WO2023273267A1 - 故障诊断方法、装置、电子设备及存储介质 - Google Patents

故障诊断方法、装置、电子设备及存储介质 Download PDF

Info

Publication number
WO2023273267A1
WO2023273267A1 PCT/CN2021/143070 CN2021143070W WO2023273267A1 WO 2023273267 A1 WO2023273267 A1 WO 2023273267A1 CN 2021143070 W CN2021143070 W CN 2021143070W WO 2023273267 A1 WO2023273267 A1 WO 2023273267A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
battery pack
fault
battery
determining
Prior art date
Application number
PCT/CN2021/143070
Other languages
English (en)
French (fr)
Inventor
孙焕丽
潘垂宇
李雪
张志�
于春洋
许立超
Original Assignee
中国第一汽车股份有限公司
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 中国第一汽车股份有限公司 filed Critical 中国第一汽车股份有限公司
Publication of WO2023273267A1 publication Critical patent/WO2023273267A1/zh

Links

Images

Classifications

    • 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
    • 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
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • 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

Definitions

  • the embodiments of the present application relate to the technical field of automobiles, for example, to a fault diagnosis method, device, electronic equipment, and storage medium.
  • the core component of an electric vehicle is a lithium-ion power battery.
  • the basic unit of the battery pack is a battery module composed of cells, and the battery sampler connected to the battery module collects the battery data of the battery module and gives real feedback to the battery module. working status.
  • Most of the related technologies use the cloud server to monitor the consistency of the battery data to predict whether the battery pack is faulty. This method cannot rule out the wrong prediction caused by the faulty battery sampler that makes the collected battery data incorrect. If the battery sampler fails, the collected battery data is incorrect and cannot accurately feed back the working status of the battery module, which may cause vehicle failure or serious safety accidents. At the same time, the analysis of battery data on the cloud server will also bring had a great impact. Therefore, there is an urgent need for a fault diagnosis method that can accurately diagnose whether the cause of the fault is the fault of the battery pack or the fault of the battery sampler.
  • Embodiments of the present application provide a fault diagnosis method, device, electronic equipment, and storage medium, which can realize accurate diagnosis of fault components and fault causes, and improve the safety of vehicles in use.
  • the embodiment of the present application provides a fault diagnosis method, the method comprising:
  • the battery pack data In response to determining that the battery pack data is faulty data, performing transformation analysis on the battery pack data, and determining a faulty component according to the transformation analysis result; wherein the faulty component is the battery sampler or the battery pack;
  • the fault category of the faulty component is determined.
  • the embodiment of the present application provides a fault diagnosis device, which includes:
  • the data acquisition module is configured to acquire the battery pack data collected by the battery sampler in the vehicle;
  • the first analysis module is configured to perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result;
  • the second analysis module is configured to perform conversion analysis on the battery pack data in response to determining that the battery pack data is faulty data, and determine a faulty component according to the conversion analysis result; wherein the faulty component is the battery sampler or said battery pack;
  • the fault determination module is configured to determine the fault category of the faulty component according to the faulty component.
  • an electronic device which includes:
  • processors one or more processors
  • a storage device configured to store one or more programs
  • the one or more processors are made to implement the fault diagnosis method described in any embodiment of the present application.
  • an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, wherein, when the program is executed by a processor, the fault diagnosis method described in any embodiment of the present application is implemented.
  • Fig. 1 is the schematic diagram of the fault diagnosis system of the embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a first fault diagnosis method provided in an embodiment of the present application
  • FIG. 3 is a second schematic flowchart of a fault diagnosis method provided in an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a fault diagnosis device provided in an embodiment of the present application.
  • Fig. 5 is a block diagram of an electronic device used to implement a fault diagnosis method according to an embodiment of the present application.
  • Fig. 1 is a schematic diagram of the fault diagnosis system of the embodiment of the present application
  • Fig. 2 is a schematic diagram of the first flow chart of a fault diagnosis method provided by the embodiment of the present application, and this embodiment is applicable to the battery report data collected by the battery sampler Perform fault diagnosis to determine the faulty component and the cause of the fault.
  • a fault diagnosis method provided in this embodiment can be executed by the fault diagnosis device provided in the embodiment of this application, and the device can be realized by means of software and/or hardware, and integrated in the electronic device that executes the method.
  • the electronic equipment in the embodiment of the present application is carried by the fault diagnosis system.
  • the fault diagnosis system includes: a vehicle-mounted terminal 11 , a cloud server 12 , and a background server 13 .
  • the vehicle is equipped with a vehicle-mounted terminal (such as a vehicle-mounted Telematics-BOX) that can upload vehicle data to a cloud server.
  • the background server can download the data of the vehicle from the cloud server, and perform fault diagnosis on it. If the fault diagnosis result shows that there is a fault in the vehicle, a fault information notification is sent to the vehicle-mounted terminal.
  • the method of the present embodiment includes but not limited to the following steps:
  • a battery sampler and a battery pack are arranged in the vehicle, and a voltage sensor and a temperature sensor are arranged in the battery pack, which are respectively set to monitor voltage data and temperature data of the battery pack.
  • the battery sampler is set to collect battery pack data of the battery pack, such as voltage data and temperature data; in addition, the battery pack data also includes at least one item.
  • the vehicle terminal first uploads and stores the battery pack data of the vehicle to the cloud server.
  • the background server can obtain the battery pack data from the cloud server.
  • the vehicle-mounted terminal can also directly upload the data of the battery pack of the vehicle to the background server, so that the background server can perform fault analysis on it.
  • the data can be uploaded to the cloud server or background server according to the data upload standard stipulated in the Chinese standard GBT32960 (such as data upload frequency, data field), so the battery pack data uploaded to the cloud server or background server is more universal sex.
  • the battery sampler in the vehicle collects the temperature data and voltage data of the battery pack every 10 seconds, and the on-board terminal aggregates the temperature data and voltage data of the battery pack with the serial number of the vehicle, the driving data of the vehicle, and the collection time of the data It is the battery pack data and uploaded to the cloud server or background server.
  • S120 Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.
  • the background server after the background server acquires the battery pack data of the vehicle, it performs feature analysis on the battery pack data. For example, if the battery pack data of a vehicle is faulty data, it will show various characteristics. Set a preset diagnosis period (such as 1 day), analyze all the battery pack data of the vehicle within the preset diagnosis period, and if there is a battery with this characteristic Packet data is faulty data, and battery pack data without this feature is normal data.
  • a preset diagnosis period such as 1 day
  • the battery pack includes at least one battery module, and each battery module has at least one battery cell.
  • a battery module is equipped with voltage sensors and temperature sensors equal to the number of battery cells, and is configured to detect the voltage value and temperature value of each battery cell. If the number of voltage sensors and temperature sensors on a module may be inconsistent, for example, there are four battery cells in a battery module, and four voltage sensors and two temperature sensors are configured, then the temperature sensor data is matched to Each battery cell, that is, each battery cell has a voltage value and a temperature value.
  • the fault data identification and characteristics in Table 1 are specific examples, and the conditions of other battery packs need to be adjusted accordingly according to the specific state.
  • the fault data feature recognition is shown in Table 1 below:
  • the faulty component is a battery sampler or a battery pack.
  • the battery sampler fails and the battery pack works normally, the battery sampler cannot accurately feed back the working data of the battery pack, so the frame data corresponding to the battery pack data collected by the battery sampler appears as a discrete signal mutation. If the battery sampler works normally and the battery pack fails, the battery sampler can accurately feed back the working data of the battery pack, so the frame data corresponding to the battery pack data collected by the battery sampler appears as a continuous signal.
  • the battery pack data is converted and analyzed to analyze whether the battery pack data is a discrete signal or a continuous signal, so as to determine whether the faulty component of the vehicle is a battery sampler or a battery pack.
  • the transformation method for transforming and analyzing the battery pack data is not specifically limited, for example, continuous wavelet transform may be used to transform the battery pack data from the time domain to the frequency domain.
  • the above steps are used to analyze the battery pack data, and after determining the faulty component corresponding to the faulty data, it is also necessary to determine the fault category of the faulty component.
  • the temperature data of the battery cell is high and close to an open circuit, it may be a problem with the temperature sensor installation process or the battery sampler sampling failure, etc.; if the temperature data of the battery cell is low and close to a short circuit, it may be The temperature sensor is in poor contact, the sampling circuit is rusted and the sensor welding process leads to cracking, etc.; if all the temperature sensors of the same battery module are too high and close to open circuit, it may be that the sampling terminal of the battery sampler is abnormal, or the communication of the single-chip microcomputer is abnormal, etc.; All the temperature sensors of the module are low and close to short circuit, which may be caused by water vapor entering the battery pack, abnormal power supply of the battery sampler, etc. Cause; if the voltage data of adjacent battery cells shows an open circuit and open circuit state, it may be an open circuit of the battery sampler; if the voltage data of the same battery module shows an open circuit state, it may be the sampling terminal of the battery sampler abnormal.
  • the technical solution provided by this embodiment obtains the battery pack data collected by the battery sampler in the vehicle; performs feature analysis on the battery pack data, and determines whether the battery pack data is fault data according to the feature analysis results; in the case of fault data , transform and analyze the battery pack data, and determine the faulty component according to the transformation analysis result; determine the fault category of the faulty component according to the faulty component.
  • performing transformation analysis on the battery pack data, and determining the faulty component according to the transformation analysis result includes: taking the frame data corresponding to the battery pack data as the current frame data, and selecting the current frame data before At least two frames of data differing from the preset time difference, and at least two frames of data are subjected to continuous wavelet transformation to obtain a transformation analysis result; wherein, the preset time difference is greater than the time difference between adjacent frame data; if the transformation analysis result is greater than or equal to the preset threshold, the faulty component is the battery sampler; if the transform analysis result is less than the preset threshold, the faulty component is the battery pack.
  • one battery pack data corresponds to one frame of data, so all the battery pack data in the preset diagnosis cycle includes several frames of data.
  • S120 analyze all the battery pack data of the vehicle within the preset diagnosis period acquired, and after determining the faulty data, use the frame data corresponding to the battery pack data determined as the faulty data as the current frame data, and select the current frame data At least two frames of data that are different from the preset time difference before, and perform continuous wavelet transformation on the at least two frames of data.
  • Analyzing the continuous wavelet transform data corresponding to the at least two frames of data if the transform analysis result is greater than or equal to a preset threshold (such as 3 ⁇ ), indicating that the frame data corresponding to the battery pack data is a discrete value, the faulty component is a battery sampler; If the transformation analysis result is less than the preset threshold, it indicates that the frame data corresponding to the battery pack data is a continuous value, and the faulty component is the battery pack.
  • a preset threshold such as 3 ⁇
  • the temperature data in the current battery pack data is faulty data
  • select the temperature data of all temperature sensors with an expected difference of a preset time between the current battery pack data and perform continuous wavelet transformation on these temperature data.
  • select the voltage data of all voltage sensors with an expected difference between the current battery pack data and the preset time and perform continuous wavelet transformation on these voltage data
  • the preset time difference is greater than the time difference between adjacent frames of data.
  • the reason for this setting is that there may be data frame loss between two frames of data or adjacent frames caused by long-term parking of the vehicle. If the time difference is relatively large, an appropriate preset time difference should be selected to avoid such a situation.
  • FIG. 3 is a second schematic flowchart of a fault diagnosis method provided by an embodiment of the present application.
  • the embodiment of the present application is refined on the basis of the above-mentioned embodiments, adding a detailed explanation of the process of judging the accuracy of the battery pack data and the process of determining the risk level and risk warning.
  • the method of the present embodiment includes but not limited to the following steps:
  • S220 Determine whether the battery pack data is accurate according to a preset judgment rule, and delete the battery pack data if the battery pack data is inaccurate.
  • the background server can analyze the accuracy of the battery pack data according to the judgment rules for the accuracy of the battery pack data, and delete false positive data at the same time.
  • the voltage value of the battery pack that the battery sampler can collect is 0-5V. When the voltage value is 5.3V, it must be caused by signal transmission, and the battery should be excluded. package data.
  • Table 2 below shows the battery pack data accuracy judgment rules in this embodiment.
  • the battery pack data accuracy judgment in this embodiment is a specific example, and other battery pack data accuracy judgment methods are also within the scope of protection.
  • the fault data includes voltage data and temperature data, determine that the risk level corresponding to the fault data is a high level.
  • step S230 all the battery pack data of the vehicle within the preset diagnosis period acquired are analyzed to determine the fault data. If the fault data includes both voltage data and temperature data, it indicates that the cause of the fault is not caused by the sampling failure of the sampler, but may be caused by water vapor entering the battery pack or other external reasons. Since this situation may cause the vehicle to break down, and may cause a serious safety accident, the risk level of this situation is determined as a high level.
  • the fault data is voltage data or temperature data
  • determine the risk corresponding to the fault data according to the number of occurrences of the fault data, the abnormal frequency of the fault data, and the total number of frame data in the preset diagnosis cycle grade.
  • the risk level is low level, medium level or high level.
  • the risk level can be determined according to the risk assessment formula, which is as follows:
  • score represents the risk assessment score corresponding to the risk level
  • n is the number of faulty sensors corresponding to the fault data, correspondingly, the value of i is a natural integer between 1 and n, where all the sensors of the same battery module and The abnormal voltage of adjacent battery cells and the abnormal voltage value of multiple points are recorded as the failure of the same sensor
  • N is the number of occurrences of the fault category corresponding to the fault data
  • M is the value that theoretically all battery pack data are fault data
  • f is the abnormal frequency of fault data, that is, the ratio of the number of frame data corresponding to fault data to the total number of frame data in the preset diagnosis period
  • 100 in brackets in the risk assessment formula means that the normalized formula is expressed as a percentage, etc. The 100 on the right side of the number indicates that the higher the score, the better the risk assessment plan.
  • steps S240-S250 correspond to the process of determining the risk level
  • step S260 corresponds to the process of determining the faulty component.
  • the risk level in S240-S250 and the risk early warning standard in S280 can be revised according to the actual situation.
  • the corrected solution may be: when the owner returns the faulty vehicle to the factory for repair and maintenance, disassemble the battery pack, analyze whether there is a phenomenon corresponding to the fault category in the above embodiment, and correct the fault in S240-S250 according to the fault phenomenon.
  • the risk level and the risk early warning standard in S280 can also be: make a fault data sample, and use a machine learning model to correct the assessment of the risk level corresponding to the fault category.
  • the technical solution provided by this embodiment obtains the battery pack data collected by the battery sampler in the vehicle; judges whether the battery pack data is accurate according to the preset judgment rules, and if the battery pack data is inaccurate, then deletes the battery pack data; Perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis results; within the preset diagnosis period, if the fault data includes voltage data and temperature data, determine that the risk level corresponding to the fault data is high; In the preset diagnosis period, if the fault data is voltage data or temperature data, the risk level corresponding to the fault data is determined according to the number of occurrences of the fault data, the abnormal frequency of the fault data and the total number of frame data in the preset diagnosis cycle; In the case of failure data, transform and analyze the battery pack data, and determine the faulty component according to the transformation analysis result; determine the fault category of the faulty component according to the faulty component; set the risk early warning standard, if the risk level is higher than the risk early warning standard, Then display the fault category and risk level
  • FIG. 4 is a schematic structural diagram of a fault diagnosis device provided in an embodiment of the present application. As shown in FIG. 4, the device 400 may include:
  • the data acquisition module 410 is configured to acquire the battery pack data collected by the battery sampler in the vehicle.
  • the first analysis module 420 is configured to perform feature analysis on the battery pack data, and determine whether the battery pack data is fault data according to the feature analysis result.
  • the second analysis module 430 is configured to perform transformation analysis on the battery pack data in the case of fault data, and determine the faulty component according to the transformation analysis result; wherein, the faulty component is the battery sampler or the battery pack.
  • the fault determination module 440 is configured to determine the fault category of the faulty component according to the faulty component.
  • the above-mentioned second analysis module 430 is configured to: use the frame data corresponding to the battery pack data as the current frame data, select at least two frames of data that are different from the current frame data by a preset time difference, and analyze the at least two frames of data Transformation analysis is performed on the frame data to obtain a transformation analysis result; wherein, the preset time difference is greater than the time difference between adjacent frame data; if the transformation analysis result is greater than or equal to a preset threshold, the faulty component is a battery sampler ; If the transformation analysis result is less than a preset threshold, the faulty component is a battery pack.
  • the above-mentioned fault diagnosis device may further include: a data judging module; the data judging module is configured to judge whether the battery pack data is accurate or not according to a preset judging rule before performing feature analysis on the battery pack data , if the battery pack data is inaccurate, delete the battery pack data.
  • the battery pack data includes voltage data of the battery pack monitored by a voltage sensor and temperature data of the battery pack monitored by a temperature sensor.
  • the above-mentioned fault diagnosis device may further include: a risk determination module; the risk determination module is configured to, if the fault data includes the voltage data and the temperature data, determine the The risk level corresponding to the fault data is high level.
  • the risk determination module is further configured to, within a preset diagnosis period, if the fault data is the voltage data or the temperature data, according to the number of occurrences of the fault data, the abnormal frequency of the fault data and The total amount of frame data in the preset diagnosis period is used to determine the risk level corresponding to the fault data; wherein, the risk level is low level, medium level or high level.
  • the above-mentioned fault diagnosis device may also include: a risk early warning module; the risk early warning module is configured to set a risk early warning standard, and if the risk level is higher than the risk early warning standard, it will be displayed to the vehicle-mounted terminal of the vehicle. The fault category and the risk level, and send the fault data and fault category of the vehicle to the after-sales system.
  • a risk early warning module is configured to set a risk early warning standard, and if the risk level is higher than the risk early warning standard, it will be displayed to the vehicle-mounted terminal of the vehicle.
  • the fault category and the risk level and send the fault data and fault category of the vehicle to the after-sales system.
  • the battery pack data further includes at least one of vehicle driving data, vehicle charging data, vehicle serial number, and battery pack data collection time.
  • the fault diagnosis device provided in this embodiment can be applied to the fault diagnosis method provided in any of the above embodiments, and has corresponding functions and beneficial effects.
  • Fig. 5 is a block diagram of an electronic device used to implement a fault diagnosis method of the embodiment of the present application
  • Fig. 5 shows a block diagram of an exemplary electronic device suitable for implementing the implementation of the embodiment of the present application.
  • the electronic device shown in FIG. 5 is only an example, and should not limit the functions and scope of use of this embodiment of the present application.
  • the electronic device may be a smart phone, a tablet computer, a notebook computer, a vehicle terminal, a wearable device, and the like.
  • electronic device 500 takes the form of a general-purpose computing device.
  • Components of the electronic device 500 may include, but are not limited to: one or more processors or processing units 516, a memory 528, and a bus 518 connecting different system components (including the memory 528 and the processing unit 516).
  • Bus 518 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures.
  • These architectures include, by way of example, but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.
  • ISA Industry Standard Architecture
  • MAC Micro Channel Architecture
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • Electronic device 500 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 500 and include both volatile and nonvolatile media, removable and non-removable media.
  • Memory 528 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 530 and/or cache memory 532 .
  • the electronic device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media.
  • storage system 534 may be configured to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive”).
  • a disk drive for reading and writing to removable nonvolatile disks e.g., "floppy disks”
  • removable nonvolatile optical disks e.g., CD-ROM, DVD-ROM or other optical media
  • each drive may be connected to bus 518 through one or more data media interfaces.
  • the memory 528 may include at least one program product, and the program product has a group (for example, at least one) of program modules configured to execute the functions of the various embodiments of the embodiments of the present application.
  • Program/utility 540 may be stored, for example, in memory 528 as a set (at least one) of program modules 542 including, but not limited to, an operating system, one or more application programs, other program modules, and program data , each or some combination of these examples may include implementations of network environments.
  • the program module 542 generally executes the functions and/or methods in the embodiments described in the embodiments of this application.
  • the electronic device 500 may also communicate with one or more external devices 514 (such as a keyboard, pointing device, display 524, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500, and/or communicate with Any device (eg, network card, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 522 .
  • the electronic device 500 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 520 . As shown in FIG.
  • the network adapter 520 communicates with other modules of the electronic device 500 through the bus 518 .
  • other hardware and/or software modules may be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape Drives and data backup storage systems, etc.
  • the processing unit 516 executes various functional applications and data processing by running the programs stored in the memory 528 , such as realizing the fault diagnosis method provided by any embodiment of the present application.
  • the embodiment of the present application also provides a computer-readable storage medium, on which a computer program (or called computer-executable instructions) is stored.
  • a computer program or called computer-executable instructions
  • the program When the program is executed by a processor, it can be used to perform the operation provided by any of the above-mentioned embodiments of the present application. fault diagnosis method.
  • the computer storage medium in the embodiments of the present application may use any combination of one or more computer-readable media.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (non-exhaustive list) of computer readable storage media include: electrical connections with one or more leads, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • the computer readable storage medium may be a non-transitory computer
  • a computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including - but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program codes for performing the operations of the embodiments of the present application may be written in one or more programming languages or combinations thereof, the programming languages including object-oriented programming languages—such as Java, Smalltalk, C++, including A conventional procedural programming language - such as the "C" language or a similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

Abstract

本申请实施例公开了一种故障诊断方法、装置、电子设备及存储介质。其中，该方法包括：获取车辆中电池采样器采集的电池包数据；响应于确定所述电池包数据为故障数据，对电池包数据进行特征分析，并根据特征分析结果确定电池包数据是否为故障数据；在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件；根据故障部件，确定故障部件的故障类别。

Description

故障诊断方法、装置、电子设备及存储介质
本申请要求在2021年6月30日提交中国专利局、申请号为202110734519.2的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及汽车技术领域，例如涉及一种故障诊断方法、装置、电子设备及存储介质。
背景技术
电动汽车的核心部件是锂离子动力电池，电池包基本单元是由电芯构成的电池模组，而与电池模组相连接的电池采样器通过采集电池模组的电池数据，真实反馈电池模组的工作状态。相关技术中大多通过云端服务器监控电池数据的一致性来预测电池包是否故障，该方法不能排除因电池采样器故障使得所采集的电池数据有误而导致的错误预测。如果电池采样器发生了故障，所采集的电池数据有误不能准确反馈电池模组的工作状态，轻则导致车辆发生故障，严重的可能造成安全事故，同时对云端服务器的电池数据分析也带来了很大的影响。因此，亟需一种故障诊断的方法，能够精确诊断故障原因是电池包故障还是电池采样器故障。
发明内容
本申请实施例提供了一种故障诊断方法、装置、电子设备及存储介质，可以实现对故障部件以及故障原因的精确诊断，提升了车辆使用中的安全性。
第一方面，本申请实施例提供了一种故障诊断方法，该方法包括：
获取车辆中电池采样器采集的电池包数据；
对所述电池包数据进行特征分析，并根据特征分析结果确定所述电池包数据是否为故障数据；
响应于确定所述电池包数据为故障数据，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件；其中，所述故障部件为所述电池采样器或所述电池包；
根据所述故障部件，确定所述故障部件的故障类别。
第二方面，本申请实施例提供了一种故障诊断装置，该装置包括：
数据获取模块，设置为获取车辆中电池采样器采集的电池包数据；
第一分析模块，设置为对所述电池包数据进行特征分析，并根据特征分析结果确定所述电池包数据是否为故障数据；
第二分析模块，设置为响应于确定所述电池包数据为故障数据，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件；其中，所述故障部件为所述电池采样器或所述电池包；
故障确定模块，设置为根据所述故障部件，确定所述故障部件的故障类别。
第三方面，本申请实施例提供了一种电子设备，该电子设备包括：
一个或多个处理器；
存储装置，设置为存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现本申请任意实施例所述的故障诊断方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，其上存储有计算机程序，其中，该程序被处理器执行时实现本申请任意实施例所述的故障诊断方法。
附图说明
附图用于更好地理解本方案，不构成对本申请的限定。其中：
图1为本申请实施例的故障诊断系统的示意图；
图2为本申请实施例提供的一种故障诊断方法的第一流程示意图；
图3为本申请实施例提供的一种故障诊断方法的第二流程示意图；
图4为本申请实施例提供的一种故障诊断装置的结构示意图；
图5是用来实现本申请实施例的一种故障诊断方法的电子设备的框图。
具体实施方式
图1为本申请实施例的故障诊断系统的示意图；图2为本申请实施例提供的一种故障诊断方法的第一流程示意图，本实施例可适用于对电池采样器所采集的电池报数据进行故障诊断，确定出出现故障的故障部件以及故障原因的情况。本实施例提供的一种故障诊断方法可以由本申请实施例提供的故障诊断装 置来执行，该装置可以通过软件和/或硬件的方式实现，并集成在执行本方法的电子设备中。本申请实施例中的电子设备由故障诊断系统承载。
参见图1，为本申请实施例的故障诊断系统的示意图，如图所示故障诊断系统包括：车载终端11、云端服务器12、后台服务器13。所述车辆中配有车载终端(如车载Telematics-BOX)可以将车辆的数据上传至云端服务器。后台服务器可从云端服务器中下载所述车辆的数据，并对其进行故障诊断。若故障诊断结果显示所述车辆存在故障，则向车载终端发送故障信息通知。
参见图2，本实施例的方法包括但不限于如下步骤：
S110、获取车辆中电池采样器采集的电池包数据。
其中，在车辆中配置有电池采样器和电池包，电池包中配置有电压传感器和温度传感器，分别设置为监测电池包的电压数据和温度数据。电池采样器设置为采集电池包的电池包数据，如电压数据和温度数据；此外，电池包数据还包括车辆的行驶数据、车辆的充电数据、车辆的编号和电池包数据的采集时间中的至少一项。
在本申请实施例中，车辆终端将该车辆的电池包数据先上传并存储至云端服务器。当后台服务器在对该车辆进行故障分析时，后台服务器可从云端服务器获取该电池包数据。例如，车载终端还可以将该车辆的电池包数据直接上传至后台服务器，以使后台服务器对其进行故障分析。
例如，可以按照中国标准GBT32960中所规定的数据上传标准(如数据上传的频率、数据的字段)上传数据至云端服务器或者后台服务器，那么上传至云端服务器或者后台服务器的电池包数据更具普适性。示例性的，车辆中电池采样器每10秒钟采集一次电池包的温度数据和电压数据，车载终端将电池包的温度数据和电压数据与车辆的编号、车辆的行驶数据以及数据的采集时间聚合为电池包数据，并上传至云端服务器或者后台服务器。
S120、对电池包数据进行特征分析，并根据特征分析结果确定电池包数据是否为故障数据。
在本申请实施例中，后台服务器获取到该车辆的电池包数据之后，对该电池包数据进行特征分析。例如，车辆的电池包数据若为故障数据会表现出多种特征，设置预设诊断周期(如1天)，分析预设诊断周期内的该车辆的全部电池包数据，若有该特征的电池包数据即为故障数据，若无该特征的电池包数据即为正常数据。
其中，电池包中包含至少一个电池模组，每个电池模组中有至少一个电池单体。一个电池模组上配置有与电池单体等数量的电压传感器和温度传感器，设置为检测每个电池单体的电压值和温度值。假若一个模块上的电压传感器和温度传感器数量可能不一致，示例性的，一个电池模组中有四个电池单体，并配置有四个电压传感器两个温度传感器，则将温度传感器的数据匹配到每一个电池单体，即形成每个电池单体都有电压值和温度值。
表1中的故障数据识别及特征为特定示例，其他电池包情况需根据具体状态进行相应调整。故障数据特征识别如下表1所示：
表1、故障数据特征识别表格
Figure PCTCN2021143070-appb-000001
S130、在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件。
其中，故障部件为电池采样器或电池包。
在本申请实施例中，经上述步骤，根据特征分析结果确定电池包数据是否为故障数据之后，需要进一步分析导致电池包数据为故障数据的故障部件，也就是，确定是电池采样器发生了故障还是电池包发生了故障。假若电池采样器发生故障且电池包正常工作，则电池采样器不能够准确反馈电池包的工作数据，因而电池采样器所采集的电池包数据所对应的帧数据表现为离散信号的突变。假若电池采样器正常工作且电池包发生故障，则电池采样器能够准确反馈电池包的工作数据，因而电池采样器所采集的电池包数据所对应的帧数据表现为连 续信号。
在本申请实施例中，对电池包数据进行变换分析，分析电池包数据是离散信号还是连续信号，从而判断车辆的故障部件是电池采样器还是电池包。
例如，对电池包数据进行变换分析的变换方法不做具体限定，例如，可以使用连续小波变换将电池包数据从时域变换为频域。
S140、根据故障部件，确定故障部件的故障类别。
在本申请实施例中，将上述步骤，对电池包数据进行分析，确定故障数据对应的故障部件之后，还需确定故障部件的故障类别。
示例性的，若电池单体的温度数据偏高且接近开路，则可能为温度传感器安装工艺问题或者电池采样器采样出现故障等；若电池单体的温度数据偏低且接近短路，则可能为温度传感器接触不良、采样回路锈蚀传感器焊接工艺导致开裂等；若同一电池模组的所有温度传感器偏高且接近开路，则可能为电池采样器的采样端异常、或单片机通信异常等；若同一电池模组的所有温度传感器偏低且接近短路，则可能为水汽进入电池包、电池采样器供电异常等故障；若电池单体的电压数据表现为断路状态，则可能为电池采样器的键合开裂导致；若相邻电池单体的电压数据表现为开路和断路状态，则可能为电池采样器的回路开路；若同一电池模组的电压数据表现为开路状态，则可能为电池采样器的采样端异常。
本实施例提供的技术方案，通过获取车辆中电池采样器采集的电池包数据；对电池包数据进行特征分析，并根据特征分析结果确定电池包数据是否为故障数据；在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件；根据故障部件，确定故障部件的故障类别。本申请通过对电池包数据进行特征分析和变换分析，可以避免相关技术不能排除因电池采样器故障使得所采集的电池数据有误而导致的错误预测，可以在不增加成本的情况下实现对故障部件以及故障原因的精确诊断，极大地提升了车辆使用中的安全性。
在一些实施例中，在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件包括：将电池包数据对应的帧数据作为当前帧数据，选取当前帧数据之前与其相差预设时间差的至少两帧数据，并对至少两帧数据进行连续小波变换，得到变换分析结果；其中，预设时间差大于相邻帧数据之间的时间差；若变换分析结果大于等于预设阈值，则故障部件为电池 采样器；若变换分析结果小于预设阈值，则故障部件为电池包。
例如，一个电池包数据对应一帧数据，因而预设诊断周期内的全部电池包数据包含若干帧数据。经S120对所获取的预设诊断周期内的该车辆的全部电池包数据进行分析，确定出故障数据之后，将确定为故障数据的电池包数据对应的帧数据作为当前帧数据，选取当前帧数据之前与其相差预设时间差的至少两帧数据，并对该至少两帧数据进行连续小波变换。分析该至少两帧数据对应的连续小波变换数据，若变换分析结果大于或等于预设阈值(如3σ)，表明该电池包数据所对应的帧数据为离散值，则故障部件为电池采样器；若变换分析结果小于预设阈值，表明该电池包数据所对应的帧数据为连续值，则故障部件为电池包。
示例性的，若确定当前电池包数据中的温度数据为故障数据，选取当前电池包数据之间预期相差预设时间的所有温度传感器的温度数据，并对这些温度数据进行连续小波变换。若确定当前电池包数据中的电压数据为故障数据，选取当前电池包数据之间预期相差预设时间的所有电压传感器的电压数据，并对这些电压数据进行连续小波变换，
在本申请实施例中，需要说明的是，预设时间差大于相邻帧数据之间的时间差，这样设置的原因在于两帧数据之间可能存在数据丢帧或者车辆长时间停放导致的相邻帧时间差较大，应该选取一个合适的预设时间差来避免这样的情况。
图3为本申请实施例提供的一种故障诊断方法的第二流程示意图。本申请实施例是在上述实施例的基础上进行细化，增加了对电池包数据的准确性的判断过程和风险等级与风险预警的确定过程进行详细的解释说明。
参见图3，本实施例的方法包括但不限于如下步骤：
S210、获取车辆中电池采样器采集的电池包数据。
S220、根据预先设置的判断规则，判断电池包数据是否准确，若电池包数据不准确，则删除该电池包数据。
在本申请实施例中，后台服务器在接收到电池包数据之后，可以根据对电池包数据准确性的判断规则，分析电池包数据的准确性，同时删除误报数据。示例性的，以电池单体为例，电池采样器能够采集到的电池包的电压值范围为0-5V，当电压值为5.3V时，则一定是信号传输原因导致的，应当排除该电池包数据。
如下表2所示为本实施例中的电池包数据准确性判断规则，本实施例的电池包数据准确性判断为特定示例，其余电池包数据准确性判断方法也在保护范围以内。
表2、电池包数据准确性判断规则
信号类型 准确性判断规则
电池单体电压值 0-5V，删除超出该范围的电池包数据
电池单体温度值 -40℃-150℃，删除超出该范围的电池包数据
电压默认值 删除0V或设定默认值(如3.65)的电池包数据
温度默认值 删除0℃或设定默认值(如20℃)的电池包数据
S230、对电池包数据进行特征分析，并根据特征分析结果确定电池包数据是否为故障数据。
S240、在预设诊断周期内，若故障数据包括电压数据和温度数据，则确定故障数据对应的风险等级为高等级。
在本申请实施例中，经上述步骤S230，对所获取的预设诊断周期内的该车辆的全部电池包数据进行分析，确定出故障数据。若故障数据中既包括电压数据又包括温度数据，表明故障原因并不是因采样器采样故障所导致的，可能是电池包中进入水汽或者其他外界原因所导致的。由于这种情况可能会导致车辆发生故障，严重的可能造成安全事故，则将这种情况的风险等级确定为高等级。
S250、在预设诊断周期内，若故障数据为电压数据或者温度数据，则根据故障数据出现的次数、故障数据的异常频率和预设诊断周期内帧数据的总数量，确定故障数据对应的风险等级。
其中，风险等级为低等级、中等级或高等级。
在本申请实施例中，在预设诊断周期内，若故障数据为电压数据或者包括温度数据，则统计该诊断周期内故障数据的次数和帧数据的总数量，并且计算故障数据的异常频率，以确定故障数据对应的风险等级。具体的可以根据风险评估公式来确定风险等级，风险评估公式如下所示：
Figure PCTCN2021143070-appb-000002
其中，score表示风险等级对应的风险评估得分；n为故障数据对应的出现故障的传感器数量，相应的，i取值为1到n之间的自然整数，其中，同一电池模组的所有传感器和相邻电池单体电压异常、多点电压值异常均记为同一个传 感器出现故障；N为故障数据对应的故障类别出现的次数；M为理论上所有电池包数据均为故障数据的数值；f为故障数据的异常频率，即故障数据对应的帧数据的数量占预设诊断周期内帧数据的总数量的比值；风险评估公式中括号内的100代表将归一化的公式以百分制表示，等号右边的100表示将score以越高分评价值越好为风险评估方案。
根据风险评估公式来确定车辆的风险等级，若score＝100则代表电池包完全没有故障风险；若score＝0则代表电池包的故障风险为最高。例如，可以设置3个风险等级，如低等级、中等级或高等级。若score>＝80，则风险等级险为低等级；若80>score>＝60，则风险等级险为中等级；若score<60，则风险等级险为高等级。
S260、在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件。
S270、根据故障部件，确定故障部件的故障类别。
S280、设置风险预警标准，若风险等级高于风险预警标准，则向车辆的车载终端显示故障类别和风险等级，并向售后系统发送车辆的故障数据和故障类别。
在本申请实施例中，设置风险预警标准，如风险评估公式的score＝60。若风险评估公式的score<60，则向车辆的车载终端显示故障类别和当前的风险等级为高等级，以警示车主当前车辆处于高风险，并且向售后系统发送车辆的故障数据和故障类别，以帮助售后部门更好的准备备件及维修车辆。
需要说明的是，步骤S240-S250对应的是确定风险等级的过程，步骤S260对应的是确定故障部件的过程，这两个过程是相互独立的，本申请实施例不对这两个过程的执行顺序进行限定，需要根据实际情况来确定两个过程的具体执行顺序，所以实际执行时可以是按照本申请上述实施例介绍的顺序执行，也可以是先执行S260，再执行S240-S250。
例如，可以根据实际情况对S240-S250中的风险等级以及对S280中的风险预警标准进行修正。修正的方案可以是：车主将故障车辆返厂维修及保养时，对电池包进行拆解，分析是否有上述实施例中的故障类别对应的现象，并根据该故障现象来修正S240-S250中的风险等级和S280中的风险预警标准；还可以是：制作故障数据样本，并利用机器学习模型，对故障类别对应的风险等级的评估进行修正。
本实施例提供的技术方案，通过获取车辆中电池采样器采集的电池包数据；根据预先设置的判断规则，判断电池包数据是否准确，若电池包数据不准确，则删除该电池包数据；对电池包数据进行特征分析，并根据特征分析结果确定电池包数据是否为故障数据；在预设诊断周期内，若故障数据包括电压数据和温度数据，则确定故障数据对应的风险等级为高等级；在预设诊断周期内，若故障数据为电压数据或者温度数据，则根据故障数据出现的次数、故障数据的异常频率和预设诊断周期内帧数据的总数量，确定故障数据对应的风险等级；在为故障数据的情况下，对电池包数据进行变换分析，并根据变换分析结果确定故障部件；根据故障部件，确定故障部件的故障类别；设置风险预警标准，若风险等级高于风险预警标准，则向车辆的车载终端显示故障类别和风险等级，并向售后系统发送车辆的故障数据和故障类别。本申请对电池包数据进行准确性判断以及故障分析，确定为故障数据之后对其进行风险等级评估并进行风险预警，可以实现监测车辆是否故障以及对在其出现故障时进行风险预警。
图4为本申请实施例提供的一种故障诊断装置的结构示意图，如图4所示，该装置400可以包括：
数据获取模块410，设置为获取车辆中电池采样器采集的电池包数据。
第一分析模块420，设置为对所述电池包数据进行特征分析，并根据特征分析结果确定所述电池包数据是否为故障数据。
第二分析模块430，设置为在为故障数据的情况下，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件；其中，所述故障部件为所述电池采样器或所述电池包。
故障确定模块440，设置为根据所述故障部件，确定所述故障部件的故障类别。
例如，上述第二分析模块430设置为：将所述电池包数据对应的帧数据作为当前帧数据，选取所述当前帧数据之前与其相差预设时间差的至少两帧数据，并对所述至少两帧数据进行变换分析，得到变换分析结果；其中，所述预设时间差大于相邻帧数据之间的时间差；若所述变换分析结果大于或等于预设阈值，则所述故障部件为电池采样器；若所述变换分析结果小于预设阈值，则所述故障部件为电池包。
例如，上述故障诊断装置，还可以包括：数据判断模块；所述数据判断模块，设置为在对所述电池包数据进行特征分析之前，根据预先设置的判断规则， 判断所述电池包数据是否准确，若所述电池包数据不准确，则删除该电池包数据。
例如，所述电池包数据包括通过电压传感器监测的电池包的电压数据和通过温度传感器监测的电池包的温度数据。
例如，上述故障诊断装置，还可以包括：风险确定模块；所述风险确定模块，设置为在预设诊断周期内，若所述故障数据包括所述电压数据和所述温度数据，则确定所述故障数据对应的风险等级为高等级。
所述风险确定模块，还设置为在预设诊断周期内，若所述故障数据为所述电压数据或者所述温度数据，则根据所述故障数据出现的次数、所述故障数据的异常频率和预设诊断周期内帧数据的总数量，确定所述故障数据对应的风险等级；其中，所述风险等级为低等级、中等级或高等级。
例如，上述故障诊断装置，还可以包括：风险预警模块；所述风险预警模块，设置为设置风险预警标准，若所述风险等级高于所述风险预警标准，则向所述车辆的车载终端显示所述故障类别和所述风险等级，并向售后系统发送所述车辆的故障数据和故障类别。
例如，所述电池包数据还包括车辆的行驶数据、车辆的充电数据、车辆的编号和电池包数据的采集时间中的至少一项。
本实施例提供的故障诊断装置可适用于上述任意实施例提供的故障诊断方法，具备相应的功能和有益效果。
图5是用来实现本申请实施例的一种故障诊断方法的电子设备的框图，图5示出了适于用来实现本申请实施例实施方式的示例性电子设备的框图。图5显示的电子设备仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。该电子设备典型可以是智能手机、平板电脑、笔记本电脑、车载终端以及可穿戴设备等。
如图5所示，电子设备500以通用计算设备的形式表现。电子设备500的组件可以包括但不限于：一个或者多个处理器或者处理单元516，存储器528，连接不同系统组件(包括存储器528和处理单元516)的总线518。
总线518表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(ISA)总线，微通道体系结构(MAC)总线，增强型ISA总线、视频电子标 准协会(VESA)局域总线以及外围组件互连(PCI)总线。
电子设备500典型地包括多种计算机系统可读介质。这些介质可以是任何能够被电子设备500访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
存储器528可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(RAM)530和/或高速缓存存储器532。电子设备500可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统534可以设置为读写不可移动的、非易失性磁介质(图5未显示，通常称为“硬盘驱动器”)。尽管图5中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如CD-ROM,DVD-ROM或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线518相连。存储器528可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本申请实施例各实施例的功能。
具有一组(至少一个)程序模块542的程序/实用工具540，可以存储在例如存储器528中，这样的程序模块542包括但不限于操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块542通常执行本申请实施例所描述的实施例中的功能和/或方法。
电子设备500也可以与一个或多个外部设备514(例如键盘、指向设备、显示器524等)通信，还可与一个或者多个使得用户能与该电子设备500交互的设备通信，和/或与使得该电子设备500能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口522进行。并且，电子设备500还可以通过网络适配器520与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图5所示，网络适配器520通过总线518与电子设备500的其它模块通信。应当明白，尽管图5中未示出，可以结合电子设备500使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
处理单元516通过运行存储在存储器528中的程序，从而执行各种功能应用以及数据处理，例如实现本申请任一实施例所提供的故障诊断方法。
本申请实施例还提供一种计算机可读存储介质，其上存储有计算机程序(或称为计算机可执行指令)，该程序被处理器执行时可以用于执行本申请上述任一实施例所提供的故障诊断方法。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。计算机可读存储介质可以是非暂态计算机可读存储介质。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请实施例操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。

Claims (10)

  1. 一种故障诊断方法，包括：
    获取车辆中电池采样器采集的电池包数据；
    对所述电池包数据进行特征分析，并根据特征分析结果确定所述电池包数据是否为故障数据；
    响应于确定所述电池包数据为故障数据，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件；其中，所述故障部件为所述电池采样器或所述电池包；
    根据所述故障部件，确定所述故障部件的故障类别。
  2. 根据权利要求1所述的方法，其中，所述响应于确定所述电池包数据为故障数据，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件，包括：
    将所述电池包数据对应的帧数据作为当前帧数据，选取所述当前帧数据之前与其相差预设时间差的至少两帧数据，并对所述至少两帧数据进行变换分析，得到变换分析结果；其中，所述预设时间差大于相邻帧数据之间的时间差；
    响应于确定所述变换分析结果大于或等于预设阈值，所述故障部件为电池采样器；
    响应于确定所述变换分析结果小于预设阈值，所述故障部件为电池包。
  3. 根据权利要求1所述的方法，其中，在对所述电池包数据进行特征分析之前，还包括：
    根据预先设置的判断规则，判断所述电池包数据是否准确，基于所述电池包数据不准确的判断结果，删除所述电池包数据。
  4. 根据权利要求1所述的方法，其中，所述电池包数据包括：通过电压传感器监测的电池包的电压数据和通过温度传感器监测的电池包的温度数据；
    在所述根据特征分析结果确定所述电池包数据是否为故障数据之后，还包括：
    在预设诊断周期内，响应于确定所述故障数据包括所述电压数据和所述温度数据，确定所述故障数据对应的风险等级为高等级；
    在预设诊断周期内，响应于确定所述故障数据为所述电压数据或者所述温度数据，根据所述故障数据出现的次数、所述故障数据的异常频率和预设诊断周期内帧数据的总数量，确定所述故障数据对应的风险等级；其中，所述风险 等级为低等级、中等级或高等级。
  5. 根据权利要求4所述的方法，还包括：
    设置风险预警标准，响应于确定所述风险等级高于所述风险预警标准，向所述车辆的车载终端显示所述故障类别和所述风险等级，并向售后系统发送所述车辆的故障数据和故障类别。
  6. 根据权利要求1-5任一所述的方法，其中，所述电池包数据还包括车辆的行驶数据、车辆的充电数据、车辆的编号和电池包数据的采集时间中的至少一项。
  7. 一种故障诊断装置，包括：
    数据获取模块，设置为获取车辆中电池采样器采集的电池包数据；
    第一分析模块，设置为对所述电池包数据进行特征分析，并根据特征分析结果确定所述电池包数据是否为故障数据；
    第二分析模块，设置为响应于确定所述电池包数据为故障数据，对所述电池包数据进行变换分析，并根据变换分析结果确定故障部件；其中，所述故障部件为所述电池采样器或所述电池包；
    故障确定模块，设置为根据所述故障部件，确定所述故障部件的故障类别。
  8. 根据权利要求7所述的装置，其中，所述第二分析模块还设置为：
    将所述电池包数据对应的帧数据作为当前帧数据，选取所述当前帧数据之前与其相差预设时间差的至少两帧数据，并对所述至少两帧数据进行变换分析，得到变换分析结果；其中，所述预设时间差大于相邻帧数据之间的时间差；
    响应于确定所述变换分析结果大于或等于预设阈值，所述故障部件为电池采样器；
    响应于确定所述变换分析结果小于预设阈值，所述故障部件为电池包。
  9. 一种电子设备，包括：
    一个或多个处理器；
    存储装置，设置为存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-6中任一项所述的故障诊断方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-6中任一项所述的故障诊断方法。
PCT/CN2021/143070 2021-06-30 2021-12-30 故障诊断方法、装置、电子设备及存储介质 WO2023273267A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110734519.2 2021-06-30
CN202110734519.2A CN113415165B (zh) 2021-06-30 2021-06-30 一种故障诊断方法、装置、电子设备及存储介质

Publications (1)

Publication Number Publication Date
WO2023273267A1 true WO2023273267A1 (zh) 2023-01-05

Family

ID=77717892

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/143070 WO2023273267A1 (zh) 2021-06-30 2021-12-30 故障诊断方法、装置、电子设备及存储介质

Country Status (2)

Country Link
CN (1) CN113415165B (zh)
WO (1) WO2023273267A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116373688A (zh) * 2023-06-06 2023-07-04 深圳市拓普泰克技术股份有限公司 多元电池包的充放电控制方法、装置、设备及存储介质
CN117104073A (zh) * 2023-10-19 2023-11-24 中印云端(深圳)科技有限公司 一种基于物联网的新能源汽车电池管理系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113415165B (zh) * 2021-06-30 2022-01-18 中国第一汽车股份有限公司 一种故障诊断方法、装置、电子设备及存储介质
CN114013388A (zh) * 2021-09-28 2022-02-08 江铃汽车股份有限公司 车辆预警提示方法、装置、可读存储介质及车辆

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103064032A (zh) * 2013-01-08 2013-04-24 重庆长安汽车股份有限公司 一种动力电池的故障诊断系统
CN108761337A (zh) * 2018-05-03 2018-11-06 自贡同发荣实业有限公司 一种锂电池管理系统
CN109143091A (zh) * 2018-10-15 2019-01-04 四川长虹电器股份有限公司 基于双冗余的电池管理系统故障fdir系统及方法
WO2020037060A1 (en) * 2018-08-14 2020-02-20 Cps Technology Holdings Llc Advanced battery health diagnostic
CN111361454A (zh) * 2020-03-18 2020-07-03 一汽解放汽车有限公司 动力电池热管理系统的诊断方法、装置、设备和存储介质
CN113415165A (zh) * 2021-06-30 2021-09-21 中国第一汽车股份有限公司 一种故障诊断方法、装置、电子设备及存储介质

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5978144B2 (ja) * 2013-01-25 2016-08-24 株式会社ケーヒン 蓄電池システム
CN106603002B (zh) * 2016-12-13 2018-08-31 烟台中飞海装科技有限公司 一种光伏电站故障检测系统
CN110069810B (zh) * 2019-03-11 2023-04-07 北京百度网讯科技有限公司 电池故障预测方法、装置、设备和可读存储介质
CN110224160B (zh) * 2019-06-12 2021-07-27 北京亿华通科技股份有限公司 一种燃料电池系统故障诊断方法
CN111038291B (zh) * 2019-12-16 2021-06-11 国网江苏省电力有限公司电力科学研究院 一种电动汽车充电故障智能诊断系统及方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103064032A (zh) * 2013-01-08 2013-04-24 重庆长安汽车股份有限公司 一种动力电池的故障诊断系统
CN108761337A (zh) * 2018-05-03 2018-11-06 自贡同发荣实业有限公司 一种锂电池管理系统
WO2020037060A1 (en) * 2018-08-14 2020-02-20 Cps Technology Holdings Llc Advanced battery health diagnostic
CN109143091A (zh) * 2018-10-15 2019-01-04 四川长虹电器股份有限公司 基于双冗余的电池管理系统故障fdir系统及方法
CN111361454A (zh) * 2020-03-18 2020-07-03 一汽解放汽车有限公司 动力电池热管理系统的诊断方法、装置、设备和存储介质
CN113415165A (zh) * 2021-06-30 2021-09-21 中国第一汽车股份有限公司 一种故障诊断方法、装置、电子设备及存储介质

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116373688A (zh) * 2023-06-06 2023-07-04 深圳市拓普泰克技术股份有限公司 多元电池包的充放电控制方法、装置、设备及存储介质
CN116373688B (zh) * 2023-06-06 2023-08-18 深圳市拓普泰克技术股份有限公司 多元电池包的充放电控制方法、装置、设备及存储介质
CN117104073A (zh) * 2023-10-19 2023-11-24 中印云端(深圳)科技有限公司 一种基于物联网的新能源汽车电池管理系统
CN117104073B (zh) * 2023-10-19 2023-12-22 中印云端(深圳)科技有限公司 一种基于物联网的新能源汽车电池管理系统

Also Published As

Publication number Publication date
CN113415165B (zh) 2022-01-18
CN113415165A (zh) 2021-09-21

Similar Documents

Publication Publication Date Title
WO2023273267A1 (zh) 故障诊断方法、装置、电子设备及存储介质
WO2021249269A1 (zh) 预警方法、装置、设备及存储介质
WO2022151819A1 (zh) 一种基于聚类分析的电池系统在线故障诊断方法和系统
CN112436968B (zh) 一种网络流量的监测方法、装置、设备及存储介质
WO2023273971A1 (zh) 电池包泄露检测的方法、装置、电子设备及存储介质
CN114148216B (zh) 电池自放电率异常检测方法、系统、设备及存储介质
CN110674009B (zh) 应用服务器性能监测方法、装置、存储介质及电子设备
JP7116103B2 (ja) 光モジュールの故障を予測するための方法、装置、およびデバイス
WO2023045790A1 (zh) Soc校准方法、建模方法、建模装置、计算机设备及介质
WO2023155619A1 (zh) 故障诊断方法、装置、车辆及存储介质
CN104076808A (zh) 工控设备的故障诊断系统和方法
CN114464906A (zh) 动力电池预警方法和装置
CN114487856A (zh) 换电站电池的热失控预警方法及系统
CN115186502A (zh) 车辆的异常数据识别方法、装置、电子设备及存储介质
CN115188688A (zh) 异常检测方法及装置、电子设备和存储介质
US20220381848A1 (en) Method and system for detecting vehicle battery cell imbalance
CN114460519A (zh) 基于电力互感器检验的现场与终端融合管理系统和方法
CN115436789A (zh) 继电器故障检测方法、装置、电子设备和存储介质
WO2024078601A1 (zh) 图标检测脚本的生成方法、设备及存储介质
CN112462744A (zh) 一种坦克火控系统状态评估与故障诊断系统
CN117129815A (zh) 一种基于物联网的多劣化绝缘子综合检测方法和系统
CN112140891A (zh) 一种基于物联网的新能源汽车用三元锂电池工作监测系统
WO2023147731A1 (zh) 异常数据的处理方法、装置及电子设备
CN115933619A (zh) 一种远程诊断方法、系统、电子设备及存储介质
CN113547879A (zh) 轮胎漏气的检测方法、装置、车辆及计算机存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21948189

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE