Dynamometer and HIL integrated whole vehicle multi-working-condition automatic test calibration system

Abstract

The invention relates to the technical field of automobile power system testing, in particular to a dynamometer and HIL integrated finished automobile multi-working-condition automatic testing and calibrating system, which combines a dynamometer rack with a hardware-in-the-loop simulation technology, develops a dynamics scene model covering conventional and limiting working conditions, and improves the testing accuracy of the dynamometer rack and the hardware-in-the-loop simulation technology. The conversion and interaction of virtual and real signals are realized through road load simulation and energy consumption simulation; the dynamometer and HIL integrated whole vehicle multi-working-condition automatic test calibration system is composed of IO, DOE, automatic calibration, objective evaluation and other functional modules, can automatically execute test cases, complete parameter calibration and optimization analysis, and perform objective evaluation on test results, and can realize multi-working-condition test, reduce the dependence on real vehicle road test, improve the test efficiency and comprehensiveness, and reduce the test cost. The controller development period is shortened, and the cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of automotive powertrain testing technology, specifically a multi-condition automatic testing and calibration system for a whole vehicle that integrates a dynamometer and a high-intensity isolation unit (HIL). Background Technology

[0002] With the rapid development of new energy vehicle technology, the testing requirements for powertrain systems are becoming increasingly complex. Traditional testing methods are no longer sufficient to meet the demands of modern automotive powertrain systems for efficient, accurate, and comprehensive testing. Modern automotive powertrain systems not only need to verify their performance under normal operating conditions but also require testing under extreme conditions to ensure their reliability and safety. Hardware-in-the-loop (HIL) simulation technology plays a crucial role in control system development. By combining real controllers with virtual vehicle models, it can highly realistically simulate various actual operating conditions in a laboratory environment, providing an efficient, safe, and economical testing platform for control system development. This allows for rapid verification of the controller's functional logic and reproduction of complex dynamic scenarios. Meanwhile, traditional dynamometer benches also play an important role in powertrain and vehicle testing, used for testing key areas such as power performance, efficiency, durability, dynamic performance, economic performance, energy recovery, and environmental adaptability.

[0003] However, existing technologies have significant limitations. While HIL technology has made progress in functional testing, performance testing of many controllers and overall system performance still relies on traditional methods. These traditional methods require testing on actual vehicles or equipment, resulting in long testing cycles, high costs, and difficulty in covering extreme operating conditions or rare fault modes. For example, performance testing of new energy vehicle battery management systems in extreme temperature or high-altitude environments, as well as the reproduction of complex traffic scenarios in autonomous driving systems, are all challenging. The testing scope of traditional dynamometer benches is also limited, mainly focusing on straight-line testing of the entire vehicle. This cannot fully reflect the vehicle's power performance in complex dynamic scenarios such as acceleration, braking, hill climbing, and high-speed driving. Furthermore, there are shortcomings in testing controller control strategies. Due to the lack of fault injection simulation capabilities, it is difficult to effectively verify the controller's ability to cope with fault conditions, affecting the integrity of vehicle safety and reliability testing.

[0004] Therefore, in view of the above situation, there is an urgent need to develop a vehicle multi-condition automatic test and calibration system that integrates a dynamometer and HIL to overcome the shortcomings in current practical applications. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic testing and calibration system for a vehicle under multiple operating conditions that integrates a dynamometer and a high-intensity dynamometer (HIL) to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A multi-condition automated test and calibration system for a vehicle integrating a dynamometer and a hardware-in-the-loop (HIL) system includes: a dynamometer bench, an HIL system, a battery simulator, and a test management module; wherein: The HIL hardware-in-the-loop system integrates a virtual simulation module for constructing dynamic scenario models that include complex operating conditions. The test management module connects the dynamometer bench, the HIL hardware-in-the-loop system, and the battery simulator, and is used to execute the following closed-loop process: (1) Road load simulation: Receive the wheel end drive torque collected by the dynamometer test bench, calculate the wheel end speed through the dynamic model and feed it back to the dynamometer test bench; (2) Energy consumption simulation: Receive the DC bus current collected by the battery simulator, calculate the battery terminal voltage through the second-order battery model and feed it back to the battery simulator; (3) Automatic testing and evaluation: Run preset test cases, collect sensor and bus data, and score the vehicle power system based on the objective evaluation module; (4) Automatic calibration and optimization: The calibration factor is designed through the DOE module, the parameters are injected into the controller through the automatic calibration module, and the calibration parameters are optimized cyclically based on the evaluation results.

[0007] As a further aspect of the present invention: the road load simulation specifically includes: The dynamic scenario model includes at least one of the following: NEDC working condition, intersection working condition, and desert shoal working condition. The model inputs include vehicle parameters based on real vehicle data, regulatory road environment data, and driving style data of road test personnel; The signal stream is transmitted in the following order: (1) Driver behavior signals are sent to the controller; (2) The controller outputs the motor drive force to the torque sensor; (3) The torque sensor collects the driving force and feeds it back to the dynamic model; (4) The dynamic model outputs the wheel end speed signal to the dynamometer stand.

[0008] As a further aspect of the present invention: the energy consumption simulation specifically includes: The second-order battery model is constructed based on the Thevenin equivalent circuit principle, and the model parameters are obtained through HPPC hybrid pulse power characteristic experiments. The signal stream is transmitted in the following order: (1) The DC bus current is collected by the sensor and then input into the battery model; (2) The battery model calculates the battery terminal voltage based on the current and initial conditions; (3) Feed back the calculated battery terminal voltage to the battery simulator.

[0009] As a further aspect of the present invention: the test management module further includes: IO function module: Implements signal unit conversion, virtual node simulation, and security logic design; Automated testing module: Used to write and execute test cases that cover multiple operating conditions; Data acquisition module: Real-time acquisition of sensor and bus data of the device under test.

[0010] As a further aspect of the present invention: the automatic calibration module realizes the automatic injection of controller parameters through Vector CANape secondary development.

[0011] As a further aspect of the present invention: the objective evaluation module generates a quantitative scoring report based on the power performance data.

[0012] As a further aspect of the present invention: the system timing actions are executed cyclically according to the following steps: S1, DOE module design calibration factor; S2. The automatic calibration module injects the first set of factor parameters into the controller; S3. Start the high and low voltage power supplies of the system under test and the test bench; S4 and HIL systems interact with the bus of the system under test, enabling the controller to enter the vehicle start-up state. The S5 and HIL systems run a virtual model and send signals to the driver. S6: Receive the wheel end drive torque and DC bus current, and calculate the wheel end speed and battery terminal voltage; S7 controls the dynamometer speed and the battery simulator voltage; S8, Acquire data from bench sensors and bus; S9. The objective evaluation module performs system scoring; S10 and DOE modules optimize calibration parameters and proceed to the next loop.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. Save time and manpower: Automated testing reduces manual operations, improves testing efficiency, and shortens the testing cycle; 2. Reduce safety risks of road testing: By simulating various working conditions in a laboratory environment, the reliance on real vehicle road testing is reduced, thereby lowering the safety risks associated with real vehicle testing; 3. Shorten the controller development cycle: Through automatic calibration and optimization analysis functions, the initial parameter calibration and optimization of the controller can be completed quickly, accelerating the controller development process. Attached Figure Description

[0014] Figure 1This is a connection diagram of the vehicle multi-condition test calibration system architecture in an embodiment of the present invention; Figure 2 This is a block diagram illustrating the road load simulation signal interaction principle in an embodiment of the present invention; Figure 3 This is a block diagram illustrating the signal transmission principle for battery energy consumption simulation in an embodiment of the present invention. Figure 4 This is a driver model diagram of AMEsim in an embodiment of the present invention; Figure 5 This is a vehicle model diagram from an embodiment of the present invention; Figure 6 This is a diagram of the tire and terrain model in an embodiment of the present invention; Figure 7 This is an interface model diagram in an embodiment of the present invention; in, Figure 4 collection Figure 5 Vehicle information and send driver instructions to Figure 5 , Figure 5 collection Figure 6 Tire information and vehicle information are sent to Figure 6 , Figure 7 This serves as an interface module, facilitating information exchange between the entire model and external systems. Figure 8 This is a flowchart of the automatic calibration process in an embodiment of the present invention. Detailed Implementation

[0015] 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.

[0016] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0017] Please see Figures 1-5 This invention provides a multi-condition automated test and calibration system for vehicles integrating a dynamometer and HIL (High-Intensity Logic Analyzer). This system overcomes the limitations of existing test platforms in terms of operating condition coverage, functional testing, and automation. By integrating the dynamometer bench and hardware with HIL technology, it enables multi-condition (including extreme condition) testing, automated calibration, optimization analysis, and objective evaluation of the powertrain system and the entire vehicle of new energy vehicles. This reduces reliance on real-vehicle road testing, improves development efficiency, reduces costs, and enhances the comprehensiveness and reliability of testing. Specific details are as follows: I. Basic Principles of the Platform; (a) Principle of road load simulation; Road load simulation is the core foundation for this platform to achieve multi-condition testing. By constructing a virtual dynamic scenario and coordinating with real test equipment, it accurately reproduces the vehicle's stress and dynamic response under various driving conditions.

[0018] 1. Development of dynamic scene models: Develop dynamic scenario models that cover a variety of complex operating conditions, including but not limited to conventional operating conditions (such as the NEDC New European Driving Cycle, urban operating conditions, coasting tests, etc.) and extreme operating conditions (such as desert shoal, mud extrication, extreme drifting, complex interactions at intersections, etc.).

[0019] The model it developed consists of four parts, namely Figure 4 The driver model in Figure 5 Vehicle models in Figure 6 Medium tires and road models and Figure 7 The system comprises interface modules. The driver model, vehicle model, and tire and road models utilize conventional technologies. However, the road model requires a novel data acquisition method: terrain data is collected via drone aerial photography, generated with coordinates using mapping software, and finally segmented at equal intervals using 3D software to create a terrain point cloud. This cloud is then imported into the road model to recreate the real terrain at a 1:1 scale. All other interfaces are custom-defined within the project, and their meanings are shown in the table below.

[0020] The vehicle parameters in the model (such as mass, wheelbase, drag coefficient, etc.) are calibrated based on real vehicle data to ensure consistency with the physical characteristics of the vehicle under test; Road environment parameters (such as slope, road surface friction coefficient, altitude, etc.) are set according to relevant testing regulations, or three-dimensional data are obtained by scanning the actual road scene with a drone and then imported into the model to ensure the authenticity of the environmental simulation. The driving style model is based on the actual operation data of road testers (such as the rate of change of accelerator pedal opening, brake pedal response speed, and shift timing) to ensure that virtual driving behavior is consistent with real driving.

[0021] 2. Signal Interaction and Load Simulation Process: The aforementioned dynamic model and input / output (IO) model are integrated into the test management software to achieve efficient conversion between virtual and real signals, ensuring stable communication between the platform and the device under test (such as VCU, MCU, BMS, and other controllers and power system components). The specific signal flow is as follows: The driver's behavior signals (such as accelerator pedal signals, brake pedal signals, gear signals, etc.) are sent to the controller under test; The controller executes control logic based on the received signals, driving the motor to generate driving force. The driving force is collected by a torque sensor and fed back to the dynamic scene model in real time; The dynamic model is based on the received driving force and combined with the current working conditions (such as road slope, vehicle mass, etc.) to perform virtual driving simulation and calculate the wheel end speed signal; The wheel-end speed signal is sent to the dynamometer, which adjusts the load according to the signal to achieve accurate simulation of the actual wheel-end load.

[0022] II. Platform Functional Modules; This platform consists of multiple functional modules that work together to automate testing, calibration, optimization, and evaluation. The specific functions of each module are as follows: 1. IO Function Module: Responsible for signal processing and conversion, including signal unit conversion (such as converting voltage signals into physical quantity signals), virtual node simulation (simulating signals from sensors or actuators that are not directly connected), and safety logic design (such as signal over-limit protection and abnormal signal filtering), to ensure the accurate transmission of virtual signals and real signals and the stability of system communication.

[0023] 2. DOE (Design of Experiments) module: Integrates calibration parameter optimization and factor design functions. It uses scientific experimental design methods (such as orthogonal experiments and response surface methodology) to determine key parameters affecting the performance of the power system (such as motor torque coefficient, battery SOC correction coefficient, etc.) and plans parameter test combinations to achieve efficient optimization of parameters.

[0024] 3. Automatic calibration module: (1) Data source input The system first receives data input from an Excel file. This data contains parameters, signals, or control commands to be processed.

[0025] (2) API interface call This project utilizes the Python programming language and the CANape API to read, parse, and preprocess Excel data. Python acts as an intermediate layer, handling data format conversion, validation, and interface integration with subsequent functional modules.

[0026] (3) Function processing Data processed by the CANape API is passed to the CANape Function module. This module implements the specific data processing logic, including signal conversion, parameter mapping, and function execution.

[0027] (4) Script generation and execution The processing results output by the CANape Function module are further passed to the CANape Script module. This module is responsible for generating and executing CANape scripts to automate testing, configuration, or process control.

[0028] (5) Target device communication The CANape Script module communicates with the target ECU (Electronic Control Unit) to enable data transmission, parameter configuration, or signal interaction.

[0029] (6) System integration and expansion interface The Python module interacts with both the Veristand system and the DOE module. It enables data synchronization, status monitoring, and integration with the Python module for extended functionality.

[0030] 4. Objective Evaluation Module: Receives all sensor data (such as torque, speed, current, voltage, temperature, etc.) and bus signals (such as controller status signals, fault codes, etc.) from the test bench. Based on preset evaluation indicators (such as power response speed, energy consumption economy, fault handling capability, etc.), it performs quantitative analysis on the performance of the vehicle power system, generates objective scores and evaluation reports, and provides data support for system optimization.

[0031] The evaluation criteria for steering transient response (steering wheel angle step input) are as follows: The lateral acceleration value is 2 m / s². 2 The response time T of the car's yaw rate at that time was evaluated.

[0032] The yaw rate response time T of a vehicle is calculated as follows: for vehicles with a maximum speed greater than 120 km / h, the range is 0.20–0.05 s; for vehicles with a maximum speed less than 120 km / h, the range is 0.30–0.10 s; for vehicles with a maximum gross vehicle weight less than or equal to 6 t, the yaw rate response time is scored as follows:

[0033] In the formula: NT is the evaluation score for the vehicle's yaw rate response time; T60 is the lower limit of the vehicle's yaw rate response time, in seconds; T100 is the upper limit of the vehicle's yaw rate response time, in seconds; T is the lateral acceleration value of 2 m / s². 2 The experimental value of the yaw rate response time of the car, in seconds.

[0034] 5. Virtual Simulation Module: Used to build virtual models of vehicles, batteries, motors, and engines, reproducing real-world operating conditions (such as extreme temperature environments and complex road conditions) through virtual simulation technology. The virtual model works in conjunction with the dynamometer bench and HIL cabinet to simulate scenarios that are impossible or difficult to replicate in a real vehicle within a laboratory environment, improving the flexibility and comprehensiveness of testing.

[0035] 6. Test Management Module: Responsible for monitoring and managing the entire test system, including timing management (such as time synchronization of actions of each module and control of signal transmission delay) and safety management (such as high voltage safety protection, equipment overload protection, and emergency shutdown logic), to ensure that the test process is orderly and safe.

[0036] 7. Automated Testing Module: Supports users to write detailed test cases in advance (covering operating conditions, parameter ranges, execution steps, etc.) and automatically complete the basic settings of the test bench (such as initial operating parameters and safety thresholds) and the operation of preset operating conditions, reducing manual intervention and improving testing efficiency and repeatability.

[0037] 8. Data Acquisition Module: Responsible for acquiring data from all sensors of the device under test (such as controller, motor, battery simulator, etc.) and bus communication data, including real-time torque, speed, current, voltage, temperature, SOC, controller output commands, etc., to provide an accurate data foundation for subsequent analysis, evaluation and optimization.

[0038] III. System timing action flow; The testing and calibration process of this platform can be performed cyclically. The following example, using a complete cycle, illustrates the timing actions of the system: Step 1: The DOE module designs calibration factors based on the test objective (such as optimizing dynamic response speed) to determine the parameters to be optimized and the test combination; Step 2: The automatic calibration module calls the preset calibration program and automatically writes the first set of parameter combinations generated by the DOE module into the controller of the system under test (such as VCU, MCU). Step 3: The test management module controls the system under test and the test bench to complete the power-on operation of high voltage and low voltage to ensure that the system enters the standby state; Step 4: The HIL system establishes communication with the system under test via a bus (such as a CAN bus), sends an initialization signal, and enables the device under test (controller and powertrain components) to enter the vehicle start-up state; Step 5: The HIL system runs the dynamic scene model in the virtual simulation module and sends driver signals (such as accelerator pedal opening and gear signal) to the controller under test according to the preset working conditions (such as desert shoal). Step 6: The controller under test drives the motor to generate driving force according to the driver's signal. The torque sensor collects the driving torque and feeds it back to the HIL system. At the same time, the current sensor collects the current signal on the DC bus and feeds it back to the battery model. The HIL system calculates the wheel end speed based on the received driving torque and calculates the battery terminal voltage based on the battery model. Step 7: The HIL system sends the calculated wheel-end speed signal to the dynamometer to control the dynamometer to adjust the load; at the same time, it sends the battery-end voltage signal to the battery simulator to control the battery simulator to output the corresponding voltage. Step 8: The data acquisition module continuously collects data from all sensors on the test bench (such as torque, speed, current, and temperature) and bus signals (such as controller status and fault codes), and stores them in the database; Step 9: The objective evaluation module calls the data stored in the data acquisition module, scores the overall vehicle power performance (such as power output stability and energy consumption economy) based on preset evaluation indicators, and generates an evaluation report; Step 10: The DOE module performs parameter optimization analysis based on the objective evaluation results, calculates the next optimal parameter combination, and then returns to Step 2 to enter the next test loop until the calibration parameters that meet the target are obtained.

[0039] This invention combines the real physical testing capabilities of a dynamometer bench with the virtual simulation advantages of HIL through the above technical solution, realizing comprehensive testing under multiple operating conditions (including extreme operating conditions). At the same time, through automated testing, calibration and optimization functions, it significantly improves testing efficiency, shortens the controller development cycle, and reduces reliance on real vehicle road tests, making it suitable for the development and verification of new energy vehicle power systems.

[0040] It should be noted that, in this invention, although the specification describes the embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A vehicle automatic test and calibration system integrated with a dynamometer and a HIL, characterized in that, The system comprises a dynamometer bench, a HIL hardware-in-the-loop system, a battery simulator and a test management module, wherein: The HIL hardware-in-the-loop system is integrated with a virtual simulation module for constructing a dynamic scene model containing complex working conditions; The test management module is connected with the dynamometer bench, the HIL hardware-in-the-loop system and the battery simulator, and is used for executing the following closed-loop processes: (1) Road load simulation: receiving wheel end driving torque collected by the dynamometer bench, calculating wheel end rotating speed through a dynamic model and feeding back to the dynamometer bench; (2) Energy consumption simulation: receiving direct current bus current collected by the battery simulator, calculating battery end voltage through a second-order battery model and feeding back to the battery simulator; (3) Automatic testing and evaluation: running preset test cases, collecting sensor and bus data and scoring the whole vehicle power system based on an objective evaluation module; (4) Automatic calibration and optimization: designing calibration factors through a DOE module, injecting parameters into the controller by an automatic calibration module and cyclically optimizing calibration parameters according to evaluation results. The road load simulation specifically comprises:

2. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, The dynamic scene model contains at least one of NEDC working condition, intersection working condition and desert rush slope working condition; Model input includes whole vehicle parameters based on real vehicle data, regulation road environment data and road test personnel driving style data; Signal flow is transmitted in the following order: (1) Driver behavior signal is sent to the controller; (2) The controller outputs motor driving force to the torque sensor; (3) The torque sensor collects driving force and feeds back to the dynamic model; (4) The dynamic model outputs wheel end rotating speed signal to the dynamometer bench. The energy consumption simulation specifically comprises:

3. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, The second-order battery model is constructed based on the Thevenin equivalent circuit principle, and model parameters are obtained through HPPC hybrid pulse power characteristic experiment; Signal flow is transmitted in the following order: (1) Direct current bus current is collected by a sensor and input into the battery model; (2) The battery model calculates battery end voltage based on current and initial conditions; (3) The calculated battery end voltage is fed back to the battery simulator. The test management module further comprises:

4. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, An IO function module for realizing signal unit conversion, virtual node simulation and safety logic design; An automatic testing module for writing and executing test cases covering multiple working conditions; A data acquisition module for collecting sensor and bus data of the measured object in real time. The automatic calibration module realizes automatic injection of parameters of the controller through secondary development of Vector CANape.

5. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, The objective evaluation module generates a quantitative scoring report based on dynamic performance data.

6. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, The system timing action is cyclically executed in the following steps:

7. The automatic test and calibration system of whole vehicle in multiple working conditions integrated with dynamometer and HIL according to claim 1, characterized in that, S1, the DOE module designs calibration factors; S2, the automatic calibration module injects a first group of factor parameters into the controller; S3, the measured system and the bench high and low voltage power supply are started; S4, the HIL system and the measured system bus interact, so that the controller enters the vehicle starting state; S5, the HIL system runs the virtual model and sends the driver signal; S6, wheel end driving torque and direct current bus current are received, and wheel end rotating speed and battery end voltage are calculated; S7, the dynamometer rotating speed and the battery simulator voltage are controlled; ​ S8, collect gantry sensors and bus data; S9, objective evaluation module scores the system; S10, DOE module optimizes calibration parameters and enters the next cycle.

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