CN116736120A - Low-voltage sampling anomaly detection method, device, equipment and readable storage medium - Google Patents

Low-voltage sampling anomaly detection method, device, equipment and readable storage medium Download PDF

Info

Publication number
CN116736120A
CN116736120A CN202310623809.9A CN202310623809A CN116736120A CN 116736120 A CN116736120 A CN 116736120A CN 202310623809 A CN202310623809 A CN 202310623809A CN 116736120 A CN116736120 A CN 116736120A
Authority
CN
China
Prior art keywords
voltage
low
determining
voltage sampling
sampling
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.)
Pending
Application number
CN202310623809.9A
Other languages
Chinese (zh)
Inventor
李云隆
曹强
岳泓亚
黄小清
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing Seres New Energy Automobile Design Institute Co Ltd
Original Assignee
Chongqing Seres New Energy Automobile Design Institute Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chongqing Seres New Energy Automobile Design Institute Co Ltd filed Critical Chongqing Seres New Energy Automobile Design Institute Co Ltd
Priority to CN202310623809.9A priority Critical patent/CN116736120A/en
Publication of CN116736120A publication Critical patent/CN116736120A/en
Pending legal-status Critical Current

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]
    • G01R31/3644—Constructional arrangements
    • G01R31/3646—Constructional arrangements for indicating electrical conditions or variables, e.g. visual or audible indicators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/003—Measuring mean values of current or voltage during a given time interval
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/10—Measuring sum, difference or ratio
    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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
    • 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]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • 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

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Secondary Cells (AREA)
  • Tests Of Electric Status Of Batteries (AREA)

Abstract

The embodiment of the invention relates to the technical field of data processing, and discloses a low-voltage sampling anomaly detection method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: acquiring all cell voltage data corresponding to the power-on time of a target vehicle; determining a cell voltage average value according to the cell voltage data; determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value; and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling result according to the voltage difference value sequence. By applying the technical scheme provided by the invention, the low-voltage sampling structure in the preset time period can be obtained according to the voltage value of the battery cell, so that the intelligent detection of low-voltage sampling is realized, the abnormal low-voltage sampling of the battery is effectively and rapidly early-warned in advance, and the battery low-voltage sampling device has the effects of high reliability, strong practicability and good economy.

Description

Low-voltage sampling anomaly detection method, device, equipment and readable storage medium
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a low-voltage sampling abnormality detection method, a device, equipment and a readable storage medium.
Background
Along with the rapid development of new energy automobiles in recent years, the real automobiles encounter a plurality of new problems never encountered in the use process, the problems of unbalanced battery core, power interruption and the like caused by abnormal low-voltage sampling are common problems in the operation process of the new energy automobiles, the normal operation of the new energy automobiles is influenced, and further, accidents of the vehicles are caused, however, no technical scheme for realizing the method for detecting the abnormality of the low-voltage sampling exists at present.
Disclosure of Invention
In view of the above problems, embodiments of the present invention provide a method, an apparatus, a device, and a readable storage medium for detecting low-voltage sampling anomalies, which are used to solve the technical problem in the prior art that low-voltage sampling anomalies cannot be detected.
According to an aspect of an embodiment of the present invention, there is provided a low-voltage sampling abnormality detection method, including:
acquiring all cell voltage data corresponding to the power-on time of a target vehicle;
determining a cell voltage average value according to the cell voltage data;
determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
In an optional manner, after the obtaining all the cell voltage data corresponding to the power-on time of the target vehicle, the method further includes:
preprocessing the cell voltage data, deleting abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, the determining the average value of the cell voltage according to the cell voltage value comprises the following steps:
and determining the average value of the cell voltage according to the processed cell voltage data.
In an optional manner, after the determining the low-voltage sampling abnormal result according to the voltage difference sequence, the method further includes:
and when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal, determining to perform early warning.
In an optional manner, the determining the abnormal low-voltage sampling result according to the voltage difference sequence includes:
acquiring a preset threshold value;
and determining the low-voltage sampling abnormal result according to the comparison result of the preset threshold value and the voltage difference value sequence.
In an optional manner, the determining the low-voltage sampling abnormal result according to the comparison result of the preset threshold value and the voltage difference value sequence includes:
determining a low-voltage sampling abnormal level according to a comparison result of the preset threshold value and the voltage difference value sequence; wherein the low-voltage sampling anomaly level comprises at least two levels.
In an optional manner, the obtaining a voltage difference sequence corresponding to the target voltage offset in the preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence, includes:
acquiring the voltage difference value sequence corresponding to the target voltage offset in real time according to
The voltage difference sequence determines the low-voltage sampling abnormal result; wherein the preset time period includes a duration.
In an optional manner, the obtaining a voltage difference sequence corresponding to the target voltage offset in the preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence, includes:
and acquiring each adjacent voltage difference sequence of the battery cells corresponding to the target voltage offset in the preset time period, and determining the low-voltage sampling abnormal result according to the adjacent voltage difference sequence of the battery cells.
According to another aspect of the embodiment of the present invention, there is provided a low-voltage sampling abnormality detection apparatus including:
the battery cell voltage data acquisition module is used for acquiring all battery cell voltage data corresponding to the power-on time of the target vehicle;
the cell voltage average value determining module is used for determining a cell voltage average value according to the cell voltage data;
the target voltage offset determining module is used for determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
the low-voltage sampling abnormal result determining module is used for obtaining a voltage difference value sequence corresponding to the target voltage offset in a preset time period and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
According to another aspect of the embodiment of the present invention, there is provided a low-voltage sampling abnormality detection apparatus including:
a memory for storing a computer program;
and the processor is used for realizing the low-voltage sampling abnormality detection method when executing the computer program.
According to another aspect of the embodiments of the present invention, there is provided a computer-readable storage medium having stored therein computer-executable instructions that, when loaded and executed by a processor, implement the above-described low-voltage sampling anomaly detection method.
According to the low-voltage sampling abnormality detection method provided by the embodiment of the invention, all the cell voltage data corresponding to the power-on time of the target vehicle are obtained; determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data; and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, thereby determining a low-voltage sampling abnormal result according to the voltage difference value sequence. Compared with the existing method that abnormality detection cannot be carried out on low-voltage sampling, and the problems of unbalanced faults of the battery cells, power interruption and the like are caused, the method can be used for intelligently detecting abnormality of low-voltage sampling in time, so that the safety of vehicle running is improved.
The low-voltage sampling abnormality detection device, the low-voltage sampling abnormality detection equipment and the readable storage medium provided by the embodiment of the invention also have the beneficial effects.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present invention can be more clearly understood, and the following specific embodiments of the present invention are given for clarity and understanding.
Drawings
The drawings are only for purposes of illustrating embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of a first embodiment of a low-voltage sampling anomaly detection method provided by the present invention;
fig. 2 is a schematic flow chart of a second embodiment of a low-voltage sampling anomaly detection method according to the present invention;
fig. 3 is a schematic flow chart of a third embodiment of a low-voltage sampling anomaly detection method according to the present invention;
fig. 4 is a schematic structural diagram of a first embodiment of a low-voltage sampling abnormality detection device according to the present invention;
fig. 5 shows a schematic structural diagram of an embodiment of a low-voltage sampling abnormality detection apparatus provided by the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein.
Fig. 1 is a schematic flow chart of a first embodiment of a low-voltage sampling anomaly detection method provided by the invention, which is executed by an intelligent device. As shown in fig. 1, the method comprises the steps of:
s100, acquiring all cell voltage data corresponding to the power-on time of the target vehicle.
The embodiment is not limited to a specific target vehicle as long as the vehicle has a cell voltage. For example, the target vehicle may be an electric car; or the target vehicle may be an electric vehicle.
S101, determining an average value of the cell voltage according to the cell voltage data.
The embodiment is not limited to a particular number of cell voltage values that the cell voltage data includes. For example, the cell voltage data may include 10 cell voltage values; it is understood that the average cell voltage is the average vavg= (v1+v2+ … +vi)/i of all cell voltages of the battery pack. Where i is the total number of cells, vi is the voltage of the ith cell, and Vavg is the average of the cell voltages.
S102, determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the average value of the battery cell voltages.
The embodiment does not limit the number of target voltage offsets as long as it corresponds to the number of cores. For example, the target vehicle includes 6 cells, then there are 6 target voltage offsets; or the target vehicle includes 15 cells, then there are 15 target voltage offsets. It will be appreciated that this embodiment aims to find the offset Δvi=vi-Vavg of the voltage values of all the cells and the average of the cell voltages at the power-up time.
And S103, acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
The embodiment is not limited to a specific period of the preset period, and may be set according to the need. It can be understood that, in order to obtain the low-voltage sampling abnormal result in time, a voltage difference sequence corresponding to the standard voltage offset needs to be obtained in real time, and the low-voltage sampling abnormal result is determined according to the voltage difference sequence. It is understood that the voltage difference sequence corresponding to the target voltage offset in this embodiment refers to the difference value calculated in the same manner as the target voltage offset. The embodiment is not limited to a specific manner of determining the low-voltage sampling anomaly result from the sequence of voltage difference values. For example, the abnormal low-voltage sampling result can be determined according to the voltage offset corresponding to all adjacent time points of the battery cells; or determining a low-voltage sampling abnormal result according to a comparison result of the voltage offset corresponding to the battery cell at any moment and the target voltage offset at the power-on moment; or, an abnormal threshold value can be obtained, and a low-voltage sampling abnormal result is determined according to the comparison result of the difference value between adjacent moments and the abnormal threshold value. This embodiment is not limited to the actions performed when the low pressure sampling anomaly is determined. For example, a prompt may be issued; or may alarm; or may be pre-warned on a level basis.
The method comprises the steps of obtaining all battery cell voltage data corresponding to the power-on time of a target vehicle; determining a cell voltage average value according to the cell voltage data; and determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value. And acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence. Compared with the prior art that the low-voltage sampling abnormality cannot be detected, the method and the device for detecting the low-voltage sampling abnormality determine the voltage difference sequence according to the voltage data of the battery cell, so that the low-voltage sampling abnormality result is intelligently determined according to the voltage difference sequence, and the safety of vehicle running is improved.
Further, in order to improve the accuracy of detection, after acquiring all the cell voltage data corresponding to the power-on time of the target vehicle, the method may further include:
preprocessing the cell voltage data, deleting the abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, determining the average value of the cell voltage according to the cell voltage value may include:
and determining a cell voltage average value according to the processed cell voltage data.
The embodiment may delete or correct abnormal data in the cell voltage data at the time of power-up. It can be understood that the battery cell voltage of the target vehicle is unstable at the power-on time and may be abnormal, so that the abnormal data of the battery cell voltage needs to be processed, and generally, the abnormal data of the battery cell voltage needs to be simply corrected, so that the accuracy of the subsequent abnormal detection according to the power-on time data can be improved.
Further, in order to improve the safety of the vehicle running, after determining the low-voltage sampling abnormality result according to the voltage difference sequence, it may further include:
and when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal, determining to perform early warning.
This embodiment is not limited to the manner of pre-warning. For example, early warning can be performed by sending prompt information; or the early warning can be performed by making an early warning sound. Or the early warning can be carried out by drawing the early warning grade. Because early warning can be timely carried out when low-pressure sampling abnormality occurs, the safety of vehicle running can be improved, and the vehicle is prevented from being in dangerous running state all the time.
Further, to improve the timeliness of the anomaly detection, obtaining a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling anomaly result according to the voltage difference sequence may include:
acquiring a voltage difference value sequence corresponding to the target voltage offset in real time, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence; wherein the preset time period includes a duration.
According to the embodiment, the voltage difference value sequence is obtained in real time, the voltage condition of the vehicle can be observed in real time, and the abnormal condition can be found in time, so that the timeliness of abnormal detection can be improved.
Further, to improve the accuracy of low-voltage sampling anomaly detection, acquiring a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling anomaly result according to the voltage difference sequence may include:
and acquiring each adjacent voltage difference sequence of the battery cells corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the adjacent voltage difference sequence of the battery cells.
According to the embodiment, through each cell adjacent voltage difference value sequence, a low-voltage sampling abnormal result is determined according to the cell adjacent voltage difference value sequence. It will be appreciated that when the target vehicle includes more cells, any abnormal cell may cause dangerous running of the vehicle, so that it is necessary to determine the abnormal low-voltage sampling result according to a sequence consisting of differences between two moments where the respective cells are closely adjacent. The detection is more comprehensive, so that the accuracy of low-voltage sampling abnormality detection can be improved.
Compared with the prior art that abnormality detection cannot be carried out on low-voltage sampling, so that unbalanced faults of the battery cells, power interruption and the like are caused, the embodiment of the invention can be used for intelligently detecting abnormality of low-voltage sampling in time, thereby improving the driving safety of vehicles. In addition, the abnormal data of the battery cell voltage can be processed, and the abnormal data of the battery cell voltage can be simply corrected under normal conditions, so that the accuracy of the subsequent abnormal detection according to the data of the power-on time can be improved; in addition, early warning can be timely carried out when low-voltage sampling abnormality occurs, so that the safety of vehicle running can be improved, and the vehicle is prevented from being in a dangerous running state all the time; and the low-voltage sampling abnormal result is determined according to a sequence formed by the difference values of two adjacent time points of each battery cell, so that the accuracy of low-voltage sampling abnormal detection is improved.
Fig. 2 is a schematic flow chart of a second embodiment of a low-voltage sampling anomaly detection method provided by the invention, which is executed by an intelligent device. As shown in fig. 2, the method comprises the steps of:
s200, acquiring all cell voltage data corresponding to the power-on time of the target vehicle.
According to the embodiment, all the battery cell voltage data corresponding to the power-on time of the target vehicle are obtained, and all the battery cells can be monitored later.
S201, determining an average value of the cell voltage according to the cell voltage data.
S202, determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the average value of the battery cell voltage.
S203, acquiring a preset threshold value.
The embodiment is not limited to the number of preset thresholds, and may be set according to the need. For example, there may be two preset thresholds; or the preset threshold may be three.
S204, acquiring a voltage difference sequence corresponding to the target voltage offset in a preset time period.
S205, determining a low-voltage sampling abnormal level according to a comparison result of a preset threshold value and a voltage difference value sequence; wherein the anomaly level includes at least two levels.
The embodiment can set the abnormal level of the low-voltage sampling according to the requirement, and can be set according to the requirement of the user in order to improve the experience of the user.
Therefore, the invention determines the low-voltage sampling abnormality level according to the cell voltage data by acquiring the cell voltage data, thereby detecting the low-voltage sampling abnormality more intelligently.
Fig. 3 is a schematic flow chart of a third embodiment of a low-voltage sampling anomaly detection method provided by the present invention, where the method is executed by an intelligent device. As shown in fig. 3, the method comprises the steps of:
s300, all voltage values corresponding to the power-on time of the target vehicle are obtained.
The embodiment can read the voltage value Vi of the target new energy vehicle on the cloud platform at the power-on time.
S301, determining the average value of the cell voltage according to all the voltage values.
This embodiment can pack all cell voltages average, vavg= (v1+v2+ … +vi)/i. Where i is the number of cells and Vavg is the average of the cell voltages.
S302, determining voltage target offset corresponding to all the battery cells according to all the voltage values and the average value of the battery cell voltages.
This embodiment determines the voltage target offset Δvi=vi-Vavg for all cells.
S303, determining a voltage difference value sequence of the voltage target offset in real time.
The embodiment determines the voltage difference sequence of the voltage target offset in real time, and can determine the time-dependent change chart of each Δvi, namely the voltage difference sequence change chart.
S304, a first preset threshold value and a second preset threshold value are obtained, and the abnormal level of the low-voltage sampling is determined according to the relation between the adjacent voltage difference value and the first preset threshold value and the second preset threshold value.
In this embodiment, if ΔVi of any two adjacent cells has a high-low phenomenon, i.e., ΔVi > M and ΔVi+1 < -M, or ΔVi < -M and ΔVi+1 > M, the low voltage loop is abnormal. Meanwhile, judging the value of M, and if the value reaches DeltaVi > M and DeltaVi+1 < -M, carrying out primary early warning; and if the delta Vi >2M and delta Vi+1 < -2M are reached, carrying out secondary early warning. It can be understood that the manner of the first preset threshold and the second preset threshold is just one embodiment provided by the embodiment of the present invention, and when the user needs three levels, if Δvi >3M and Δvi+1 < -3M are reached, three levels of early warning can be set.
Therefore, the technical scheme of the invention utilizes the advantage that the cloud platform data can monitor the historical data of the vehicle, and the abnormal faults of the low-voltage sampling loop can be quickly, efficiently and accurately early warned in advance by observing the comparison between the single voltage at the power-on time and the average value of all the single voltages in the battery pack and the phenomenon that the voltage of the adjacent battery cells deviates from one another; and the problems of extra measurement of the resistance of the low-voltage sampling loop, detection of a circuit, welding of nickel sheets and the like are avoided, and the practicality, the universality and the economical efficiency of the abnormal faults of the low-voltage sampling loop in the engineering application process are effectively improved.
Fig. 4 is a schematic structural diagram of a first embodiment of a low-voltage sampling abnormality detection device according to the present invention. As shown in fig. 4, the apparatus may include:
the battery cell voltage data acquisition module 100 is used for acquiring all battery cell voltage data corresponding to the power-on time of the target vehicle;
the cell voltage average value determining module 200 is configured to determine a cell voltage average value according to the cell voltage data;
the target voltage offset determining module 300 is configured to determine a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the average value of the battery cell voltages;
the low-voltage sampling abnormal result determining module 400 is configured to obtain a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determine a low-voltage sampling abnormal result according to the voltage difference sequence.
In an optional manner, the low-voltage sampling abnormality detection device may further include:
the correction module is used for preprocessing the cell voltage data, deleting abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, the above-mentioned cell voltage average value determining module 200 may include:
and the cell voltage average value determining unit is used for determining the cell voltage average value according to the processed cell voltage data.
In an optional manner, the low-voltage sampling abnormality detection device may further include:
and the early warning module is used for determining to perform early warning when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal.
In an alternative manner, the low-voltage sampling anomaly result determining module 400 may include:
the preset threshold value acquisition module is used for acquiring a preset threshold value;
and the abnormal result determining unit is used for determining the low-voltage sampling abnormal result according to the comparison result of the preset threshold value and the voltage difference value sequence.
In an alternative manner, the abnormal result determining unit may include:
an abnormal level determining subunit, configured to determine a low-voltage sampling abnormal level according to a comparison result of the preset threshold value and the voltage difference sequence; wherein the low-voltage sampling anomaly level comprises at least two levels.
In an alternative manner, the low-voltage sampling anomaly result determining module 400 may include:
the real-time abnormality determining unit is used for acquiring the voltage difference value sequence corresponding to the target voltage offset in real time and determining the low-voltage sampling abnormality result according to the voltage difference value sequence; wherein the preset time period includes a duration.
In an alternative manner, the low-voltage sampling anomaly result determining module 400 may include:
and the battery cell abnormality determining unit is used for obtaining each battery cell adjacent voltage difference value sequence corresponding to the target voltage offset in the preset time period and determining the low-voltage sampling abnormality result according to the battery cell adjacent voltage difference value sequence.
The invention provides a low-voltage sampling abnormality detection device, comprising: the battery cell voltage data acquisition module 100 is used for acquiring all battery cell voltage data corresponding to the power-on time of the target vehicle; the cell voltage average value determining module 200 is configured to determine a cell voltage average value according to the cell voltage data; the target voltage offset determining module 300 is configured to determine a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the average value of the battery cell voltages; the low-voltage sampling abnormal result determining module 400 is configured to obtain a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determine a low-voltage sampling abnormal result according to the voltage difference sequence. Compared with the existing method that abnormality detection cannot be carried out on low-voltage sampling, and the problems of unbalanced faults of the battery cells, power interruption and the like are caused, the method can be used for intelligently detecting abnormality of low-voltage sampling in time, so that the safety of vehicle running is improved. In addition, the abnormal data of the battery cell voltage can be processed, and the abnormal data of the battery cell voltage can be simply corrected under normal conditions, so that the accuracy of the subsequent abnormal detection according to the data of the power-on time can be improved; in addition, early warning can be timely carried out when low-voltage sampling abnormality occurs, so that the safety of vehicle running can be improved, and the vehicle is prevented from being in a dangerous running state all the time; in addition, a low-voltage sampling abnormal result is determined according to a sequence formed by the difference values of two adjacent time points of each battery cell, so that the accuracy of low-voltage sampling abnormal detection is improved; and a plurality of abnormal grades can be set, so that the experience of the user is improved.
Fig. 5 shows a schematic structural diagram of an embodiment of a low-voltage sampling abnormality detection apparatus according to the present invention, and the embodiment of the present invention is not limited to a specific implementation of a low-voltage sampling abnormality detection apparatus.
As shown in fig. 5, the low-voltage sampling abnormality detection apparatus may include: a processor (processor) 402, a communication interface (communication interface) 404, a memory (memory) 406, and a communication bus 408.
Wherein: processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. A communication interface 404 for communicating with network elements of other devices, such as clients or other servers. Processor 402 is configured to execute program 410, and may specifically perform the relevant steps described above for one embodiment of a low-voltage sampling anomaly detection method.
In particular, program 410 may include program code including computer-executable instructions.
The processor 402 may be a central processing unit CPU, or a specific integrated circuit ASIC (ApplicationSpecificIntegratedCircuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the low-voltage sampling abnormality detection apparatus may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 406 for storing programs 410. Memory 406 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
Program 410 may be specifically invoked by processor 402 to cause a low voltage sampling anomaly detection device to:
acquiring all cell voltage data corresponding to the power-on time of a target vehicle;
determining a cell voltage average value according to the cell voltage data;
determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
In an optional manner, after acquiring all the cell voltage data corresponding to the power-on time of the target vehicle, the method may further include:
preprocessing the cell voltage data, deleting the abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, determining the average value of the cell voltage according to the cell voltage value may include:
and determining a cell voltage average value according to the processed cell voltage data.
In an alternative manner, after determining the low-voltage sampling abnormal result according to the voltage difference value sequence, the method may further include:
and when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal, determining to perform early warning.
In an alternative manner, determining the low-voltage sampling anomaly result according to the voltage difference sequence may include:
acquiring a preset threshold value;
and determining a low-voltage sampling abnormal result according to a comparison result of the preset threshold value and the voltage difference value sequence.
In an alternative manner, determining the abnormal low-voltage sampling result according to the comparison result of the preset threshold value and the voltage difference value sequence may include:
determining a low-voltage sampling abnormal level according to a comparison result of a preset threshold value and a voltage difference value sequence; the low-voltage sampling abnormality level at least comprises two levels.
In an optional manner, obtaining a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence may include:
acquiring the voltage difference value sequence corresponding to the target voltage offset in real time, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence; wherein the preset time period includes a duration.
In an optional manner, obtaining a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence may include:
and acquiring each adjacent voltage difference sequence of the battery cells corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the adjacent voltage difference sequence of the battery cells.
Compared with the existing method that abnormality detection cannot be carried out on low-voltage sampling, and the problems of unbalanced faults of the battery cells, power interruption and the like are caused, the method can be used for intelligently detecting abnormality of low-voltage sampling in time, so that the safety of vehicle running is improved. In addition, the abnormal data of the battery cell voltage can be processed, and the abnormal data of the battery cell voltage can be simply corrected under normal conditions, so that the accuracy of the subsequent abnormal detection according to the data of the power-on time can be improved; in addition, early warning can be timely carried out when low-voltage sampling abnormality occurs, so that the safety of vehicle running can be improved, and the vehicle is prevented from being in a dangerous running state all the time; in addition, a low-voltage sampling abnormal result is determined according to a sequence formed by the difference values of two adjacent time points of each battery cell, so that the accuracy of low-voltage sampling abnormal detection is improved; and a plurality of abnormal grades can be set, so that the experience of the user is improved.
An embodiment of the present invention provides a computer readable storage medium storing at least one executable instruction that, when executed on a low-voltage sampling abnormality detection apparatus/device, causes the low-voltage sampling abnormality detection apparatus/device to perform a low-voltage sampling abnormality detection method according to any of the above-described method embodiments.
The executable instructions may be specifically configured to cause a low voltage sampling anomaly detection apparatus/device to:
acquiring all cell voltage data corresponding to the power-on time of a target vehicle;
determining a cell voltage average value according to the cell voltage data;
determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
In an optional manner, after acquiring all the cell voltage data corresponding to the power-on time of the target vehicle, the method may further include:
preprocessing the cell voltage data, deleting the abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, determining the average value of the cell voltage according to the cell voltage value may include:
and determining a cell voltage average value according to the processed cell voltage data.
In an alternative manner, after determining the low-voltage sampling abnormal result according to the voltage difference value sequence, the method may further include:
and when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal, determining to perform early warning.
In an alternative manner, determining the low-voltage sampling anomaly result according to the voltage difference sequence may include:
acquiring a preset threshold value;
and determining a low-voltage sampling abnormal result according to a comparison result of the preset threshold value and the voltage difference value sequence.
In an alternative manner, determining the abnormal low-voltage sampling result according to the comparison result of the preset threshold value and the voltage difference value sequence may include:
determining a low-voltage sampling abnormal level according to a comparison result of a preset threshold value and a voltage difference value sequence; the low-voltage sampling abnormality level at least comprises two levels.
In an optional manner, obtaining a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence may include:
acquiring the voltage difference value sequence corresponding to the target voltage offset in real time, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence; wherein the preset time period includes a duration.
In an optional manner, obtaining a voltage difference sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference sequence may include:
and acquiring each adjacent voltage difference sequence of the battery cells corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the adjacent voltage difference sequence of the battery cells.
Compared with the existing method that abnormality detection cannot be carried out on low-voltage sampling, and the problems of unbalanced faults of the battery cells, power interruption and the like are caused, the method can be used for intelligently detecting abnormality of low-voltage sampling in time, so that the safety of vehicle running is improved. In addition, the abnormal data of the battery cell voltage can be processed, and the abnormal data of the battery cell voltage can be simply corrected under normal conditions, so that the accuracy of the subsequent abnormal detection according to the data of the power-on time can be improved; in addition, early warning can be timely carried out when low-voltage sampling abnormality occurs, so that the safety of vehicle running can be improved, and the vehicle is prevented from being in a dangerous running state all the time; in addition, a low-voltage sampling abnormal result is determined according to a sequence formed by the difference values of two adjacent time points of each battery cell, so that the accuracy of low-voltage sampling abnormal detection is improved; and a plurality of abnormal grades can be set, so that the experience of the user is improved.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. In addition, embodiments of the present invention are not directed to any particular programming language.
In the description provided herein, numerous specific details are set forth. It will be appreciated, however, that embodiments of the invention may be practiced without such specific details. Similarly, in the above description of exemplary embodiments of the invention, various features of embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. Wherein the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Except that at least some of such features and/or processes or elements are mutually exclusive.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.

Claims (10)

1. The low-voltage sampling abnormality detection method is characterized by comprising the following steps of:
acquiring all cell voltage data corresponding to the power-on time of a target vehicle;
determining a cell voltage average value according to the cell voltage data;
determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
and acquiring a voltage difference value sequence corresponding to the target voltage offset in a preset time period, and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
2. The method for detecting a low-voltage sampling abnormality according to claim 1, further comprising, after the acquisition of all the cell voltage data corresponding to the power-on time of the target vehicle:
preprocessing the cell voltage data, deleting abnormal data or correcting the abnormal data to obtain processed cell voltage data;
correspondingly, the determining the average value of the cell voltage according to the cell voltage value comprises the following steps:
and determining the average value of the cell voltage according to the processed cell voltage data.
3. The low-voltage sampling abnormality detection method according to claim 1, characterized by further comprising, after said determining a low-voltage sampling abnormality result from said sequence of voltage difference values:
and when the low-voltage sampling abnormal result is that the low-voltage sampling loop is abnormal, determining to perform early warning.
4. A low-voltage sampling anomaly detection method according to any one of claims 1 to 3, wherein the determining a low-voltage sampling anomaly result from the sequence of voltage differences comprises:
acquiring a preset threshold value;
and determining the low-voltage sampling abnormal result according to the comparison result of the preset threshold value and the voltage difference value sequence.
5. The method according to claim 4, wherein determining the low-voltage sampling abnormality result according to the comparison result of the preset threshold value and the voltage difference sequence comprises:
determining a low-voltage sampling abnormal level according to a comparison result of the preset threshold value and the voltage difference value sequence; wherein the low-voltage sampling anomaly level comprises at least two levels.
6. The method for detecting a low-voltage sampling abnormality according to claim 1, wherein the obtaining a voltage difference sequence corresponding to the target voltage offset in the preset time period, and determining a low-voltage sampling abnormality result according to the voltage difference sequence, includes:
acquiring the voltage difference value sequence corresponding to the target voltage offset in real time, and determining the low-voltage sampling abnormal result according to the voltage difference value sequence; wherein the preset time period includes a duration.
7. The method for detecting a low-voltage sampling abnormality according to claim 1, wherein the obtaining a voltage difference sequence corresponding to the target voltage offset in the preset time period, and determining a low-voltage sampling abnormality result according to the voltage difference sequence, includes:
and acquiring each adjacent voltage difference sequence of the battery cells corresponding to the target voltage offset in the preset time period, and determining the low-voltage sampling abnormal result according to the adjacent voltage difference sequence of the battery cells.
8. A low-voltage sampling abnormality detection apparatus, characterized by comprising:
the battery cell voltage data acquisition module is used for acquiring all battery cell voltage data corresponding to the power-on time of the target vehicle;
the cell voltage average value determining module is used for determining a cell voltage average value according to the cell voltage data;
the target voltage offset determining module is used for determining a target voltage offset corresponding to the battery cell according to the battery cell voltage data and the battery cell voltage average value;
the low-voltage sampling abnormal result determining module is used for obtaining a voltage difference value sequence corresponding to the target voltage offset in a preset time period and determining a low-voltage sampling abnormal result according to the voltage difference value sequence.
9. A low-voltage sampling abnormality detection apparatus, characterized by comprising:
a memory for storing a computer program;
a processor for implementing the low-voltage sampling anomaly detection method according to any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium having stored therein computer executable instructions which when loaded and executed by a processor implement the low voltage sampling anomaly detection method of any one of claims 1 to 7.
CN202310623809.9A 2023-05-30 2023-05-30 Low-voltage sampling anomaly detection method, device, equipment and readable storage medium Pending CN116736120A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310623809.9A CN116736120A (en) 2023-05-30 2023-05-30 Low-voltage sampling anomaly detection method, device, equipment and readable storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310623809.9A CN116736120A (en) 2023-05-30 2023-05-30 Low-voltage sampling anomaly detection method, device, equipment and readable storage medium

Publications (1)

Publication Number Publication Date
CN116736120A true CN116736120A (en) 2023-09-12

Family

ID=87910783

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310623809.9A Pending CN116736120A (en) 2023-05-30 2023-05-30 Low-voltage sampling anomaly detection method, device, equipment and readable storage medium

Country Status (1)

Country Link
CN (1) CN116736120A (en)

Similar Documents

Publication Publication Date Title
CN108931725B (en) Method and device for detecting battery fault
CN109001636B (en) Method and device for determining battery health degree of battery pack, vehicle and computing equipment
CN112406545B (en) Relay temperature rise monitoring method and device and electric automobile
CN112485685B (en) Power bearing capacity parameter determining method and device and electronic equipment
CN113415165B (en) Fault diagnosis method and device, electronic equipment and storage medium
CN113466721B (en) Failure identification method and device for lithium ion battery, electronic equipment and medium
CN112345955B (en) Multi-fault online diagnosis method and system for power battery
CN105356535B (en) A kind of battery management system total voltage processing method based on functional safety
CN115951230B (en) Abnormality detection method and system for lithium battery energy storage box
CN110749829A (en) Power supply equipment abnormality detection method and device
CN112285413A (en) Current value determination method, device, controller, medium and battery management system
CN116953556B (en) Method, system, medium and equipment for online detection of multivariable redundant fault battery
CN116736120A (en) Low-voltage sampling anomaly detection method, device, equipment and readable storage medium
CN112540301B (en) Battery detection method, device and storage medium
US9501347B2 (en) Documentation of faults in a fault memory of a motor vehicle
CN116106758A (en) Battery fault diagnosis method and system based on data driving
CN115684976A (en) Online fault diagnosis and positioning method for energy storage battery, electronic equipment and medium
CN113533985B (en) Identification method of battery pack internal resistance abnormal module and storage medium thereof
CN115972982A (en) Thermal runaway early warning method, device, equipment and readable storage medium
CN115980585A (en) Battery fault detection method and device, computer equipment and storage medium
CN112937302A (en) High-voltage monitoring method, device, storage medium and system
CN113935423A (en) Power battery fault early warning method and system for coupling fuzzy control rule with Elman neural network
CN113646648A (en) Method and system for detecting connection failure of parallel battery units
CN115113056A (en) Method and device for detecting health state of fuel cell system
CN112924887B (en) Battery pack health detection method and device, readable storage medium and electronic equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination