A detection method, device and equipment of a vehicle tri-electric system

Abstract

The application provides a kind of detection method, device and equipment of vehicle three electric systems, it is related to new energy automobile manufacturing and detection technical field, comprising: in response to the static test request that vehicle is sent in test site, receive the static test data of vehicle that vehicle end system gathers;In response to the dynamic test request that vehicle is sent in test site, receive the dynamic test data of vehicle test driving process that vehicle end system gathers;Through the analysis of static test data, obtain the static test result of vehicle;Through static test data and dynamic test data, the driving process of vehicle is analyzed, and the dynamic test result of vehicle is obtained.The technical scheme of the embodiment of the application can realize the full-process automatic detection of dynamic test and static test, improve the detection efficiency, provide a stable data transmission channel for the data transmission process, and fuse and analyze the static test data and the dynamic test data, improve the accuracy of detection result.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle manufacturing and testing technology, and in particular to a testing method, device and equipment for the vehicle's three-electric system. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the proportion of pure electric vehicles in the automotive market is increasing year by year. End-of-line testing (EOL), as a key quality control step in the automotive manufacturing process, directly affects the overall quality of the vehicle upon delivery. The performance parameter testing of the three core components of pure electric vehicles—battery system, motor system, and electronic control system—is of paramount importance during the EOL process.

[0003] In existing technologies, end-of-life (EOL) testing of the three-electric system (battery, motor, and electronic control system) is mainly divided into two modes: static testing and dynamic testing. Static testing is usually conducted when the vehicle is stationary. Dedicated testing equipment is connected via an OBD (On-Board Diagnostics) interface to read fault codes and real-time data streams stored in each control unit; alternatively, the CAN bus communication protocol is used to achieve parallel testing and data acquisition of multiple electronic control units. Dynamic testing, on the other hand, requires the vehicle to be driven under actual driving conditions. Testers must drive the vehicle through a preset test route and judge the dynamic performance of the three-electric system based on subjective driving experience or with the aid of simple data recording devices.

[0004] Because static and dynamic tests are mostly separate and the testing process is not continuous, the testing process relies heavily on manual operation, observation and judgment. Therefore, the testing efficiency is low and it is impossible to guarantee the consistency of testing conditions for different vehicles and different drivers. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a testing method, apparatus, and equipment for a vehicle's three-electric system, which enables continuous static and dynamic testing of the vehicle in a test site, as well as real-time reporting and automatic analysis of static and dynamic test data collected during the testing process. This improves the stability of data transmission between the cloud and the vehicle, ensures the consistency of vehicle test conditions, and enhances the objectivity and accuracy of the evaluation results. Furthermore, it enables the fusion analysis of dynamic and static test data, thereby improving the accuracy of determining dynamic test results.

[0006] In a first aspect, embodiments of the present invention provide a method for testing a vehicle's three-electric system, applied to a cloud-based intelligent analysis system. The cloud-based intelligent analysis system can communicate with the vehicle-side system via a wireless network communication system set up in a test site. The method includes: In response to a static test request issued by the vehicle at the test site, the system receives static test data of the vehicle collected by the vehicle-side system. In response to the dynamic test request issued by the vehicle in the test site, it receives dynamic test data collected by the vehicle-side system during the vehicle test driving process. The static test results of the vehicle are obtained by analyzing the static test data. The vehicle's driving process is analyzed using the static and dynamic test data to obtain the vehicle's dynamic test results.

[0007] In a preferred embodiment of the present invention, the above-described analysis of the vehicle's driving process using the static test data and the dynamic test data to obtain the vehicle's dynamic test results includes: Multiple dynamically collected data corresponding to the first test item are determined from the dynamic test data; Match the benchmark reference data corresponding to the first test item from the static test data; The dynamic test results of the vehicle are determined based on the multiple dynamically acquired data and the benchmark reference data.

[0008] In a preferred embodiment of the present invention, the determination of multiple dynamically acquired data corresponding to the first test item from the dynamic test data includes: Based on the vehicle's location data, determine the road spectrum type of the road segment the vehicle traveled; Based on the road spectrum type, determine at least one first test item that needs to be tested for the driving section; From the dynamic test data, select multiple dynamic data collected at different data collection times on the driving section under the first test item.

[0009] In a preferred embodiment of the present invention, the above-mentioned matching of the benchmark reference data corresponding to the first test item from the static test data includes: From the static test data, each second test item for testing the vehicle and the static data collected for the second test item are determined; From each of the second test items, identify the second test item that belongs to the same project as the first test item, and use the static collection data of the identified second test item as the benchmark reference data of the first test item.

[0010] In a preferred embodiment of the present invention, determining the dynamic test result of the vehicle based on the plurality of dynamically acquired data and the reference data includes: Obtain the preset security threshold for the first test item; Each of the dynamically acquired data points is compared with the preset security threshold to obtain a first test result on whether the first test item meets the static state. For each of the dynamically acquired data, the rate of change of the dynamically acquired data relative to the benchmark reference data is determined to obtain a second test result on whether the first test item satisfies dynamic stability. The dynamic test results of the vehicle are obtained by fusing the first test results and the second test results.

[0011] In a preferred embodiment of the present invention, the above-described determination of the rate of change of each dynamically acquired data point relative to the benchmark reference data, to obtain a second test result on whether the first test item satisfies dynamic stability, includes: For each of the dynamically acquired data, calculate the difference between the acquired value corresponding to the dynamically acquired data and the reference value corresponding to the benchmark reference data; The absolute value of the ratio of the difference to the reference value is taken as the rate of change corresponding to the dynamically collected data; If the rate of change of each of the dynamically acquired data is less than a preset change threshold, the first test item is determined to meet the dynamic stability of the vehicle. The second test result includes the linkage test pass result indicating that the dynamic stability of the vehicle is met.

[0012] In a preferred embodiment of the present invention, the method further includes: By performing data fitting on the multiple dynamically collected data, the state fitting curve of the first test item is obtained; If the rate of change of each of the dynamically acquired data is less than a preset change threshold, determining that the first test item meets the vehicle's dynamic stability includes: If the rate of change of each of the dynamically acquired data is less than a preset change threshold, and the curvature of each point on the state fitting curve is less than a preset curvature, then the first test item is determined to meet the dynamic stability of the vehicle.

[0013] In a preferred embodiment of the present invention, the above-mentioned wireless network communication system includes multiple local area network access points for wireless communication between the cloud-based intelligent analysis system and the vehicle-mounted system. The multiple local area network access points operate on the same channel. During the process of receiving the static test data and / or the dynamic test data, the method further includes: The system receives a network switching request sent by the vehicle-mounted system; wherein the vehicle-mounted system can dynamically determine the working status information of each local area network access point, determine whether each local area network access point is sufficient to meet network connection requirements and data transmission requirements through the corresponding working status information, and determine the connection method for the cloud-based intelligent analysis system to connect with the vehicle-mounted system. In response to the network switching request, the network connection with the vehicle system is switched from the current connection method to the target connection method.

[0014] Secondly, embodiments of the present invention also provide a testing device for a vehicle's three-electric system, comprising: an application to a cloud-based intelligent analysis system, wherein the cloud-based intelligent analysis system is capable of communicating with the vehicle-side system via a wireless network communication system set up in a test site, and the device comprising: The static data receiving module is used to receive static test data of the vehicle collected by the vehicle-side system in response to the static test request issued by the vehicle in the test site. The dynamic data receiving module is used to respond to dynamic test requests issued by the vehicle in the test site and receive dynamic test data collected by the vehicle-side system during the vehicle test driving process. The static test analysis module is used to obtain the static test results of the vehicle by analyzing the static test data. The dynamic test analysis module is used to analyze the driving process of the vehicle using the static test data and the dynamic test data to obtain the dynamic test results of the vehicle.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the vehicle three-electric system detection method of the first aspect described above.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the vehicle three-electric system detection method described in the first aspect.

[0017] The embodiments of the present invention bring the following beneficial effects: This invention provides a testing method for a vehicle's three-electric system (battery, motor, and electronic control system). By establishing a test site equipped with a cloud-based intelligent analysis system and a wireless network communication system, the vehicle can undergo continuous static and dynamic testing within the test site. Furthermore, the vehicle-side system can communicate with the cloud-based intelligent analysis system via the wireless network communication system, effectively improving the stability of data transmission between the cloud and the vehicle. This facilitates real-time reporting and automatic analysis of dynamic and static test data collected during the testing process, significantly reducing interference from human factors and ensuring consistency in vehicle testing conditions. All evaluations are based on a unified data and algorithm model, improving the objectivity and accuracy of the evaluation results. Moreover, static test data is introduced during the analysis of dynamic test data, achieving fusion analysis of dynamic and static test data, which effectively improves the accuracy of determining dynamic test results.

[0018] Other features and advantages of the invention will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the overall system architecture for detecting the three-electric system of a vehicle, as provided in an embodiment of the present invention. Figure 2 A flowchart illustrating a testing method for a vehicle's three-electric system provided in an embodiment of the present invention; Figure 3 A flowchart of another method for testing a vehicle's three-electric system provided in an embodiment of the present invention; Figure 4 A flowchart of another method for testing a vehicle's three-electric system provided in an embodiment of the present invention; Figure 5 A flowchart illustrating another method for testing a vehicle's three-electric system provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a vehicle's three-electric system testing device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] End-of-line (EOL) testing is a critical step in the automobile manufacturing process. For pure electric vehicles, the three-electric system (battery, motor, and electronic control system) is its core component, and its performance directly affects the safety, reliability, and lifespan of the entire vehicle. EOL testing of the three-electric system typically employs static or dynamic testing. Static testing involves using diagnostic tools or data acquisition equipment to read fault codes and data streams from each controller while the vehicle is stationary, verifying its functionality. Dynamic testing involves a driver navigating the vehicle through a specific test track, with testers making subjective judgments based on their experience or using simple data recording equipment.

[0024] In existing technologies, many solutions rely on the vehicle's own mobile network (4G / 5G) or specific wired connections for data transmission. These are limited by signal coverage and carrier plans, leading to high costs and unstable performance during large-scale deployment in testing sites. Network solutions are inconsistent between static and dynamic testing areas. Static and dynamic testing are often separate processes, with disjointed workflows and ineffective data integration and analysis. The testing process heavily relies on manual operation, observation, and judgment, resulting in low automation and integration and low testing efficiency. Furthermore, dynamic test results heavily depend on driver experience and operation, making it difficult to ensure consistency of testing conditions across different vehicles and drivers, thus affecting the objectivity and accuracy of the evaluation results.

[0025] Based on this, the vehicle three-electric system testing method provided by the present invention can analyze static and dynamic test data by establishing a cloud-based intelligent analysis system, thereby automating dynamic and static testing, improving the objectivity and accuracy of test results, and realizing communication between the cloud-based intelligent analysis system and the vehicle system through a wireless network communication system, thus achieving stable data transmission between the cloud and the vehicle and reducing the dependence and limitations of data transmission on its own mobile network or specific wired connection.

[0026] To facilitate understanding of this embodiment, a detailed description of the vehicle's three-electric system detection system disclosed in this embodiment of the invention will be provided first.

[0027] Figure 1 This is a schematic diagram of the architecture of a vehicle's three-electric system testing system provided in an embodiment of the present invention. In the technical solution of this embodiment, the core of the vehicle's three-electric system testing system lies in constructing an integrated "vehicle-cloud-device" EOL intelligent testing system that relies entirely on a wireless network (such as WiFi) set up in the testing site for data communication, thereby achieving automated, in-depth, and intelligent testing of the vehicle's three-electric system. Figure 1 As shown, the overall testing system for the vehicle's three-electric system includes: a cloud-based intelligent analysis system, a wireless network communication system, a vehicle-side system, and a factory terminal display system. This overall testing system for the vehicle's three-electric system can be applied to pure electric vehicles as well as hybrid vehicles, such as plug-in hybrids, hybrid electric vehicles, and range-extended hybrids. Furthermore, if a gasoline-powered vehicle possesses an intelligent three-electric system, it can also be tested using the overall testing system provided in this application and the testing scheme described below. In this overall testing system for the vehicle's three-electric system, the vehicle-side system includes the EOL APP pre-installed on the vehicle's central control screen via electrical testing equipment at the static testing station (CP7 station) in the test site; a data acquisition device connected via the OBD-II interface; and the vehicle's overall network (CAN / CAN FD). The central control screen and data acquisition device automatically connect to the test site's WiFi network (factory WiFi) at the static testing station. The EOL APP provides a graphical display interface for test item selection, instructions, and results, and responds to cloud-based instructions. The data acquisition unit is responsible for collecting real-time message data from the vehicle's three-electric system and related controllers, namely static and dynamic test data. The network communication system is a high-speed WiFi (IEEE 802.11ax) network covering the dynamic test area of ​​the full-coverage test site, providing stable, high-bandwidth wireless access for the vehicle's central control screen (EOL APP) and the data acquisition unit, ensuring the real-time and reliable uploading of massive amounts of message data. By establishing a wireless network communication system, wireless communication and message data transmission between the cloud-based intelligent analysis system and the vehicle-side system are achieved. Compared to existing technologies where data transmission relies on the vehicle's own mobile network (4G / 5G) or a specific wired connection, the technical solution of this invention solves the problems of signal coverage and operator package limitations when using mobile networks (4G / 5G) or specific wired connections for data transmission, significantly improving the stability of data transmission between the cloud-based intelligent analysis system and the vehicle-side system.

[0028] The cloud-based intelligent analysis system is a cloud service platform deployed in the test site's data center, comprising a big data processing platform and an intelligent analysis algorithm engine. The cloud service platform receives and stores vehicle message data uploaded via WiFi; the algorithm engine has built-in dedicated analysis models for different test items (static items, dynamic road profiles) and the three-electric components, performing real-time calculations, feature extraction, and comprehensive evaluation. The plant terminal display system includes display terminals located next to the static testing station, unloading station, and dynamic testing area, used to display operation instructions and final test results issued by the cloud-based intelligent analysis system, guiding operators in their work.

[0029] In the technical solution of this invention embodiment, the WiFi network of the test site adopts a WiFi 6 wireless network based on the IEEE 802.11ax standard, using multiple enterprise-grade wireless APs (access points), such as the Cisco Catalyst 9130 series, to achieve seamless full coverage of the static test station, unloading station, and the entire dynamic test area. A dedicated network SSID and security policy (such as WPA2-Enterprise) are deployed for EOL system devices to access. The vehicle's central control system has a pre-installed WiFi module. The EOL APP is developed based on Android Automotive OS and has the function of automatically connecting to a specified WiFi SSID. The data acquisition device is a portable device supporting dual-band WiFi (2.4GHz / 5GHz), with a built-in WiFi module, which can automatically connect to the preset WiFi network of the test site after power-on. It also supports CAN bus connection, and the sampling rate is configurable. The cloud service platform is deployed in the test site data center, using virtual machines or containerization; an MQTT Broker (such as EMQX) is built to receive WiFi data streams from the data acquisition device and the EOL APP; Flink is used for stream processing, InfluxDB is used to store time-series data, and MySQL is used to store business data. Develop analysis models using Python, and pre-set models for different road spectra and test items (such as a motor torque response analysis model for ABS road sections and an insulation resistance change rate calculation model for water-crossing areas).

[0030] Understandably, in existing technologies, the testing mode of the three-electric system can be summarized as "static fixed-point wired / wireless testing + dynamic manual recording or offline analysis". Static testing is usually carried out at a fixed workstation (such as a static test station), communicating with the test server via a diagnostic cable (wired) or relying on the vehicle's own 4G / 5G module (wireless). This mode is limited by cable length or public network signal coverage and quality. After the vehicle leaves the workstation, dynamic testing either loses its real-time data connection with the test system (becoming a "black box" test, with data downloaded manually afterwards), or barely relies on an unstable public network for sporadic data backhaul, unable to support massive, real-time, and continuous data stream transmission and analysis. The technical solution of this invention, by constructing a wireless network communication system, utilizes WiFi network coverage to extend from the static test station / unloading station to the entire dynamic test area, changing dynamic testing from an "offline" or "semi-offline" state to a completely "online" state. No matter where the vehicle is in the test site, its vehicle-side system and the cloud-based intelligent analysis system always maintain a stable connection with high bandwidth and low latency. This enables the cloud-based intelligent analysis system to receive and analyze vehicle data streams in real time. Therefore, the technical solution of this invention is not a simple "replacement of network connection method", but a "systematic reconstruction" involving test mode, system architecture and terminal form.

[0031] To address this, the technical solution of this invention constructs a vehicle-side system specifically designed for the WiFi environment of the test site. The EOL APP within the vehicle-side system is not a simple display program, but a core in-vehicle software integrating test control, network communication, and human-machine interaction. It is responsible for receiving cloud commands, controlling the test process, sending diagnostic requests to the vehicle controller, and displaying progress and results through a graphical interface. After the vehicle is powered on, the EOL APP automatically searches for and connects to a preset, high-priority WiFi SSID of the test site, rather than the vehicle's built-in mobile network. As the in-vehicle "test commander," it maintains a heartbeat connection with the cloud-based intelligent analysis system via the WiFi network. The data acquisition device in the vehicle-side system is not an ordinary OBD reader, but a dedicated device integrating a high-performance dual-band WiFi module (supporting WiFi 6), a large cache, and support for protocols such as CAN FD. It is physically connected to the vehicle via the OBD-II interface, but the data output is WiFi. Upon power-up, it also automatically connects to the WiFi network of the test site, uploading the massive amounts of raw data packets collected from the entire vehicle network to the cloud in real-time and raw streaming via the WiFi network, rather than simply parsing or storing them locally.

[0032] Therefore, the test site WiFi network in the technical solution of this invention is a self-built dedicated network, which can improve network stability and reliability, optimize coverage and bandwidth as needed, optimize deployment for the test site environment, achieve seamless and fast roaming, ensure the continuity of network connection for vehicles during dynamic testing, support truly uninterrupted real-time mobile testing, and form an efficient, safe, and low-latency test.

[0033] The following section will provide a detailed explanation of the specific testing scheme provided in this application, in conjunction with the aforementioned testing system for the vehicle's three electrical systems.

[0034] This invention provides a method for testing the three-electric system of a vehicle. Figure 2 This is a flowchart illustrating a testing method for a vehicle's three-electric system (battery, motor, and electronic control system) according to an embodiment of the present invention. The testing method for the vehicle's three-electric system provided in this application is applied to the aforementioned overall testing system for the vehicle's three-electric system, such as... Figure 2 As shown, the testing method for the vehicle's three-electric system may include the following steps: Step S101: In response to the static test request issued by the vehicle at the test site, receive the static test data of the vehicle collected by the vehicle-side system.

[0035] Specifically, a static test request can be a request or notification used to instruct the cloud-based intelligent analysis system to prepare for processing the vehicle's static test data. It can be a test start command issued by the operator at the static test station via the vehicle-side EOL APP. Correspondingly, after the vehicle-side system detects the static test start command, it can start the static test. In addition, after detecting the test start command, the vehicle-side system can send a static test request to the cloud-based intelligent analysis system to inform it of the static test that has started. Moreover, after the static test starts, the vehicle-side system can upload test data, and the cloud-based intelligent analysis system can then receive the collected static test data for subsequent data processing.

[0036] Static test data describes the data of various controllers, battery system, motor system, and electronic control system when the vehicle is stationary. Static test data includes at least the fault codes reported by each controller (VCU (Vehicle Control Unit), BMS (Battery Management System), and MCU (Motor Control Unit)), static parameters of the battery system (cell voltage, insulation resistance, total voltage, etc.), static parameters of the motor controller (resolver initial angle, temperature, etc.), and software version number and checksum of the electronic control system.

[0037] Specifically, at the static testing station, the operator installs the EOL APP onto the vehicle's central control system using electrical testing equipment, displaying it on the vehicle's central control screen. The central control system automatically connects to the test site's WiFi network. The operator connects the data acquisition device to the vehicle's OBD interface; after powering on, the data acquisition device automatically connects to the test site's WiFi network. The operator selects "Static Test" and static test items (such as VCU self-test, BMS data reading, MCU parameter verification, etc.) in the EOL APP and clicks "Start." At this point, the EOL APP generates a static test request based on the operator's actions, including the specific test items. The EOL APP sends the static test request to the cloud-based intelligent analysis system via the WiFi network and sends test commands to relevant controllers, battery systems, motor systems, and electronic control systems via the vehicle network to perform the static test. The test commands describe the operator's actions on the EOL APP. The data acquisition instrument collects the response messages from each controller, battery system, motor system, and electronic control system after the test command is issued, and uploads the response messages as static test data to the cloud intelligent analysis system via the WiFi network of the test site.

[0038] For example, at the static testing station, the operator clicks the "Static Test" button on the EOL APP on the vehicle's central control screen and selects the "BMS Data Reading" item. After clicking "Start," the EOL APP sends a static test request to the cloud-based intelligent analysis system via WiFi. Upon receiving the static test request, the cloud-based intelligent analysis system begins listening to the response messages collected by the data acquisition unit. Simultaneously, the data acquisition unit sends diagnostic commands to the BMS via the OBD-II interface. The BMS responds and reports the following data: total battery pack voltage 396V, insulation resistance 1020 kΩ, individual cell voltage range 18 mV, highest individual cell temperature 28.5℃, and no fault codes. The data acquisition unit then uploads the data responded and reported by the BMS to the cloud-based intelligent analysis system in real time via the WiFi network.

[0039] Step S102: In response to the dynamic test request issued by the vehicle in the test site, receive the dynamic test data collected by the vehicle-side system during the vehicle test driving process.

[0040] A dynamic test request can be a request or notification used to instruct the cloud-based intelligent analysis system to prepare for processing the vehicle's dynamic test data. It is a dynamic test start command issued by the operator on the EOL APP after the vehicle has completed static testing and entered the dynamic test area. Correspondingly, after the vehicle-side system detects the dynamic test start command, it can start the dynamic test. In addition, after detecting the dynamic test start command, the vehicle-side system can send a dynamic test request to the cloud-based intelligent analysis system to inform it of the static test that has started. After the dynamic test starts, the vehicle-side system can upload test data, and the cloud-based intelligent analysis system can then receive the collected dynamic test data for subsequent data processing.

[0041] Dynamic test data describes the data from various controllers, battery systems, motor systems, and electronic control systems during vehicle operation. Dynamic test data consists of massive amounts of message data continuously collected and uploaded in real time by a data acquisition device. This data can include time-series data: various signals continuously reported with millisecond-level timestamps, such as vehicle speed, accelerator pedal opening, actual motor torque, battery current, and individual cell voltage. Location information identifies the corresponding road profile (uneven road, tortuous road, ABS road, highway, wading area, etc.) for the dynamic test data.

[0042] Specifically, after the static test is completed, the operator drives the vehicle into the dynamic test area. The operator selects "Dynamic Test" in the EOL APP and clicks "Start." The driver traverses each dynamic roadmap in a preset order. At this time, the EOL APP generates a dynamic test request based on the operator's actions, including the specific test items. The EOL APP sends the dynamic test request to the cloud-based intelligent analysis system via WiFi and sends test commands to the relevant controllers, battery system, motor system, and electronic control system via the vehicle network to conduct the dynamic test. The data acquisition unit collects the response messages from each controller, battery system, motor system, and electronic control system after the test commands are issued and uploads these response messages as static test data to the cloud-based intelligent analysis system via the test site's WiFi network.

[0043] For example, after the static test is completed, the operator clicks the "Dynamic Test" button on the EOL APP. After clicking "Start," the EOL APP sends a dynamic test request to the cloud-based intelligent analysis system via WiFi. Simultaneously, the operator drives the vehicle into the dynamic test area, following instructions to navigate through uneven roads, ABS sections, and a water crossing. Throughout the entire driving process, the data acquisition device continuously collects and uploads dynamic test data at a certain frequency (e.g., 100Hz). The cloud-based intelligent analysis system receives the dynamic test data in real time.

[0044] Step S103: By analyzing the static test data, the static test results of the vehicle are obtained.

[0045] Static test results describe whether the various controllers, battery system, motor system, and electronic control system are operating normally when the vehicle is stationary. Specifically, after receiving the static test data, the cloud-based intelligent analysis system analyzes the data to determine whether the functions of each controller, battery system, motor system, and electronic control system are normal, and whether performance parameters (such as battery cell voltage consistency and insulation resistance value) are within the calibration range, thus obtaining the vehicle's static test results. The calibration range is a pre-set threshold for the performance parameters. If the functions of each controller, battery system, motor system, and electronic control system are normal, and the performance parameters are within the calibration range, the static test result is qualified; if there is a malfunction in any controller, battery system, motor system, or electronic control system, or if the performance parameters are outside the calibration range, the static test result is unqualified.

[0046] For example, if fault codes are present in the response messages of each controller, battery system, motor system, and electronic control system, it indicates that the controller is malfunctioning, and the static test result is abnormal. Performance parameters in the battery system include insulation resistance and single-cell voltage range. The calibration range is insulation resistance > 500 kΩ and single-cell voltage range < 50 mV. If the insulation resistance value in the static test data is greater than 500 kΩ, it indicates that the performance parameter is within the calibration range; if the insulation resistance value in the static test data is less than or equal to 500 kΩ, it indicates that the performance parameter is outside the calibration range. Similarly, if the single-cell voltage range in the static test data is less than 50 mV, it indicates that the performance parameter is within the calibration range; if the single-cell voltage range in the static test data is greater than or equal to 50 mV, it indicates that the performance parameter is outside the calibration range.

[0047] Step S104: Analyze the driving process of the vehicle using the static test data and the dynamic test data to obtain the dynamic test results of the vehicle.

[0048] When the cloud intelligent analysis system analyzes dynamic test data, static test data is introduced as a reference. Specifically, the cloud intelligent analysis platform can use the static test data obtained in the static test stage as benchmark parameters to establish a "digital physical examination file" for the vehicle. In the dynamic test stage, the obtained dynamic test data is aligned with the static test data in terms of time and space, that is, the dynamic test data and the static test data are associated through a unified time stamp sequence and the unique vehicle identifier (such as the VIN code). In the cloud intelligent analysis platform, a dynamic test analysis model can be established to analyze the dynamic test data and determine whether the functions of each controller, battery system, motor system or electronic control system of the vehicle are normal during driving. The static test data will be used as a parameter in the dynamic test analysis model and participate in the analysis process of the dynamic test data. Among them, the dynamic test analysis model can be a pre-trained intelligent analysis algorithm.

[0049] In an implementable manner, after obtaining the static test data, the cloud intelligent analysis system can analyze the static test data of the vehicle to obtain the static test result of the vehicle. If the static test result is unqualified, the test is directly ended and the dynamic test is no longer carried out. After the test is ended, the vehicle will drive into the maintenance area for maintenance. In this way, the detection time of the vehicle's three-electric system can be saved and the data processing volume of the cloud intelligent analysis system can be reduced.

[0050] Furthermore, when both the static test result and the dynamic test result are qualified, the vehicle will drive into the unloading station to complete the entire test process. The static test result and the dynamic test result can be presented in the form of a test report and sent to the EOL APP on the vehicle's central control through the test site WiFi network for display, and at the same time displayed on the display screen of the unloading station. The operator unloads the EOL APP accordingly.

[0051] In another implementable manner, during the process of receiving the dynamic test data, the cloud intelligent analysis system analyzes the dynamic test data in real time. Exemplarily, after the vehicle drives out of the bumpy road and before entering the ABS section, the analysis of the dynamic test data of the bumpy road is completed to obtain the dynamic test result of the vehicle on the bumpy road. The cloud intelligent analysis system can also analyze the dynamic test data of each road spectrum in turn after receiving all the dynamic test data of the road spectrum to obtain the dynamic test result.

[0052] In another implementable manner, in the cloud intelligent analysis system, the dynamic test data and the static test data can be associated through a unified time stamp sequence and the unique vehicle identifier (such as the VIN code) to ensure that the static test data and the dynamic test data are uploaded by the data collector of the same vehicle.

[0053] The vehicle three-electric system testing method provided in this invention, by setting up a cloud-based intelligent analysis system, achieves automatic analysis of dynamic and static test data, eliminating interference from human factors and ensuring consistency of test conditions for different vehicles and drivers. All evaluations are based on a unified data and algorithm model, improving the objectivity and accuracy of the evaluation results. Simultaneously, the cloud-based intelligent analysis system incorporates static test data during the analysis of dynamic test data, achieving fusion analysis of dynamic and static test data and improving the accuracy of determining dynamic test results. By establishing a wireless network communication system, wireless communication between the cloud-based intelligent analysis system and the vehicle-side system is achieved, solving the problems of data transmission relying on the vehicle's own mobile network or specific wired connections, which are limited by signal coverage and operator packages in existing technologies, thus improving the stability of data transmission between the cloud and the vehicle.

[0054] This invention also provides another method for testing a vehicle's three-electric system; this method is implemented based on the method described in the above embodiments; the method focuses on describing the specific implementation of analyzing the vehicle's driving process through the static test data and the dynamic test data to obtain the vehicle's dynamic test results.

[0055] Figure 3 A flowchart of another detection method for a vehicle's three-electric system provided in an embodiment of the present invention is shown below. Figure 3 As shown, the testing method for the vehicle's three-electric system may include the following steps: Step S201: In response to the static test request issued by the vehicle at the test site, receive the static test data of the vehicle collected by the vehicle-side system.

[0056] Step S202: In response to the dynamic test request issued by the vehicle in the test site, receive the dynamic test data collected by the vehicle-side system during the vehicle test driving process.

[0057] Step S203: By analyzing the static test data, the static test results of the vehicle are obtained.

[0058] Step S204: Determine multiple dynamically collected data corresponding to the first test item from the dynamic test data.

[0059] The first test item describes the content to be tested during dynamic testing and is a specific detection item in the dynamic test. Test items can be pre-set according to testing needs. For example, the first test item could be insulation performance on a flooded road section, performance under high-speed rapid acceleration conditions, or vibration conditions on uneven roads. Dynamic acquisition data refers to the message data uploaded in real time by the data acquisition device when the vehicle passes through the road spectrum corresponding to the first test item. Different first test items correspond to different dynamic acquisition data. For example, when the first test item is insulation performance on a flooded road section, it is necessary to analyze the trend of insulation resistance changes reported by the BMS, and the corresponding dynamic acquisition data is insulation resistance; when the first test item is performance under high-speed rapid acceleration conditions, it is necessary to analyze the torque response of the MCU and the power output of the BMS under rapid acceleration and deceleration conditions, and the corresponding dynamic acquisition data is the battery pack individual cell voltage and the motor torque; when the first test item is vibration conditions on uneven roads, it is necessary to analyze the monitoring data of the BMS on battery box vibration and battery connector status, and the corresponding dynamic acquisition data is the vibration acceleration of the battery pack individual cells and the BMS fault codes.

[0060] The first test item corresponds to multiple dynamic data acquisitions, which describe the message data related to the test item that the data acquisition device collects and uploads in real time as the vehicle passes through the corresponding road spectrum.

[0061] Specifically, the cloud-based intelligent analysis system, based on the road spectrum recognition results or the identifier of the first test item reported by the EOL APP, filters out time segments and data signals related to the continuously received dynamic test data stream. The cloud-based intelligent analysis system can determine the vehicle's current road spectrum location based on its GPS positioning and use this as the road spectrum recognition result.

[0062] For example, the vehicle sequentially traverses three road segments in the dynamic test area (uneven road, ABS section, and wading area). The cloud-based intelligent analysis system continuously receives the raw message stream uploaded by the data acquisition device. The raw message stream is marked with the acquisition time. During acquisition time t=0-30s, the vehicle traverses the uneven road, and the GPS location is displayed in the uneven road area; during t=31-60s, the vehicle traverses the ABS section, and the GPS location is displayed in the ABS section area; during t=61-90s, the vehicle traverses the wading area, and the GPS location is displayed in the wading area area. When the first test item is the insulation performance test of the wading section, the cloud-based intelligent analysis system identifies the raw message data in the time period t=61-90s as data related to the wading section, i.e., the dynamic test data corresponding to the first test item. The insulation resistance value can be identified from this dynamic test data.

[0063] Step S205: Match the benchmark reference data corresponding to the first test item from the static test data.

[0064] Reference data refers to reference values ​​extracted from static test data and used for analyzing dynamic test data. It can be understood that reference data can be a portion of the static test data obtained during the static testing phase, or it can be the result of processing the static test data. Different first test items require different static references. The cloud-based intelligent analysis system queries the corresponding reference data from the vehicle's static test data based on the first test item. For example, when the first test item is the insulation performance test on a flooded road section, the cloud-based intelligent analysis system queries the insulation resistance from the static test data and uses it as the reference data for the first test item; when the first test item is the high-speed rapid acceleration performance test, the cloud-based intelligent analysis system queries the battery pack cell voltage and motor torque from the static test data and uses them as the reference data for the first test item; when the first test item is the vibration condition on an uneven road, the cloud-based intelligent analysis system queries the vibration acceleration of the battery pack from the static test data and uses it as the reference data for the first test item.

[0065] Step S206: Determine the dynamic test result of the vehicle based on the multiple dynamically acquired data and the benchmark reference data.

[0066] The cloud-based intelligent analysis system compares, correlates, and fuses dynamically collected data with benchmark reference data to derive the analysis results for the first test item, which serve as the dynamic test results. It is understood that the cloud-based intelligent analysis system can employ different analysis models for different first test items. For example, the analysis models may include: a trend deviation model to calculate the rate of change and trend of change of the dynamically collected data relative to the benchmark reference data; a consistency verification model to verify the consistency between the dynamically collected data and the benchmark reference data; and a prediction model to predict trends by combining the dynamically collected data and the benchmark reference data.

[0067] For example, the first test item is the insulation performance test of the flooded section. When the vehicle passes through the flooded area, the dynamic test data uploaded by the data acquisition device is selected as the insulation resistance value sequence R_real(t). The benchmark reference data is the insulation resistance R_base = 1020 kΩ selected from the static test data. For the first test item, the cloud-based intelligent analysis system adopts a trend deviation model: calculates the rate of change relative to the benchmark at each moment: ΔR(t) = [R_base - R_real(t)] / R_base × 100%; calculates the variance of the rate of change to evaluate stability; and checks whether the absolute value exceeds the standard: R_real(t) > 500 kΩ. If ΔR(t) is always less than 5% (empirical threshold) and the curve is stable (the variance of the rate of change is less than the preset value), it is judged as "linkage pass", that is, the dynamic test result is qualified. This shows that the dynamic harsh working conditions did not have a substantial impact on the insulation system, and the static test results were dynamically verified. The first test item is the motor torque response test on the ABS section. When the vehicle passes through the ABS section, the dynamic data uploaded by the data acquisition device includes the accelerator pedal position, motor requested torque, actual motor torque, and motor speed. The benchmark reference data consists of the accelerator pedal position, motor torque, and motor speed selected from the static test data. For the first test item, the cloud-based intelligent analysis system uses a consistency verification model and a prediction model: it detects the time of the accelerator pedal change and calculates the response delay. The accelerator pedal position from the benchmark reference data is input into the prediction model to obtain the predicted response curve. The accelerator pedal position acquired from the dynamic data is fitted to a curve and compared with the predicted response curve to calculate the matching degree. If the response delay is less than a preset delay threshold and the matching degree is greater than the matching threshold, it is judged as "linkage passed," meaning the dynamic test result is qualified, indicating that the motor's dynamic response performance meets the design requirements and is consistent with the static calibration.

[0068] In another example, a dedicated analysis model can be designed for each of the first test items. Specifically, when the vehicle enters the wading area of ​​the dynamic test zone, the first test item is the insulation performance of the wading section. Correspondingly, the cloud-based intelligent analysis system activates the "insulation monitoring model." Insulation resistance is obtained from the static test data as a benchmark reference and used as a parameter in the insulation monitoring model. Simultaneously, for the dynamic test data, it is determined whether each value in the insulation resistance value sequence exceeds a safety threshold, and the relative rate of change of each value in the insulation resistance value sequence relative to the benchmark reference data is calculated. If, throughout the entire wading area, each value in the insulation resistance value sequence is less than the safety threshold, and the relative rate of change is consistently less than the empirical threshold, then it is judged as "passed." This indicates that the dynamic adverse conditions did not have a substantial impact on the insulation system, and the static test results were dynamically verified.

[0069] The vehicle three-electric system testing method provided in this invention determines multiple dynamically acquired data corresponding to the first test item from the dynamic test data, realizes the division of dynamic test data by item, prepares an independent analysis dataset for each test item, avoids the mixing of data from different road spectra and different test items, and establishes a data link between static and dynamic data by matching the benchmark reference data corresponding to the first test item from the static test data, providing a static anchor point for the analysis of dynamic test results, enabling the analysis of dynamic test results to evaluate changes relative to the static benchmark, realizing in-depth detection combining dynamic and static data, thereby improving the accuracy of dynamic test results.

[0070] This invention also provides another method for testing a vehicle's three-electric system; this method is implemented based on the method described in the above embodiments; the method focuses on describing the specific implementation of analyzing the vehicle's driving process through the static test data and the dynamic test data to obtain the vehicle's dynamic test results.

[0071] Figure 4 A flowchart of another detection method for a vehicle's three-electric system provided in an embodiment of the present invention is shown below. Figure 4 As shown, the testing method for the vehicle's three-electric system may include the following steps: Step S301: In response to the static test request issued by the vehicle at the test site, receive the static test data of the vehicle collected by the vehicle-side system.

[0072] Step S302: In response to the dynamic test request issued by the vehicle in the test site, receive the dynamic test data collected by the vehicle-side system during the vehicle test driving process.

[0073] Step S303: By analyzing the static test data, the static test results of the vehicle are obtained.

[0074] Step S304: Determine the road spectrum type of the road segment traveled by the vehicle based on the vehicle's positioning data.

[0075] Location data is used to describe the vehicle's location. In this embodiment of the invention, the location data can be any of the following: GPS location data, UWB location data, road spectrum entry trigger signal, timing logic, etc. Specifically, GPS location data consists of the latitude and longitude coordinates uploaded by the vehicle via a WiFi network, matched with a preset electronic map. UWB (Ultra-Wideband) location data consists of UWB location tags deployed at each road spectrum starting point, with the vehicle equipped with a UWB receiver, and the location tag identification results uploaded by the UWB receiver. The road spectrum entry trigger signal is a signal triggered by sensors (geomagnetic, RFID, etc.) installed at the road spectrum entry point. The timing logic uses a preset time window based on the test sequence (e.g., "30-60 seconds after entering the dynamic test area is the ABS section"). The road spectrum type refers to the functional classification of different characteristic road segments in the dynamic test area. For example, road types may include uneven roads (simulating bumpy road surfaces), twisted roads (simulating vehicle body twisting conditions), ABS road sections (simulating emergency braking conditions), wading areas (simulating flooded road surfaces), high-speed ring roads (simulating high-speed driving conditions), and uphill sections (simulating heavy load conditions), etc.

[0076] Specifically, based on the vehicle's location data, the road spectrum type of the route the vehicle traveled can be queried.

[0077] Step S305: Based on the road spectrum type, determine at least one first test item that needs to be tested for the driving section.

[0078] Each road spectrum type corresponds to one or more items that need to be tested; that is, one road spectrum type corresponds to one or more first test items. This mapping relationship is preset in the cloud-based intelligent analysis system. Specifically, the cloud-based intelligent analysis system queries the preset "road spectrum-test item mapping table" based on the identified road spectrum type to obtain all the first test items that need to be performed for that road segment type.

[0079] Step S306: Select multiple dynamic data points collected at different data collection times on the driving section under the first test item from the dynamic test data.

[0080] Dynamically acquired data refers to time-series data points collected at different times on a specific road segment, related to the first test item, forming a time-series dataset. Specifically, when a vehicle is tested in the dynamic test area, its location data needs to be acquired in real time. Therefore, based on the vehicle's location data, the time period during which the vehicle travels on a road segment of a certain road spectrum type can be determined. Based on this time period, dynamic test data within that time period is extracted from the dynamic test data. For each first test item, the corresponding dynamic test data is selected from the extracted dynamic test data and used as the dynamically acquired data.

[0081] Step S307: From the static test data, determine each second test item to be tested for the vehicle and the static acquisition data of the second test item.

[0082] The second test item refers to the specific testing items performed during the static testing phase. Each second test item corresponds to a specific test objective and data set. The second test items can be set according to testing requirements. For example, the second test items may include BMS insulation resistance testing, battery cell voltage consistency testing, BMS temperature sensor calibration, MCU resolver initial angle testing, and VCU software version verification. Static data acquisition refers to the data collected during the execution of the second test item in the static test data. For example, when the second test item is BMS insulation resistance testing, the static data acquisition is static insulation resistance; when the second test item is battery cell voltage consistency testing, the static data acquisition is the battery cell voltage range; when the second test item is BMS temperature sensor calibration, the static data acquisition is the maximum temperature difference; when the second test item is MCU resolver initial angle testing, the static data acquisition is the initial angle; and when the second test item is battery pack installation torque verification, the static data acquisition is the installation quality level.

[0083] Step S308: Determine the second test item that belongs to the same project as the first test item from each of the second test items, and use the static collection data of the determined second test item as the benchmark reference data of the first test item.

[0084] Specifically, a mapping between the first and second test items can be pre-established on the cloud-based intelligent analysis system. This mapping is preset in the "Static / Dynamic Item Mapping Table" of the cloud-based intelligent analysis system. When the first test item needs to be analyzed, the cloud-based intelligent analysis system queries the static / dynamic item mapping table to find the second test item corresponding to the first test item. After finding the matching second test item, the parameters in the static data collected by the second test item are extracted as benchmark reference data. One first test item may correspond to multiple benchmark reference data, all of which need to be extracted.

[0085] Step S309: Determine the dynamic test result of the vehicle based on the multiple dynamically acquired data and the benchmark reference data.

[0086] The vehicle three-electric system detection method provided in this invention identifies the road spectrum type of the road segment the vehicle is traveling on by using positioning data. This enables the cloud-based intelligent analysis system to constantly determine the type of operating condition the vehicle is currently experiencing, providing a basis for subsequent data analysis. Based on the road spectrum type, the first test item required for the driving segment is determined. Different test items can be triggered according to different road spectra, ensuring that the performance of the three-electric system under each operating condition is evaluated specifically. By filtering the dynamically collected data under the first test item from the dynamic test data for subsequent analysis, an independent, complete, and relevant analysis dataset is prepared for each test item. This avoids data processing chaos when different test items are analyzed simultaneously, ensuring the accuracy of subsequent data analysis results.

[0087] This invention also provides another method for testing a vehicle's three-electric system; this method is implemented based on the method described in the above embodiments; the method focuses on describing the specific implementation of determining the dynamic test results of the vehicle based on the plurality of dynamically acquired data and the benchmark reference data.

[0088] Figure 5 A flowchart of another detection method for a vehicle's three-electric system provided in an embodiment of the present invention is shown below. Figure 5 As shown, the testing method for the vehicle's three-electric system may include the following steps: Step S401: In response to the static test request issued by the vehicle at the test site, receive the static test data of the vehicle collected by the vehicle-side system.

[0089] Step S402: In response to the dynamic test request issued by the vehicle in the test site, receive the dynamic test data collected by the vehicle-side system during the vehicle test driving process.

[0090] Step S403: By analyzing the static test data, the static test results of the vehicle are obtained.

[0091] Step S404: Determine multiple dynamically collected data corresponding to the first test item from the dynamic test data.

[0092] Step S405: Match the benchmark reference data corresponding to the first test item from the static test data.

[0093] Step S406: Obtain the preset security threshold for the first test item.

[0094] The preset safety threshold refers to the boundary value set for the dynamically collected data that needs to be analyzed in the first test item. The cloud-based intelligent analysis system allows setting preset safety thresholds for the first test item in advance.

[0095] Step S407: Compare each of the dynamically collected data with the preset safety threshold to obtain a first test result on whether the first test item meets the static state.

[0096] The first test result is used to describe whether the first test item meets the static state. Compare each dynamically collected data with the preset safety threshold to determine whether the dynamically collected data is within the preset safety threshold, and obtain the comparison result of each dynamically collected data. When all the comparison results indicate that the dynamically collected data is within the preset safety threshold, it is determined that the first test item meets the static state, that is, the first test result is qualified. When there is dynamically collected data outside the preset safety threshold, it is determined that the first test item does not meet the static state, that is, the first test result is unqualified.

[0097] Exemplarily, the first test item is the dynamic test of the insulation performance of the water-crossing section. The dynamically collected data is the insulation resistance uploaded in real time during the vehicle's driving in the water-crossing area, and the preset safety threshold is the insulation resistance > 500 kΩ. If each dynamically collected data is greater than 500 kΩ, it is determined that the first test item meets the static state. The first test item is the motor torque response test on the ABS section. The dynamically collected data is the response delay uploaded in real time during the vehicle's driving on the ABS section, and the preset safety threshold is the response delay < 120 ms. If each dynamically collected data is less than 120 ms, it is determined that the first test item meets the static state.

[0098] Step S408: For each of the dynamically collected data, determine the change rate of the dynamically collected data relative to the reference reference data to obtain a second test result on whether the first test item meets the dynamic stability.

[0099] Specifically, for each of the dynamically collected data, calculate the difference between the collected value corresponding to the dynamically collected data and the reference value corresponding to the reference reference data; take the absolute value of the ratio of the difference to the reference value as the change rate corresponding to the dynamically collected data; if the change rate of each of the dynamically collected data is less than the preset change threshold, it is determined that the first test item meets the dynamic stability of the vehicle, where the second test result includes a linkage test pass result indicating that the dynamic stability of the vehicle is met.

[0100] The collected value refers to the numerical value of dynamically collected data, and the reference value refers to the numerical value of benchmark reference data. For each dynamically collected data point, the rate of change of the dynamically collected data relative to the benchmark reference data can be calculated using the following formula: Rate of change = |(Reference value - Collected value)| / Reference value. The rate of change is compared with a preset change threshold. If the rate of change is less than or equal to the preset change threshold, it indicates that the dynamically collected data meets dynamic stability. If the rate of change is greater than the preset change threshold, it indicates that the dynamically collected data does not meet dynamic stability. Only when all dynamically collected data meet dynamic stability can the first test item be determined to meet dynamic stability, i.e., the first test result is qualified. If any dynamically collected data does not meet dynamic stability, the first test item is determined to not meet dynamic stability, i.e., the first test result is unqualified.

[0101] In another possible implementation, a state fitting curve for the first test item is obtained by fitting the multiple dynamically acquired data; the step of determining that the first test item meets the dynamic stability of the vehicle if the rate of change of each of the dynamically acquired data is less than a preset change threshold includes: if the rate of change of each of the dynamically acquired data is less than a preset change threshold, and the curvature of each point on the state fitting curve is less than a preset curvature, then the first test item meets the dynamic stability of the vehicle.

[0102] Curvature describes the smoothness of a curve. A larger curvature indicates a steeper curve and more dramatic changes, while a smaller curvature indicates a smoother curve and more stable changes. For a state-fitted curve, the curvature at each point on the fitted curve can be calculated using existing curvature calculation formulas. A preset curvature is a pre-set threshold for judging curvature; if the curvature is greater than or equal to the preset curvature, the first test item does not meet the vehicle's dynamic stability requirements.

[0103] Specifically, assuming that the rate of change of each dynamically acquired data point is less than a preset threshold, the curvature of each point on the state fitting curve is further calculated. If the curvature of each point is less than the preset curvature, the first test item is deemed to meet the vehicle's dynamic stability. If there is a point where the curvature is greater than or equal to the preset curvature, the first test item is deemed not to meet the vehicle's dynamic stability.

[0104] By introducing curvature analysis, it is possible to effectively identify situations where the rate of change at each point is small, but the overall trend exhibits drastic fluctuations or abnormal trends. These situations may be caused by sensor problems, poor contact, or system oscillations. Such anomalies cannot be detected by traditional rate of change analysis, but can be identified by curvature analysis. Through dual stability determination of rate of change and curvature, a more refined and scientific assessment of the dynamic performance of the three-electric system is achieved, making the stability determination more comprehensive and accurate.

[0105] The state fitting curve is used to describe the overall change trend of dynamically collected data over time. Specifically, the discrete dynamically collected data is converted into a continuous and smooth curve to obtain the state fitting curve. The state fitting curve can be obtained by means of polynomial fitting, interpolation fitting, exponential fitting, logarithmic fitting, etc.

[0106] Step S409, obtain the dynamic test result of the vehicle according to the fusion of the first test result and the second test result.

[0107] Specifically, when the first test result is qualified and the second test result is qualified, the dynamic test result of the vehicle is qualified. If at least one of the first test result or the second test result is unqualified, the dynamic test result of the vehicle is unqualified.

[0108] In the detection method of the vehicle's three-electric system provided by the embodiments of the present invention, the first test result of the static state and the second test result of the dynamic stability are introduced in the process of determining the vehicle's dynamic test result. Only after the two are fused can the dynamic test result be obtained, realizing a deep evaluation of the integration of static and dynamic, that is, ensuring basic safety and being able to identify the trend of performance degradation. When the first test result of the static state is qualified, it is further found that there is a degradation trend within the safety boundary of the three-electric system, that is, it is indicated that there may be hidden dangers that are worthy of attention although they do not exceed the standard in the three-electric system, thus triggering a more in-depth investigation. This is exactly the manifestation of the deep guiding role of static test data on dynamic test, which can greatly improve the accuracy of the dynamic test result.

[0109] In another feasible implementation manner, the wireless network communication system includes multiple local network access points for wireless communication connection between the cloud intelligent analysis system and the vehicle terminal system, and the multiple local network access points operate on the same channel. The vehicle terminal system can dynamically determine the working state information of each of the local network access points, determine whether each of the local network access points is sufficient to meet the network connection requirements and data transmission requirements through the corresponding working state information, and determine the connection method for the cloud intelligent analysis system to connect with the vehicle terminal system.

[0110] Specifically, the data acquisition unit in the vehicle-mounted system has a built-in network management module that monitors the WiFi network quality in real time. Specifically, it determines whether the WiFi network quality meets network switching requirements by using signal strength index (RSSI), link packet loss rate, and round-trip time (RTT). For example, if the signal strength remains below a preset threshold (e.g., -75dBm) for more than 3 seconds, the link packet loss rate exceeds 5% within a 1-second time window, or the round-trip time consistently exceeds the service-tolerable threshold (e.g., 200ms), it can be determined that the WiFi network quality is poor and meets the network switching requirements, necessitating a network switch. At this time, the network management module will generate a network switching request, instructing the data acquisition unit to switch the network connection method. The network switching request will then be sent to the cloud-based intelligent analysis system, instructing it to switch the network connection method and receive static and / or dynamic test data through the new network connection method.

[0111] Furthermore, the cloud-based intelligent analysis system can also determine the real-time operating status of each local area network (LAN) access point. Based on this status information, it determines whether each LAN access point is sufficient to meet network connectivity and data transmission requirements, and determines the connection method between the cloud-based intelligent analysis system and the vehicle-side system. Specifically, the cloud-based intelligent analysis system can intelligently analyze each LAN access point using preset AI algorithms, automatically diagnosing over 95% of common faults and achieving closed-loop network optimization, reducing maintenance costs and improving network availability. When the cloud-based intelligent analysis system detects a LAN access point fault or receives maintenance information from a LAN access point, it sends a network switching command to the vehicle-side system, instructing it to switch the network connection method from WiFi to a 5G link connection.

[0112] In this embodiment, during the test in the dynamic testing area, the vehicle is in a driving state, meaning it moves between different local area network (LAN) access points. Normally, the data acquisition device on the vehicle selects the nearest LAN access point for network connection and data transmission. Therefore, as the vehicle moves, the data acquisition device continuously changes LAN access points for network connection. During this process, the data acquisition device needs to undergo a "disconnect-reconnect" channel switching process, which inevitably causes data transmission delays and packet loss. This embodiment of the invention uses a technique that virtualizes multiple physical LAN access points into a single logical "large AP," enabling all LAN access points to operate on the same channel. The vehicle terminal does not need to switch channels during movement, thus achieving a "zero roaming" experience and fundamentally avoiding service interruptions caused by switching, ensuring continuous data transmission. In the dynamic testing area, especially on highways, enterprise-grade / industrial-grade LAN access points supporting high-speed mobility enhancement algorithms are selected. For example, through algorithm optimization, a backhaul bandwidth of 1Gbps can be achieved at a speed of 260 km / h, with a switching latency of less than 3 milliseconds. These types of access points (APs) are typically equipped with high-gain directional antennas, which can effectively extend the coverage distance and optimize signal stability in high-speed scenarios. In the dynamic test area, APs are deployed in overlapping coverage along each road spectrum, especially in areas where signals are prone to attenuation, such as curves and hilltops, to ensure no dead zones in the signal coverage.

[0113] Therefore, during the process of receiving static test data and / or dynamic test data, a network switching request sent by the vehicle-mounted system is received. In response to the network switching request, the network connection with the vehicle-mounted system is switched from the current connection mode to the target connection mode.

[0114] The current connection method refers to the network connection method currently used between the vehicle-side system and the cloud-based intelligent analysis system. The target connection method refers to the network connection method to be used after switching.

[0115] For example, during normal operation, the data acquisition device's 5G module maintains a low-power connection (inactive data transmission) with the 5G private network at the test site, ensuring minimal switching latency. Upon receiving a network switching request, the data acquisition device, through its network management module, quickly synchronizes key context information such as the vehicle's VIN code, test session ID, and data sequence number to the cloud-based intelligent analysis system via the 5G link, ensuring seamless data flow integration. The data acquisition device has a built-in circular data buffer (capable of caching 10 seconds of data). At the moment of network connection switching, continuously collected data is temporarily stored in the buffer; once the 5G link is confirmed to be operational, the buffered data is transmitted first, followed by the real-time data stream, ensuring zero data loss. Network connection switching only applies to test message data streams (static and dynamic test data) that require real-time reporting. For latency-insensitive services such as EOL APP interaction commands, switching can be temporarily suspended or different threshold strategies can be employed.

[0116] Understandably, after switching to 5G for data transmission, the network management module will continuously monitor the network quality of the WiFi network. If the WiFi network quality does not meet the network switching requirements, a network switching request will be generated, instructing the data acquisition device to switch the network connection method. This request will then be sent to the cloud-based intelligent analysis system, instructing it to switch the network connection method and receive static and / or dynamic test data through the new connection method. At this point, the current connection method is the 5G link, and the target connection method is the WiFi network.

[0117] In another possible implementation, for the analysis of dynamic test results, the cloud-based intelligent analysis system can "sink" some computing tasks to the network edge closer to the data source (vehicle), thereby achieving millisecond-level analysis response and a more robust system architecture. Specifically, an edge computing (MEC) server is deployed in the overall testing system of the vehicle's three-electric system. While sending static and dynamic test data to the MEC server, the data acquisition device also sends a complete copy to the cloud-based intelligent analysis system via WiFi for long-term storage, in-depth analysis, and global analysis. After processing the static and dynamic test data, the MEC server uploads the generated lightweight real-time results (such as "pass / fail" Boolean values, key indicators) and high-dimensional feature data to the cloud-based intelligent analysis system via a high-speed internal network (WiFi network). Examples of suitable algorithm types for MEC server processing include motor torque response analysis algorithms, battery peak power and temperature rise early warning algorithms, vibration signal feature analysis algorithms (for uneven roads), and rapid diagnostic algorithms for electronic control system coordination. The motor torque response analysis algorithm is used to calculate the delay, overshoot, and steady-state error from the change in accelerator pedal opening to the actual torque output. It requires processing of accelerator pedal position, requested motor torque, actual motor torque, and motor speed. This reduces the analysis delay from 100-500ms (a round trip from the cloud) to 10-50ms, enabling real-time and accurate assessment of road conditions such as ABS. The motor torque response analysis algorithm is used to estimate the available charge / discharge power at the current SOC and temperature in real time, and monitor the temperature rise rate. It requires processing of total battery voltage, total current, individual cell temperature, and SOC mechanical energy. Under conditions such as rapid acceleration / deceleration and continuous uphill climbing, it can identify power exceeding limits or abnormal temperature rise risks in milliseconds, providing timely alarms or interventions to ensure test safety. The vibration signal feature analysis algorithm is used to calculate the RMS and FFT spectral characteristics of vibration acceleration in components such as the battery pack and motor controller in real time. It needs to process triaxial high-frequency acceleration data from the BMS or additional vibration sensors. It can extract lightweight feature values ​​from massive amounts of raw vibration waveforms (>1kHz sampling) at the source, saving more than 90% of uplink bandwidth and enabling real-time judgment of road spectrum passage. The electronic control system coordination rapid diagnostic algorithm is used to verify whether the command coordination and state switching of VCU, MCU, and BMS are timely and conflict-free under specific road spectrum (such as coasting recovery). It needs to process VCU operating mode, MCU torque status, BMS charging and discharging status, and related fault codes. It can complete the verification of complex state logic between multiple controllers in real time at the edge and immediately feed back the "coordination anomaly" result to guide the driver or trigger subsequent test process adjustments.

[0118] The cloud-based intelligent analysis system receives and stores lightweight real-time results and high-dimensional feature data from the MEC server, while also storing complete raw data (static and dynamic test data). This association can be achieved using the vehicle's VIN code. After receiving the lightweight real-time results and high-dimensional feature data, the cloud-based intelligent analysis system performs subsequent processing to obtain dynamic and static test results. Simultaneously, the system can utilize the raw data to perform secondary verification of the lightweight real-time results and high-dimensional feature data, or more complex offline analysis (such as machine learning model training, batch vehicle quality statistics, and performance degradation trend analysis). Finally, when generating the "EOL Test Report for the Three-Electric System," the cloud-based intelligent analysis system integrates the lightweight real-time results from the MEC server with its own in-depth analysis conclusions. For example, the report will explicitly state: "ABS section motor torque response delay: 95ms (real-time analysis by edge MEC), response characteristics matching the calibration model: 98% (offline in-depth analysis by the cloud)."

[0119] By introducing MEC servers, analysis tasks that affect real-time evaluation and security can be placed on MEC servers, which improves response speed by an order of magnitude. The initial feature extraction of massive amounts of raw data can also be completed on MEC servers, greatly reducing the data processing pressure and bandwidth consumption in the cloud. In addition, in the extreme case of a brief interruption of the cloud intelligent analysis system, MEC servers can still ensure the operation of core security monitoring and real-time diagnostic functions, and the testing process can be degraded and continued.

[0120] The vehicle three-electric system testing method provided in this invention detects network quality in real time during the transmission of static test data and dynamic test data. When the network quality is poor, it switches the network connection mode in a timely manner, ensuring the real-time performance of data transmission, realizing rapid response to network changes, improving data transmission efficiency, and providing a highly reliable, low-latency, and seamless wireless communication guarantee system for data transmission, thus providing a solid network foundation for real-time data upload.

[0121] Corresponding to the above method embodiments, this invention provides a testing device for a vehicle's three-electric system, applied to a cloud-based intelligent analysis system. This cloud-based intelligent analysis system can communicate with the vehicle-side system via a wireless network communication system set up in the testing area. Figure 6 This is a schematic diagram of the structure of a vehicle's three-electric system testing device provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the testing device for the vehicle's three-electric system may include: The static data receiving module 501 is used to receive static test data of the vehicle collected by the vehicle-end system in response to a static test request issued by the vehicle in the test site. The dynamic data receiving module 502 is used to respond to the dynamic test request issued by the vehicle in the test site and receive the dynamic test data collected by the vehicle-end system during the vehicle test driving process. The static test analysis module 503 is used to obtain the static test results of the vehicle by analyzing the static test data. The dynamic test analysis module 504 is used to analyze the driving process of the vehicle using the static test data and the dynamic test data to obtain the dynamic test results of the vehicle.

[0122] The vehicle three-electric system testing device provided in this invention, through the establishment of a cloud-based intelligent analysis system, achieves automatic analysis of dynamic and static test data, eliminating interference from human factors and ensuring consistency of test conditions for different vehicles and drivers. All evaluations are based on a unified data and algorithm model, improving the objectivity and accuracy of the evaluation results. Simultaneously, the cloud-based intelligent analysis system incorporates static test data during the analysis of dynamic test data, achieving fusion analysis of dynamic and static test data and improving the accuracy of determining dynamic test results. By establishing a wireless network communication system, wireless communication between the cloud-based intelligent analysis system and the vehicle-side system is achieved, solving the problems of data transmission relying on the vehicle's own mobile network or specific wired connections, which are limited by signal coverage and operator packages in existing technologies, thus improving the stability of data transmission between the cloud and the vehicle.

[0123] In some embodiments, the dynamic test analysis module 504 includes: A data filtering unit is used to determine multiple dynamically collected data corresponding to the first test item from the dynamic test data; A benchmark determination unit is used to match benchmark reference data corresponding to the first test item from the static test data; The dynamic test analysis unit is used to determine the dynamic test results of the vehicle based on the multiple dynamically acquired data and the benchmark reference data.

[0124] In some embodiments, the data filtering unit is further configured to: Based on the vehicle's location data, determine the road spectrum type of the road segment the vehicle traveled; Based on the road spectrum type, determine at least one first test item that needs to be tested for the driving section; From the dynamic test data, select multiple dynamic data collected at different data collection times on the driving section under the first test item.

[0125] In some embodiments, the reference determination unit is further configured to: From the static test data, each second test item for testing the vehicle and the static data collected for the second test item are determined; From each of the second test items, identify the second test item that belongs to the same project as the first test item, and use the static collection data of the identified second test item as the benchmark reference data of the first test item.

[0126] In some embodiments, the dynamic test analysis unit includes: The threshold acquisition subunit is used to acquire a preset security threshold for the first test item; The first test result determination subunit is used to compare each of the dynamically acquired data with the preset security threshold to obtain the first test result of whether the first test item meets the static state. The second test result determination subunit is used to determine the rate of change of each of the dynamically acquired data relative to the benchmark reference data, and to obtain a second test result on whether the first test item satisfies dynamic stability. The result fusion subunit is used to obtain the dynamic test results of the vehicle based on the fusion of the first test results and the second test results.

[0127] In some embodiments, the second test result determining the subunit is further used for: For each of the dynamically acquired data, calculate the difference between the acquired value corresponding to the dynamically acquired data and the reference value corresponding to the benchmark reference data; The absolute value of the ratio of the difference to the reference value is taken as the rate of change corresponding to the dynamically collected data; If the rate of change of each of the dynamically acquired data is less than a preset change threshold, the first test item is determined to meet the dynamic stability of the vehicle. The second test result includes the linkage test pass result indicating that the dynamic stability of the vehicle is met.

[0128] In some embodiments, the device further includes: The fitting module is used to obtain the state fitting curve of the first test item by performing data fitting on the multiple dynamically acquired data. The second test result determines the sub-unit and is also used for: If the rate of change of each of the dynamically acquired data is less than a preset change threshold, and the curvature of each point on the state fitting curve is less than a preset curvature, then the first test item is determined to meet the dynamic stability of the vehicle.

[0129] In some embodiments, the wireless network communication system includes multiple local area network access points for wireless communication between the cloud-based intelligent analysis system and the vehicle-mounted system, wherein the multiple local area network access points operate on the same channel, and the device further includes: The switching request receiving module is used to receive a network switching request sent by the vehicle-side system during the process of receiving the static test data and / or the dynamic test data; wherein, the vehicle-side system can dynamically determine the working status information of each of the local area network access points, determine whether each of the local area network access points is sufficient to meet the network connection requirements and data transmission requirements through the corresponding working status information, and determine the connection method for the cloud intelligent analysis system to connect with the vehicle-side system. The network switching module is used to switch the network connection with the vehicle system from the current connection mode to the target connection mode in response to the network switching request.

[0130] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0131] This invention also provides an electronic device for testing the aforementioned vehicle's three-electric system; see [link to related documentation]. Figure 7 The diagram shows the structure of an electronic device, which includes a memory 600 and a processor 601. The memory 600 stores one or more computer instructions, which are executed by the processor 601 to implement the aforementioned detection method for the vehicle's three-electric system.

[0132] Furthermore, Figure 7 The electronic device shown also includes a bus 602 and a communication interface 603, with the processor 601, communication interface 603 and memory 600 connected via the bus 602.

[0133] The memory 600 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 603 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 602 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0134] Processor 601 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 601 or by instructions in software form. Processor 601 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 600, and processor 601 reads information from memory 500 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0135] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the aforementioned detection method for the vehicle's three-electric system. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0136] The computer program product for testing the vehicle's three-electric system provided in this embodiment of the invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0137] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0141] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0142] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for testing a vehicle's three-electric system, characterized in that, The method, applied to a cloud-based intelligent analysis system capable of communicating with a vehicle-side system via a wireless network communication system set up in a test site, includes: In response to a static test request issued by the vehicle at the test site, the system receives static test data of the vehicle collected by the vehicle-side system. In response to the dynamic test request issued by the vehicle in the test site, it receives dynamic test data collected by the vehicle-side system during the vehicle test driving process. The static test results of the vehicle are obtained by analyzing the static test data. The vehicle's driving process is analyzed using the static and dynamic test data to obtain the vehicle's dynamic test results.

2. The method according to claim 1, characterized in that, The step of analyzing the vehicle's driving process using the static test data and the dynamic test data to obtain the vehicle's dynamic test results includes: Multiple dynamically collected data corresponding to the first test item are determined from the dynamic test data; Match the benchmark reference data corresponding to the first test item from the static test data; The dynamic test results of the vehicle are determined based on the multiple dynamically acquired data and the benchmark reference data.

3. The method according to claim 2, characterized in that, The step of determining multiple dynamically collected data corresponding to the first test item from the dynamic test data includes: Based on the vehicle's location data, determine the road spectrum type of the road segment the vehicle traveled; Based on the road spectrum type, determine at least one first test item that needs to be tested for the driving section; From the dynamic test data, select multiple dynamic data collected at different data collection times on the driving section under the first test item.

4. The method according to claim 2, characterized in that, The step of matching the benchmark reference data corresponding to the first test item from the static test data includes: From the static test data, each second test item for testing the vehicle and the static data collected for the second test item are determined; From each of the second test items, identify the second test item that belongs to the same project as the first test item, and use the static collection data of the identified second test item as the benchmark reference data of the first test item.

5. The method according to claim 2, characterized in that, Determining the dynamic test results of the vehicle based on the multiple dynamically acquired data and the benchmark reference data includes: Obtain the preset security threshold for the first test item; Each of the dynamically acquired data points is compared with the preset security threshold to obtain a first test result on whether the first test item meets the static state. For each of the dynamically acquired data, the rate of change of the dynamically acquired data relative to the benchmark reference data is determined to obtain a second test result on whether the first test item satisfies dynamic stability. The dynamic test results of the vehicle are obtained by fusing the first test results and the second test results.

6. The method according to claim 5, characterized in that, For each piece of dynamically acquired data, the rate of change of the dynamically acquired data relative to the benchmark reference data is determined to obtain a second test result on whether the first test item satisfies dynamic stability, including: For each of the dynamically acquired data, calculate the difference between the acquired value corresponding to the dynamically acquired data and the reference value corresponding to the benchmark reference data; The absolute value of the ratio of the difference to the reference value is taken as the rate of change corresponding to the dynamically collected data; If the rate of change of each of the dynamically acquired data is less than a preset change threshold, the first test item is determined to meet the dynamic stability of the vehicle. The second test result includes the linkage test pass result indicating that the dynamic stability of the vehicle is met.

7. The method according to claim 6, characterized in that, The method further includes: By performing data fitting on the multiple dynamically collected data, the state fitting curve of the first test item is obtained; If the rate of change of each of the dynamically acquired data is less than a preset change threshold, determining that the first test item meets the vehicle's dynamic stability includes: If the rate of change of each of the dynamically acquired data is less than a preset change threshold, and the curvature of each point on the state fitting curve is less than a preset curvature, then the first test item is determined to meet the dynamic stability of the vehicle.

8. The method according to claim 1, characterized in that, The wireless network communication system includes multiple local area network access points for wireless communication between the cloud-based intelligent analysis system and the vehicle-mounted system. These multiple local area network access points operate on the same channel. During the process of receiving the static test data and / or the dynamic test data, the method further includes: The system receives a network switching request sent by the vehicle-mounted system; wherein the vehicle-mounted system can dynamically determine the working status information of each local area network access point, determine whether each local area network access point is sufficient to meet network connection requirements and data transmission requirements through the corresponding working status information, and determine the connection method for the cloud-based intelligent analysis system to connect with the vehicle-mounted system. In response to the network switching request, the network connection with the vehicle system is switched from the current connection method to the target connection method.

9. A testing device for a vehicle's three-electric system, characterized in that, An application in a cloud-based intelligent analysis system, wherein the cloud-based intelligent analysis system can communicate with the vehicle-side system via a wireless network communication system set up in the test site, the device includes: The static data receiving module is used to receive static test data of the vehicle collected by the vehicle-side system in response to the static test request issued by the vehicle in the test site. The dynamic data receiving module is used to respond to dynamic test requests issued by the vehicle in the test site and receive dynamic test data collected by the vehicle-side system during the vehicle test driving process. The static test analysis module is used to obtain the static test results of the vehicle by analyzing the static test data. The dynamic test analysis module is used to analyze the driving process of the vehicle using the static test data and the dynamic test data to obtain the dynamic test results of the vehicle.

10. An electronic device, characterized in that, The system includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the detection method for the vehicle's three-electric system as described in any one of claims 1 to 8.

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