Test system and method based on road load simulation test bench

Abstract

The invention provides a test system and method based on a road load simulation test bench, and belongs to the technical field of commercial vehicle power assembly and whole vehicle testing, and the system comprises a road load simulation test bench which comprises a dynamometer subsystem, a data acquisition subsystem, a controller, an upper computer and a vehicle simulation model; the calibration tool is in communication connection with the to-be-tested vehicle; the dynamometer subsystem is connected with the to-be-tested vehicle and is used for applying a simulation load; the data acquisition subsystem is used for acquiring vehicle data of the to-be-tested vehicle and dynamometer data of the dynamometer subsystem; a road information spectrum based on distance is pre-stored in the upper computer, and a vehicle simulation model is operated; the vehicle simulation model generates a control instruction according to the road information spectrum, and issues the control instruction to the controller to drive the dynamometer subsystem to simulate the load of the target road; and the calibration tool is used for reading and modifying the parameters of the to-be-tested vehicle. The road load is efficiently simulated, the test process is optimized, the cost is reduced, and the safety and accuracy are improved.

Description

Technical Field

[0001] This application belongs to the field of commercial vehicle powertrain and vehicle testing technology, specifically relating to a testing system and method based on a road load simulation test bench. Background Technology

[0002] As the performance of commercial vehicles continues to improve, higher demands are being placed on the accuracy, efficiency, and cost control of vehicle testing. Currently, commercial vehicle testing mainly relies on a complementary strategy that combines road testing and bench testing.

[0003] Road testing, conducted in real and complex environments, can verify the overall performance of a vehicle and the human-machine interaction experience, offering advantages such as high realism and comprehensiveness, with relatively low initial equipment investment. However, road testing has the following limitations. First, consistency is poor; due to the uncontrollable nature of the outdoor environment and the subjective factors of the driver, it is difficult to reproduce and compare test data. Second, costs are high; long-term testing involves significant personnel, travel, logistics, and time costs. Third, safety is low; extreme condition tests (such as maximum gradeability and maximum speed) pose a safety threat to drivers and passengers. Finally, data acquisition is difficult; sensor placement is limited by factors such as space, vibration, and environment, making it difficult to guarantee data quality and integrity.

[0004] Bench testing, conducted in a controlled laboratory environment, offers excellent consistency, safety, and efficiency, making it particularly suitable for durability testing and rapid software iteration. However, a drawback of bench testing is its insufficient simulation accuracy. Existing bench tests typically employ time-based standard operating conditions (such as CHTC and CLTC), whose load models cannot accurately reproduce the spatial characteristics of real roads (such as road surface unevenness excitation, road slope and curvature varying with distance), resulting in discrepancies between simulation results and real-world road experiences.

[0005] Therefore, there is an urgent need for an innovative testing method that can integrate the realism of road testing with the efficiency, consistency and safety of bench testing in order to solve the above problems, reduce R&D costs and shorten the R&D cycle. Summary of the Invention

[0006] In a first aspect, embodiments of this application provide a test system based on a road load simulation test bench, including a road load simulation test bench and calibration tools; The road load simulation test bench includes a dynamometer subsystem, a data acquisition subsystem, a controller, a host computer, and a vehicle simulation model; The calibration tool is connected to the vehicle under test via communication. The dynamometer subsystem, connected to the vehicle under test, is used to apply simulated loads; The data acquisition subsystem is used to collect vehicle data of the vehicle under test and dynamometer data of the dynamometer subsystem. The controller is connected to the dynamometer subsystem, the data acquisition subsystem, and the host computer. The host computer pre-stores distance-based road information spectra and uses them to run vehicle simulation models; The vehicle simulation model generates control commands based on the road information spectrum and sends the control commands to the controller to drive the dynamometer subsystem to simulate the load of the target road; Calibration tools are used to read and modify the parameters of the vehicle under test.

[0007] Furthermore, the dynamometer subsystem includes at least one dynamometer for correspondingly driving at least one wheel of the vehicle under test.

[0008] Furthermore, it also includes visualization tools; The visualization tool communicates with the host computer to convert road information spectrum into a visualized road scene containing slope, curvature, road surface type and traffic elements, and then displays it.

[0009] Furthermore, it also includes panoramic screens; The panoramic screen is connected to visualization tools to provide a driving environment for testers.

[0010] Secondly, embodiments of this application also provide a testing method based on a road load simulation test bench, comprising the following steps: S1. Obtain the road features of the target road segment, construct a road information database, and generate a distance-based road information spectrum after processing the data in the road information database; S2. Build a vehicle load simulation test bench and import the road information spectrum into the host computer of the vehicle load simulation test bench; S3. On a vehicle load simulation test bench, control the test vehicle to perform simulated driving in a simulated road scenario based on the road information spectrum; S4. Collect vehicle data and dynamometer data during the simulated driving test, and analyze the test results of the test vehicle based on the collected data.

[0011] Furthermore, the specific steps of step S1 are as follows: S11. Drive a vehicle equipped with a data acquisition instrument to continuously collect raw data while traveling on the target road segment; the raw data includes at least the road type and the vehicle's azimuth angle. Vehicle trajectory, vehicle altitude H, vehicle coordinates Vehicle speed v and vehicle travel time t; S12. Input the raw data into the host computer to build a road information database; S13. The host computer determines the road friction coefficient by querying a preset friction coefficient table based on the road type and preset weather conditions. The friction coefficient table defines the friction coefficient values ​​for different road types under different weather conditions. S14. The host computer calculates the vehicle's operating azimuth angle. Calculate the vehicle's turning radius based on its trajectory. ; S15. The host computer uses the vehicle's altitude H and vehicle coordinates... Calculate road slope ; S16. The host computer calculates the vehicle speed. and vehicle running time Calculate road length ; S17. The host computer filters the raw data to remove data distortion; S18. The host computer uses road length... As an index, integrate the friction coefficients at the corresponding locations. Turning radius and road slope Generate a distance-based road information spectrum.

[0012] Furthermore, the specific steps of step S2 are as follows: S21. Modify the vehicle to be tested to fit the mechanical mounting interface of the test bench; S22. The modified vehicle under test is installed and fixed on the dynamometer subsystem of the road load simulation test bench through flanges and bearing units; S23. Arrange sensors on the vehicle under test and the dynamometer subsystem, and connect the sensors to the data acquisition subsystem; S24. In the host computer of the road load simulation test bench, create a test mode based on the vehicle running distance and import the road information spectrum.

[0013] Furthermore, step S3 specifically includes: S31. The host computer sets the road load simulation test bench to road simulation mode and loads the road information spectrum into the vehicle simulation model; S32. The vehicle simulation model calculates the road load at the current distance in real time based on the road information spectrum, generates control commands, and sends them to the controller; S33. The controller drives the dynamometer subsystem to apply a simulated force corresponding to the road load to the vehicle under test; S34. Display the road scene ahead, generated based on road information spectrum, to test personnel using visualization tools; S35. Respond to the tester's instructions to operate the vehicle's accelerator and brake pedals, and control the vehicle's speed in the simulated scenario.

[0014] Furthermore, prior to step S31, a resistance iteration step is also included: The driving resistance curve of the vehicle under test is obtained through vehicle coasting tests or model simulation, and then input into the vehicle simulation model for iterative matching.

[0015] Furthermore, step S4 specifically includes: S41. During the simulated driving process, the data acquisition subsystem synchronously and continuously acquires the vehicle CAN signal, powertrain status, energy consumption and response time of the vehicle under test, as well as the dynamometer signal of the dynamometer subsystem. S42. The collected data is transmitted to the host computer for storage and real-time display; S43. Respond to the tester's request to read the vehicle's CAN information using the calibration tool, and respond to the tester's instructions to modify the vehicle's control parameters based on the test results; S44. Repeat steps S3 and S4, and conduct iterative tests based on the new vehicle control parameters to optimize the vehicle's performance or economy.

[0016] As can be seen from the above technical solutions, this application has the following advantages: The testing system and method based on a road load simulation test bench provided in this application achieve accurate testing and optimization of vehicle performance by simulating real road scenarios, reducing R&D costs, shortening the R&D cycle, improving testing efficiency and safety, and integrating the realism of road testing with the efficiency, consistency and safety of bench testing. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the test system based on the road load simulation test bench of the present invention.

[0019] Figure 2 This is a flowchart illustrating the testing method based on a road load simulation test bench according to the present invention. Detailed Implementation

[0020] The various embodiments of this disclosure will be described more fully in the following detailed description of the test system based on the road load simulation test bench. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0021] This embodiment provides a testing system based on a road load simulation test bench. By simulating real road loads, it enables efficient and safe vehicle testing, reduces R&D costs, shortens the cycle, and improves testing accuracy.

[0022] The technical solutions of the embodiments 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, and 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] Please see Figure 1 The diagram shown is a schematic of a test system based on a road load simulation test bench in a specific embodiment. The system includes a road load simulation test bench and calibration tools. The road load simulation test bench includes a dynamometer subsystem, a data acquisition subsystem, a controller, a host computer, and a vehicle simulation model; The calibration tool is connected to the vehicle under test via communication. The dynamometer subsystem, connected to the vehicle under test, is used to apply simulated loads; The data acquisition subsystem is used to collect vehicle data of the vehicle under test and dynamometer data of the dynamometer subsystem. The controller is connected to the dynamometer subsystem, the data acquisition subsystem, and the host computer. The host computer pre-stores distance-based road information spectra and uses them to run vehicle simulation models; The vehicle simulation model generates control commands based on the road information spectrum and sends the control commands to the controller to drive the dynamometer subsystem to simulate the load of the target road; Calibration tools are used to read and modify the parameters of the vehicle under test; It should be noted that the road load simulation test bench integrates multiple subsystems and can simulate various load conditions in real road environments, providing an efficient, safe and controllable platform for vehicle testing, effectively solving the limitations of traditional testing methods; The dynamometer subsystem, by connecting to the vehicle under test and applying simulated loads, can accurately simulate the vehicle's driving resistance under different road conditions, providing a testing basis for testing the vehicle's power and economy. The data acquisition subsystem is used to collect vehicle data of the vehicle under test and dynamometer data of the dynamometer subsystem. It can acquire various data in real time and accurately during the test process, providing important data support for data analysis and vehicle performance optimization. The controller connects the dynamometer subsystem, the data acquisition subsystem, and the host computer, playing a coordinating and controlling role to ensure efficient collaboration and data transmission between the subsystems, thereby improving the overall performance and stability of the testing system. The host computer pre-stores a distance-based road information spectrum and uses it to run the vehicle simulation model. By processing and analyzing the data, the host computer generates control commands to drive the dynamometer subsystem to simulate the load on the target road, thus achieving precise control and management of the simulation test process. Vehicle simulation models can calculate road loads in real time and generate corresponding control commands, improving the accuracy and real-time performance of simulation tests.

[0024] The calibration tool communicates with the vehicle under test to read and modify its parameters. It provides a user-friendly interface for testers, enabling them to adjust vehicle parameters in real time based on test results, thereby optimizing vehicle performance.

[0025] This embodiment combines the advantages of road testing and bench testing by simulating real road loads, achieving efficient and safe vehicle performance testing. It can reduce R&D costs, shorten the testing cycle, and improve testing accuracy and reliability.

[0026] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another test system based on a road load simulation test bench is provided. This system includes a road load simulation test bench and calibration tools. The road load simulation test bench includes a dynamometer subsystem, a data acquisition subsystem, a controller, a host computer, and a vehicle simulation model; The calibration tool is connected to the vehicle under test via communication. The dynamometer subsystem, connected to the vehicle under test, is used to apply simulated loads; The data acquisition subsystem is used to collect vehicle data of the vehicle under test and dynamometer data of the dynamometer subsystem. The controller is connected to the dynamometer subsystem, the data acquisition subsystem, and the host computer. The host computer pre-stores distance-based road information spectra and uses them to run vehicle simulation models; The vehicle simulation model generates control commands based on the road information spectrum and sends the control commands to the controller to drive the dynamometer subsystem to simulate the load of the target road; Calibration tools are used to read and modify the parameters of the vehicle under test; The dynamometer subsystem includes at least one dynamometer for driving at least one wheel of the vehicle under test. It also includes visualization tools; The visualization tool communicates with the host computer to convert road information spectrum into a visualized road scene containing slope, curvature, road surface type and traffic elements and display it. It also includes panoramic screens; The panoramic screen is connected to visualization tools to provide a driving environment for testers; For example, the road load simulation test bench includes a dynamometer subsystem, a data acquisition subsystem, a controller, a host computer, and a vehicle simulation model. The dynamometer subsystem uses four identical AC dynamometers (e.g., model SG-1600), each driving one of the four wheels of the 6x4 tractor under test. Each dynamometer has a rated power of 1600kW and a rated speed of 3000r / min, accurately simulating traction resistance, braking resistance, and cornering differential load during vehicle operation. The data acquisition subsystem uses an NI PXIe-1085 acquisition platform, equipped with an 8-channel analog input module, a 16-channel digital input / output module, and a CAN bus acquisition module. The sampling frequency can reach up to 1MHz, simultaneously acquiring vehicle CAN signals, dynamometer speed / torque signals, and sensor detection data. The controller uses a Siemens S7-1500 series PLC with a computation cycle ≤1ms, realizing dynamometer load control, data interaction, and command execution. The host computer is equipped with an Intel Core i9 processor, 64GB of memory, and a 4TB solid-state drive, and runs LabVIEW. The 2023 development environment and vehicle dynamics simulation software (such as CarSim2024) pre-store distance-based road information spectra and run vehicle simulation models; the vehicle simulation models are built based on MATLAB / Simulink, integrating vehicle powertrain models, tire models and road load models, and can calculate load requirements in real time based on road information spectra. The calibration tool uses Vector CANape 19.0, which communicates with the vehicle under test via CAN bus. It supports reading internal parameters of the vehicle ECU (such as fuel injection pulse width, ignition advance angle, transmission shift strategy parameters, etc.) and can modify parameter values ​​online to achieve rapid iterative optimization of vehicle control strategy. The visualization tool is developed using the Unity 3D engine, converting road information spectrum into a 3D visualized road scene that includes slope, curvature, road surface type (asphalt / cement / unpaved) and traffic elements (traffic lights, obstacles, traffic flow). The panoramic system consists of six 55-inch high-definition display screens, spliced ​​together to form a 360° circular display area with a resolution of 3840×2160 and a refresh rate of 60Hz, providing testers with an immersive driving environment and intuitively presenting the road conditions ahead (such as uphill warnings, curve warnings, and slippery road markings).

[0027] like Figure 2 As shown, the following are embodiments of the test method based on a road load simulation test bench provided in this disclosure. This method and the test system based on a road load simulation test bench in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the test method based on a road load simulation test bench, please refer to the embodiments of the test system based on a road load simulation test bench described above.

[0028] The method includes the following steps: S1. Obtain the road features of the target road segment, construct a road information database, and generate a distance-based road information spectrum after processing the data in the road information database; It should be noted that this step provides accurate basic data for the simulation test, ensuring the realism and reliability of the simulated road environment and providing a foundation for the testing process; S2. Build a vehicle load simulation test bench and import the road information spectrum into the host computer of the vehicle load simulation test bench; It should be noted that this step realizes the assembly and configuration of the test system, ensuring that the vehicle load simulation test bench can accurately simulate the load conditions of the target road according to the road information spectrum, thus making full preparations for simulated driving tests. S3. On a vehicle load simulation test bench, control the test vehicle to perform simulated driving in a simulated road scenario based on the road information spectrum; It should be noted that by controlling the test vehicle to simulate driving in a simulated road scenario, and by loading the road information spectrum and calculating the road load in real time, the driving process of the vehicle on the target road can be accurately simulated, making the test results closer to the real road conditions and improving the authenticity and effectiveness of the test. S4. Collect vehicle data and dynamometer data during the simulated driving test, and analyze the test results of the test vehicle based on the collected data; It should be noted that this step, through real-time data collection and analysis, provides a basis for the evaluation and optimization of vehicle performance, achieving efficient management and utilization of the testing process.

[0029] This embodiment uses a road load simulation test bench to accurately reproduce real road scenarios, optimize vehicle testing processes, reduce R&D costs, shorten testing cycles, and improve testing efficiency and safety.

[0030] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another testing method based on a road load simulation test bench is provided. Taking a certain model of 6X4 tractor as the test vehicle, and the target road section selected as a combination of "mountain expressway + rural unpaved road" (total mileage 10km) as an example, the method includes the following steps: S1. Obtain the road features of the target road segment, construct a road information database, and process the data in the road information database to generate a distance-based road information spectrum; the specific steps of step S1 are as follows: S11. Drive a vehicle equipped with a data acquisition instrument to continuously collect raw data while traveling on the target road segment; the raw data includes at least the road type and the vehicle's azimuth angle. Vehicle trajectory, vehicle altitude H, vehicle coordinates Vehicle speed v and vehicle travel time t; S12. Input the raw data into the host computer to build a road information database; S13. The host computer determines the road friction coefficient by querying a preset friction coefficient table based on the road type and preset weather conditions. The friction coefficient table defines the friction coefficient values ​​for different road types under different weather conditions. S14. The host computer calculates the vehicle's operating azimuth angle. Calculate the vehicle's turning radius based on its trajectory. ; S15. The host computer uses the vehicle's altitude H and vehicle coordinates... Calculate road slope ; S16. The host computer calculates the vehicle speed. and vehicle running time Calculate road length ; S17. The host computer filters the raw data to remove data distortion; S18. The host computer uses road length... As an index, integrate the friction coefficients at the corresponding locations. Turning radius and road slope Generate distance-based road information spectra; For example, for raw data acquisition, a test vehicle equipped with a data acquisition instrument (including a GPS positioning module, an inertial measurement unit (IMU), a vehicle speed sensor, and a time synchronization module) was selected to conduct a real-vehicle driving test on the target road section. During the driving process, the data acquisition instrument continuously acquired raw data at a frequency of 100Hz, including: road type (asphalt road for highway sections, unpaved road for rural sections), vehicle azimuth angle (range: 0°-360°), vehicle trajectory (GPS coordinate accuracy ±1m), vehicle altitude H (accuracy ±0.5m), vehicle coordinates (latitude and longitude), vehicle speed v (accuracy ±0.1km / h), and vehicle travel time t (synchronized UTC time, accuracy ±1ms). The road information database is constructed by importing the collected raw data into the host computer, storing it according to the categories of "road segment-time-data type", and constructing a road information database containing a total of 1 million valid data records. Data processing and parameter calculation: The friction coefficient was determined, and the preset test weather was "dry". The preset friction coefficient table is shown in Table 1. The friction coefficient C21 for high-speed asphalt roads is 0.85, and the friction coefficient C11 for rural unpaved roads is 0.60. Table 1

[0031] The turning radius is calculated using the formula R = Δs / Δθ (in radians), based on the change in the vehicle's azimuth angle Δθ and the travel distance Δs. For example, if the vehicle's azimuth angle changes from 30° to 60° on a certain road section (Δθ = π / 6 rad), the corresponding travel distance Δs = 52.36 m, and the calculated turning radius R = 52.36 / (π / 6) = 100 m. Road gradient is calculated based on the vehicle's altitude change ΔH and horizontal distance ΔL (calculated from GPS coordinates), using the formula i = ΔH / ΔL × 100%. For example, if a road segment has an altitude of 146m at the start and 89m at the end, with a horizontal distance of 1250m, the gradient is calculated as i = (89-146) / 1250 × 100% = -4.56% (the negative sign indicates a downhill slope). The road length is calculated based on the vehicle speed v and the running time t, using the integral formula L=∫v(t)dt to calculate the length of each road segment. The highway segment is 6km long, the rural unpaved road segment is 4km long, and the total mileage is 10km. Data distortion removal involves using a Kalman filter algorithm to filter the original data, removing abnormal data caused by sudden acceleration / braking (driving factors) and road surface potholes (road smoothness) (such as sudden changes in instantaneous vehicle speed, changes in azimuth angle, etc.), and retaining valid data. The road information spectrum is generated by using the calculated road length (in meters) as an index and integrating the friction coefficient, turning radius, and road slope data at the corresponding locations. For example, the road information spectrum for mileage 0-6000m (highway section) is: friction coefficient 0.85, slope range -4.56%-8.67%, turning radius 300-1500m; the road information spectrum for mileage 6000-10000m (rural unpaved road section) is: friction coefficient 0.60, slope range -3.2%-5.8%, turning radius 100-800m. The road information spectrum is stored in tabular form on the host computer, as shown in Table 2. Table 2

[0032] S2. Set up a vehicle load simulation test bench and import the road information spectrum into the host computer of the vehicle load simulation test bench; the specific steps of step S2 are as follows: S21. Modify the vehicle to be tested to fit the mechanical mounting interface of the test bench; S22. The modified vehicle under test is installed and fixed on the dynamometer subsystem of the road load simulation test bench through flanges and bearing units; S23. Arrange sensors on the vehicle under test and the dynamometer subsystem, and connect the sensors to the data acquisition subsystem; S24. In the host computer of the road load simulation test bench, create a test mode based on the vehicle running distance and import the road information spectrum; For example, the vehicle is modified and installed by modifying the 6X4 tractor under test, removing the original wheels, and installing an adapter flange and bearing unit at the output end of the axle. The flange is rigidly connected to the output shaft of the four dynamometers on the test bench to ensure that the power transmission is without deviation. The modified vehicle is fixed to the base of the test bench and the frame is clamped by hydraulic clamps to prevent the vehicle from shifting during the test. Sensor placement and data acquisition connection: Torque sensors (accuracy ±0.1%FS) are installed at the engine output, transmission input / output, and drive axle input of the vehicle under test; acceleration sensors (to measure vibration data) are installed at key locations on the vehicle body; and a fuel level sensor (to measure energy consumption) is installed in the fuel tank. All sensor signals are connected to the NI PXIe acquisition module of the data acquisition subsystem, and the vehicle ECU is connected to the data acquisition subsystem via the CAN bus to achieve real-time acquisition of vehicle CAN signals. Test mode creation and spectrum import: Create a "distance-based road spectrum test mode" in the LabVIEW software on the host computer, set the data acquisition trigger conditions (with mileage as the trigger node, data storage is triggered once every 1m); import the road information spectrum generated in step S1 into the host computer, match it with the parameter interface of the vehicle simulation model, and complete the test bench construction and configuration; S3. On a vehicle load simulation test bench, control the test vehicle to perform simulated driving in a simulated road scenario based on road information spectrum; step S3 specifically includes: S31. The host computer sets the road load simulation test bench to road simulation mode and loads the road information spectrum into the vehicle simulation model; S32. The vehicle simulation model calculates the road load at the current distance in real time based on the road information spectrum, generates control commands, and sends them to the controller; S33. The controller drives the dynamometer subsystem to apply a simulated force corresponding to the road load to the vehicle under test; S34. Display the road scene ahead, generated based on road information spectrum, to test personnel using visualization tools; S35. Respond to the tester's instructions to operate the vehicle's accelerator and brake pedals, and control the vehicle's speed in the simulated scenario; Before step S31, a resistance iteration step is also included: The driving resistance curve of the vehicle under test is obtained through vehicle coasting tests or model simulation, and then input into the vehicle simulation model for iterative matching. For example, in the resistance iterative matching process, before setting the road simulation mode, the driving resistance curve of the vehicle under test is obtained through a vehicle coasting test. The vehicle is placed in an unloaded state, and after starting, the accelerator pedal is released, allowing the vehicle to coast freely. The data acquisition subsystem collects vehicle speed-time data during the coasting process, and calculates the driving resistance using the formula F_resistance = ma (where m is the vehicle's curb weight and a is the acceleration), generating a resistance curve. The resistance curve is then input into the vehicle simulation model and subjected to three iterative matching iterations to ensure that the error between the model-calculated resistance and the actual coasting resistance is ≤3%. The road simulation mode is activated. The host computer sets the test bench to road simulation mode, loads the imported road information spectrum into the vehicle simulation model, and reads the friction coefficient, slope, and turning radius parameters corresponding to the current mileage in real time. The current road load (including rolling resistance, air resistance, slope resistance, and turning resistance) is calculated through the vehicle dynamics model, and control commands (such as dynamometer torque commands and differential control commands) are generated and sent to the controller. Simulated load is applied, and after receiving instructions, the controller drives four dynamometers to work collaboratively, applying simulated forces corresponding to the current road load. For example, when the vehicle travels to 1300m (8.67% gradient, 300m turning radius), the dynamometers apply slope resistance (Fslope = mgsinθ, m = 18000kg, g = 9.8m / s², θ is the inclination angle corresponding to the slope) and turning differential (the inner dynamometer rotates at a lower speed than the outer dynamometer, and the speed difference is inversely proportional to the turning radius) based on the calculated results, simulating real uphill and turning conditions. Visualized driving and speed control: The visualization tool projects the current road scene (uphill road surface, curve markings, asphalt road texture) onto a 360° panoramic screen. Testers observe the road conditions ahead displayed on the panoramic screen and subjectively control the vehicle speed by operating the accelerator and brake pedals (target speed of 80-100km / h on highways and 30-50km / h on rural roads). The vehicle speed is fed back to the host computer in real time through the vehicle speed sensor, forming a closed-loop control. S4. Collect vehicle data and dynamometer data during the simulated driving test, and analyze the test results of the test vehicle based on the collected data; Step S4 specifically includes: S41. During the simulated driving process, the data acquisition subsystem synchronously and continuously acquires the vehicle CAN signal, powertrain status, energy consumption and response time of the vehicle under test, as well as the dynamometer signal of the dynamometer subsystem. S42. The collected data is transmitted to the host computer for storage and real-time display; S43. Respond to the tester's request to read the vehicle's CAN information using the calibration tool, and respond to the tester's instructions to modify the vehicle's control parameters based on the test results; S44. Repeat steps S3 and S4, and conduct iterative tests based on the new vehicle control parameters to optimize the vehicle's performance or economy; For example, during the simulated driving test, the data acquisition subsystem synchronously collects data at a frequency of 100Hz, including: vehicle CAN signals (injection pulse width, transmission gear, ECU control commands), powertrain status (engine speed / torque, transmission input / output torque, drive axle temperature), energy consumption (fuel consumption rate, cumulative fuel consumption), response time (time from accelerator pedal travel to engine torque response), and dynamometer signals (speed, torque, output power). The collected data is transmitted to the host computer for storage and display in real time. Parameter modification and iterative testing: Testers use calibration tools to read and analyze vehicle performance data. For example, it was found that the vehicle's fuel consumption was high (35L / 100km) and acceleration response time was long (0.8s) on a 1300m uphill section (8.67% gradient). The calibration tools were used to modify the vehicle ECU's injection pulse width parameter (from 20ms to 22ms) and the transmission shift strategy (delaying the RPM threshold for shifting from 1st to 2nd gear, from 1500r / min to 1800r / min). After modification, steps S3-S4 were repeated to conduct iterative testing and compare the energy consumption and response time data before and after modification. After three iterations of testing, the vehicle's fuel consumption on uphill sections decreased to 32L / 100km, and acceleration response time was reduced to 0.5s, resulting in improved power and fuel economy. The testers analyzed the final collected data to assess the vehicle's overall performance, generating a test report that clarified the vehicle's adaptability to the target road conditions and identified areas for further optimization.

[0033] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0034] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A test system based on a road load simulation test bench, characterized in that, Includes a road load simulation test bench and calibration tools; The road load simulation test bench includes a dynamometer subsystem, a data acquisition subsystem, a controller, a host computer, and a vehicle simulation model; The calibration tool is connected to the vehicle under test via communication. The dynamometer subsystem, connected to the vehicle under test, is used to apply simulated loads; The data acquisition subsystem is used to collect vehicle data of the vehicle under test and dynamometer data of the dynamometer subsystem. The controller is connected to the dynamometer subsystem, the data acquisition subsystem, and the host computer. The host computer pre-stores distance-based road information spectra and uses them to run vehicle simulation models; The vehicle simulation model generates control commands based on the road information spectrum and sends the control commands to the controller to drive the dynamometer subsystem to simulate the load of the target road; Calibration tools are used to read and modify the parameters of the vehicle under test.

2. The test system based on a road load simulation test bench according to claim 1, characterized in that, The dynamometer subsystem includes at least one dynamometer for driving at least one wheel of the vehicle under test.

3. The test system based on a road load simulation test bench according to claim 1, characterized in that, It also includes visualization tools; The visualization tool communicates with the host computer to convert road information spectrum into a visualized road scene containing slope, curvature, road surface type and traffic elements, and then displays it.

4. The test system based on a road load simulation test bench according to claim 3, characterized in that, It also includes panoramic screens; The panoramic screen is connected to visualization tools to provide a driving environment for testers.

5. A test method based on a road load simulation test bench, characterized in that, Includes the following steps: S1. Obtain the road features of the target road segment, construct a road information database, and generate a distance-based road information spectrum after processing the data in the road information database; S2. Build a vehicle load simulation test bench and import the road information spectrum into the host computer of the vehicle load simulation test bench; S3. On a vehicle load simulation test bench, control the test vehicle to perform simulated driving in a simulated road scenario based on the road information spectrum; S4. Collect vehicle data and dynamometer data during the simulated driving test, and analyze the test results of the test vehicle based on the collected data.

6. The test method based on a road load simulation test bench according to claim 5, characterized in that, The specific steps of step S1 are as follows: S11. Drive a vehicle equipped with a data acquisition instrument to continuously collect raw data while traveling on the target road segment; the raw data includes at least the road type and the vehicle's azimuth angle. Vehicle trajectory, vehicle altitude H, vehicle coordinates Vehicle speed v and vehicle travel time t; S12. Input the raw data into the host computer to build a road information database; S13. The host computer determines the road friction coefficient by querying a preset friction coefficient table based on the road type and preset weather conditions. The friction coefficient table defines the friction coefficient values ​​for different road types under different weather conditions. S14. The host computer calculates the vehicle's operating azimuth angle. Calculate the vehicle's turning radius based on its trajectory. ; S15. The host computer uses the vehicle's altitude H and vehicle coordinates... Calculate road slope ; S16. The host computer calculates the vehicle speed. and vehicle running time Calculate road length ; S17. The host computer filters the raw data to remove data distortion; S18. The host computer uses road length... As an index, integrate the friction coefficients at the corresponding locations. Turning radius and road slope Generate a distance-based road information spectrum.

7. The test method based on a road load simulation test bench according to claim 5, characterized in that, The specific steps of step S2 are as follows: S21. Modify the vehicle to be tested to fit the mechanical mounting interface of the test bench; S22. The modified vehicle under test is installed and fixed on the dynamometer subsystem of the road load simulation test bench through flanges and bearing units; S23. Arrange sensors on the vehicle under test and the dynamometer subsystem, and connect the sensors to the data acquisition subsystem; S24. In the host computer of the road load simulation test bench, create a test mode based on the vehicle running distance and import the road information spectrum.

8. The test method based on a road load simulation test bench according to claim 5, characterized in that, Step S3 specifically includes: S31. The host computer sets the road load simulation test bench to road simulation mode and loads the road information spectrum into the vehicle simulation model; S32. The vehicle simulation model calculates the road load at the current distance in real time based on the road information spectrum, generates control commands, and sends them to the controller; S33. The controller drives the dynamometer subsystem to apply a simulated force corresponding to the road load to the vehicle under test; S34. Display the road scene ahead, generated based on road information spectrum, to test personnel using visualization tools; S35. Respond to the tester's instructions to operate the vehicle's accelerator and brake pedals, and control the vehicle's speed in the simulated scenario.

9. The test method based on a road load simulation test bench according to claim 8, characterized in that, Before step S31, a resistance iteration step is also included: The driving resistance curve of the vehicle under test is obtained through vehicle coasting tests or model simulation, and then input into the vehicle simulation model for iterative matching.

10. The test method based on a road load simulation test bench according to claim 5, characterized in that, Step S4 specifically includes: S41. During the simulated driving process, the data acquisition subsystem synchronously and continuously collects the vehicle CAN signal, powertrain status, energy consumption and response time of the vehicle under test, as well as the dynamometer signal of the dynamometer subsystem. S42. The collected data is transmitted to the host computer for storage and real-time display; S43. Respond to the tester's request to read the vehicle's CAN information using the calibration tool, and respond to the tester's instructions to modify the vehicle's control parameters based on the test results; S44. Repeat steps S3 and S4, and conduct iterative tests based on the new vehicle control parameters to optimize the vehicle's performance or economy.

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