CN117301949A - Vehicle, method for identifying abnormal battery cell of vehicle, storage medium and electronic device - Google Patents
Vehicle, method for identifying abnormal battery cell of vehicle, storage medium and electronic device Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000012216 screening Methods 0.000 claims abstract description 7
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- 238000012423 maintenance Methods 0.000 abstract description 5
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- 208000032953 Device battery issue Diseases 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/24—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention discloses a vehicle and a method for identifying abnormal battery cells thereof, a storage medium and electronic equipment, wherein the vehicle comprises a power battery, the power battery comprises a plurality of battery cells, and the method comprises the following steps: acquiring historical operation data of a vehicle, and screening charging data of a plurality of charging stages from the historical operation data, wherein the charging data comprises voltages of a plurality of electric cores; calculating characteristic values of the battery cells in each charging stage according to the voltages of the battery cells, wherein the characteristic values comprise a mean value and/or a standard deviation; calculating local outlier factor LOF values of all the electric cores in each charging stage according to the characteristic values, and clustering the LOF values to obtain outlier electric cores; and determining abnormal cells in the power battery according to the outlier cells. The method can early warn the abnormality of the battery in advance based on the running data of the vehicle, quickly and accurately locate the abnormal battery core, and avoid thermal runaway of the vehicle through timely maintenance.
Description
Technical Field
The present invention relates to the field of battery technologies, and in particular, to a vehicle, a method for identifying an abnormal battery cell of the vehicle, a storage medium, and an electronic device.
Background
Because of the different manufacturing process levels of the power battery and the running conditions of the vehicle, a plurality of problems are exposed, such as a vehicle endurance problem, a sudden battery failure or a sudden fire of the vehicle in a static or charge-discharge stage and other safety problems, which can influence the use experience of a user and even endanger personal and property safety.
The safety problem of the lithium ion power battery is a key problem in the new energy automobile, so that the utilization rate of the battery is improved, the service life of the battery is prolonged, the power battery of the new energy automobile is monitored in real time, and the early warning of the power battery problem is very important. In addition, the identification efficiency can be improved and the maintenance efficiency can be improved by accurately positioning the abnormal battery cells.
In the related art, abnormal detection is performed on charging and discharging of a lithium battery cell based on PCA (Principal Component Analysis ) decomposition, and abnormal self-discharging of the power battery cell is recognized by adopting a four-bit distance method based on operation data, wherein the methods are all used for recognizing abnormal battery cells according to data characteristics of the battery in a certain state. The battery early warning methods in the prior art have the advantages of pressure difference early warning, working condition early warning and the like, the methods have higher requirements on data, and in the actual use of a vehicle, the requirements on data precision and the high requirements on charging data can not be met due to the problems in the data acquisition and data transmission processes, the charging habit of a user and the like, so that available data is possibly reduced, and data calculation can not be performed due to insufficient effective data.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention aims to provide a vehicle, a method for identifying abnormal battery cells of the vehicle, a storage medium and electronic equipment, so that the abnormal battery cells can be early warned in advance based on the running data of the vehicle, the abnormal battery cells can be quickly and accurately positioned, and the thermal runaway of the vehicle can be avoided through timely maintenance.
To achieve the above object, an embodiment of a first aspect of the present invention provides a method for identifying an abnormal battery cell of a vehicle, the vehicle including a power battery including a plurality of battery cells, the method including: acquiring historical operation data of the vehicle, and screening charging data of a plurality of charging stages from the historical operation data, wherein the charging data comprises voltages of a plurality of battery cells; calculating characteristic values of the battery cells in the charging stages according to the voltages of the battery cells, wherein the characteristic values comprise a mean value and/or a standard deviation; calculating local outlier factor LOF (Local Outlier Factor ) values of the battery cells in each charging stage according to the characteristic values, and clustering the LOF values to obtain outlier battery cells; and determining abnormal cells in the power battery according to the outlier cells.
In addition, the method for identifying the abnormal battery cell of the vehicle according to the embodiment of the invention may further have the following additional technical features:
according to one embodiment of the present invention, the historical operation data further includes a collection time of charging data and a remaining power of the power battery, and the screened charging data of the plurality of charging phases satisfies the following conditions: the power battery residual electric quantity is larger than a preset electric quantity threshold value, and the acquisition time is in a preset time interval.
According to one embodiment of the present invention, the calculating the characteristic value of each battery cell in each charging stage according to the voltage of each battery cell, the method further includes: and determining that the data frame number of the charging stage is greater than or equal to a preset frame number threshold.
According to one embodiment of the present invention, the clustering the LOF values includes: hierarchical clustering is performed on the LOF values in time sequence.
According to one embodiment of the present invention, the determining an abnormal cell in the power battery according to the outlier cell includes: and if the number of the outlier cells is smaller than a preset number threshold, or is larger than or equal to the preset number threshold and the distance between the outlier cells is larger than a preset distance threshold, determining that the outlier cells are abnormal cells.
According to one embodiment of the invention, the method further comprises: and if the number of the outlier cells is greater than or equal to the preset number threshold and the distance between the outlier cells is smaller than or equal to the preset distance threshold, determining that the outlier cells are normal cells.
According to one embodiment of the invention, after determining the abnormal cell, the method further comprises: and carrying out early warning on the vehicle according to the abnormal battery cell.
To achieve the above object, a second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, implements the above-mentioned method for identifying abnormal cells of a vehicle.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, where the computer program, when executed by the processor, implements the method for identifying abnormal battery cells of a vehicle.
To achieve the above object, a fourth aspect of the present invention provides a vehicle including the electronic device according to the above.
According to the vehicle, the identification method of the abnormal battery cell, the storage medium and the electronic equipment, disclosed by the embodiment of the invention, the abnormality of the battery is early warned based on the running data of the vehicle, the abnormal battery cell is rapidly and accurately positioned, and the thermal runaway of the vehicle can be avoided through timely maintenance.
Drawings
FIG. 1 is a flow chart of a method of identifying abnormal cells of a vehicle in accordance with one embodiment of the present invention;
FIG. 2 is a statistical plot of LOF values for individual cells at a certain charge stage according to one embodiment of the invention;
FIG. 3 is a statistical plot of LOF values for individual cells at various charge phases in accordance with one embodiment of the present invention;
FIG. 4 is a statistical plot of the individual cell classifications of one embodiment of the present invention;
FIG. 5 is a statistical plot of the individual cell classifications of another embodiment of the present invention;
FIG. 6 is a block diagram of an electronic device according to one embodiment of the invention;
fig. 7 is a block diagram of a vehicle according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The following describes a vehicle and a method for identifying an abnormal cell thereof, a storage medium, and an electronic apparatus according to an embodiment of the present invention with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method of identifying abnormal battery cells of a vehicle according to an embodiment of the present invention.
The vehicle includes a power battery including a plurality of electrical cells, as shown in fig. 1, the method includes:
s11, acquiring historical operation data of the vehicle, and screening charging data of a plurality of charging stages from the historical operation data, wherein the charging data comprises voltages of a plurality of battery cells.
In particular, historical operating data of the vehicle may be obtained from a remote monitoring platform. The remote monitoring platform can acquire and store the running data of the vehicle in real time. The operation data of the vehicle contains operation State information of the vehicle and basic information such as VIN (Vehicle Identification Number, vehicle identification code), time, remaining power of the power battery, state of the vehicle, gear signal, brake signal, total voltage of the power battery, charge/discharge current, maximum voltage of the battery, minimum voltage of the battery, voltages of the respective battery cells, available capacity of the power battery, and temperatures at the respective temperature detection points. Whether the historical operating data is charging data can be judged through the charging current. Screening out charging data for a plurality of charging phases includes: and selecting charging data of a plurality of charging stages to identify abnormal battery cells according to a charging control strategy and battery characteristics of the vehicle.
S12, calculating characteristic values of the battery cells in each charging stage according to the voltages of the battery cells, wherein the characteristic values comprise a mean value and/or a standard deviation.
Specifically, the average and standard deviation of the cell voltages during a charging phase are shown in table 1, where the average may represent the average state of the voltages and the standard deviation may represent the deviation state of the voltages.
TABLE 1
| Battery cell 1 | Cell 2 | Cell 3 | Cell 4 | Battery cell 5 | |
| Average value (V) | 3.99152 | 3.992355 | 3.993107 | 3.994649 | 3.960937 |
| Standard deviation (V) | 0.090789 | 0.090795 | 0.090804 | 0.090545 | 0.089337 |
S13, calculating local outlier factor LOF values of the battery cells in each charging stage according to the characteristic values, and clustering the LOF values to obtain outlier battery cells.
Specifically, calculating the local outlier factor LOF value of each cell in each charging stage according to the eigenvalue includes: the average value and standard deviation of each cell voltage can be used as the abscissa of a point, the inverse of the average reachable distance from the point to the point p in the k-distance neighborhood of the point p is the local reachable density of the point p, and the ratio of the average of the local reachable densities of all the points in the k-distance neighborhood of the point p to the local reachable density of the point p is the local anomaly factor LOF value of the point p. Wherein the kth distance represents: a distance value from the kth point p; k distance neighborhood represents: all points in the kth neighborhood of the point p, namely, all points within the kth distance of the point p; the reachable distance of point o to point p is the maximum of both the kth distance of point o and the euclidean distance of point o to point p. In any of the charging phases, each cell may calculate a local outlier LOF, as shown in fig. 2, where the abscissa indicates the cell number and the ordinate indicates the local outlier LOF.
S14, determining abnormal battery cells in the power battery according to the outlier battery cells.
Specifically, in the use process of the power battery, the voltage of different battery cells can show different change rules along with the reasons of battery aging, battery unbalance and the like. Compared with a normal cell, the abnormal cell generally shows a change rule that the abnormal cell has obvious outliers and has an outlier expansion trend without obvious outliers. If the change of the abnormal battery core can be observed in advance, namely the abnormal battery core can be identified in advance, early warning can be made in advance, the abnormal battery core is treated, and the risk of thermal runaway of the vehicle can be effectively avoided.
Thermal runaway of the vehicle occurs more often when the vehicle is charged, one of the most obvious data features being voltage. Based on a charging control strategy and battery characteristics of the vehicle, specific charging stage data are selected, and an LOF algorithm is adopted to identify abnormal factors of battery cell voltage characteristics. And calculating LOF values based on each selected charging stage, hierarchical clustering the LOF values in a preset time interval, dividing the LOF values into an outlier type and a non-outlier type, judging whether the abnormal battery cells are abnormal according to the number of the outlier type and the degree of difference between the battery cells in the outlier type, and completing the advanced identification of the abnormal battery cells.
According to the method for identifying the abnormal battery cell of the vehicle, disclosed by the embodiment of the invention, the abnormality of the battery is early warned on the basis of the running data of the vehicle, the abnormal battery cell is rapidly and accurately positioned, and the thermal runaway of the vehicle can be avoided through timely maintenance.
In some embodiments, the historical operating data further includes a collection time of the charging data and a remaining power of the power battery, and the charging data of the plurality of charging stages selected satisfies the following conditions: the power battery residual electric quantity is larger than a preset electric quantity threshold value, and the acquisition time is in a preset time interval.
Specifically, before calculating the characteristic value, the historical operation data can be subjected to null value elimination processing and screening. Considering that the internal state of the power battery is relatively stable in the terminal charging stage, the data acquired when the residual electric quantity of the power battery is higher, namely the residual electric quantity is more than 70%, is selected as charging data. In order to ensure timeliness of the data, 3 months can be used as a preset time interval, and subsequent identification can be performed based on charging data of the acquisition time in the preset time interval.
In some embodiments, the method further includes calculating a characteristic value of each cell in each charging stage according to the voltage of each cell, respectively: and determining that the data frame number of the charging stage is greater than or equal to a preset frame number threshold.
Specifically, when the data in one charging stage is less, if the data is affected by voltage acquisition, data transmission and the like, when the difference between a certain frame of data and other cells is larger, the cell has larger influence on calculation, so that the finally obtained calculation result contains larger error. To eliminate these errors, a lower limit on the number of data frames per charging phase may be set, ignoring for charging phases with a number of data frames less than 100 frames.
In some embodiments, clustering the LOF values includes: hierarchical clustering of LOF values occurs in time order. The clustering can be used for a large number of unlabeled data sets, the data sets are divided into a plurality of different categories according to the data characteristics existing in the data, so that the data in the same category are similar, and the data similarity between the different categories is smaller.
Specifically, as shown in fig. 3, the abscissa is the number of charging stages, the ordinate is the LOF value, and it can be observed that the LOF value of each cell changes with time, for example, curve 1 is the curve of the LOF value of cell 1 in each charging stage, and curve 2 is the curve of the LOF value of cell 2 in each charging stage; hierarchical clustering is carried out on the curves of LOF values of the battery cells shown in the figure 3, each curve is regarded as a sample, and the samples are respectively classified into classes; then, two kinds of wells with the Euclidean distance closest to each other are combined, a new kind is established, and the operation is repeated until all the cells are divided into an isolated cell and a non-isolated cell.
In some embodiments, determining an abnormal cell in the power cell from the outlier cell comprises: if the number of the outlier cells is smaller than a preset number threshold, or is larger than or equal to the preset number threshold and the distance between the outlier cells is larger than a preset distance threshold, determining that the outlier cells are abnormal cells.
Specifically, the preset number threshold may be 2, and the preset distance may be chebyshev distance, that is, if the number of cells in the outlier cells is 1, or if the number of cells is greater than or equal to two cells but the inter-cell chebyshev distance is greater than the preset distance threshold, the outlier cells are determined to be abnormal cells. As shown in fig. 4, the abscissa is time (S), the ordinate is voltage (V), the cell 3 represented by the curve 3 is an outlier cell, the remaining curves represent non-outlier cells, the number of cells in the outlier cell is 1, and the cell 3 represented by the curve 3 is determined to be an abnormal cell.
In some embodiments, the method further comprises: if the number of the outlier cells is greater than or equal to a preset number threshold and the distance between the outlier cells is less than or equal to a preset distance threshold, determining that the outlier cells are normal cells.
Specifically, the preset number threshold may be 2, and the preset distance may be a chebyshev distance, that is, if the number of cells in the outlier cells is greater than or equal to 2 and the inter-cell chebyshev distance is less than or equal to the preset distance threshold, the outlier cells are determined to be normal cells. As shown in fig. 5, the abscissa is time (S), the ordinate is voltage (V), the cells 4 and 5 represented by the curves 4 and 5 are outlier cells, the rest curves represent non-outlier cells, the number of cells in the outlier cells is 2, and the chebyshev distance between the cells 4 and 5 is less than or equal to a preset distance threshold, and it is determined that an abnormal cell is between the cells 4 and 5.
In some embodiments, after determining the abnormal cell, the method further comprises: and early warning is carried out on the vehicle according to the abnormal battery cell.
Specifically, after determining the abnormal cell, an early warning signal and an abnormal cell number may be sent to a vehicle VCU (Vehicle control unit, vehicle controller).
In summary, according to the method for identifying the abnormal battery cell of the vehicle, the remote monitoring platform data is obtained to calculate according to the real-time data; the method considers the voltage characteristics (mean value and standard deviation) of the power battery and fully utilizes vehicle operation data, and combines LOF values and clusters by collecting time, residual electric quantity of the power battery and data frame number screening data, extracts the voltage characteristics of the battery cells and identifies abnormal battery cells. The utilization rate of the data is improved, the abnormal battery cell can be accurately positioned, a vehicle early warning signal is sent in advance, and the risk of thermal runaway of the vehicle is reduced.
Based on the method for identifying the abnormal battery cell of the vehicle in the embodiment, the invention also provides a computer readable storage medium.
In this embodiment, a computer program is stored on a computer readable storage medium, and when the computer program is executed by a processor, the method for identifying abnormal cells of a vehicle described above is implemented.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the invention.
As shown in fig. 6, the electronic device 600 includes: a processor 601 and a memory 603. The processor 601 is coupled to a memory 603, such as via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that, in practical applications, the transceiver 604 is not limited to one, and the structure of the electronic device 600 is not limited to the embodiment of the present invention.
The processor 601 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure. The processor 601 may also be a combination that performs computing functions, such as including one or more microprocessors, a combination of a DSP and a microprocessor, and the like.
Bus 602 may include a path to transfer information between the components. Bus 602 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect Standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 602 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
The memory 603 is used for storing a computer program corresponding to the method of identifying an abnormal cell of a vehicle according to the above-described embodiment of the present invention, which is controlled to be executed by the processor 601. The processor 601 is arranged to execute a computer program stored in the memory 603 for realizing what is shown in the foregoing method embodiments.
Among other things, electronic device 600 includes, but is not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device 600 shown in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
Fig. 7 is a block diagram of a vehicle according to an embodiment of the present invention.
As shown in fig. 7, a vehicle 700 includes: the electronic device 600 described above.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A method of identifying abnormal cells of a vehicle, the vehicle comprising a power cell including a plurality of cells, the method comprising:
acquiring historical operation data of the vehicle, and screening charging data of a plurality of charging stages from the historical operation data, wherein the charging data comprises voltages of a plurality of battery cells;
calculating characteristic values of the battery cells in the charging stages according to the voltages of the battery cells, wherein the characteristic values comprise a mean value and/or a standard deviation;
calculating local outlier factor LOF values of the battery cells in each charging stage according to the characteristic values, and clustering the LOF values to obtain outlier battery cells;
and determining abnormal cells in the power battery according to the outlier cells.
2. The method for identifying abnormal battery cells of a vehicle according to claim 1, wherein the historical operation data further includes a collection time of charging data and a remaining power of the power battery, and the screened charging data of the plurality of charging stages satisfies the following conditions:
the power battery residual electric quantity is larger than a preset electric quantity threshold value, and the acquisition time is in a preset time interval.
3. The method for identifying abnormal cells of a vehicle according to claim 2, wherein the calculating the characteristic value of each cell in each charging stage according to the voltage of each cell, respectively, further comprises:
and determining that the data frame number of the charging stage is greater than or equal to a preset frame number threshold.
4. The method for identifying abnormal cells of a vehicle according to claim 1, wherein the clustering the LOF values includes:
hierarchical clustering is performed on the LOF values in time sequence.
5. The method for identifying abnormal cells of a vehicle according to claim 1, wherein the determining abnormal cells in the power battery according to the outlier cells comprises:
and if the number of the outlier cells is smaller than a preset number threshold, or is larger than or equal to the preset number threshold and the distance between the outlier cells is larger than a preset distance threshold, determining that the outlier cells are abnormal cells.
6. The method for identifying an abnormal cell of a vehicle of claim 5, further comprising:
and if the number of the outlier cells is greater than or equal to the preset number threshold and the distance between the outlier cells is smaller than or equal to the preset distance threshold, determining that the outlier cells are normal cells.
7. The method for identifying an abnormal cell of a vehicle according to claim 1, wherein after determining the abnormal cell, the method further comprises:
and carrying out early warning on the vehicle according to the abnormal battery cell.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method for identifying abnormal cells of a vehicle according to any one of claims 1-7.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory, which when executed by the processor, implements a method of identifying an abnormal cell of a vehicle as claimed in any one of claims 1-7.
10. A vehicle characterized by comprising an electronic device according to claim 9.
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| CN202311501637.4A CN117301949A (en) | 2023-11-10 | 2023-11-10 | Vehicle, method for identifying abnormal battery cell of vehicle, storage medium and electronic device |
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| CN202311501637.4A CN117301949A (en) | 2023-11-10 | 2023-11-10 | Vehicle, method for identifying abnormal battery cell of vehicle, storage medium and electronic device |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647748A (en) * | 2024-01-30 | 2024-03-05 | 宁德时代新能源科技股份有限公司 | Abnormal cell detection method, device, equipment and storage medium |
| CN117907844A (en) * | 2024-03-19 | 2024-04-19 | 中国汽车技术研究中心有限公司 | Method for detecting short circuit abnormality in battery system, electronic device, and medium |
| CN118033467A (en) * | 2024-04-15 | 2024-05-14 | 北汽福田汽车股份有限公司 | Abnormality recognition method and device for power battery, vehicle, medium, and program |
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2023
- 2023-11-10 CN CN202311501637.4A patent/CN117301949A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117647748A (en) * | 2024-01-30 | 2024-03-05 | 宁德时代新能源科技股份有限公司 | Abnormal cell detection method, device, equipment and storage medium |
| CN117647748B (en) * | 2024-01-30 | 2024-05-28 | 宁德时代新能源科技股份有限公司 | Abnormal cell detection method, device, equipment and storage medium |
| CN117907844A (en) * | 2024-03-19 | 2024-04-19 | 中国汽车技术研究中心有限公司 | Method for detecting short circuit abnormality in battery system, electronic device, and medium |
| CN118033467A (en) * | 2024-04-15 | 2024-05-14 | 北汽福田汽车股份有限公司 | Abnormality recognition method and device for power battery, vehicle, medium, and program |
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