 | Battery abnormity detection method and device, electronic equipment and storage medium |
| CN113030740A (en) * | 2021-03-02 | 2021-06-25 | 北京嘀嘀无限科技发展有限公司 | Storage battery abnormity detection method and device, electronic equipment and storage medium |
| CN113219353A (en) * | 2021-03-24 | 2021-08-06 | 浙江合众新能源汽车有限公司 | BMS-based thermal runaway control method and system |
-
2021
- 2021-11-30 CN CN202111442794.3A patent/CN114137421B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105954615A (en) * | 2016-04-29 | 2016-09-21 | 国家电网公司 | State assessment method and assessment system after transformer short circuit |
| CN109323363A (en) * | 2018-09-30 | 2019-02-12 | 广东美的制冷设备有限公司 | Secondary refrigerant leakage fault detection method, detection system and the air conditioner of air conditioner |
| CN109669143A (en) * | 2019-01-30 | 2019-04-23 | 中航锂电（洛阳）有限公司 | A kind of battery capacity evaluating method |
| JP2020136247A (en) * | 2019-02-26 | 2020-08-31 | 株式会社豊田自動織機 | Abnormality detection device of parallel unit |
| CN110579716A (en) * | 2019-10-22 | 2019-12-17 | 东软睿驰汽车技术（沈阳）有限公司 | Battery detection method and device |
| CN110715678A (en) * | 2019-10-22 | 2020-01-21 | 东软睿驰汽车技术（沈阳）有限公司 | Sensor abnormity detection method and device |
| CN110687457A (en) * | 2019-11-13 | 2020-01-14 | 东软睿驰汽车技术（沈阳）有限公司 | Battery pack abnormity detection method and device, storage medium and electronic equipment |
| CN111537939A (en) * | 2020-04-17 | 2020-08-14 | 武汉格蓝若智能技术有限公司 | Voltage transformer state evaluation method and device based on multi-index fusion |
| CN112092675A (en) * | 2020-08-31 | 2020-12-18 | 长城汽车股份有限公司 | Battery thermal runaway early warning method, system and server |
| CN112444748A (en) * | 2020-10-12 | 2021-03-05 | 武汉蔚来能源有限公司 | Battery abnormality detection method, battery abnormality detection device, electronic apparatus, and storage medium |
| CN112289379A (en) * | 2020-10-15 | 2021-01-29 | 天津诺禾致源生物信息科技有限公司 | Method and device for determining cell type, storage medium and electronic device |
| CN112580961A (en) * | 2020-12-15 | 2021-03-30 | 国网电力科学研究院有限公司 | Power grid information system based operation risk early warning method and device |
| CN112816885A (en) * | 2021-01-06 | 2021-05-18 | 北京嘀嘀无限科技发展有限公司 | Battery abnormity detection method and device, electronic equipment and storage medium |
| CN113030740A (en) * | 2021-03-02 | 2021-06-25 | 北京嘀嘀无限科技发展有限公司 | Storage battery abnormity detection method and device, electronic equipment and storage medium |
| CN113219353A (en) * | 2021-03-24 | 2021-08-06 | 浙江合众新能源汽车有限公司 | BMS-based thermal runaway control method and system |
| CN112766429A (en) * | 2021-04-09 | 2021-05-07 | 北京瑞莱智慧科技有限公司 | Method, device, computer equipment and medium for anomaly detection |
Non-Patent Citations (2)
| Title |
|---|
| BT project actualized mode analysis;ZHANG Y K，LIU W G;《Municipal Engineering Technology》;89 － 92 * |
| BT 项目风险模糊综合评判理论及方法研究;熊光红;《武汉理工大学学报( 信息与管理工程版)》;270-273 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114137421A (en) | 2022-03-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114137421B (en) | Battery abnormality detection method, device, equipment and storage medium | |
| CN109828220B (en) | Linear evaluation method for health state of lithium ion battery | |
| CN113138340A (en) | Method for establishing battery equivalent circuit model and method and device for estimating state of health | |
| CN116381514B (en) | Cell differential pressure early warning method, device, storage medium and equipment | |
| CN113608132B (en) | Method, system and storage medium for determining residual capacity of lithium ion battery | |
| CN112989569B (en) | Lithium battery sensor fault filtering diagnosis method with temperature constraint condition | |
| CN118251603A (en) | Battery state of health estimation system, parameter extraction system and method for same | |
| CN114169154A (en) | Expansive force prediction method, expansive force prediction system, electronic device, and storage medium | |
| CN109061482B (en) | Battery health degree prediction method and device | |
| CN116540108B (en) | Method, device, storage medium and equipment for early warning of capacity attenuation of battery cell | |
| CN115796708B (en) | Big data intelligent quality inspection method, system and medium for engineering construction | |
| CN110888065A (en) | Battery pack state of charge correction method and device | |
| Zhou et al. | Research on Online Capacity Estimation of Power Battery Based on EKF‐GPR Model | |
| CN114371408A (en) | Estimation method of battery charge state, and extraction method and device of charging curve | |
| CN113985178A (en) | Charging pile state detection method, device, equipment and storage medium | |
| CN113125965B (en) | Method, device and equipment for detecting lithium separation of battery and storage medium | |
| CN112230152B (en) | Method and system for measuring internal resistance increment of single battery cell | |
| CN115923592A (en) | Battery state detection method and device for electric vehicle | |
| Khaki et al. | Vanadium redox battery parameter estimation using electrochemical model by reduced number of sensors | |
| CN116090353A (en) | Product remaining life prediction method and device, electronic equipment and storage medium | |
| CN113608133B (en) | Method, system and storage medium for determining residual capacity of lithium iron phosphate battery | |
| CN116243199A (en) | Method and device for detecting abnormal battery cell and computer storage medium | |
| CN113777497B (en) | Online SOC and SOH joint estimation method and device for degraded battery, storage medium and electronic equipment | |
| CN111751732A (en) | Electric quantity calculation method based on self-adaptive Gaussian convolution component method | |
| CN114994542B (en) | Method and device for estimating open-circuit voltage of battery, electronic equipment and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 201824 J, building 2, No. 3131, Jinshajiang Road, Jiading District, Shanghai Applicant after: Dr. Octopus Intelligent Technology (Shanghai) Co.,Ltd. Address before: 201824 J, building 2, No. 3131, Jinshajiang Road, Jiading District, Shanghai Applicant before: Honeycomb energy (Shanghai) Co.,Ltd. |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |