A new energy vehicle remote fault diagnosis method and system

Abstract

The application provides a new energy vehicle remote fault diagnosis method and system, and relates to the technical field of new energy vehicle control.The new energy vehicle remote fault diagnosis method comprises the following steps: when a vehicle end vehicle controller performs online fault diagnosis on a vehicle, generating a vehicle end diagnosis conclusion and fault data; a monitoring platform performs platform end fault diagnosis based on the fault data, obtaining a platform end diagnosis conclusion; generating a fault diagnosis task list and a working condition list according to the vehicle end diagnosis conclusion and the platform end diagnosis conclusion; the vehicle end vehicle controller verifies a remote instruction sent by the monitoring platform; if the remote instruction verification is passed and the vehicle meets preset conditions, locking a specific fault source or fault component based on the fault diagnosis task list and the working condition list, and obtaining a final diagnosis conclusion.The new energy vehicle remote fault diagnosis method and system solve the technical problem that the conventional remote diagnosis in the related art can only narrow the fault range but cannot directly lock the fault source.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle control technology, specifically to a method and system for remote fault diagnosis of new energy vehicles. Background Technology

[0002] Currently, with the rapid development of the automotive industry, vehicle fault diagnosis has become a crucial link in the industrial chain. With the widespread adoption of vehicle monitoring platforms and vehicle cloud data technology, the remote vehicle fault diagnosis industry has emerged. Its core technology utilizes TBOX, the internet, and other means to achieve real-time monitoring and analysis of vehicle operating status, thereby assisting in fault diagnosis.

[0003] In existing technologies, remote vehicle fault diagnosis mainly relies on remote vehicle status monitoring systems combined with preset status thresholds for judgment, or on downloading relevant vehicle data for offline analysis via vehicle-to-cloud collaboration when a vehicle malfunctions, in order to pinpoint the source of the fault. Especially in the field of new energy special-purpose vehicles, the development of remote fault diagnosis has been relatively slow, with the common approach being the use of TBOX combined with vehicle-to-cloud collaboration for vehicle status monitoring and offline data analysis.

[0004] However, this remote diagnostic method has significant shortcomings in practical applications: in most cases, remote diagnostics can only determine the general system problem of the vehicle, and further narrow down the fault range through offline data analysis, but it is still difficult to directly pinpoint the source of the fault. After-sales personnel or technicians often still need to conduct on-site troubleshooting of the actual vehicle to ultimately determine the source of the fault. Especially for fault types such as insulation faults and communication faults, where multiple systems may report faults simultaneously, existing remote diagnostic methods and offline data analysis are almost unable to accurately pinpoint the fault location, relying entirely on on-site personnel for troubleshooting.

[0005] Therefore, existing remote fault diagnosis methods are essentially "passive" fault diagnosis measures. Their diagnostic results often contain a mixture of multiple fault types, some being the root cause and others subordinate, making it impossible to accurately identify the root cause. This diagnostic method can only narrow down the fault range, but cannot directly determine the fault source and provide clear repair guidance. Furthermore, on-site troubleshooting is not only time-consuming, but also presents significant challenges due to the harsh and dirty working environments of special-purpose vehicles, and the often inaccessible and remote locations of some fault points. Existing remote fault diagnosis technologies have failed to fully leverage their advantages in the field of new energy special-purpose vehicles. The state and operating conditions of the faulty vehicle at the time of the fault are relatively known; that is, the faulty vehicle "can speak," even for intermittent or fleeting faults. However, existing technologies have failed to effectively utilize this information, resulting in a passive diagnostic approach.

[0006] Therefore, existing technologies need further development. Summary of the Invention

[0007] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a remote fault diagnosis method and system for new energy vehicles, so as to solve the technical problem that conventional remote diagnosis in related technologies can only narrow down the fault range but cannot directly pinpoint the fault source.

[0008] To achieve the above technical objectives, the present invention adopts the following technical solution: A remote fault diagnosis method for new energy vehicles is provided, comprising: real-time detection of vehicle status and acquisition of real-time vehicle status parameters; determination of whether a vehicle fault has occurred based on the real-time vehicle status parameters; when a vehicle fault occurs, the vehicle-side controller performs online fault diagnosis, generating vehicle-side diagnostic conclusions and fault data; the monitoring platform performs platform-side fault diagnosis based on the fault data, obtaining platform-side diagnostic conclusions; a fault diagnosis task list and a working condition list are generated based on the vehicle-side diagnostic conclusions and the platform-side diagnostic conclusions; the vehicle-side controller verifies remote commands issued by the monitoring platform; if the remote command verification is successful and the vehicle meets preset conditions, the specific fault source or faulty component is identified based on the fault diagnosis task list and the working condition list, obtaining a final diagnostic conclusion; the monitoring platform generates maintenance guidance information for direct maintenance guidance based on the final diagnostic conclusion.

[0009] Furthermore, the fault data includes: preliminary conclusions of online fault diagnosis on the vehicle, real-time vehicle data after data cleaning, operating condition datasets before and after the fault occurred, and the original dataset for the time period of the fault occurrence.

[0010] Furthermore, the method for platform-side fault diagnosis based on fault data includes: the monitoring platform performs authenticity and integrity verification on the received fault data; after the verification is passed, the monitoring platform performs platform-side fault diagnosis on the real-time status parameters of the vehicle according to the preset diagnostic strategy, and at the same time downloads the original dataset before and after the fault to the local machine and performs offline fault diagnosis according to the status sequence, diagnostic strategy and related status relationship.

[0011] Furthermore, the method for generating the fault diagnosis task list and operating condition list includes: comparing and analyzing the vehicle-side diagnosis conclusions and the platform-side diagnosis conclusions to form preliminary diagnosis conclusions; generating the fault diagnosis task list and operating condition list based on the preliminary diagnosis conclusions and fault data; wherein, the operating condition list is prioritized according to the degree of correlation with the operating conditions at the time of the fault occurrence, with higher correlation resulting in higher priority.

[0012] Furthermore, the method for verifying remote commands issued by the monitoring platform includes: the vehicle controller at the vehicle end uses the public key in the preset certificate to verify the digital signature of the remote command and the hash value signature of the remote command data packet; and, performs redundancy verification on the remote command according to a preset custom verification algorithm.

[0013] Furthermore, the preset conditions include one or more of the following: vehicle battery status, drive system status, power battery system status, handbrake status, and vehicle speed status.

[0014] Furthermore, the method for obtaining the final diagnostic conclusion includes: setting diagnostic parameters according to the fault diagnosis task list, including insulation resistance threshold points, contactor status parameters, and operating condition parameters for each high-voltage circuit; setting the closing, opening, and detection timing logic of the high-voltage contactor according to the high-voltage circuit to be tested; conducting real-time control and information interaction with the monitoring platform through an encrypted channel, and receiving remote control commands from the monitoring platform to stop, add, remove, or step-by-step; controlling the controllers and components involved in the current high-voltage circuit to be tested, and sequentially checking and diagnosing wiring harness loose connections, short circuits, open circuits, and the components themselves.

[0015] Furthermore, the monitoring platform generates maintenance guidance information based on the final diagnostic conclusion to directly guide the repair process. Specifically, the monitoring platform receives the final diagnostic conclusion uploaded by the vehicle controller and the diagnostic data generated during the diagnostic task, performs a second verification of the final diagnostic conclusion, and inputs the verified final diagnostic conclusion into the monitoring platform to generate a maintenance instruction.

[0016] A remote fault diagnosis system for new energy vehicles is also provided, comprising: a detection unit for real-time detection of vehicle status and acquisition of real-time vehicle status parameters; a basic fault diagnosis unit for determining whether a vehicle has malfunctioned based on the real-time vehicle status parameters; a vehicle-side diagnosis unit for online fault diagnosis by the vehicle controller when a malfunction occurs, generating vehicle-side diagnosis conclusions and fault data; a platform-side diagnosis unit for monitoring the platform to perform platform-side fault diagnosis based on the fault data and obtain platform-side diagnosis conclusions; a collaborative diagnosis unit for generating a fault diagnosis task list and a working condition list based on the vehicle-side and platform-side diagnosis conclusions; a verification unit for verifying remote commands issued by the monitoring platform by the vehicle controller; a fault locking unit for locking the specific fault source or faulty component based on the fault diagnosis task list and working condition list if the remote command verification is successful and the vehicle meets preset conditions, thus obtaining a final diagnosis conclusion; and a maintenance guidance unit for the monitoring platform to generate maintenance guidance information for direct maintenance guidance based on the final diagnosis conclusion.

[0017] A computer-readable storage medium is also provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, they implement the various steps of the above-mentioned remote fault diagnosis method for new energy vehicles or the remote fault diagnosis system for new energy vehicles.

[0018] Beneficial effects: The remote fault diagnosis method for new energy vehicles of this invention integrates vehicle-side online diagnosis with platform-side online and offline diagnosis to form a preliminary diagnostic conclusion. Based on this conclusion, a fault diagnosis task list and a working condition list containing specific components to be tested and reproducible operating conditions are generated, thus providing an execution basis for proactive diagnosis. On this basis, only when the remote command has undergone multiple safety verifications and the vehicle meets preset safety conditions, the vehicle-side controller actively drives the target components to collaboratively execute diagnostic tasks (such as sequentially switching high-voltage circuits, adjusting contactor states, and reproducing fault conditions) in a remote control manner according to the task list and working condition list. This transforms passively waiting for data reporting into remotely and proactively controlling the vehicle to perform self-checks, directly locking the fault source or faulty component. Finally, the monitoring platform outputs maintenance guidance information that can directly guide repairs, forming a technical closed loop of deep collaboration between "remote control + fault diagnosis". This directly locks the fault source and outputs operable maintenance instructions, truly realizing a leap from passively narrowing the scope to actively locking the source and guiding repairs. This significantly improves the accuracy, safety, and practicality of remote fault diagnosis for new energy vehicles and solves the fundamental defect of conventional remote diagnostic technologies that cannot directly lock the fault source. Attached Figure Description

[0019] Figure 1 This is a flowchart of the remote fault diagnosis method for new energy vehicles used in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of the remote fault diagnosis system for new energy vehicles used in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the structure of the remote fault diagnosis system for new energy vehicles used in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the vehicle system used in Embodiment 2 of the present invention; Figure 5 This is a diagram illustrating the architecture of the remote fault diagnosis method for new energy vehicles used in Embodiment 3 of the present invention. Figure 6 This is a flowchart of the remote fault diagnosis method for new energy vehicles used in Embodiment 4 of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0021] Example 1: See Figure 1 This embodiment provides a remote fault diagnosis method for new energy vehicles, including: The S100 detects vehicle status in real time and obtains real-time vehicle status parameters. Specifically, the vehicle real-time status parameters refer to the dynamic and static status data collected in real time by electronic control units such as the vehicle controller (VCU), battery management system (BMS), and motor controller (MCU) during the operation of the new energy special vehicle via CAN bus or hard-wired signals. The vehicle real-time status parameters include: Powertrain parameters include: drive motor speed, drive motor torque, drive motor temperature, bus voltage, phase current, motor controller operating status, motor controller fault codes, and motor controller temperature; if the vehicle has a transmission, the powertrain parameters also include transmission-related parameters.

[0022] Power battery system parameters: total battery voltage, total battery current, state of charge (SOC), state of health (SOH), maximum voltage of a single battery cell, minimum voltage of a single battery cell, maximum temperature of a single battery cell, minimum temperature of a single battery cell, insulation resistance of the positive terminal to ground, insulation resistance of the negative terminal to ground, status of the high-voltage contactor (closed / open), and status of the pre-charge relay, etc.

[0023] Vehicle control and operation parameters: vehicle speed (from wheel speed sensor or GPS), gear (P / R / N / D / L), handbrake status (pull-up / release), accelerator pedal opening, brake pedal opening, key status (OFF / ON / START), and low-pressure handle switch status, etc. Environmental and operating parameters: ambient temperature, ambient humidity, vehicle load (estimated by airbag pressure sensor or suspension height sensor) and charging status (whether the charging gun is plugged in, charging power), etc. Fault diagnosis related parameters: fault codes (DTCs) and fault flag bits of each system, key thresholds in the preset diagnostic strategy (such as insulation resistance alarm threshold of 500Ω / V, over-temperature threshold of 60℃, etc.), and feature values ​​extracted after data cleaning (such as insulation resistance change rate, number of component status jumps, etc.).

[0024] S200 determines whether the vehicle has malfunctioned based on the vehicle's real-time status parameters; In this embodiment, the VCU has a pre-set fault diagnosis strategy and a classification strategy. The fault diagnosis strategy includes state monitoring thresholds and logical judgment rules for each system (such as the power battery system, drive motor system, high-voltage power distribution system, insulation monitoring system, etc.).

[0025] When a vehicle malfunctions, the vehicle-side controller of S300 performs online fault diagnosis on the vehicle and generates vehicle-side diagnostic conclusions and fault data. In practice, fault data includes: preliminary conclusions of online fault diagnosis on the vehicle, real-time vehicle data after data cleaning, operating condition datasets before and after the fault, and the original dataset for the time period of the fault.

[0026] The S400 monitoring platform performs platform-side fault diagnosis based on the fault data to obtain platform-side diagnostic conclusions. In practice, the methods for the monitoring platform to perform platform-side fault diagnosis based on the fault data include: The monitoring platform performs authenticity and integrity checks on the received fault data; After verification, the monitoring platform performs platform-side fault diagnosis on the real-time status parameters of the vehicle according to the preset diagnostic strategy. At the same time, it downloads the original dataset before and after the fault to the local machine and performs offline fault diagnosis based on the status sequence, diagnostic strategy and related status relationships.

[0027] S500 generates a fault diagnosis task list and a working condition list based on the vehicle-side diagnostic conclusion and the platform-side diagnostic conclusion. In practice, the methods for generating the fault diagnosis task list and operating condition list include: The vehicle-side diagnostic conclusions are compared and analyzed with the platform-side diagnostic conclusions to form a preliminary diagnostic conclusion. Based on the preliminary diagnostic conclusions and the fault data, a fault diagnosis task list and a working condition list are generated; wherein, the working condition list is prioritized according to its correlation with the working condition at the time of the fault occurrence, with higher correlation resulting in higher priority.

[0028] The vehicle controller at the S600 verifies the remote commands issued by the monitoring platform; In practice, methods for verifying remote commands issued by the monitoring platform include: The vehicle-side controller uses the public key in a preset certificate to verify the digital signature of the remote command and the hash value signature of the remote command's data packet; and, The remote command is redundancy checked according to a preset custom verification algorithm.

[0029] If the remote command verification is successful and the vehicle meets the preset conditions, S700 will locate the specific fault source or faulty component based on the fault diagnosis task list and the operating condition list, and obtain the final diagnosis conclusion. In practice, the preset conditions include one or more of the vehicle's battery status, drive system status, power battery system status, handbrake status, and vehicle speed status.

[0030] In practice, methods for obtaining a final diagnostic conclusion include: The diagnostic parameters are set according to the fault diagnosis task list. The diagnostic parameters include the insulation resistance threshold point of each high-voltage circuit, contactor status parameters, and operating condition parameters. Set the closing, opening, and detection timing logic of the high-voltage contactor according to the high-voltage circuit to be tested; The system communicates with the monitoring platform in real time via an encrypted channel, and receives remote control commands from the monitoring platform to stop, add, remove, or perform step-by-step actions. Control the controllers and components involved in the high-voltage circuit to be tested, and sequentially check and diagnose issues such as loose connections, short circuits, open circuits in the wiring harness, and the components themselves.

[0031] The monitoring platform S800 generates maintenance guidance information based on the final diagnostic conclusion to directly guide maintenance.

[0032] In practice, the monitoring platform generates maintenance guidance information based on the final diagnostic conclusion to directly guide maintenance, specifically including: The monitoring platform receives the final diagnostic conclusion and diagnostic data generated during the diagnostic task uploaded by the vehicle controller, and performs a second verification on the final diagnostic conclusion. The verified final diagnostic conclusion is input into the monitoring platform to generate a maintenance instruction.

[0033] See Figure 2 This embodiment also provides a remote fault diagnosis system for new energy vehicles, including: a detection unit, which is used to detect the vehicle status in real time and obtain real-time vehicle status parameters; a basic fault diagnosis unit, which is used to determine whether the vehicle has a fault based on the real-time vehicle status parameters; a vehicle-side diagnosis unit, which is used to perform online fault diagnosis on the vehicle by the vehicle-side vehicle controller when a fault occurs, and generate vehicle-side diagnosis conclusions and fault data; a platform-side diagnosis unit, which is used to monitor the platform to perform platform-side fault diagnosis based on the fault data and obtain platform-side diagnosis conclusions; and a collaborative diagnosis unit. The collaborative diagnostic unit is used to generate a fault diagnosis task list and a working condition list based on the vehicle-side diagnostic conclusion and the platform-side diagnostic conclusion; the verification unit is used by the vehicle-side vehicle controller to verify the remote commands issued by the monitoring platform; the fault locking unit is used to lock the specific fault source or faulty component based on the fault diagnosis task list and the working condition list if the remote command is verified and the vehicle meets preset conditions, and obtain the final diagnostic conclusion; the maintenance guidance unit is used by the monitoring platform to generate maintenance guidance information for direct maintenance guidance based on the final diagnostic conclusion.

[0034] By adopting the above configuration, the remote fault diagnosis method for new energy vehicles of the present invention integrates vehicle-side online diagnosis with platform-side online and offline diagnosis to form a preliminary diagnostic conclusion. Based on this conclusion, a fault diagnosis task list and a working condition list containing specific components to be tested and reproducible working conditions are generated, thus providing an execution basis for proactive diagnosis. On this basis, only when the remote command has undergone multiple safety verifications and the vehicle meets preset safety conditions, the vehicle-side controller actively drives the target components to collaboratively execute diagnostic tasks (such as sequentially switching high-voltage circuits, adjusting contactor states, reproducing fault conditions, etc.) in a remote control manner according to the task list and working condition list. This transforms passively waiting for data reporting into remotely and proactively controlling the vehicle to perform self-checks, directly locking the fault source or faulty component. Finally, the monitoring platform outputs maintenance guidance information that can directly guide repairs, forming a technical closed loop of deep collaboration between "remote control + fault diagnosis". This directly locks the fault source and outputs operable maintenance instructions, truly realizing a leap from passively narrowing the scope to actively locking the source and guiding repairs. This significantly improves the accuracy, safety, and practicality of remote fault diagnosis for new energy vehicles and solves the fundamental defect that conventional remote diagnosis technologies cannot directly lock the fault source.

[0035] This embodiment also provides a computer-readable storage medium storing computer-readable instructions. When the computer-readable instructions are executed by a processor, they implement the various steps of the above-described remote fault diagnosis method for new energy vehicles or the remote fault diagnosis system for new energy vehicles.

[0036] Example 2: This embodiment provides a remote fault diagnosis system for new energy vehicles. (See also...) Figure 3 This includes: vehicle control unit (VCU), monitoring platform fault diagnosis module, T-BOX, and faulty system components (the scope of components involved is determined in real time based on the specific fault).

[0037] The monitoring platform's fault diagnosis module remains in vehicle status monitoring mode throughout the vehicle's entire lifecycle in non-faulty conditions, monitoring, issuing warnings, and alarms for real-time vehicle data. When a vehicle malfunctions, the VCU fault diagnosis module immediately performs a diagnosis and uploads the diagnostic results and relevant key real-time data (after data cleaning) to the monitoring platform's fault diagnosis module. Simultaneously, offline diagnosis is performed on this data and the diagnostic results, generating analytical conclusions. At this point, remote control and fault diagnosis collaborative control of the vehicle are required. Relevant personnel access the monitoring platform's fault diagnosis module, confirming the vehicle is in a safe and repairable state (the VCU will simultaneously perform prerequisite verification; if the vehicle's current state does not allow remote diagnostic control, the VCU will reject the diagnostic request until the vehicle's state meets the prerequisite requirements), and then click the "Vehicle Diagnosis" command to issue it. After the relevant command is encrypted, decrypted, verified, and confirmed by the VCU, vehicle diagnosis immediately begins. First, switch the vehicle to debugging and diagnostic mode; second, set diagnostic tasks and operating conditions based on online and offline diagnostic conclusions; then, control relevant components to perform collaborative diagnostic work according to the set tasks; next, control the "active" diagnostic task through collaborative control between the vehicle and platform ends, and finally locate the faulty components and the source of the fault or circuit problem, and output the final diagnostic conclusion to the fault diagnosis module of the monitoring platform; finally, use the above conclusions to directly guide maintenance personnel to carry out vehicle repairs.

[0038] In this architecture, the T-Box serves to transmit relevant commands and datasets between the VCU and the monitoring platform.

[0039] In this embodiment, the system mainly involves two system states: a vehicle fault-free state and a vehicle fault state. See [link / reference] Figure 4When the vehicle is in normal condition, both the vehicle-side and platform-side monitor the vehicle's real-time status. At this time, the collaborative diagnostic system mainly consists of a vehicle-side vehicle status monitoring module, a data cleaning module, a monitoring platform vehicle status monitoring module, and an information security module. The system's primary task is to collaboratively monitor the vehicle's real-time status between the vehicle-side and the cloud, and to predict vehicle status trends and provide risk warnings. When the vehicle is in a faulty state, the remote control and fault diagnosis collaborative control system mainly consists of a vehicle-side online fault diagnosis module, a data cleaning module, a monitoring platform online diagnosis module, an offline diagnosis module, a fault diagnosis task and scheduling module, a fault diagnosis module (remote control + fault diagnosis collaborative control), a diagnosis conclusion and maintenance guidance dashboard module, and an information security module. The system's primary task at this time is to generate preliminary diagnostic conclusions through online and offline diagnostics, then generate fault diagnosis tasks and operating condition lists. After the user issues a "fault diagnosis" command, remote vehicle control and fault diagnosis collaborative control are performed. After the diagnosis is completed, the final diagnostic conclusion is pushed to the monitoring platform fault diagnosis module, and maintenance guidance is simultaneously pushed to the maintenance guidance dashboard, directly guiding maintenance personnel in subsequent maintenance work.

[0040] Specifically, the main task of the information security module is to verify the authenticity and integrity of interactive instructions and related data information during the collaborative control process, and to encrypt and verify identities based on asymmetric encryption and digital certificates, thereby ensuring the absolute security and reliability of data and control instructions transmission and interaction during remote control and fault diagnosis collaborative control.

[0041] Example 3: This embodiment provides a remote fault diagnosis method for new energy vehicles. (See also...) Figure 5 This includes: When the vehicle is in a fault-free state, it will enter a real-time status monitoring mode. The vehicle-side will monitor the vehicle status in real time according to a preset monitoring and diagnostic strategy mechanism. To avoid wasting unnecessary bandwidth, in this state, the relevant data uploaded by the vehicle-side VCU to the platform's fault diagnosis module needs to undergo a predefined data cleaning strategy to perform operations such as deduplication, filtering, and extraction of important feature values. Then, the VCU uses its private key to sign a random number and sends the signature result and the complete data packet hash value together to the monitoring platform. After the monitoring platform decrypts and verifies the signature, it immediately receives the data packet and synchronously performs real-time vehicle status monitoring. It also uses machine learning, trend analysis, and other methods to predict and warn about the vehicle status, thereby carrying out relevant preventive work to "prevent problems before they occur".

[0042] When the vehicle is in a faulty state, it will enter the vehicle collaborative fault diagnosis mode. The vehicle-side system first performs online diagnosis of vehicle faults in real time according to the preset fault diagnosis strategy mechanism. It then packages the preliminary diagnosis conclusions, real-time data before and after the fault, and real-time vehicle status data under the current state. The system also processes the related data through a predefined data cleaning strategy, including deduplication, filtering, and extraction of important feature values, and then packages the data together. Specifically, taking the insulation fault diagnosis strategy as an example, the preset fault diagnosis mechanism is as follows: The VCU calculates the insulation fault threshold point based on the vehicle's actual driving status, voltage status, etc. (generally, two threshold points are designed: a warning fault and a shutdown fault). Simultaneously, it monitors the positive and negative insulation values ​​of each point in real time and compares them with the aforementioned fault threshold points. If the actual value is lower than the relevant threshold point, a corresponding fault is triggered, and a preliminary diagnosis conclusion is generated and pushed to the monitoring platform's fault diagnosis module. Since the vehicle's high-voltage wiring harness is in a connected state, generally, if an insulation fault occurs, almost all connected high-voltage components will report an insulation fault. Therefore, the above preliminary diagnosis can only indicate that a fault exists, but cannot pinpoint the fault location or faulty component. Similarly, many similar faults in the vehicle cannot be identified through preliminary diagnosis. Therefore, this embodiment adopts a method of remote control and fault diagnosis collaborative control to solve the technical problem in existing technologies where conventional remote diagnosis can only narrow down the fault range but cannot directly pinpoint the fault source.

[0043] Then the VCU uses its private key to sign a random number, and then sends the signature result, along with the complete data packet hash signature result, to the monitoring platform's fault diagnosis module. After the platform decrypts and verifies the signature, it will immediately perform an online diagnosis according to the preset diagnostic strategy based on the current real-time vehicle status data, compare the preliminary diagnosis conclusion with the vehicle-side diagnosis conclusion, and record all preliminary conclusions and comparison conclusions in the fault diagnosis log of the fault diagnosis module. Next, based on the real-time data before and after the fault uploaded by the vehicle, an offline diagnosis is performed according to the preset diagnostic strategy, and the offline diagnosis results are also recorded synchronously in the same fault diagnosis log mentioned above. In this embodiment, the system will generate a list of collaborative control tasks and a list of operating conditions for remote control and fault diagnosis system based on the above preliminary diagnostic conclusions. After the user selects and confirms the diagnostic task and confirms that the vehicle is in a safe and troubleshootable state, the system will issue a vehicle "fault diagnosis" command from the platform. Following the information security module to perform tasks such as identity encryption, decryption, signature verification, and integrity verification of the command, if the verification is correct, the VCU will immediately perform vehicle prerequisite verification. If the prerequisites are not met, the task will be rejected. If the prerequisites are met, the relevant remote control and fault diagnosis collaborative control tasks will be received immediately and the vehicle will be switched to debug mode. The VCU status will be switched to remote control and fault diagnosis collaborative control mode. It should be noted that the workflow of the information security module in this embodiment is as follows: First, a hash check value is generated for the data address range involved in the complete data packet (or instruction set data packet) using a secure hash algorithm (SHA-256), and this value is packaged into the data packet (for data integrity verification); a digital signature is generated using the private key (using the Elliptic Curve Digital Signature Algorithm (ECDSA)) for data authenticity verification; and a random signature number is generated using custom rules for redundancy verification.

[0044] Then, the data or instruction recipient calculates a hash value for the complete dataset using the same SHA-256 algorithm and compares it with the hash value calculated by the sender. If they match, the integrity verification is successful. The recipient verifies the digital signature using the public key. If the verification is successful, the authenticity verification is successful. The recipient also verifies the random signature number using custom rules. If the verification is successful, the redundancy verification is successful.

[0045] Finally, after all the above verifications are passed, the data decryption is successful, and the recipient begins to parse the data and carry out subsequent work tasks.

[0046] It should be noted that, in addition, communication between the platform and the remote communication terminal (TBOX) will be further encrypted using Transport Layer Security Protocol 1.2 (TLS1.2) to ensure that data can be transmitted over the encrypted channel.

[0047] Finally, the platform remotely controls the vehicle based on the aforementioned collaborative control task list and operating condition list, and conducts "proactive" fault diagnosis through collaborative control to ultimately pinpoint the source of the problem. During this collaborative control task, all collaborative interactions require encryption, decryption, signature verification, and integrity checks of relevant instructions and data through the information security module. After the diagnosis is completed, the final diagnosis conclusion and maintenance guidance will be pushed to the maintenance guidance dashboard in the fault diagnosis module of the monitoring platform, thereby directly guiding maintenance personnel to carry out subsequent vehicle maintenance work.

[0048] See Figure 5 In this embodiment, online fault diagnosis refers to diagnosis based on real-time vehicle status parameters and real-time data, and in accordance with a fault diagnosis strategy; offline fault diagnosis refers to downloading the original data before and after the vehicle fault occurred to the local machine and diagnosing it according to a fault diagnosis strategy.

[0049] Example 4: Since the high-voltage circuit is a unified whole, the insulation test values ​​at the positive and negative terminals are the same for all components in the circuit. When an insulation fault occurs, almost all components will report an abnormal insulation status. Existing remote and offline diagnostic methods are largely ineffective for this type of fault, failing to pinpoint the fault source and, in most cases, even failing to narrow down the fault area. Maintenance personnel still need to systematically check and locate the insulation anomaly point. Therefore, this implementation provides a remote fault diagnosis method for new energy vehicles, taking insulation faults as an example.

[0050] See Figure 6 The remote fault diagnosis method for new energy vehicles in this embodiment includes: Step 0: Turn the low-voltage handle switch to the ON position to supply low-voltage power, and ensure that the vehicle is in a powered-on state, such as the key being in the ON position or the charging gun being plugged in (i.e., the VCU is in the wake-up state).

[0051] (Steps 1-1 and 1-2 run in parallel during program execution and have no order of priority.)

[0052] Step 1-1: The vehicle-side VCU monitors the insulation status of the entire vehicle based on relevant insulation status monitoring thresholds and parameters; through data mining and big data analysis, a vehicle insulation status trend table is obtained (only the status trend of this vehicle), and the current insulation status trend of the vehicle is predicted based on this table.

[0053] Steps 1-2: The vehicle-side performs data cleaning operations on the whole vehicle data, such as deduplication, filtering, and extraction of important feature values ​​(insulation status value, component status, etc.).

[0054] Step 2-a: The VCU uses its private key to sign a random number on the vehicle insulation status trend prediction conclusion in Step 1-1 and the dataset after data cleaning in Step 1-2. Then, it sends the signature result, the complete data packet hash value signature result, and the custom private key encrypted signature value to the monitoring platform.

[0055] Step 2-b: The vehicle-side VCU continuously monitors the insulation status of the entire vehicle based on relevant insulation status monitoring thresholds and parameters until the vehicle has an insulation-related fault condition.

[0056] It should be noted that steps 2-a and 2-b are performed in parallel mode during program execution and have no order of precedence; step 2-a is the next step after steps 1-1 and 1-2, and step 2-b is the next step after another branch of the task in part of step 1-1; in subsequent steps, n+1-a corresponds to the next step after step na above, and n+1-b corresponds to the next step after step nb above.

[0057] Step 3-a: The monitoring platform uses the public key in the certificate to verify the signature (authenticity verification), and uses the public key to verify the hash value signature of the complete data packet (integrity verification). At the same time, it verifies the custom signature according to the custom verification algorithm (redundancy verification).

[0058] Step 3-b: If the vehicle has an insulation-related fault, the vehicle-side VCU will immediately perform online fault diagnosis based on the preset fault diagnosis strategy and classification strategy by using information such as the vehicle's real-time status parameters.

[0059] Step 4-a: If the signature verification is incorrect, the monitoring platform will reject the data transmission; if the signature verification is correct, the monitoring platform will receive the data and parse it, and at the same time continuously monitor the insulation status of the whole vehicle according to the relevant insulation status monitoring thresholds and parameters.

[0060] Step 4-b: The VCU uses its private key to sign a random number, along with the dataset packaged after data cleaning operations such as deduplication, filtering, and extraction of important feature values ​​(insulation status value, component status, related component feature values, etc.) of the preliminary conclusions formed by online fault diagnosis, relevant real-time vehicle data, operating condition dataset (relevant vehicle status and operating conditions before and after the fault), and the original dataset of the insulation fault occurrence time period, and then sends the signature result, the complete data packet hash value signature result, and the custom private key encrypted signature value to the monitoring platform.

[0061] Step 5-a: The monitoring platform obtains a trend table of vehicle insulation status through data mining and big data analysis (this trend table is derived from the analysis of the relevant status of more than 100 vehicles). This status trend is combined with the insulation status trend predicted by the VCU to obtain the final insulation status prediction conclusion of this vehicle. If there is any related risk, an early warning will be issued on the monitoring platform according to the graded strategy, so as to carry out preventive work to "prevent problems before they occur".

[0062] Step 5-b: The monitoring platform uses the public key in the certificate to verify the signature (authenticity verification), and uses the public key to verify the hash value signature of the complete data packet (integrity verification). At the same time, it verifies the custom signature according to the custom verification algorithm (redundancy verification).

[0063] Step 6-b: If the signature verification is incorrect, the monitoring platform will reject the data transmission; if the signature verification is correct, the monitoring platform will receive the data and parse it. At the same time, it will perform online fault diagnosis on the platform based on the preset fault diagnosis strategy and classification strategy using data such as real-time vehicle status parameters. Simultaneously, it will download the original dataset to the local machine and perform offline fault diagnosis based on the relevant status sequence, diagnosis strategy and related status relationships.

[0064] Step 7-b: After comparing and analyzing the online and offline fault diagnosis conclusions of the vehicle and platform, a preliminary diagnosis conclusion is formed. Then, based on the preliminary diagnosis conclusion and the working condition dataset, a task list for remote control of insulation fault and collaborative control of fault diagnosis and a working condition matching list are generated.

[0065] It should be noted that the operating condition matching list refers to the relevant operating conditions in the fault diagnosis collaborative control task formulated based on the vehicle operating condition status dataset before and after the fault occurred. The operating condition matching list and priority are formulated according to the degree of fit with the operating conditions at the time of the fault. The higher the correlation and relevance of the operating conditions with the fault, the higher the priority in the operating condition list of the fault diagnosis collaborative control task, and vice versa. The purpose here is to prioritize the collaborative diagnosis task that is most consistent with the operating conditions at the time of the fault when carrying out the fault diagnosis collaborative control task, thereby improving the accuracy of diagnosis. At the same time, for intermittent faults, the fault state may only exist in the operating conditions at the time of the fault. This kind of fault diagnosis collaborative control operating condition list setting can identify the fault source of intermittent problems to the greatest extent, thereby improving the detectability, traceability and diagnostic accuracy of intermittent faults.

[0066] Step 8-b: Users can select the remote control and fault diagnosis collaborative control tasks for insulation faults to be executed according to their needs. Of course, they can also directly select all. The system will execute the relevant tasks according to the preset priority of the list. After the selection is completed, the user can issue a "fault diagnosis" command. Then, the monitoring platform will use the private key to sign a random number on the list of remote control fault diagnosis commands and remote control and fault diagnosis collaborative control tasks to be executed (including operating conditions). Then, the signing result, the complete data packet hash value signing result, and the custom private key encrypted signature value are sent to the VCU.

[0067] Step 9-b: The VCU verifies the signature using the public key in the certificate (authenticity verification), and verifies the hash signature value of the complete data packet using the public key (integrity verification). At the same time, it verifies the custom signature according to the custom verification algorithm (redundancy verification).

[0068] Step 10-b: If the signature and verification are incorrect, the VCU will reject the remote control and fault diagnosis collaborative control task; if the signature and verification are correct, the VCU will proceed to vehicle prerequisite verification (including such as battery level, drive system status, power battery system status, handbrake status, etc.).

[0069] Step 11-b: If the vehicle prerequisite requirements are not met, the VCU will reject the remote control and fault diagnosis collaborative control task and upload the reason to the monitoring platform through an encrypted channel; if the requirements are met, the VCU will immediately receive and parse the remote control and fault diagnosis collaborative control task and related instruction set.

[0070] Step 12-b: The VCU immediately switches the control mode to the remote control and fault diagnosis collaborative control mode, and switches the vehicle status to the debugging mode.

[0071] It should be noted that the purpose of adopting the above measures is to prevent accidents caused by the loss of control of drive and power battery systems, and to fundamentally avoid accidents such as "runaway cars".

[0072] Step 13-b: The VCU sets insulation diagnosis-related parameters based on the received insulation fault remote control and fault diagnosis collaborative control task list containing the operating condition list. These parameters mainly include insulation resistance threshold points for each high-voltage circuit, contactor status, and operating conditions.

[0073] It should be noted that the introduction of contactor status parameters is to avoid abnormal insulation monitoring values ​​caused by abnormal contactor status or poor closure; at the same time, this problem may also be one of the causes of insulation faults.

[0074] Step 14-b: The VCU sets up an insulation fault diagnosis circuit (i.e., simulates the operation of maintenance personnel to check the insulation value of each component one by one), and sets up the relevant high-voltage contactor closing, opening, and detection timing logic according to the diagnosis circuit mentioned above to prevent new faults from occurring due to timing errors.

[0075] Step 15-b: After completing the above-mentioned parameter and timing settings, proceed to the remote control and fault diagnosis collaborative control task for insulation faults according to the task list. At this time, control and information exchange will be conducted in real time with the monitoring platform through an encrypted channel. The monitoring platform can add, remove, or stop remote control tasks at any time, and can also remotely control the fault diagnosis process in a step-by-step manner. The VCU will coordinate control with the monitoring platform and the controllers involved in the current diagnostic loop. First, for most faults, the first fault diagnosis collaborative control task is to rule out whether the fault is caused by loose wiring harness connections, open circuits, short circuits, etc.

[0076] It should be noted that currently, the troubleshooting of such problems in new energy special vehicles on the market is mainly based on manual inspection, which cannot accurately analyze such faults from the perspective of data, and information and related status cannot be interactively coordinated. This embodiment aims to enable the system to perform remote diagnosis for such problems by redesigning the vehicle's communication and diagnostic architecture, so that each type of fault and related components can be controlled collaboratively. The VCU and various components are designed collaboratively through CAN signals, hard-wired signals, power supply signals, wake-up signals, etc. The goal is to achieve remote fault diagnosis and collaborative control through the "active" fault diagnosis method designed in this embodiment, using CAN, hard-wired signals, etc., for data interaction and collaborative control, "actively" diagnosing the above-mentioned wiring harness and related problems, and being able to diagnose them directly from data or signals, thereby realizing remote fault diagnosis of such problems. For example, in remote fault diagnosis and coordination control, communication problems will be addressed by controlling the power supply and wake-up signals of components to check whether the power supply parameters and dynamic parameters of the power supply harness are appropriate. If the above parameters are not problematic, the relevant communication harness detection circuit will be controlled to automatically detect relevant parameters such as resistance and voltage in real time and feed them back to the VCU via hard wire or CAN signal. The VCU will then make a unified judgment on whether there are short circuits, open circuits, or loose connections. If there are no problems, subsequent remote collaborative control will be used to check whether there are interference or hardware problems, until the source of the problem is found and the troubleshooting is guided.

[0077] Furthermore, if the above steps are not the cause of the problem, then the following types of tasks are carried out: on the one hand, information interaction with the monitoring platform, real-time response to the remote control commands and task scheduling commands of the monitoring platform, and collaborative control with the monitoring platform; on the other hand, based on the set parameters, timing, etc., control the relevant components of the diagnostic circuit and high-voltage contactors to collaboratively carry out insulation fault diagnosis until the insulation fault circuit and components are located.

[0078] Step 16-b: The VCU completes the remote control and fault diagnosis collaborative control tasks, locates the fault source and faulty components, forms a fault diagnosis conclusion, and packages it with all diagnostic data; then, it uses a private key to sign a random number, and sends the signature result, the complete data packet hash value signature result, and the custom private key encrypted signature value to the monitoring platform.

[0079] Step 17-b: The monitoring platform verifies the signature using the public key in the certificate (authenticity verification) and verifies the hash signature value of the complete data packet using the public key (integrity verification). At the same time, it verifies the custom signature according to the custom verification algorithm (redundancy verification). If the signature verification and validation are correct, the monitoring platform immediately receives and parses the fault diagnosis conclusion and diagnostic data. Then, the monitoring platform will perform a second verification of the conclusion based on the diagnostic data and push the final verified conclusion to the fault diagnosis module.

[0080] Step 18-b: Finally, based on the fault diagnosis conclusion, generate a maintenance instruction manual in the maintenance instruction board to directly guide maintenance personnel in performing maintenance work.

[0081] It should be noted that current remote fault diagnosis for new energy special-purpose vehicles mostly relies on measures such as vehicle status monitoring and offline data download and analysis via TBOX. While this narrows down the range of fault sources to a certain extent, it cannot directly pinpoint the root cause and guide maintenance personnel in their work; on-site personnel are still required to investigate the problem. Troubleshooting some faults is time-consuming, and due to the special nature of special-purpose vehicles, locating certain fault points is extremely difficult, and some fault points are inaccessible to personnel. Therefore, current remote diagnostic methods still have problems and do not fulfill the intended purpose of remote fault diagnosis. This current method can be considered a "passive" form of remote fault diagnosis.

[0082] This embodiment combines the vehicle's status and operating conditions at the time of the fault, along with preliminary conclusions from online and offline fault diagnosis, to form a collaborative control task and operating condition list for fault diagnosis. Based on this list, the vehicle is remotely controlled via a monitoring platform to perform collaborative fault diagnosis. Ultimately, the fault source and faulty components can be identified through these diagnostic tasks, generating repair guidance that directly instructs repair personnel to perform subsequent vehicle maintenance. This approach can be considered "proactive" remote fault diagnosis. Through collaborative control between the monitoring platform, the VCU, and potentially faulty components, remote fault diagnosis is no longer limited to the current vehicle status and data during the fault period. It also proactively controls the vehicle to execute relevant fault diagnosis tasks and allows the faulty vehicle to "speak," thus addressing current problems in remote fault diagnosis and enabling remote fault diagnosis to identify the fault source and faulty components, directly guiding repair personnel to perform maintenance. This embodiment transforms the "passive" remote diagnosis into an "active" one, thereby improving the accuracy of remote fault diagnosis and greatly reducing the on-site troubleshooting tasks of personnel. It enables remote diagnosis to replace the entire process of on-site vehicle troubleshooting, allowing remote fault diagnosis technology to truly and directly participate in guiding maintenance personnel to troubleshoot, and realizing true remote fault diagnosis collaborative control.

[0083] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0084] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0085] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0086] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0087] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A remote fault diagnosis method for new energy vehicles, characterized in that, include: Real-time vehicle status detection and acquisition of real-time vehicle status parameters; Determine whether the vehicle has malfunctioned based on the vehicle's real-time status parameters; When a vehicle malfunctions, the vehicle controller performs online fault diagnosis and generates vehicle-side diagnostic conclusions and fault data. The monitoring platform performs platform-side fault diagnosis based on the fault data and obtains platform-side diagnostic conclusions. Based on the vehicle-side diagnostic conclusions and the platform-side diagnostic conclusions, a fault diagnosis task list and a working condition list are generated, which include specific components to be tested and reproducible working conditions. The vehicle-mounted controller verifies the remote commands issued by the monitoring platform; If the remote command verification is successful and the vehicle meets the preset conditions, the specific fault source or faulty component is located based on the fault diagnosis task list and the operating condition list, and a final diagnostic conclusion is obtained. The preset conditions include one or more of the vehicle's battery status, drive system status, power battery system status, handbrake status, and vehicle speed status. The method for obtaining the final diagnostic conclusion is as follows: Diagnostic parameters are set according to the fault diagnosis task list, including insulation resistance thresholds for each high-voltage circuit, contactor status parameters, and operating condition parameters; the closing, opening, and detection timing logic of the high-voltage contactor is set according to the high-voltage circuit to be tested; real-time control and information interaction are performed with the monitoring platform through an encrypted channel, receiving remote control commands from the monitoring platform to stop, add, remove, or step-by-step; the controllers and components involved in the current high-voltage circuit to be tested are controlled to sequentially check and diagnose wiring harness connections, short circuits, open circuits, and the components themselves. The monitoring platform generates maintenance guidance information based on the final diagnostic conclusion to directly guide the maintenance process.

2. The remote fault diagnosis method for new energy vehicles according to claim 1, characterized in that, The fault data includes: preliminary conclusions of online fault diagnosis on the vehicle, real-time vehicle data after data cleaning, operating condition datasets before and after the fault, and the original dataset for the time period of the fault.

3. The remote fault diagnosis method for new energy vehicles according to claim 1, characterized in that, The method for the monitoring platform to perform platform-side fault diagnosis based on the fault data includes: The monitoring platform performs authenticity and integrity checks on the received fault data; After verification, the monitoring platform performs platform-side fault diagnosis on the real-time status parameters of the vehicle according to the preset diagnostic strategy. At the same time, it downloads the original dataset before and after the fault to the local machine and performs offline fault diagnosis based on the status sequence, diagnostic strategy and related status relationships.

4. The remote fault diagnosis method for new energy vehicles according to claim 1, characterized in that, The method for generating a fault diagnosis task list and a working condition list that include specific components under test and reproducible working conditions includes: The vehicle-side diagnostic conclusions are compared and analyzed with the platform-side diagnostic conclusions to form a preliminary diagnostic conclusion. Based on the preliminary diagnostic conclusions and the fault data, a fault diagnosis task list and a working condition list are generated, which include specific components to be tested and reproducible working conditions. The working condition list is prioritized according to its correlation with the working conditions at the time of the fault occurrence; the higher the correlation, the higher the priority.

5. The remote fault diagnosis method for new energy vehicles according to claim 1, characterized in that, The method for verifying remote commands issued by the monitoring platform includes: The vehicle-side controller uses the public key in a preset certificate to verify the digital signature of the remote command and the hash value signature of the remote command's data packet; and, The remote command is redundancy checked according to a preset custom verification algorithm.

6. The remote fault diagnosis method for new energy vehicles according to claim 1, characterized in that, The monitoring platform generates maintenance guidance information based on the final diagnostic conclusion to directly guide maintenance, specifically including: The monitoring platform receives the final diagnostic conclusion and diagnostic data generated during the diagnostic task uploaded by the vehicle controller, and performs a second verification on the final diagnostic conclusion. The verified final diagnostic conclusion is input into the monitoring platform to generate a maintenance instruction.

7. A remote fault diagnosis system for new energy vehicles, characterized in that, include: The detection unit is used to detect the vehicle status in real time and obtain real-time vehicle status parameters. A basic fault diagnosis unit is used to determine whether the vehicle has a fault based on the vehicle's real-time status parameters. The vehicle-side diagnostic unit is used to perform online fault diagnosis on the vehicle when a vehicle fault occurs, and generate vehicle-side diagnostic conclusions and fault data. A platform-side diagnostic unit is used to monitor the platform and perform platform-side fault diagnosis based on the fault data to obtain platform-side diagnostic conclusions. A collaborative diagnostic unit is used to generate a fault diagnosis task list and a working condition list containing specific components to be tested and reproducible working conditions based on the vehicle-side diagnostic conclusion and the platform-side diagnostic conclusion. A verification unit is used by the vehicle controller to verify the remote commands issued by the monitoring platform. A fault locking unit is configured to, if the remote command verification is successful and the vehicle meets preset conditions, lock the specific fault source or faulty component based on the fault diagnosis task list and the operating condition list, and obtain a final diagnostic conclusion. The preset conditions include one or more of the vehicle's battery status, drive system status, power battery system status, handbrake status, and vehicle speed status. The method for obtaining the final diagnostic conclusion specifically involves: setting diagnostic parameters according to the fault diagnosis task list, including insulation resistance thresholds for each high-voltage circuit, contactor status parameters, and operating condition parameters; setting the closing, opening, and detection timing logic of the high-voltage contactor according to the high-voltage circuit to be tested; conducting real-time control and information interaction with the monitoring platform through an encrypted channel, receiving remote control commands from the monitoring platform to stop, add, remove, or perform step-by-step actions; and controlling the controllers and components involved in the current high-voltage circuit to be tested, sequentially checking and diagnosing wiring harness connections, short circuits, open circuits, and the components themselves. A maintenance guidance unit is used by the monitoring platform to generate maintenance guidance information for direct maintenance guidance based on the final diagnostic conclusion.

8. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed by the processor, they implement the steps of the remote fault diagnosis method for new energy vehicles as described in any one of claims 1-6 or the remote fault diagnosis system for new energy vehicles as described in claim 7.

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