New energy automobile electrical parameter measurement method and system based on sensor

Abstract

The invention relates to the technical field of new energy automobile electrical parameter measurement, and discloses a sensor-based new energy automobile electrical parameter measurement method and system, and the method comprises the steps: obtaining the operation parameters of a high-voltage electrical system of a new energy automobile through a plurality of types of sensors disposed at a preset part of the high-voltage electrical system of the new energy automobile; mapping the operation parameters to a preset digital virtual mirror image of the electrical system, performing dynamic deduction of the operation state, and generating expected operation state data; the running state data, obtained in real time, of the high-voltage electrical system of the new energy automobile are compared with the expected running state data, the abnormal state of the high-voltage electrical system of the new energy automobile is recognized according to the comparison result, and diagnosis information is generated; and through an augmented reality or virtual reality technology, the running state data, the expected running state data and the diagnosis information which are acquired in real time are visually presented. According to the invention, the problems of data acquisition and safety monitoring in a high-voltage system in a traditional measurement method are effectively solved.

Description

Technical Field

[0001] This application relates to the field of electrical parameter measurement technology for new energy vehicles, and more specifically, to a sensor-based method and system for measuring electrical parameters of new energy vehicles. Background Technology

[0002] The booming development of the new energy vehicle industry has led to rapid advancements in its electrical system technology, particularly in core components such as high-voltage platforms, battery management systems, and electronic control systems, which place higher demands on the measurement of electrical parameters. However, existing measurement methods and related teaching resources often struggle to keep pace with technological progress, resulting in numerous challenges in practical applications and talent cultivation.

[0003] The measurement of electrical parameters in new energy vehicles currently faces several practical challenges. Traditional measurement methods, such as using offline tools or simple simulation equipment, struggle to achieve real-time data acquisition and safety monitoring under high-voltage systems. Furthermore, existing virtual simulation resources are relatively weak in interactivity, failing to accurately recreate dynamic scenarios such as fault diagnosis in battery management systems. This results in high operational risks and inaccurate data during practical training. In addition, a shortage of qualified technical personnel creates a gap between measurement technology and actual enterprise needs, limiting the standardization and intelligent development of parameter measurement.

[0004] Given the rapid iteration of high-voltage electrical system technology in new energy vehicles, the stringent requirements for real-time data acquisition and safety monitoring, and the significant deficiencies of existing teaching resources and traditional measurement methods in terms of interactivity, data accuracy, and operational safety, it is crucial to design an electrical parameter measurement method and system that can overcome the limitations of traditional offline measurement tools in real-time monitoring and safety protection under high-voltage systems. This system should also address the issues of poor interactivity, low data fidelity, and high operational risks in complex dynamic scenarios such as battery management system fault diagnosis using virtual simulation, while effectively compensating for the shortage of professional instructors. Ultimately, this will promote the standardization and intelligent development of measurement technology, and is a pressing technical challenge.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this application provides a sensor-based method and system for measuring electrical parameters of new energy vehicles, which solves problems such as the inability of existing methods to collect high-voltage system data in real time, insufficient safety monitoring, poor virtual simulation interactivity, and lack of professional technicians.

[0007] In a first aspect, this application discloses a sensor-based method for measuring electrical parameters of new energy vehicles, comprising the following steps: The operating parameters of the high-voltage electrical system of new energy vehicles are obtained by using various types of sensors deployed at predetermined locations in the high-voltage electrical system of new energy vehicles. The operating parameters are mapped to a preset digital virtual image of the electrical system to dynamically simulate the operating status and generate expected operating status data. By comparing the real-time operational status data of the high-voltage electrical system of new energy vehicles with the expected operational status data, abnormal states of the high-voltage electrical system of new energy vehicles can be identified based on the comparison results, and diagnostic information can be generated. Augmented reality or virtual reality technologies are used to visualize real-time operational status data, expected operational status data, and diagnostic information.

[0008] This technical solution enables real-time monitoring, anomaly diagnosis, and intuitive presentation of the operating status of high-voltage electrical systems in new energy vehicles. It effectively solves the problems of data acquisition and safety monitoring under high-voltage systems using traditional measurement methods, and improves the accuracy and efficiency of fault diagnosis.

[0009] Furthermore, based on the above, the steps for acquiring the operating parameters of the high-voltage electrical system of a new energy vehicle through multiple types of sensors deployed at predetermined locations within the high-voltage electrical system include: Operating parameters, including voltage, current, and temperature parameters, are obtained through voltage sensors, current sensors, and temperature sensors. Data preprocessing, including filtering and range conversion, is performed on the operating parameters.

[0010] This technical solution ensures the accuracy and reliability of the acquired operating parameters, providing a high-quality data foundation for subsequent dynamic simulations and anomaly identification.

[0011] In some preferred embodiments, the preset digital virtual image of the electrical system is a simulation model established based on the physical structure, electrical connection relationship and component operating characteristics of the high-voltage electrical system of new energy vehicles.

[0012] This technical solution enables the construction of a highly realistic digital virtual image, laying the foundation for accurate dynamic simulation of operational status.

[0013] Based on this, the steps for dynamically extrapolating the operational status and generating expected operational status data include: In a pre-defined digital virtual image of an electrical system, based on pre-defined physical laws, electrical connection relationships, and component operating characteristics, the expected operating state data under the executed operating parameters is deduced.

[0014] Furthermore, the steps of comparing the real-time acquired operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identifying abnormal states of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generating diagnostic information include: When the deviation between the operating status data and the expected operating status data exceeds the first preset threshold, the high-voltage electrical system of the new energy vehicle will be marked as abnormal. By combining the dynamic deduction process of abnormal and operational states, the causes of abnormalities are analyzed, faulty components or locations are located, and diagnostic information is generated.

[0015] As a technological improvement, the steps to visualize real-time acquired operational status data, expected operational status data, and diagnostic information using augmented reality or virtual reality technologies include: Augmented reality or virtual reality technologies are used to overlay real-time operational status data and expected operational status data onto the corresponding positions of real components or virtual models of the high-voltage electrical system of new energy vehicles in the form of digital tags, color codes, or dynamic indicators. Based on the diagnostic information generation system, the optimized step-by-step operation guide and safety calibration prompts are overlaid and displayed on the corresponding positions of the real components or virtual models of the high-voltage electrical system of new energy vehicles.

[0016] Based on the above, after the step of mapping the operating parameters to a preset digital virtual image of the electrical system, the following is also included: The system acquires environmental parameters of the high-voltage electrical system of new energy vehicles in real time, maps the environmental parameters to the physical damage assessment module in the preset digital virtual image of the electrical system, and generates the physical damage accumulation index of electrical components. The physical damage accumulation index is used to determine whether electrical components have aging damage.

[0017] As a further improvement, the steps for determining whether electrical components have aging damage based on the physical damage accumulation index include: When the physical damage accumulation index reaches the second preset threshold, it is determined that the electrical components have aging damage. Based on a pre-defined library of aging damage types, the system determines the type of aging damage to electrical components and generates a notification message that includes the type, location, and impact of the aging damage to the electrical components. The prompts will be presented visually.

[0018] To improve the solution, the steps to visualize the prompts include: It displays warning text including the type, location, and impact of aging and damage to electrical components, and highlights the electrical components.

[0019] Secondly, this application also discloses a sensor-based electrical parameter measurement system for new energy vehicles, used to perform the aforementioned sensor-based electrical parameter measurement method for new energy vehicles. The system includes: The parameter acquisition module is used to acquire the operating parameters of the high-voltage electrical system of the new energy vehicle through various types of sensors deployed at preset locations in the high-voltage electrical system of the new energy vehicle. The virtual image simulation module is used to map operating parameters to a preset digital virtual image of the electrical system, perform dynamic simulation of the operating status, and generate expected operating status data. The anomaly identification module is used to compare the real-time acquired operating status data of the high-voltage electrical system of new energy vehicles with the expected operating status data, identify the abnormal status of the high-voltage electrical system of new energy vehicles based on the comparison results, and generate diagnostic information. The visualization module is used to visualize real-time operational status data, expected operational status data, and diagnostic information through augmented reality or virtual reality technologies.

[0020] This technical solution provides a complete system that integrates data acquisition, virtual simulation, anomaly diagnosis, and visualization, offering a comprehensive solution for measuring electrical parameters in new energy vehicles.

[0021] In summary, this application provides a sensor-based method and system for measuring electrical parameters of new energy vehicles. The method involves deploying multiple types of sensors to acquire real-time operating parameters of the high-voltage electrical system of the new energy vehicle, mapping these parameters to a digital virtual image for dynamic simulation, and generating expected operating state data. This method can compare actual operating data with expected data in real time, thereby accurately identifying abnormal states of the electrical system and generating diagnostic information. Furthermore, by using augmented reality or virtual reality technology, real-time data, expected data, and diagnostic information are visualized, greatly improving the user's understanding of the system status and the efficiency of fault handling. Attached Figure Description

[0022] Figure 1 This is a schematic flowchart illustrating a sensor-based method for measuring electrical parameters of new energy vehicles, provided in an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of a sensor-based electrical parameter measurement system for new energy vehicles, provided as an embodiment of this application.

[0024] Labeling Explanation: 210, Parameter Acquisition Module; 220, Virtual Mirror Simulation Module; 230, Anomaly Identification Module; 240, Visualization Presentation Module. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Traditional methods for measuring electrical parameters in new energy vehicles often struggle to achieve real-time, safe data acquisition and monitoring when dealing with high-voltage electrical systems. Furthermore, existing virtual simulation resources lack interactivity and accuracy, failing to effectively recreate dynamically changing fault diagnosis scenarios, leading to high risks and inaccurate data in practical training. In addition, the lack of qualified technical personnel creates a gap between measurement technology and actual enterprise needs, limiting the standardization and intelligent development of parameter measurement.

[0028] In this regard, firstly, referring to Figure 1 This application proposes a sensor-based method for measuring electrical parameters of new energy vehicles, including: S1. Obtain the operating parameters of the high-voltage electrical system of the new energy vehicle by using multiple types of sensors installed at preset locations in the high-voltage electrical system of the new energy vehicle. S2. Map the operating parameters to a preset digital virtual image of the electrical system, perform dynamic simulation of the operating status, and generate expected operating status data; S3. Compare the real-time acquired operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identify the abnormal status of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generate diagnostic information. S4. Visualize the real-time acquired operating status data, expected operating status data, and diagnostic information using augmented reality or virtual reality technology.

[0029] The high-voltage electrical system of new energy vehicles refers to the high-voltage electrical components in new energy vehicles, typically including core components such as power batteries, drive motors, high-voltage power distribution units, on-board chargers, and DC / DC converters. Its operating parameters directly affect vehicle performance and safety. Operating parameters refer to various physical quantities describing the working state of the high-voltage electrical system, such as voltage, current, and temperature. A digital virtual mirror of the electrical system refers to a virtual model constructed using computer modeling and simulation technology that is highly consistent with the actual high-voltage electrical system in terms of physical structure, electrical connections, and component operating characteristics, capable of simulating the operating state of the real system. Expected operating state data refers to the theoretical data of the system under normal operating conditions, deduced from preset conditions and physical laws in the digital virtual mirror. Operating state data refers to the actual operating data of the high-voltage electrical system collected in real time by sensors. Diagnostic information refers to the analysis and description of the cause of the abnormality, the faulty component, or its location after an abnormal state is identified. Augmented reality (AR) and virtual reality (VR) technologies are technologies used to integrate virtual information with the real world or create a completely virtual environment; in this application, they are used to achieve the visualization of data.

[0030] Specifically, various methods can be used to acquire the operating parameters of the high-voltage electrical system of new energy vehicles. For example, various sensors can be deployed at key nodes of the high-voltage electrical system, such as the battery pack output, motor controller input, and high-voltage wiring harness connections. These sensors can be independent voltage, current, and temperature sensors, or integrated multi-functional sensors. The raw data collected by the sensors, such as analog signals, will be converted into digital signals and transmitted to the data processing unit via the vehicle network (such as the CAN bus). Alternatively, data transmission can be achieved through wireless sensor networks, reducing wiring complexity.

[0031] In mapping operating parameters to a pre-defined digital virtual image of the electrical system and performing dynamic simulations of its operating state, the first step is to establish an accurate digital virtual image. This image can be constructed based on the CAD model, circuit diagram, and physical characteristic parameters (such as resistance, inductance, capacitance, and heat capacity) of the high-voltage electrical system of new energy vehicles. The mapping process for operating parameters can involve directly using real-time data collected by sensors as input to the corresponding virtual sensors in the virtual image. For example, the real-time battery voltage value can be directly assigned to the voltage parameter of the virtual battery model. Dynamic simulation, on the other hand, uses pre-defined physical laws (such as Ohm's law, Kirchhoff's laws, and thermodynamic laws) and component operating characteristics (such as battery charge / discharge curves and motor efficiency curves) within the virtual image to simulate the system's behavior under the current operating parameters. For example, when real-time current parameters are input, the virtual image can deduce the expected operating state data, such as the battery's real-time internal resistance, temperature changes, and remaining charge, based on the battery model.

[0032] In comparing real-time acquired operational status data with expected operational status data and identifying abnormal states, the comparison process can be implemented using various algorithms. For example, a simple difference comparison can be used, which calculates the absolute or relative difference between real-time data and expected data. When the difference exceeds a preset threshold, an anomaly is considered to exist. Alternatively, more complex statistical methods, such as Kalman filtering and neural networks, can be used to predict data trends and detect anomalies. For example, if real-time current data fluctuates drastically within a short period while expected current data remains stable, it may indicate an anomaly. The identification of abnormal states is not limited to anomalies in a single parameter; it can also involve correlational anomalies between multiple parameters. For instance, a voltage drop accompanied by a temperature increase may indicate a short-circuit fault.

[0033] In terms of visualization using augmented reality (AR) or virtual reality (VR) technologies, various interactive methods can be employed. For example, using AR glasses or a tablet, real-time operational status data, expected operational status data, and diagnostic information can be directly overlaid onto the actual components of the high-voltage electrical system in new energy vehicles, presented as digital labels, color codes, or dynamic indicators. For instance, when a user wears AR glasses to view the battery pack, its voltage, current, temperature, and other data will be displayed in real time, with green indicating normal operation and red indicating abnormality. Alternatively, a high-precision virtual model of the electrical system can be constructed within a VR environment, allowing users to freely explore the virtual space and view the operational status data and diagnostic information of each component. For example, a user can disassemble a motor controller in a VR environment to view the real-time temperature distribution and fault points on the internal circuit board.

[0034] The overall working principle of this application is as follows: First, the operating parameters of the high-voltage electrical system of new energy vehicles are acquired in real time and comprehensively through multiple types of sensors to ensure the real-time nature and accuracy of the data. Then, these real-time operating parameters are input into a pre-established digital virtual image of the electrical system. This digital virtual image is constructed based on the real physical structure, electrical connections, and component operating characteristics, and can simulate the system's behavior under various operating conditions. In the virtual image, the system dynamically deduces the expected operating state data of the system in the current state based on the input real-time parameters, combined with preset physical laws and component characteristics. This process is equivalent to providing a "theoretical benchmark" for the actual system. Next, the real-time acquired actual operating state data is compared with the expected operating state data deduced by the virtual image. Through this comparison, the deviation between the actual system and the theoretical model can be accurately identified, thereby determining whether there is an abnormal state. Once an abnormality is identified, the system further analyzes the cause of the abnormality, locates the faulty component or location, and generates detailed diagnostic information. Finally, to improve the efficiency of fault diagnosis and maintenance, this application utilizes augmented reality or virtual reality technology to present the real-time operating data, expected operating data, and diagnostic information in an intuitive and interactive way. This visualization method enables technicians to understand the system's operating status more intuitively, quickly locate problems, and obtain operational guidance, thereby significantly improving the intelligence level of electrical parameter measurement and fault diagnosis efficiency of new energy vehicles.

[0035] In some embodiments described above, the operating parameters of the high-voltage electrical system of new energy vehicles are acquired using various types of sensors deployed at predetermined locations within the system. However, in practical applications, the raw operating parameters directly acquired by the sensors may suffer from noise interference, range mismatch, and other issues. Without effective processing, these problems will affect the accuracy of subsequent dynamic simulations of operating status and the reliability of abnormal state identification. Therefore, this application further proposes a scheme for refined processing of the operating parameters to ensure the accuracy and usability of the data.

[0036] In this regard, this application further proposes that the steps for obtaining the operating parameters of the high-voltage electrical system of a new energy vehicle through multiple types of sensors installed at predetermined locations in the high-voltage electrical system include: Operating parameters, including voltage, current, and temperature parameters, are obtained through voltage sensors, current sensors, and temperature sensors. Data preprocessing, including filtering and range conversion, is performed on the operating parameters.

[0037] Specifically, the various types of sensors can be understood as a variety of sensing devices used to monitor the key operating status of the high-voltage electrical system in new energy vehicles. Among them, voltage sensors are used to collect voltage parameters at key points in the high-voltage electrical system in real time, such as battery pack voltage and motor controller input voltage; current sensors are used to collect current parameters flowing through each circuit of the high-voltage electrical system in real time, such as charging and discharging current and motor operating current; and temperature sensors are used to monitor the temperature parameters of key components in the high-voltage electrical system in real time, such as battery temperature, motor temperature, and power device temperature. These parameters are the basic data for assessing the operating status and health of the high-voltage electrical system in new energy vehicles.

[0038] Furthermore, data preprocessing refers to a series of operations performed on the raw sensor data to eliminate noise, calibrate the data, and convert it into a uniform, usable format. Filtering involves using digital signal processing techniques to remove random noise and interference from the sensor data. Methods such as mean filtering, median filtering, or Kalman filtering can be used to improve data smoothness and accuracy, avoiding misjudgments. Range conversion involves converting the raw sensor output signal (usually voltage or current signals) into actual physical quantities (such as volts, amperes, and degrees Celsius) and standardizing units to ensure compatibility and consistency of different types of sensor data in subsequent processing. This includes converting analog signals to digital signals and performing linear or nonlinear conversions based on the sensor's calibration curve. The goal is to ensure that the operating parameters input into the digital virtual mirror are high-quality, highly reliable data.

[0039] Through the aforementioned technical solutions, this application ensures higher accuracy and reliability of the acquired operating parameters. Specifically, by utilizing the collaborative work of multiple types of sensors, comprehensive monitoring of the operating status of the high-voltage electrical system of new energy vehicles is achieved. Data preprocessing, particularly filtering and range conversion, effectively solves the problems of noise interference and range mismatch in the original sensor data, significantly improving data quality. This not only lays the foundation for accurate simulation of the digital virtual image of the electrical system but also greatly enhances the accuracy and timeliness of abnormal state identification, thereby improving the efficiency and reliability of fault diagnosis in the high-voltage electrical system of new energy vehicles and providing strong protection for the safe operation of vehicles.

[0040] In some preferred embodiments, it is assumed that multiple voltage sensors, current sensors, and temperature sensors are deployed inside the battery pack of the new energy vehicle, at the input terminal of the motor controller, and at key connection points of the high-voltage wiring harness. When the vehicle is running, these sensors collect raw data such as battery voltage, charging and discharging current, and motor temperature in real time. For example, a current sensor may experience electromagnetic interference during data acquisition, generating instantaneous spike signals. In this case, the data preprocessing module first performs median filtering on the current data to effectively remove these spike noises and restore smoothness. Subsequently, the filtered current data undergoes range conversion, converting the analog voltage signals output by the sensors into actual ampere values ​​and standardizing the data format. Only after this high-quality processing are the operating parameters input into the digital virtual mirror of the electrical system for dynamic simulation, thereby ensuring the accuracy of the simulation results and providing reliable data support for subsequent anomaly identification.

[0041] In some embodiments of this application, the aforementioned preset digital virtual image of the electrical system is a simulation model established based on the physical structure, electrical connection relationship and component operating characteristics of the high-voltage electrical system of new energy vehicles.

[0042] Specifically, the pre-defined digital virtual image of the electrical system can be understood as a precise replica or simulation of the high-voltage electrical system of a new energy vehicle in digital space. The construction of this digital virtual image is based on the real physical structure of the high-voltage electrical system of the new energy vehicle, the electrical connections between its components, and the operating characteristics of each component under different operating conditions. For example, the physical structure can include the geometric dimensions and material properties of components such as the battery pack, motor controller, high-voltage wiring harness, and DC / DC converter; the electrical connections cover the circuit topology and signal transmission paths between these components; and the operating characteristics of the components refer to their performance curves, loss models, and failure modes under different voltage, current, and temperature parameters. By abstracting and modeling this real-world physical information and operating rules, a high-fidelity simulation model is formed.

[0043] Through the aforementioned technical solution, the digital virtual image, precisely defined as a simulation model based on the physical structure, electrical connections, and component operating characteristics of the high-voltage electrical system of new energy vehicles, can more realistically and accurately simulate the operating behavior of actual high-voltage electrical systems. This significantly improves the accuracy and reliability of dynamic simulation of operating states, thus providing a more solid data foundation for identifying abnormal states in the high-voltage electrical systems of new energy vehicles, effectively reducing false alarm and false negative rates, and improving the system's diagnostic efficiency and safety.

[0044] In some embodiments described above in this application, it is proposed to map operating parameters to a preset digital virtual image of the electrical system, perform dynamic simulation of the operating state, and generate expected operating state data. To ensure the accuracy and reliability of this dynamic simulation, the steps for performing dynamic simulation of the operating state and generating expected operating state data can be further refined.

[0045] Specifically, the steps for dynamically extrapolating the operational status and generating expected operational status data include: In a pre-defined digital virtual image of an electrical system, based on pre-defined physical laws, electrical connection relationships, and component operating characteristics, the expected operating state data under the executed operating parameters is deduced.

[0046] The pre-defined physical laws refer to various physical laws and engineering principles applicable to the operation of high-voltage electrical systems in new energy vehicles, such as Ohm's law, Kirchhoff's laws, the law of conservation of energy, and the laws of thermodynamics. These laws form the basis for constructing simulation models and conducting dynamic deductions, ensuring the scientific validity and accuracy of the deduction results.

[0047] Electrical connections refer to the actual electrical connections between components within the high-voltage electrical system of a new energy vehicle, including series, parallel, star, and delta connections, as well as the topology of each connection point. These relationships determine the current path, voltage distribution, and energy transmission method, and are crucial for accurate electrical behavior simulation.

[0048] Component operating characteristics refer to the performance parameters and behavioral patterns of various electrical components (such as batteries, motors, inverters, DC / DC converters, and high-voltage wiring harnesses) within the high-voltage electrical system of new energy vehicles under different operating conditions. Examples include the battery's charge / discharge curves, internal resistance changes, and temperature characteristics; the motor's efficiency curves and torque characteristics; and the inverter's switching losses and harmonic characteristics. These characteristics are the basis for the aforementioned digital virtual mirror to accurately simulate the behavior of real components.

[0049] The simulation can be understood as using the aforementioned physical laws, electrical connection relationships, and component operating characteristics, combined with real-time acquired operating parameters, to simulate the dynamic behavior of the high-voltage electrical system of new energy vehicles under current operating parameters through simulation calculations and model predictions, thereby predicting its future or ideal operating state data.

[0050] Through the aforementioned technical solution, the simulation process fully considers pre-set physical laws, electrical connection relationships, and component operating characteristics, resulting in highly accurate and reliable data on the expected operating state. This not only more realistically reflects the ideal operating state of the high-voltage electrical system of new energy vehicles but also provides a more precise benchmark for subsequent real-time data comparison, thereby significantly improving the sensitivity and accuracy of abnormal state identification, effectively avoiding false alarms or missed alarms, and ensuring the operational safety and reliability of new energy vehicles.

[0051] In some of the embodiments described above in this application, although a method is proposed to compare real-time acquired operating status data with expected operating status data and identify abnormal states of the high-voltage electrical system of new energy vehicles based on the comparison results, thereby generating diagnostic information, there is still room for further clarification and optimization in the implementation process regarding how to accurately determine abnormal states and how to generate detailed diagnostic information. If judgments are made solely based on vague comparison results, false alarms or missed alarms may occur, and the diagnostic information may not be specific enough to guide subsequent maintenance and handling. Therefore, this application further proposes a step of comparing real-time acquired operating status data of the high-voltage electrical system of new energy vehicles with expected operating status data, identifying abnormal states of the high-voltage electrical system of new energy vehicles based on the comparison results, and generating diagnostic information. Specifically, this includes: when the deviation between the operating status data and the expected operating status data exceeds a first preset threshold, marking the high-voltage electrical system of the new energy vehicle as abnormal; combining the abnormal state and the dynamic deduction process of the operating status, analyzing the cause of the abnormality, locating the faulty component or location, and generating diagnostic information.

[0052] The steps of comparing the real-time acquired operating status data of the high-voltage electrical system of new energy vehicles with the expected operating status data, identifying abnormal states of the high-voltage electrical system of new energy vehicles based on the comparison results, and generating diagnostic information include: When the deviation between the operating status data and the expected operating status data exceeds the first preset threshold, the high-voltage electrical system of the new energy vehicle will be marked as abnormal. By combining the dynamic deduction process of abnormal and operational states, the causes of abnormalities are analyzed, faulty components or locations are located, and diagnostic information is generated.

[0053] Specifically, the deviation between operational status data and expected operational status data refers to the difference between the actual operating parameters (such as voltage, current, and temperature) of the high-voltage electrical system of new energy vehicles monitored in real time and the theoretical or ideal operating parameters derived through digital virtual mirroring. This deviation can be quantified using various mathematical methods, such as calculating absolute difference, relative difference, and root mean square error. The first preset threshold is a pre-set critical value used to determine whether the system is abnormal. This threshold can be set according to the design specifications, safety standards, historical operating data, and expert experience of the high-voltage electrical system to ensure timely detection of potential risks in the system. When the deviation between the actual operating data and the expected operating data exceeds this threshold, the system is considered to have deviated from the normal range, thus marking the high-voltage electrical system of the new energy vehicle as an abnormal state.

[0054] The process of dynamically extrapolating abnormal and operational states to analyze the causes of anomalies, locate faulty components or locations, and generate diagnostic information refers to further utilizing the dynamic extrapolation capabilities of digital virtual mirrors to conduct in-depth analysis of the causes of anomalies after the system is marked as abnormal. Specifically, by retrospectively analyzing or simulating the extrapolation process of digital virtual mirrors before and after the anomaly occurs, combined with real-time operational data, it is possible to identify which parameter changes led to the deviation, and thus infer the electrical components that may be faulty or their specific locations. For example, if the temperature parameter in a specific area is consistently higher than expected, it may indicate a problem with the cooling system in that area or an overload of a component. Diagnostic information can be understood as a detailed description of the abnormal state, including the anomaly type, the time of occurrence, possible causes, the faulty components or locations involved, and recommended troubleshooting or repair measures. Its purpose is to provide maintenance personnel with clear and accurate fault information so as to quickly locate and resolve problems.

[0055] Through the above technical solution, this application can achieve accurate and quantitative identification of abnormal states in the high-voltage electrical systems of new energy vehicles, significantly improving the accuracy and reliability of fault detection. By using clearly defined threshold judgments, false alarms and missed alarms are effectively avoided, ensuring that the system can respond promptly when potential risks arise. More importantly, by combining the dynamic simulation process of digital virtual mirrors for anomaly cause analysis and fault location, the generated diagnostic information becomes more specific and instructive, greatly shortening fault diagnosis and repair time, reducing maintenance costs, and improving the operational safety and maintenance efficiency of new energy vehicles.

[0056] In some preferred embodiments, it is assumed that a battery module in the high-voltage electrical system of a new energy vehicle has a normal operating voltage range of 3.6V to 4.2V. In the digital virtual mirror, the expected operating voltage of this battery module is extrapolated to 3.8V. Real-time acquired operating status data shows that the actual voltage of the battery module is 3.5V. At this time, the system calculates the deviation between the actual voltage and the expected voltage, i.e., 3.8V - 3.5V = 0.3V. If the preset first threshold is 0.2V, then since 0.3V exceeds 0.2V, the system will immediately determine that the battery module is in an abnormal state. Furthermore, the system will combine the electrical connection relationship of the battery module in the digital virtual mirror, historical operating data, and the extrapolation process to analyze the reasons for the low voltage. For example, the analysis may reveal that a certain series-connected cell of the battery module may have excessive internal resistance or capacity decay, or its connection line may have poor contact. Based on this analysis, the system will generate diagnostic information, such as abnormal voltage in battery module X. Possible causes: excessive internal resistance / capacity decay in cell Y. It is recommended to check cell Y or connecting line Z and locate the faulty component as cell Y in battery module X.

[0057] In some of the embodiments described above in this application, a scheme is proposed to visualize the operating status data, expected operating status data and diagnostic information of the high-voltage electrical system of new energy vehicles through augmented reality or virtual reality technology. However, in the implementation process, if only simple data presentation is performed, it may not be possible to intuitively associate this information with the actual physical components, nor is it easy to directly provide effective operation guidance for abnormal states, thereby affecting the efficiency of fault diagnosis and maintenance.

[0058] In this regard, this application further proposes the following steps for visualizing the real-time acquired operational status data, expected operational status data, and diagnostic information using augmented reality or virtual reality technology: Augmented reality or virtual reality technologies are used to overlay real-time operational status data and expected operational status data onto the corresponding positions of real components or virtual models of the high-voltage electrical system of new energy vehicles in the form of digital tags, color codes, or dynamic indicators. Based on the diagnostic information generation system, the optimized step-by-step operation guide and safety calibration prompts are overlaid and displayed on the corresponding positions of the real components or virtual models of the high-voltage electrical system of new energy vehicles.

[0059] Specifically, digital labels can display numerical values ​​for specific electrical parameters (such as voltage, current, and temperature), color coding can indicate the health status of the system (e.g., green for normal, yellow for warning, and red for abnormal), and dynamic indicators can include flashing, animation, or arrows to highlight key data or abnormal areas. These visualization elements are designed to be directly overlaid on the actual components of the high-voltage electrical system of new energy vehicles, for example, through augmented reality glasses or handheld devices, or overlaid on virtual models, such as in a virtual reality environment, allowing operators to intuitively understand the correspondence between data and physical entities. The system optimization step-by-step operation guide provides a series of detailed, step-by-step instructions for identified abnormal states or potential optimization points, such as troubleshooting procedures, component replacement steps, or performance tuning suggestions. Safety calibration prompts aim to ensure that necessary safety regulations and calibration procedures are followed during any operation, such as power-off operation prompts, insulation check requirements, or sensor calibration steps. Once generated, these guidelines and prompts are displayed, in the form of text, illustrations, or animations, overlaid on the corresponding locations of real components or virtual models of the high-voltage electrical system of new energy vehicles using augmented reality or virtual reality technology, thereby providing maintenance personnel with immediate and contextualized operational support.

[0060] Through the aforementioned technical solutions, the operational status data, expected operational status data, and diagnostic information of the high-voltage electrical system of new energy vehicles can be presented to users in a more intuitive and contextualized manner. This visualization method not only improves the efficiency of data understanding but, more importantly, greatly simplifies the fault location and problem diagnosis process by directly linking data with physical components. Simultaneously, the system's optimized step-by-step operation guidance and safety calibration prompts provide maintenance personnel with immediate and accurate action guidance, thereby significantly shortening troubleshooting time, reducing maintenance difficulty, and effectively ensuring operational safety, ultimately improving the operational reliability and maintenance efficiency of new energy vehicles.

[0061] In some preferred embodiments, it is assumed that a high-voltage battery module in a new energy vehicle experiences a voltage anomaly. First, real-time voltage parameters acquired by a voltage sensor are processed and compared with expected voltage parameters to identify the abnormal state of the battery module and generate diagnostic information. Subsequently, through augmented reality glasses, maintenance personnel can directly see digital labels superimposed on the actual battery module, displaying the module's real-time and expected voltage values, and visually indicating its abnormal state through color coding (e.g., red). Simultaneously, based on the diagnostic information, the system will overlay step-by-step instructions for checking the battery module's connection cables and a safety calibration reminder to disconnect the high-voltage power supply before operation next to a virtual model or real component of the battery module. Maintenance personnel can follow the instructions step-by-step to perform the checks and operations, thereby resolving the problem efficiently and safely.

[0062] In some embodiments described above, abnormal states of the high-voltage electrical system of new energy vehicles are primarily identified by comparing real-time operating status data with expected operating status data. However, in actual operation, components of the high-voltage electrical system of new energy vehicles may experience not only immediate operational anomalies but also long-term environmental factors, leading to accumulated physical damage and aging. These potential physical damages and aging may not immediately manifest in abnormal operating parameters but will significantly affect the long-term reliability and safety of the system. If these problems are not addressed, the system may suddenly fail due to component aging or physical damage without being identified as an operational anomaly, resulting in safety hazards and high maintenance costs. Therefore, this application further proposes an assessment method that considers the impact of environmental parameters on the cumulative physical damage of electrical components, to more comprehensively monitor and diagnose the health status of the high-voltage electrical system of new energy vehicles.

[0063] In some embodiments of this application described above, after the step of mapping the operating parameters to a preset digital virtual image of the electrical system, the method further includes: The system acquires environmental parameters of the high-voltage electrical system of new energy vehicles in real time, maps the environmental parameters to the physical damage assessment module in the preset digital virtual image of the electrical system, and generates the physical damage accumulation index of electrical components. The physical damage accumulation index is used to determine whether electrical components have aging damage.

[0064] Specifically, real-time acquisition of environmental parameters for the high-voltage electrical system of new energy vehicles refers to obtaining real-time data on the environment in which the high-voltage electrical system operates through additional environmental sensors, such as temperature sensors, humidity sensors, and vibration sensors. These environmental parameters may include, but are not limited to, ambient temperature, humidity, altitude, vibration intensity, and impact load, with the aim of comprehensively assessing the physical stress that the external environment may cause to electrical components. The physical damage assessment module, which maps environmental parameters to a preset digital virtual image of the electrical system, can be understood as inputting the real-time acquired environmental data into a sub-module within the digital virtual image specifically for assessing physical damage. This physical damage assessment module can be a simulation unit built based on a physical model, empirical model, or machine learning model, capable of simulating the stress response and damage accumulation process of electrical components under these environmental conditions based on the input environmental parameters. In practical applications, generating the physical damage accumulation index of electrical components refers to the quantitative indicator calculated by the physical damage assessment module based on environmental parameters and a preset damage model, representing the degree of physical damage suffered by each electrical component (e.g., battery pack, motor controller, high-voltage wiring harness, etc.) over a period of time. This index can reflect the cumulative effects of component material fatigue, insulation aging, and structural deformation. Furthermore, determining whether electrical components exhibit aging damage based on the physical damage accumulation index involves comparing the calculated physical damage accumulation index with a preset damage threshold. When the accumulation index reaches or exceeds a specific threshold, the electrical component is considered to have already experienced or is about to experience aging damage, requiring further attention or maintenance.

[0065] Through the above technical solution, this application enables more comprehensive health monitoring of the high-voltage electrical system of new energy vehicles. Compared to focusing only on real-time anomalies in operating parameters, this solution, by considering the long-term impact of environmental parameters on components, can identify and warn of physical damage and aging trends in electrical components in advance. This helps to fundamentally improve the long-term operational reliability and safety of new energy vehicles, supports the implementation of preventative maintenance strategies, effectively reduces the risk of unexpected failures and maintenance costs caused by component aging, and extends the service life of the electrical system.

[0066] In some preferred embodiments, it is assumed that a new energy vehicle operates in a high-temperature and high-humidity area for an extended period. Traditional monitoring methods may only focus on whether operating parameters such as battery voltage, current, and temperature are within normal ranges. However, according to the solution of this application, in addition to operating parameters, ambient temperature and humidity are acquired in real time. These environmental parameters are input into a physical damage assessment module in a digital virtual mirror. This module may include an insulation material aging model based on the Arrhenius equation, which calculates the cumulative physical damage index of the insulation material inside the battery pack based on long-term high-temperature and high-humidity data. When this index reaches a preset threshold, the system determines that the insulation components of the battery pack have aging damage and generates corresponding early warning information. For example, even if the real-time operating parameters of the battery pack are still within the normal range, the system can alert the user or maintenance personnel in advance that the insulation performance of the battery pack may have begun to decline, and recommend inspection or preventive maintenance to avoid serious failures such as short circuits or thermal runaway caused by insulation aging.

[0067] In some of the embodiments described above in this application, a scheme for determining whether electrical components have aging damage based on the physical damage accumulation index has been proposed. However, simply determining whether aging damage exists may not provide sufficiently detailed guidance for subsequent maintenance and repair. For example, when the system indicates the presence of aging damage, maintenance personnel still need to further investigate the specific type and location of the damage and its potential impact, which undoubtedly increases the complexity and time cost of fault diagnosis.

[0068] In this regard, this application further proposes that the steps for determining whether electrical components have aging damage based on the physical damage accumulation index include: When the physical damage accumulation index reaches the second preset threshold, it is determined that the electrical components have aging damage. Based on a pre-defined library of aging damage types, the system determines the type of aging damage to electrical components and generates a notification message that includes the type, location, and impact of the aging damage to the electrical components. The prompts will be presented visually.

[0069] Specifically, the physical damage accumulation index is an indicator used by the physical damage assessment module to quantitatively evaluate the degree of damage accumulation of electrical components under different environmental parameters. When this index reaches a second preset threshold, it indicates that the damage level of the electrical component has reached a level requiring attention or action, and it is judged to have aging damage. The second preset threshold can be set according to factors such as the material properties, design life, operating conditions, and safety standards of different electrical components. For example, it can be set as the damage accumulation value corresponding to 80% of the component's design life.

[0070] Furthermore, to provide more instructive information, this application introduces a pre-defined aging damage type library. This library stores various types of aging damage that may occur in electrical components, such as insulation aging, material fatigue, loose connections, and corrosion, and may include a characteristic description, typical location, and potential impact for each damage type. When aging damage is determined to exist in an electrical component, the system will match and determine the damage type based on this library.

[0071] Therefore, the system can generate alerts that include the type, location, and impact of aging damage to electrical components. For example, the alert might specifically state that the battery module is experiencing insulation aging, located at the left-side connection terminal, which could lead to a risk of leakage. This alert details which component has experienced what type of aging damage, the specific location of the damage, and the potential consequences, providing maintenance personnel with a comprehensive diagnostic basis.

[0072] Finally, the warning information is presented visually. This can be achieved in several ways, such as displaying it as warning text on the screen, or highlighting the corresponding location of the damaged part in a virtual model or augmented reality view, thereby intuitively conveying key information to the user.

[0073] Through the above technical solution, this application can provide more refined and specific diagnostic results for aging damage. Compared to simply determining whether aging damage exists, this application can clearly identify the type, specific location, and potential impact of aging damage, greatly improving the accuracy and efficiency of fault diagnosis. This enables maintenance personnel to quickly locate problems and take targeted measures, effectively reducing maintenance costs and time, and significantly improving the operational reliability and safety of high-voltage electrical systems in new energy vehicles.

[0074] In some preferred embodiments, it is assumed that the cumulative physical damage index of a high-voltage connector in a new energy vehicle continues to rise and eventually reaches a preset second threshold. At this point, the system will determine that the high-voltage connector has aging damage. Further, the system will match the damage based on a preset aging damage type library and identify the damage type as increased contact resistance or material fatigue. Subsequently, the system will generate a prompt message, such as: "High-voltage connector, increased contact resistance, located at the battery pack output end, may cause local overheating and power reduction." This prompt message is then displayed in red warning text overlaid on the actual high-voltage connector in the augmented reality glasses of the maintenance personnel through a visualization module, while simultaneously highlighting the corresponding position of the connector in the virtual model, thus intuitively prompting the maintenance personnel to inspect and replace it.

[0075] In some of the embodiments described above in this application, a scheme for visually presenting prompts has been proposed. However, in the implementation process, if only a simple visualization is presented, it may not effectively highlight the severity and specific details of aging damage, causing operators to fail to pay attention to or accurately understand the prompts in a timely manner, thereby affecting the efficiency of troubleshooting and maintenance. Therefore, this application further proposes a more intuitive and eye-catching method for visually presenting prompts to enhance the effectiveness of information communication.

[0076] In this regard, this application further proposes the following steps for visualizing the aforementioned prompt information: It displays warning text including the type, location, and impact of aging and damage to electrical components, and highlights the electrical components.

[0077] Specifically, warning text refers to a clear textual description of the type of aging damage to electrical components, their specific location in the high-voltage electrical system of new energy vehicles, and the potential impact of this damage. For example, the warning text might state that the battery module has internal short-circuit aging damage located in the middle of the left side of the vehicle, potentially leading to thermal runaway. Its purpose is to provide operators with comprehensive and easily understandable fault information. Highlighting electrical components can be understood as using visual emphasis, such as changing color, flashing, adding borders, or magnifying the display, to make the damaged electrical components more prominent in the visualization interface (whether it's an augmented reality overlay of a real component or a display in a virtual model). Its purpose is to quickly attract the operator's attention, enabling them to quickly locate the components requiring attention.

[0078] By employing the aforementioned technical solutions, compared to simple visualization, this application significantly improves the efficiency and accuracy of conveying aging damage warning information. The detailed descriptions of the warning text prevent ambiguity, ensuring operators can accurately understand the fault situation; the highlighted display effectively solves the problem of information being overlooked, enabling operators to notice critical abnormal components immediately. Therefore, this application helps shorten fault diagnosis time, reduces safety risks caused by the failure to detect aging damage in a timely manner, and improves the maintenance and operational safety of high-voltage electrical systems in new energy vehicles.

[0079] In some preferred embodiments, assuming that a high-voltage connector in a new energy vehicle has reached a second preset threshold due to long-term operation, the system determines that it has aging damage. At this time, based on a preset aging damage type library, the system identifies the connector as having an abnormally increased contact resistance and generates a warning message. The solution of this application visualizes this warning message: in the augmented reality interface, when maintenance personnel observe the high-voltage electrical system of the new energy vehicle through AR glasses, a warning text will be overlaid above the actual location of the high-voltage connector, such as "High-voltage connector: Abnormally increased contact resistance, located at the battery pack output end, which may cause local overheating." Simultaneously, the virtual model of the high-voltage connector or its outline in the real environment will be highlighted with a flashing red border to strongly alert maintenance personnel that the component has a problem and requires immediate attention and inspection.

[0080] Traditional sensor-based methods for measuring electrical parameters in new energy vehicles can monitor the operating status and diagnose anomalies of the electrical system through a series of steps. However, in practical applications, these methods often require specific hardware and software environments to support and execute them. The lack of a structured, modular system to support these complex measurement, deduction, comparison, and presentation processes can lead to low implementation efficiency, difficulties in system integration, and increased maintenance costs.

[0081] Regarding this, secondly, refer to Figure 2 This application proposes a sensor-based electrical parameter measurement system for new energy vehicles, used to execute the aforementioned sensor-based electrical parameter measurement method for new energy vehicles. The system includes: The parameter acquisition module 210 is used to acquire the operating parameters of the high-voltage electrical system of the new energy vehicle through multiple types of sensors deployed at preset locations in the high-voltage electrical system of the new energy vehicle. The virtual image simulation module 220 is used to map the operating parameters to a preset digital virtual image of the electrical system, perform dynamic simulation of the operating status, and generate expected operating status data. The anomaly identification module 230 is used to compare the real-time acquired operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identify the abnormal status of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generate diagnostic information. The visualization module 240 is used to visualize the real-time acquired operating status data, expected operating status data and diagnostic information through augmented reality or virtual reality technology.

[0082] Specifically, the parameter acquisition module 210 is configured to interact with various sensors in the high-voltage electrical system of new energy vehicles. These sensors may include, but are not limited to, voltage sensors, current sensors, and temperature sensors. They are deployed at key nodes in the high-voltage electrical system, such as battery packs, motor controllers, and DC / DC converters, to collect operating parameters, including voltage, current, and temperature parameters, in real time. The parameter acquisition module 210 is responsible for preliminary processing of this raw data, such as filtering to remove noise and performing range conversion to adapt to the data format requirements of subsequent processing modules.

[0083] The virtual mirror simulation module 220 is designed to receive the operating parameters output by the parameter acquisition module 210. The core of this module is a preset digital virtual mirror of the electrical system, which is a simulation model based on the physical structure, electrical connections, and component operating characteristics of the high-voltage electrical system of new energy vehicles. The virtual mirror simulation module 220 maps the real-time acquired operating parameters into this digital virtual mirror and, based on preset physical laws, electrical connections, and component operating characteristics, dynamically simulates the operating state within the digital virtual mirror, thereby generating expected operating state data under the current operating parameters.

[0084] In practical applications, the anomaly identification module 230 is used to receive real-time operational status data of the high-voltage electrical system of the new energy vehicle and expected operational status data generated by the virtual mirror simulation module 220. This module compares these two sets of data to assess the deviation between them. When the deviation between the operational status data and the expected operational status data exceeds a first preset threshold, the anomaly identification module 230 marks the high-voltage electrical system of the new energy vehicle as an abnormal state. Furthermore, this module combines the abnormal state with the dynamic simulation of the operational status to deeply analyze the causes of the anomaly, locate specific faulty components or positions, and generate detailed diagnostic information.

[0085] Furthermore, the visualization module 240 is configured to receive real-time operational status data, expected operational status data, and diagnostic information generated by the anomaly identification module 230. This module utilizes augmented reality (AR) or virtual reality (VR) technology to present this data and information to the user in an intuitive and easy-to-understand manner. For example, real-time operational data and expected operational data can be overlaid on corresponding locations of real components or their virtual models in the high-voltage electrical system of new energy vehicles using digital labels, color coding, or dynamic indicators. Simultaneously, based on the diagnostic information, this module can also generate step-by-step system optimization operation guidelines and safety calibration prompts, and overlay these guidelines and prompts on corresponding locations of real components or virtual models, providing direct operational guidance for maintenance personnel.

[0086] Through the above technical solution, this application provides a sensor-based electrical parameter measurement system for new energy vehicles. This system can transform complex measurement methods into deployable and operable entities. Compared to solutions that only describe the method steps, this system, through clear functional module division, achieves automation and integration of data acquisition, virtual simulation, anomaly identification, and visualization, significantly improving the efficiency and accuracy of electrical parameter measurement for new energy vehicles. Furthermore, the modular design gives the system better scalability and maintainability, facilitating functional upgrades or component replacements according to actual needs. Through AR / VR visualization technology, operators can intuitively understand the complex electrical system status and diagnostic information, enabling faster and more accurate troubleshooting and maintenance operations. This effectively reduces the difficulty and error rate of manual operation, improving the operational safety and reliability of new energy vehicles.

[0087] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A sensor-based method for measuring electrical parameters of new energy vehicles, characterized in that, include: The operating parameters of the high-voltage electrical system of the new energy vehicle are obtained by using multiple types of sensors deployed at predetermined locations in the high-voltage electrical system of the new energy vehicle. The operating parameters are mapped to a preset digital virtual image of the electrical system to dynamically simulate the operating status and generate expected operating status data. The system compares the real-time operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identifies abnormal states of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generates diagnostic information. Augmented reality or virtual reality technologies are used to visualize the real-time acquired operating status data, expected operating status data, and diagnostic information.

2. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 1, characterized in that, The step of acquiring the operating parameters of the high-voltage electrical system of the new energy vehicle through multiple types of sensors deployed at predetermined locations in the high-voltage electrical system of the new energy vehicle includes: The operating parameters, including voltage, current, and temperature parameters, are obtained through voltage, current, and temperature sensors. The operating parameters undergo data preprocessing, including filtering and range conversion.

3. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 1, characterized in that, The preset digital virtual image of the electrical system is a simulation model established based on the physical structure, electrical connection relationship and component operating characteristics of the high-voltage electrical system of the new energy vehicle.

4. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 3, characterized in that, The steps for dynamically extrapolating the operational status and generating expected operational status data include: In the preset digital virtual image of the electrical system, based on preset physical laws, electrical connection relationships and component operating characteristics, the expected operating state data under the execution of the operating parameters is deduced.

5. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 1, characterized in that, The step of comparing the real-time acquired operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identifying abnormal states of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generating diagnostic information includes: When the deviation between the operating status data and the expected operating status data exceeds a first preset threshold, the high-voltage electrical system of the new energy vehicle is marked as abnormal. By combining the abnormal state and the dynamic deduction process of the operating state, the cause of the abnormality is analyzed, the faulty component or location is located, and the diagnostic information is generated.

6. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 1, characterized in that, The step of visualizing the real-time acquired operating status data, expected operating status data, and diagnostic information using augmented reality or virtual reality technology includes: Using augmented reality or virtual reality technology, the real-time acquired operating status data and the expected operating status data are overlaid and displayed on the corresponding positions of the real components or virtual models of the high-voltage electrical system of the new energy vehicle in the form of digital tags, color codes or dynamic indicators. Based on the diagnostic information generation system, the optimized step-by-step operation guide and safety calibration prompts are overlaid and displayed on the corresponding positions of the real components or virtual models of the new energy vehicle's high-voltage electrical system.

7. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 1, characterized in that, Following the step of mapping the operating parameters to a preset digital virtual image of the electrical system, the method further includes: The environmental parameters of the high-voltage electrical system of the new energy vehicle are acquired in real time, and the environmental parameters are mapped to the physical damage assessment module in the preset digital virtual image of the electrical system to generate the physical damage accumulation index of the electrical components. The presence of aging damage in the electrical components is determined based on the physical damage accumulation index.

8. The sensor-based method for measuring electrical parameters of new energy vehicles according to claim 7, characterized in that, The step of determining whether the electrical component has aging damage based on the physical damage accumulation index includes: When the physical damage accumulation index reaches the second preset threshold, it is determined that the electrical component has aging damage. Based on a preset library of aging damage types, the aging damage type of the electrical component is determined, and a prompt message containing the aging damage type, location, and impact of the electrical component is generated. The prompt information will be presented visually.

9. A sensor-based method for measuring electrical parameters of new energy vehicles according to claim 8, characterized in that, The step of visualizing the prompt information includes: The system displays warning text including the type of aging damage, location, and impact of the electrical component, and highlights the electrical component.

10. A sensor-based electrical parameter measurement system for new energy vehicles, used to execute the sensor-based electrical parameter measurement method for new energy vehicles as described in any one of claims 1 to 9, characterized in that, The system includes: The parameter acquisition module is used to acquire the operating parameters of the high-voltage electrical system of the new energy vehicle through various types of sensors deployed at preset locations in the high-voltage electrical system of the new energy vehicle. The virtual image simulation module is used to map the operating parameters to a preset digital virtual image of the electrical system, perform dynamic simulation of the operating status, and generate expected operating status data. An anomaly identification module is used to compare the real-time acquired operating status data of the high-voltage electrical system of the new energy vehicle with the expected operating status data, identify the abnormal state of the high-voltage electrical system of the new energy vehicle based on the comparison results, and generate diagnostic information. The visualization module is used to visualize the real-time acquired operating status data, expected operating status data, and diagnostic information using augmented reality or virtual reality technology.

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