New energy automobile diagnostic tool comprehensive system and fault diagnosis method of new energy automobile controller

Abstract

The invention relates to a new energy automobile diagnostic tool comprehensive system and a fault diagnosis method of a new energy automobile controller, and the system comprises a tool box core module which is used for carrying out the diagnosis data collection, processing and storage of a target controller on a new energy automobile according to the target controller selected by a user; and the preprocessing function module is used for selecting to-be-diagnosed data related to a diagnosis demand from the diagnosis data of the target controller according to the diagnosis demand input by a user for the target controller, performing normalization processing and verification on the to-be-diagnosed data, and then comparing the to-be-diagnosed data with the diagnosis knowledge base to obtain a preliminary diagnosis result.

Description

Technical Field

[0001] This application relates to the field of automotive controller fault diagnosis, and particularly to a comprehensive system for new energy vehicle diagnostic tools and a fault diagnosis method for new energy vehicle controllers. Background Art

[0002] Current status of intelligent diagnosis for new energy vehicles: In recent years, the market share of new energy vehicles has been growing rapidly, and core components such as vehicle control units (VCUs), battery management systems (BMSs), and drive motor controllers (MCUs) have gradually adopted more complex software architectures. However, existing diagnostic tools often have the following problems: 1. Tool decentralization: Different brands and models use different diagnostic protocols (such as UDS, KWP2000, CAN / CANFD, LIN, etc.), making it difficult to conduct unified detection.

[0003] 2. Insufficient real-time performance: Some existing tools cannot parse vehicle status in real time, affecting diagnostic efficiency.

[0004] 3. Lack of knowledge accumulation: Diagnostic data is not linked with the knowledge base, making it difficult to form intelligent fault prediction. Summary of the Invention

[0005] To solve the above problems, the present invention provides a comprehensive system for new energy vehicle diagnostic tools and a fault diagnosis method for new energy vehicle controllers.

[0006] The technical solution of the present invention is as follows: On the one hand, the present invention provides a comprehensive system for new energy vehicle diagnostic tools, including: A toolbox core module for collecting, processing, and storing diagnostic data of a target controller on a new energy vehicle according to the target controller selected by the user; A preprocessing function module for selecting, from the diagnostic data of the target controller, the data to be diagnosed related to the diagnostic requirements according to the diagnostic requirements input by the user for the target controller, performing normalization processing and verification on the data to be diagnosed, and then comparing it with the diagnostic knowledge base to obtain a preliminary diagnostic result.

[0007] Preferably, the comprehensive system for new energy vehicle diagnostic tools further includes: A plugin management module for dynamically loading professional diagnostic tools according to the user's needs.

[0008] Preferably, the comprehensive system for new energy vehicle diagnostic tools further includes: A diagnostic report generation module for outputting a comprehensive diagnostic report that meets specific format requirements according to the manual diagnostic information input by the user, the diagnostic data obtained from the toolbox core module, and the diagnostic knowledge base.

[0009] Preferably, the new energy vehicle diagnostic tool integrated system further includes: A visualization interaction module for real-time displaying the diagnostic process.

[0010] Preferably, the core module of the toolbox includes: A data acquisition interface for identifying a target controller according to a physical request address input by a user and collecting diagnostic data of the target controller on the new energy vehicle; A data processing unit for performing data formatting, data verification, and column existence check on the diagnostic data collected by the data acquisition interface; A data storage unit for storing the diagnostic data obtained after being processed by the data processing unit into a local SQLite database and a cloud MySQL database.

[0011] Preferably, the steps for the data processing unit to perform data formatting, data verification, and column existence check on the diagnostic data collected by the data acquisition interface include: Using the strip() method to remove the spaces before and after the string for each row of diagnostic data to implement data formatting processing of the diagnostic data; Using the int() function to perform hexadecimal integer conversion on the physical request address and physical response address in the diagnostic data to implement address format verification of the diagnostic data; the physical request address is used to identify the path or location where the target controller returns the diagnostic data; Verifying the column names in the diagnostic data to implement column existence check of the diagnostic data.

[0012] Preferably, the preprocessing function module includes: A preprocessing and normalization unit for performing data format unification, data integrity check, accuracy check, cross-source data comparison, and outlier correction on the data to be diagnosed; A fault analysis unit for comparing the data to be diagnosed obtained after being processed by the preprocessing and normalization unit with the diagnostic knowledge base to obtain a preliminary diagnostic result.

[0013] Preferably, the diagnostic knowledge base is formed by performing structured processing on the collected data; the collected data includes historical maintenance data, in-vehicle test data, expert experience, and industry technical documents of new energy vehicles; The steps for performing structured processing on the collected data include: Classifying the faults according to the power system, charging system, battery management system, braking system, etc.; Establishing a rule base based on fault codes and defining the possible causes and priorities of various faults; Storing actual cases, including fault phenomena, diagnostic steps, repair processes, and results.

[0014] Preferably, the plug-in management module implements: Dynamic plugin loading mechanism, which loads professional diagnostic tools in real time through the importlib library; Plugin version management, supporting incremental updates and rollback operations; Permission control: set user access rights based on user roles.

[0015] The present application also provides a fault diagnosis method for a new energy vehicle controller, which is implemented using the above-mentioned system.

[0016] The beneficial effects of the present invention are: This comprehensive diagnostic tool system for new energy vehicles collects real-time diagnostic data from new energy vehicles, including data from core components such as the vehicle control unit (VCU), battery management system (BMS), and drive motor controller (MCU). The data acquisition interface communicates with the vehicle's controller in real time, ensuring the timeliness and accuracy of the data. For example, while the vehicle is in motion, it can monitor battery parameters such as voltage, current, and temperature, as well as motor speed and torque, providing up-to-date data support for diagnosis. After collecting data, the comprehensive diagnostic tool system quickly normalizes and verifies it, compares it with a diagnostic knowledge base, and generates a real-time diagnostic report. Users can view diagnostic progress and results in real time on the interface, eliminating the need for lengthy data processing and analysis. For example, when a vehicle malfunction occurs, the comprehensive diagnostic tool system can immediately identify the fault code, analyze the cause, and provide recommended solutions, helping maintenance personnel quickly locate and resolve the problem, significantly improving diagnostic efficiency.

[0017] This comprehensive system of new energy vehicle diagnostic tools has professional tool expansion capabilities and supports the addition of new applications through plug-ins. This means that when encountering new models or new diagnostic needs, users can easily add corresponding plug-ins without having to repurchase or develop new tools. This flexible expansion method enables the toolbox to adapt to the ever-changing market and model types, and always maintain its applicability and advancement.

[0018] By building a diagnostic knowledge base, historical maintenance data, real-vehicle test data, expert experience, and technical documentation are structured and stored and managed. This diagnostic knowledge base not only contains a wealth of fault cases and solutions, but also uses machine learning algorithms to analyze and optimize data, dynamically adjusting diagnostic rules to improve diagnostic accuracy and efficiency. For example, when the toolbox encounters a new fault symptom, it automatically searches the knowledge base for similar cases and, based on existing experience, provides preliminary diagnostic guidance and solutions.

[0019] The collected diagnostic data is linked in real time with the diagnostic knowledge base. Through the analysis and learning of a large amount of historical data, potential fault risks can be predicted. For example, through the long-term monitoring and analysis of the data of the battery management system, the toolbox can predict the health status and remaining life of the battery, give early warnings of possible battery failures, provide a scientific basis for the maintenance and servicing of the vehicle, and achieve intelligent fault prediction. Brief Description of the Drawings

[0020] Figure 1 It is a schematic diagram of the comprehensive system of the new energy vehicle diagnostic tool in the embodiment of the present application. Detailed Implementation Manner

[0021] For the convenience of understanding by those skilled in the art, the following further describes and explains the present invention patent through the drawings. The description is relatively detailed and complete, but it should not be construed as a limitation on the scope of the present invention patent. Obvious deformations and replacement forms of the following examples are all within the protection scope of this patent.

[0022] Referring to Figure 1 , the embodiment of the present invention provides a comprehensive system of a new energy vehicle diagnostic tool, including: The toolbox core module is used to collect, process and store diagnostic data of the target controller on the new energy vehicle according to the target controller selected by the user. The preprocessing function module is used to select the to-be-diagnosed data related to the diagnostic requirement from the diagnostic data of the target controller according to the diagnostic requirement of the target controller input by the user, perform normalization processing and verification on the to-be-diagnosed data, and then compare it with the diagnostic knowledge base to obtain a preliminary diagnostic result.

[0023] In this embodiment, the target controller is, for example, the battery management system BMS, the vehicle controller VCU, and the motor controller MCU.

[0024] The toolbox core module includes: The data acquisition interface is used to identify the target controller according to the physical request address input by the user, and collect the diagnostic data returned by the target controller on the new energy vehicle through the physical response address. The data processing unit is used to perform data formatting, data verification, and column existence check on the diagnostic data collected by the data acquisition interface. The data storage unit stores the diagnostic data obtained after being processed by the data processing unit into the local SQLite database and the cloud MySQL database, which is convenient for long-term storage and subsequent analysis of the data.

[0025] For example, when diagnosing the battery control system BMS, the toolbox can automatically load the communication protocol dedicated to the battery control system BMS (such as the UDS protocol 0x7E4).

[0026] Preferably, the data processing unit uses the strip() method to remove the spaces before and after the string for each line of diagnostic data, achieving data formatting processing of the diagnostic data; The data processing unit uses the int() function to convert the physical request address and physical response address in the diagnostic data into hexadecimal integers, achieving address format verification of the diagnostic data; the physical request address is used to identify the path or location where the target controller returns the diagnostic data, and the physical response address is used to obtain the diagnostic data returned by the identified controller; The data processing unit verifies the column names in the diagnostic data, achieving column existence check of the diagnostic data.

[0027] Specifically, during data formatting, when reading each line of data, the data processing unit uses the strip() method to remove the spaces before and after the string to ensure the correct data format.

[0028] During address format verification, for the physical request address and physical response address, the data processing unit uses the int() function to attempt to convert them into hexadecimal integers. If the conversion fails, it indicates that the address format is incorrect, and the user is prompted via messagebox.showinfo.

[0029] During column existence check, after reading the Excel file of the diagnostic data, the data processing unit first checks whether the necessary columns (such as "controller name", "physical request address", "physical response address") exist. If any column is missing, the user will be prompted via messagebox.showinfo and the processing will be terminated.

[0030] Import success prompt: After successfully importing the controller configuration information, the user is prompted via messagebox.showinfo that the operation is successful.

[0031] If all verifications pass, the data storage unit stores the data contained in the controller name and the corresponding physical request address and physical response address into a dictionary data structure.

[0032] For example, when the user selects the target controller as the Battery Management System (BMS) through the GUI, the data acquisition interface of the core module of the toolbox automatically loads the dedicated communication protocol for the BMS (UDS protocol ID: 0x7E4); the core module of the toolbox reads the hardware version number of the controller: "HW2.1.3", obtains the software version information: "SW1.4.5_202305", reads the currently stored fault codes ['P0A7F1', 'U0121'] using service 0x19, and obtains real-time battery parameters through service 0x31AA01: maximum single-cell voltage: 3.65V; minimum temperature: 25.3°C.

[0033] The preprocessing function module includes: A preprocessing and normalization unit for unifying the data format, checking the data integrity, accuracy, cross-source data comparison, and correcting outliers of the data to be diagnosed; A fault analysis unit for comparing the data to be diagnosed obtained after being processed by the preprocessing and normalization unit with the diagnostic knowledge base to obtain a preliminary diagnostic result.

[0034] Among them, when unifying the data format, convert data from different sources into a unified format (JSON, CSV, database format).

[0035] During the integrity check, it is necessary to check whether key fields are missing (such as fault codes, timestamps, vehicle IDs) and whether associated data is consistent (such as BMS data matching the battery status).

[0036] During the accuracy check, it is necessary to perform data range verification (such as voltage should be within a reasonable range) and logical verification (such as the SOC status cannot suddenly change from 80% to 10%).

[0037] During the cross-source data comparison, it is required that the vehicle SOC (state of charge) should match the voltage data and multiple sensor data.

[0038] During the outlier correction, use statistical methods or machine learning to detect abnormal data; for partially missing data, use interpolation methods to fill it in (such as linear interpolation), if there is a unit conversion error, it can be automatically corrected; for abnormal data that cannot be determined, mark it and submit it for manual review.

[0039] After preprocessing and normalization, identify the controller version and part number information of the current vehicle; through the diagnostic questionnaire library, translate and classify the fault codes to provide clear diagnostic information.

[0040] In the embodiments of the present application, the diagnostic knowledge base is formed by structuring the collected data; the collected data includes historical maintenance data of new energy vehicles, in-vehicle test data, expert experience, and industry technical documents. The diagnostic knowledge base has established a comprehensive and dynamically updated knowledge base by structuring historical maintenance data, in-vehicle test data, expert experience, and industry technical documents.

[0041] The steps for structuring the collected data include: Classify faults according to the power system, charging system, battery management system, braking system, etc.; Establish a rule base based on fault codes, and define the possible causes and priorities of various faults; Store actual cases, including fault phenomena, diagnostic steps, repair processes, and results.

[0042] In this embodiment, the establishment process of the diagnostic knowledge base is as follows: 1. Data collection: 1.1 Historical maintenance data: Extract common fault information and its solutions from maintenance records, fault reports, etc.

[0043] 1.2 In-vehicle test data: Utilize new energy vehicle test data, including controller version, fault codes (DTCs), battery management system (BMS) data, etc.

[0044] 1.3 Expert experience: Integrate the experience of engineers and experts, especially the handling methods for special or rare faults.

[0045] 1.4 Technical documents: Analyze new energy vehicle maintenance manuals, diagnostic guides, and technical bulletins.

[0046] 2. Knowledge structuring: 2.1 Establish a standard fault classification system: Classify by power system, charging system, battery management system, braking 2.2 Associated diagnostic rules: Establish a rule base based on fault codes, and define the possible causes and priorities of various faults.

[0047] 2.3 Case library: Store actual cases, including fault phenomena, diagnostic steps, repair processes, and results.

[0048] 2.4 Machine learning optimization: Data mining and AI can be used to analyze historical data, optimize diagnostic rules, and improve accuracy. Analyze a large number of fault cases through machine learning to dynamically adjust the matching weights. Combine LSTM (Long Short-Term Memory Network) to predict fault trends and achieve early warning.

[0049] 3. Data storage and management: 3.1. Database Storage: Use SQL or NoSQL databases to store structured knowledge, such as: Fault code → Fault description → Possible cause → Solution Vehicle controller version → Compatible firmware version → Possible problems 3.2. Knowledge Graph: Build a knowledge graph for new energy vehicle diagnosis, associating different fault codes, components, and repair solutions.

[0050] 3.3. Access Rights: Set different levels of access rights according to user roles (technicians, engineers, researchers).

[0051] Data Normalization: Standardize data from different sources to match the diagnostic knowledge base.

[0052] Time Format: Standardize the time format (ISO 8601).

[0053] Numeric Units: Unify units such as temperature, rate, voltage, etc.

[0054] Fault Code Mapping: Convert the manufacturer's fault codes to a standard format (such as the OBD-II standard).

[0055] In this embodiment, the new energy vehicle diagnostic tool integrated system further includes: Plugin Management Module, used to dynamically load professional diagnostic tools according to user needs., Provides a simple and intuitive interface through tkinter, allowing users to conveniently manage and use multiple tools. Through operations on files and directories, the application can dynamically load tools, suitable for tool management and quick access scenarios.

[0056] Users can add new tool plugins according to actual needs. Two tools are default configured in the system toolbox: RDP report processing tool and actual vehicle controller version reading tool.

[0057] This plugin management module realizes the installation, update, and uninstallation of plugins through a plugin manager, providing a highly flexible tool extension ability.

[0058] Plugin Management Module Implementation: Plugin dynamic loading mechanism, real-time loading of professional diagnostic tools through the importlib library; plugin version management, supporting incremental updates and rollback operations; permission control, setting user access rights according to user roles.

[0059] In this embodiment, the new energy vehicle diagnostic tool integrated system further includes: A diagnostic report generation module, which is used to output a comprehensive diagnostic report that meets specific format requirements according to the manual diagnostic information input by the user, the diagnostic data obtained from the core module of the toolbox, and the diagnostic knowledge base.

[0060] The comprehensive diagnostic report includes: Diagnostic table: showing the controller version and fault information of the vehicle; Fault type statistics: statistically counting the occurrence frequency of fault codes by category; Solution suggestions: providing corresponding fault solutions based on the matching results of the knowledge base; Intelligent suggestions: predicting possible related faults based on the historical occurrence of fault codes and providing suggested maintenance solutions.

[0061] In this embodiment, the new energy vehicle diagnostic tool integrated system further includes: A visual interaction module, which is used to display the diagnostic process in real time.

[0062] This new energy vehicle diagnostic tool integrated system supports users to view the diagnostic progress and results in real time on the interface. Through the visualization module, users can conveniently analyze historical data and diagnostic trends.

[0063] When displaying data, a line chart is used to show the change trend of fault codes at different time points; a radar chart is used to show the health of key parameters of the controller.

[0064] In actual use, engineers connect to the OBD interface of the new energy vehicle through a CAN device: after running the toolbox, open the integrated tool application components, and the software will automatically collect the software and hardware versions and fault code information of the vehicle controller; after the data is normalized, it is matched with the diagnostic knowledge base; the system outputs a comprehensive diagnostic report, including the cause of the fault and solution suggestions; engineers perform maintenance operations according to the suggestions in the report and install professional tool plugins when necessary to complete more complex diagnostic tasks.

[0065] The above system of the present invention can significantly improve the diagnostic efficiency and accuracy of new energy vehicles. Especially through modular design and plugin management functions, the flexible expansion of the toolbox is realized to meet the test requirements in different scenarios.

[0066] On the other hand, the present application also provides a fault diagnosis method for a new energy vehicle controller, which is implemented by using the above system.

[0067] It should be noted that the various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other.

[0068] Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0069] It should also be noted that in this document, the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. This is for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention. In addition, relative terms such as "first" and "second" are used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations, nor should they be construed as indicating or implying relative importance. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements does not include those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or terminal device comprising the element.

[0070] The technical solutions provided by the present invention have been introduced in detail above. Specific examples have been used in this article to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only for helping to understand the present invention, and the content of this specification should not be construed as a limitation on the present invention. At the same time, for those of ordinary skill in the art, according to the present invention, there will be various forms of changes in the specific implementation manners and application scopes. It is not necessary and impossible to enumerate all the implementation manners here, and the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A comprehensive system for new energy vehicle diagnostic tools, characterized in that, It includes: The core module of the toolbox is used to collect, process, and store diagnostic data of the target controller on the new energy vehicle according to the target controller selected by the user; The preprocessing function module is used to select the diagnostic data related to the diagnostic requirements from the diagnostic data of the target controller according to the diagnostic requirements of the target controller input by the user, perform normalization processing and verification on the diagnostic data to be diagnosed, and then compare it with the diagnostic knowledge base to obtain a preliminary diagnostic result.

2. The integrated system of new energy vehicle diagnostic tools according to claim 1, wherein, The comprehensive new energy vehicle diagnostic tool system further includes: The plugin management module is used to dynamically load professional diagnostic tools according to the user's needs.

3. The integrated system of new energy vehicle diagnostic tools according to claim 1, characterized in that, The comprehensive new energy vehicle diagnostic tool system further includes: The diagnostic report generation module is used to output a comprehensive diagnostic report that meets specific format requirements according to the manual diagnostic information input by the user, the diagnostic data obtained from the core module of the toolbox, and the diagnostic knowledge base.

4. The integrated system of new energy vehicle diagnostic tools according to claim 1, wherein The comprehensive new energy vehicle diagnostic tool system further includes: The visualization interaction module is used to display the diagnostic process in real time.

5. The integrated system of new energy vehicle diagnostic tools according to claim 1, characterized in that, The core module of the toolbox includes: The data acquisition interface is used to identify the target controller according to the physical request address input by the user and collect diagnostic data of the target controller on the new energy vehicle; The data processing unit is used to perform data formatting, data verification, and column existence check on the diagnostic data collected by the data acquisition interface; The data storage unit is used to store the diagnostic data obtained after being processed by the data processing unit into the local SQLite database and the cloud MySQL database.

6. The integrated new energy vehicle diagnostic tool system according to claim 5, characterized in that, The steps for the data processing unit to perform data formatting, data verification, and column existence check on the diagnostic data collected by the data acquisition interface include: Using the strip() method to remove the spaces before and after the string for each row of diagnostic data to implement data formatting processing of the diagnostic data; Using the int() function to convert the physical request address and physical response address in the diagnostic data into hexadecimal integers to implement address format verification of the diagnostic data; the physical request address is used to identify the path or location where the target controller returns diagnostic data; Verifying the column names in the diagnostic data to implement column existence check of the diagnostic data.

7. The integrated system of new energy vehicle diagnostic tools according to claim 1, characterized in that The preprocessing function module includes: The preprocessing and normalization unit is used to perform data format unification, data integrity check, accuracy check, cross-source data comparison, and outlier correction on the diagnostic data to be diagnosed; The fault analysis unit is used to compare the diagnostic data to be diagnosed obtained after being processed by the preprocessing and normalization unit with the diagnostic knowledge base to obtain a preliminary diagnostic result.

8. The integrated system of new energy vehicle diagnostic tools according to claim 7, wherein, The diagnostic knowledge base is formed by structuring the collected data; the collected data includes the historical maintenance data, in-vehicle test data, expert experience, and industry technical documents of new energy vehicles; The steps for structuring the collected data include: Classifying faults according to the power system, charging system, battery management system, braking system, etc.; Establishing a rule base based on fault codes and defining the possible causes and priorities of various faults; Storing actual cases, including fault phenomena, diagnostic steps, repair processes, and results.

9. The integrated system of new energy vehicle diagnostic tools according to claim 2, wherein The plugin management module realizes: Plugin dynamic loading mechanism, which loads professional diagnostic tools in real time through the importlib library; Plugin version management, supporting incremental updates and rollback operations; Permission control, setting user access permissions according to user roles.

10. A fault diagnosis method for a new energy vehicle controller, characterized in that, Implemented by using the system described in any one of claims 1-9.

Images