A fault diagnosis method and electronic device

Abstract

Embodiments of the present application provide a fault diagnosis method and an electronic device. The method is applied to the electronic device, and the method comprises: collecting real-time data of a power battery system; reporting the real-time data; receiving a fault diagnosis prediction result and / or a first fault measure corresponding to the fault diagnosis prediction result; generating a local fault diagnosis result and / or a second fault measure corresponding to the local fault diagnosis result; arbitrating the fault diagnosis prediction result and / or the first fault measure, and the local fault diagnosis result and / or the second fault measure, to generate a third fault measure; and implementing the third fault measure. According to the method of the embodiments of the present application, the fault of the power battery system can be predicted in advance based on VHR big data, and the fault measure that best matches the real-time state of the electric vehicle can be generated by arbitration in combination with the real-time state of the electric vehicle.

Description

Technical Field

[0001] This application relates to the field of new energy electric vehicles, and in particular to a fault diagnosis method and electronic device. Background Technology

[0002] In electric vehicle applications, the power battery system is susceptible to failure. Currently, fault diagnosis of the power battery system can only be performed when or after a fault occurs. Intervention and solutions can only be implemented after a fault has already occurred or worsened. This means that early diagnosis and pre-diagnosis of power battery system faults are not possible, nor is it possible to effectively prevent the deterioration of critical components in electric vehicles, or to proactively protect and avoid faults in these critical components.

[0003] Therefore, a new fault diagnosis method is needed to diagnose faults occurring in power battery systems. Summary of the Invention

[0004] To address the problem of how to diagnose faults in power battery systems, this application provides a fault diagnosis method and an electronic device, and also provides a computer-readable storage medium.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, this application provides a fault diagnosis method, the method being applied to an electronic device, the method comprising:

[0007] Collect real-time data from the power battery system;

[0008] Report the aforementioned real-time data;

[0009] Receive fault diagnosis prediction results and / or the first fault measures corresponding to the fault diagnosis prediction results, wherein the fault diagnosis prediction results are possible faults of the power battery system predicted by comparing the real-time data with the vehicle's historical report big data;

[0010] Collect the fault flag bits and / or fault levels reported by the power battery system;

[0011] Based on the real-time data, fault diagnosis is performed according to the fault flag bit and / or the fault level, generating local fault diagnosis results and / or second fault measures corresponding to the local fault diagnosis results;

[0012] Arbitrate the fault diagnosis prediction results and / or the first fault measure, as well as the local fault diagnosis results and / or the second fault measure, to generate a third fault measure;

[0013] Implement the third fault-finding measure.

[0014] In one implementation of the first aspect, the method further includes:

[0015] By comparing the real-time data with the vehicle's historical big data report, parameters that may cause the power battery system to malfunction are extracted from the real-time data;

[0016] Based on the parameters that may cause the power battery system to malfunction, the possible malfunctions of the power battery system are predicted.

[0017] In one implementation of the first aspect, predicting potential failures of the power battery system based on the parameters that may cause the power battery system to fail includes:

[0018] The database is used to search for the faults corresponding to the parameters that may cause the power battery system to malfunction. The database is used to store the parameters that may cause the power battery system to malfunction, the possible faults of the power battery system, and the correspondence between the parameters and the faults.

[0019] In one implementation of the first aspect, the method further includes:

[0020] The database is used to search for the first fault measure corresponding to the possible faults of the power battery system. The database is used to store fault measures that can be taken for the power battery system, as well as the correspondence between the fault measures and the faults.

[0021] In one implementation of the first aspect, the fault diagnosis prediction result and the local fault diagnosis result are arbitrated to generate the third fault measure, including:

[0022] Identify the current vehicle scene of the electric vehicle;

[0023] Based on the current vehicle scenario, the fault diagnosis prediction result and the local fault diagnosis result are arbitrated to generate a fault determination result;

[0024] Based on the fault determination result, a third fault measure matching the current vehicle scenario is generated.

[0025] In one implementation of the first aspect, the fault determination result includes:

[0026] The fault diagnosis prediction result or a portion of the fault in the fault diagnosis prediction result;

[0027] Alternatively, the local fault diagnosis result or a portion of the fault in the local fault diagnosis result;

[0028] Alternatively, the fault diagnosis prediction result or a portion of the fault in the fault diagnosis prediction result may be a combination of the local fault diagnosis result or a portion of the fault in the local fault diagnosis result.

[0029] In one implementation of the first aspect, arbitrating the first fault measure and the second fault measure to generate the third fault measure includes:

[0030] Identify the current vehicle scene of the electric vehicle;

[0031] Based on the current vehicle scenario, the first fault measure and the second fault measure are arbitrated to generate the third fault measure that matches the current vehicle scenario.

[0032] In one implementation of the first aspect, the third fault measure includes:

[0033] The first fault measure or a portion thereof;

[0034] Alternatively, the second fault measure or a portion thereof;

[0035] Alternatively, the first fault measure or a portion thereof, combined with the second fault measure or a portion thereof.

[0036] In a second aspect, this application provides an electronic device, which includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the steps of the method described in the first aspect.

[0037] Thirdly, this application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0038] The technical solutions proposed in the embodiments of this application can achieve at least the following technical effects:

[0039] According to the method in the embodiments of this application, the failure of the power battery system can be predicted in advance based on VHR big data, and the failure measures that best match the real-time status of the electric vehicle can be generated by arbitration in combination with the real-time status of the electric vehicle.

[0040] According to the method in the embodiments of this application, faults in the power battery system can be diagnosed and pre-diagnosed in advance, and the deterioration of faults in key components of electric vehicles can be avoided in a timely and effective manner, thereby protecting and avoiding faults in key components of electric vehicles in advance. Attached Figure Description

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of a fault diagnosis method according to an embodiment of this application;

[0042] Figure 2 The diagram shown is a flowchart of a fault diagnosis method according to an embodiment of this application;

[0043] Figure 3 The diagram shown is a flowchart of a fault diagnosis method according to an embodiment of this application;

[0044] Figure 4 The diagram shown is a data flow diagram according to an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present application. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The terminology used in the implementation section of this application is for the purpose of explaining specific embodiments of this application only, and is not intended to limit this application.

[0048] This application provides a fault diagnosis method to address the problem of how to diagnose faults in power battery systems.

[0049] Specifically, Figure 1 This is a schematic diagram illustrating an application scenario of a fault diagnosis method according to an embodiment of this application.

[0050] like Figure 1 As shown, electronic device 110 is installed in an electric vehicle, and electronic device 110 includes data acquisition module 111. Data acquisition module 111 is used to acquire local real-time data of the electric vehicle's power battery system.

[0051] Specifically, in one embodiment, the components covered by the power battery system of an electric vehicle include the following components.

[0052] Engine Management System (EMS). EMS enables the management of the engine in an electric vehicle.

[0053] The Generator Control Unit (GCU) is the device used to start the engine. The GCU is the core component that drives the generator to provide energy, and it is responsible for starting and stopping the engine and converting energy in electric vehicles.

[0054] Battery Management System (BMS). The BMS is responsible for monitoring the battery status in an electric vehicle in real time.

[0055] Clutch Control Unit (CCU). The CCU is used during the starting or driving of an electric vehicle to control the clutch and ensure a smooth start. When starting or shifting gears, the CCU temporarily disconnects the engine from the transmission system to facilitate engine starting (reducing starting torque) and gear shifting (reducing or eliminating shift shock).

[0056] Micro Controller Unit (MCU). The MCU receives vehicle driving control commands from the vehicle controller and controls the electric motor to output specified torque and speed, enabling the electric vehicle to move.

[0057] Direct Current Converter (DCDC). A DCDC converter effectively changes the battery's output voltage, transmitting a suitable voltage to the motor driver, ultimately finding the appropriate voltage for vehicle operation and enabling vehicle starting.

[0058] On-board charger (OBC). An OBC is a device responsible for charging the battery in an electric vehicle.

[0059] Positive Temperature Coefficient (PTC) components. The primary function of PTC components is to provide heating in electric vehicles. The PTC is the heat source for the air conditioning system in electric vehicles. In traditional internal combustion engine vehicles, the heat source for the air conditioning is mainly the cooling water from the internal combustion engine. However, electric vehicles do not have an internal combustion engine and cannot provide heating. Therefore, a PTC is needed to generate heat to achieve the purpose of air conditioning heating.

[0060] Thermal management accessories are crucial components for regulating the automotive cabin environment and the operating environment of automotive parts. They comprehensively improve energy efficiency through cooling, heating, and internal heat conduction. Thermal management accessories control and regulate the internal temperature of an electric vehicle and the operating temperature of each component, ensuring the normal operation of every part and providing a comfortable driving environment.

[0061] The data acquisition module 111 collects local real-time data including, but not limited to, the status bits, validity bits, torque, torque request, speed, enable conditions, current, voltage, control status, gear position, and thermal management component status of the power battery system components. Specifically: validity bits indicate whether each component is valid (e.g., Valid for valid, Invalid for invalid); enable conditions refer to the prerequisites for fault diagnosis, such as starting diagnosis 2 seconds after power-on or 3 seconds after high voltage application; control status refers to the individual status of each component, such as low-voltage standby, high-voltage standby, torque control, speed control, fault mode, etc.

[0062] The electronic device 110 also includes a fault information acquisition module 112. The fault information acquisition module 112 is used to acquire fault flag bits (e.g., short circuit flag bit, open circuit flag bit) and fault levels reported by the components of the power battery system.

[0063] The electronic device 110 also includes a fault diagnosis module 113. The fault diagnosis module 113 is used to perform fault diagnosis and output fault diagnosis results after the fault information acquisition module 112 acquires the fault flag bits and fault levels reported by the components of the power battery system, and combines them with the local real-time data acquired by the data acquisition module 111.

[0064] The electronic device 110 also includes a fault response generation module 114. The fault response generation module 114 is used to generate corresponding fault responses based on the fault diagnosis results output by the fault diagnosis module 113.

[0065] The electronic device 110 also includes a data output module 115, which is used to output the local real-time data collected by the data acquisition module 111 to the electronic device 120.

[0066] Electronic device 120 includes database 121, which is used to store large amounts of Vehicle History Report (VHR) data. The VHR contains all message information on the vehicle communication matrix, such as the historical fault database of the power battery system, the root cause statistics of historical faults, the cause location of historical faults, and the historical data upload of power battery system components.

[0067] Electronic device 120 can be a cloud server or a local data storage device.

[0068] Electronic device 120 also includes a data comparison module 122. This module compares the local real-time data reported by electronic device 110 with the VHR big data stored in database 121. Through comparative analysis, it extracts parameters (indicators) from the local real-time data reported by electronic device 110 that may lead to power battery system failure. For example, suppose the VHR big data records that a power battery system failure occurs when the battery temperature exceeds 78 degrees Celsius. The local real-time data reported by electronic device 110 includes a battery temperature of 79 degrees Celsius. Through comparative analysis, the parameter that may lead to power battery system failure—battery temperature 79 degrees Celsius—is extracted from the local real-time data.

[0069] The parameters (indicators) extracted by the data comparison module 122 that may lead to malfunctions in the power battery system are output to the electronic device 130. The electronic device 130 can be a cloud server or a local device installed on the electric vehicle.

[0070] Electronic device 130 includes database 131, which is used to store parameters (indicators) that cause power battery system failure, possible failures (fault tree) of power battery system, and the correspondence between parameters (indicators) and failures (fault tree).

[0071] The electronic device 130 also includes a fault diagnosis module 132, which is used to diagnose the power battery system based on the data stored in the database 131 and the key parameters and key indicators output by the electronic device 120, predict the possible faults of the power battery system, and generate fault diagnosis prediction results.

[0072] The database 131 of the electronic device 130 is also used to store fault measures corresponding to different faults in the power battery system. The electronic device 130 also includes a fault measure generation module 133, which is used to generate corresponding fault measures based on the data stored in the database 131 and the fault diagnosis prediction results generated by the fault diagnosis module 132.

[0073] The fault diagnosis module 132 predicts possible faults in the power battery system, and the fault measures generated by the fault measure generation module 133 are sent to the electronic device 110.

[0074] The electronic device 110 also includes a vehicle scene recognition module 118, which is used to recognize the current vehicle scene (e.g., driving mode, road mode, vehicle mode, vehicle speed, gear, etc.).

[0075] The electronic device 110 also includes an arbitration module 116, which is used to arbitrate the fault measures output by the fault diagnosis module 113 and the fault measures generated by the fault measures generation module 133 based on the recognition results of the vehicle scene recognition module 118. The arbitration module 116 selects the fault measures that match the current vehicle scene from the fault measures output by the fault diagnosis module 113 and the fault measures generated by the fault measures generation module 133, or combines the fault measures output by the fault diagnosis module 113 and the fault measures generated by the fault measures generation module 133 into a fault measure that matches the current vehicle scene.

[0076] Specifically, in the same arbitration session of arbitration module 116, the fault diagnosis module 113 may output one or more fault measures, and the fault measure generation module 133 may also generate one or more fault measures. Furthermore, in the same arbitration session of arbitration module 116, either the fault diagnosis module 113 or the fault measure generation module 133 may not output any fault measures.

[0077] The electronic device 110 also includes a fault implementation module 117, which is used to implement the fault measures output by the arbitration module 116.

[0078] Figure 2 The diagram shown is a flowchart of a fault diagnosis method according to an embodiment of this application.

[0079] In one embodiment, the electronic device performs as follows Figure 2 The following process is shown to achieve fault diagnosis for power battery systems.

[0080] The S200 collects local real-time data from the power battery system of electric vehicles.

[0081] S210 reports local real-time data to the cloud server.

[0082] S220 compares the locally reported real-time data with VHR big data, and extracts parameters (indicators) that may cause power battery system failure from the locally reported real-time data through comparative analysis.

[0083] S221, based on pre-saved parameters (indicators) that cause power battery system failure, possible failures of the power battery system, and the correspondence between parameters (indicators) and failures, the power battery system is diagnosed according to parameters (indicators) that may cause power battery system failure extracted from local real-time data, and possible failures of the power battery system are predicted, and a failure diagnosis prediction result is generated.

[0084] S222, based on the pre-saved fault measures corresponding to the faults, generate fault measures (first fault measures) corresponding to the fault diagnosis prediction results according to the predicted faults that may occur in the power battery system (fault diagnosis prediction results).

[0085] S230 collects fault flags and fault levels reported by components of the power battery system.

[0086] S231. Based on the fault flag bits and fault levels reported by the components of the power battery system, and combined with the local real-time data of the power battery system, perform local fault diagnosis and generate local fault diagnosis results.

[0087] S232, Generate corresponding fault measures (second fault measures) based on the local fault diagnosis results generated by the local fault diagnosis.

[0088] S240 identifies the current vehicle scene.

[0089] S241, based on the current vehicle scenario, arbitrate the fault measures corresponding to the predicted possible faults of the power battery system and the fault measures corresponding to the fault diagnosis results generated by local fault diagnosis, and generate a fault measure (third fault measure) that matches the current vehicle scenario.

[0090] In S241, the third fault measure can be the first fault measure or a part of the first fault measure; the third fault measure can also be the second fault measure or a part of the second fault measure; the third fault measure can also be the first fault measure or a part of the first fault measure, combined with the second fault measure or a part of the second fault measure.

[0091] S250, implement the third fault measure generated by arbitration.

[0092] Figure 3 The diagram shown is a flowchart of a fault diagnosis method according to an embodiment of this application.

[0093] In another embodiment, the electronic device performs, as Figure 3 The following process is shown to achieve fault diagnosis for power battery systems.

[0094] S300 collects local real-time data from the electric vehicle's power battery system. Refer to S200.

[0095] S310 reports local real-time data to the cloud server. See S210.

[0096] S320 compares locally reported real-time data with VHR big data, and extracts key parameters and indicators that may lead to power battery system failure from the locally reported real-time data through comparative analysis. Refer to S220.

[0097] S321: Based on pre-saved key parameters / key indicators that may lead to power battery system failure, the fault trees that may result from these key parameters / key indicators, and the correspondence between these key parameters / key indicators and the fault trees, the power battery system is diagnosed based on key parameters and key indicators extracted from local real-time data that may lead to power battery system failure. This process predicts possible failures of the power battery system and generates a fault diagnosis prediction result. (Refer to S221.)

[0098] S330 collects fault flag bits and fault levels reported by components of the power battery system. Refer to S230.

[0099] S331: Based on the fault flag bits and fault levels reported by the components of the power battery system, and combined with the local real-time data of the power battery system, perform local fault diagnosis and generate local fault diagnosis results. Refer to S231.

[0100] S340 identifies the current vehicle scene.

[0101] S341, based on the current vehicle scenario, arbitrates the predicted faults that may occur in the power battery system (fault diagnosis prediction results) and the local fault diagnosis results generated by local fault diagnosis to generate a fault determination result.

[0102] In S341, the fault determination result can be a fault diagnosis prediction result or a partial fault in the fault diagnosis prediction result; the fault determination result can also be a local fault diagnosis result or a partial fault in the local fault diagnosis result; the fault determination result can also be a combination of a fault diagnosis prediction result or a partial fault in the fault diagnosis prediction result and a local fault diagnosis result or a partial fault in the local fault diagnosis result.

[0103] S342, based on the current vehicle scenario, generates fault measures that match the current vehicle scenario according to the fault determination results.

[0104] S350, implement fault measures generated by arbitration.

[0105] According to the method in the embodiments of this application, the failure of the power battery system can be predicted in advance based on VHR big data, and the failure measures that best match the real-time status of the electric vehicle can be generated by arbitration in combination with the real-time status of the electric vehicle.

[0106] According to the method in the embodiments of this application, faults in the power battery system can be diagnosed and pre-diagnosed in advance, and the deterioration of faults in key components of electric vehicles can be avoided in a timely and effective manner, thereby protecting and avoiding faults in key components of electric vehicles in advance.

[0107] The following describes in detail the fault diagnosis process according to an embodiment of this application, taking the fault diagnosis of the Battery Management System (BMS) in the power battery system as an example.

[0108] Figure 4 The diagram shown is a data flow diagram according to an embodiment of this application.

[0109] Collect real-time BMS data 410, which includes information such as power battery voltage, current, power parameters, single cell voltage parameters, heat, and thermal management accessory flow rate.

[0110] For example, BMS real-time data 310 includes BMS insulation detection signal 411 (CAN signal), BMS power signal 412 (CAN signal), BMS operating status signal 413 (CAN signal), BMS high voltage interlock status signal 414 (CAN signal), etc.

[0111] Send BMS real-time data 410 to the cloud.

[0112] The real-time BMS data 410 is compared with the VHR big data 420 in the cloud (including historical BMS data 421). BMS parameters 422 that match the key information such as battery voltage, current, operating status, and individual cell status during BMS failure in the historical BMS data 421 are extracted from the real-time BMS data 410.

[0113] Fault prediction and diagnosis are performed based on the extracted BMS parameter 422. When the consistency rate between BMS parameter 422 and the parameter causing BMS level 5 fault is greater than or equal to 95%, the result of fault prediction and diagnosis 430 indicates that there is a BMS level 5 fault.

[0114] The result of the fault prediction and diagnosis, 430 (there is a BMS level 5 fault), is fed back to the electric vehicle.

[0115] Based on the result 430 of the fault prediction diagnosis (there is a BMS level 5 fault), the real-time data of the electric vehicle's BMS was compared and verified, confirming the existence of a BMS level 5 fault.

[0116] Implement the fault measures corresponding to BMS Level 5 faults.

[0117] In the description of the embodiments of this application, for the sake of convenience, the device is described by dividing it into various modules according to its functions. The division of each module is only a logical functional division. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.

[0118] Specifically, the apparatus proposed in this application can be fully or partially integrated onto a single physical entity, or physically separated. These modules can be implemented entirely in software via processing element calls; entirely in hardware; or partially in software via processing element calls and partially in hardware. For example, the detection module can be a separate processing element or integrated into a chip in the electronic device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together, or implemented independently. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0119] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0120] An embodiment of this application also proposes an electronic device, which may be the electronic device 110, 120 or 130 of the embodiments of this application.

[0121] Figure 5 This is a schematic diagram of an electronic device structure according to an embodiment of this application.

[0122] like Figure 5 As shown, the electronic device 500 includes a memory 502 for storing computer program instructions and a processor 501 for executing the program instructions. When the computer program instructions are executed by the processor 501, the electronic device 500 is triggered to perform the steps of the method described in the embodiments of this application.

[0123] Specifically, in one embodiment of this application, the aforementioned one or more computer programs are stored in the aforementioned memory 502. The aforementioned one or more computer programs include instructions that, when executed by the aforementioned electronic device 500, cause the aforementioned electronic device 500 to perform the method steps described in the embodiments of this application.

[0124] It is understood that the structural description of the electronic device 500 in the embodiments of this application does not constitute a specific limitation on the electronic device 500. In other embodiments of this application, the electronic device 500 may include other components besides the processor 501 and the memory 502.

[0125] The processor 501 may be an on-chip device (SOC), which may include a central processing unit (CPU) and may further include other types of processors.

[0126] The processor 501 may include, for example, a CPU, DSP, microcontroller, or digital signal processor, and may also include a GPU, embedded neural network processing units (NPUs), and image signal processors (ISPs). The processor may also include necessary hardware accelerators or logic processing hardware circuitry, such as an ASIC, or one or more integrated circuits for controlling the execution of the program in the present application. Furthermore, the processor may have the function of operating one or more software programs, which may be stored in a storage medium.

[0127] Processor 501 may include one or more processing units. For example, a processor may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent components or integrated into one or more processors. In some embodiments, electronic device 500 may also include one or more processors 501. The controller can generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution.

[0128] In some embodiments, the processor 501 may include one or more interfaces. These interfaces may include an inter-integrated circuit (I2C) interface, an integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a SIM card interface, and / or a USB interface, etc. The USB interface is a USB standard-compliant interface, specifically a Mini USB interface, a Micro USB interface, a USB Type-C interface, etc. The USB interface can be used to connect a charger to charge the electronic device, and can also be used for data transfer between the electronic device and peripheral devices.

[0129] The electronic device 500 may also include an external memory interface for connecting an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor 501 through the external memory interface to perform data storage functions, such as saving music, video, and other files on the external memory card.

[0130] The memory 502 may include a code storage area and a data storage area. The code storage area may store the operating system. The data storage area may store data created during the use of the electronic device 500. Furthermore, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as one or more disk storage components, flash memory components, universal flash storage (UFS), etc.

[0131] The memory 502 may be a read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices. Alternatively, it may be any computer-readable medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer.

[0132] Processor 501 and memory 502 can be combined into a single processing device, but more commonly they are separate components.

[0133] Electronic device 500 may also include an antenna, a mobile communication module, a wireless communication module, a modem processor, and a baseband processor. Electronic device 500 can realize wireless communication function through the antenna, mobile communication module, wireless communication module, modem processor, and baseband processor.

[0134] Antennas are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 500 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization.

[0135] The mobile communication module can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to electronic devices 500. The mobile communication module may include at least one filter, switch, power amplifier, low-noise amplifier (LNA), etc. The mobile communication module can receive electromagnetic waves via an antenna, filter and amplify the received electromagnetic waves, and transmit them to a modem processor for demodulation. The mobile communication module can also amplify the signal modulated by the modem processor and radiate it as electromagnetic waves via the antenna. In some embodiments, at least some functional modules of the mobile communication module may be housed in the processor 501.

[0136] The modem processor may include a modulator and a demodulator. The modulator modulates a low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 501 and may be housed within the same device as the mobile communication module or other functional modules.

[0137] The wireless communication module can provide solutions for wireless communication applications on electronic device 500, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module can be one or more devices integrating at least one communication processing module. The wireless communication module receives electromagnetic waves via an antenna, modulates and filters the electromagnetic wave signal, and sends the processed signal to processor 501. The wireless communication module can also receive signals to be transmitted from processor 501, modulate and amplify them, and then convert them into electromagnetic waves for radiation via the antenna.

[0138] In some embodiments, the electronic device 500 can communicate with a network and other devices via wireless communication technologies. Wireless communication technologies may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies. The GNSS may include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0139] Furthermore, the devices, apparatuses, and modules described in the embodiments of this application may be implemented by computer chips or physical entities, or by products with certain functions.

[0140] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media containing computer-usable program code.

[0141] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0142] Specifically, one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute the method provided in the embodiment of this application.

[0143] An embodiment of this application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the method provided in the embodiment of this application.

[0144] The embodiments described in this application are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] It should also be noted that in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0148] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0149] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0150] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0151] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments of this application can be implemented using electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0153] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A fault diagnosis method, said method being applied to electronic equipment, characterized in that, The method includes: Collect real-time data from the power battery system; Report the aforementioned real-time data; Receive fault diagnosis prediction results and / or the first fault measures corresponding to the fault diagnosis prediction results, wherein the fault diagnosis prediction results are possible faults of the power battery system predicted by comparing the real-time data with the vehicle's historical report big data; Collect the fault flag bits and / or fault levels reported by the power battery system; Based on the real-time data, fault diagnosis is performed according to the fault flag bit and / or the fault level, generating local fault diagnosis results and / or second fault measures corresponding to the local fault diagnosis results; Identify the current vehicle scene of the electric vehicle; Based on the current vehicle scenario, the fault diagnosis prediction result and / or the first fault measure, as well as the local fault diagnosis result and / or the second fault measure, are arbitrated to generate a third fault measure that matches the current vehicle scenario. Implement the third fault-finding measure.

2. The method according to claim 1, characterized in that, The method further includes: By comparing the real-time data with the vehicle's historical big data report, parameters that may cause the power battery system to malfunction are extracted from the real-time data; Based on the parameters that may cause the power battery system to malfunction, the possible malfunctions of the power battery system are predicted.

3. The method according to claim 2, characterized in that, The step of predicting potential failures of the power battery system based on the parameters that may cause the power battery system to fail includes: The database is used to search for the faults corresponding to the parameters that may cause the power battery system to fail. The database is used to store the parameters that may cause the power battery system to fail, the possible faults of the power battery system, and the correspondence between the parameters and the faults.

4. The method according to claim 3, characterized in that, The method further includes: The database is used to search for the first fault measure corresponding to the possible faults of the power battery system. The database is used to store fault measures that can be taken for the power battery system, as well as the correspondence between the fault measures and the faults.

5. The method according to any one of claims 1-4, characterized in that, Based on the current vehicle scenario, the fault diagnosis prediction result and the local fault diagnosis result are arbitrated to generate a fault determination result; Based on the fault determination result, the third fault measure is generated.

6. The method according to claim 5, characterized in that, The fault determination results include: The fault diagnosis prediction result or a portion of the fault in the fault diagnosis prediction result; Alternatively, the local fault diagnosis result or a portion of the fault in the local fault diagnosis result; Alternatively, the fault diagnosis prediction result or a portion of the fault in the fault diagnosis prediction result may be a combination of the local fault diagnosis result or a portion of the fault in the local fault diagnosis result.

7. The method according to any one of claims 1-4, characterized in that, Based on the current vehicle scenario, the first fault measure and the second fault measure are arbitrated to generate the third fault measure.

8. The method according to claim 7, characterized in that, The third fault response includes: The first fault measure or a portion thereof; Alternatively, the second fault measure or a portion thereof; Alternatively, the first fault measure or a portion thereof, combined with the second fault measure or a portion thereof.

9. An electronic device, characterized in that, The electronic device includes a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the method steps as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1-8.

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