Hybrid electric vehicle controller hardware-in-the-loop simulation test method, device, equipment and storage medium

Abstract

The application discloses a hybrid electric vehicle controller hardware-in-the-loop simulation test method, device and equipment and a storage medium, relates to the vehicle simulation test technical field, and comprises the following steps: acquiring a mode instruction sent by a vehicle controller and a clutch state signal of a hybrid electric vehicle; determining a torque calculation rule of a power assembly model in a vehicle dynamics simulation model according to the mode instruction; determining a rotating speed calculation rule of the power assembly model according to the clutch state signal; performing real-time simulation on the vehicle dynamics simulation model based on the torque calculation rule and the rotating speed calculation rule, and obtaining vehicle running state data; sending the vehicle running state data to the vehicle controller for hardware-in-the-loop simulation test, and obtaining a hardware-in-the-loop simulation test result of the vehicle controller, so that the coupling dynamics of an engine and double motors in a multi-mode switching process of the hybrid electric vehicle is accurately reproduced, and the confidence of the vehicle controller hardware-in-the-loop simulation test is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle simulation testing technology, and in particular to a hardware-in-the-loop simulation testing method, apparatus, equipment, and storage medium for a hybrid electric vehicle controller. Background Technology

[0002] With the emergence of limitations in the range and refueling capabilities of pure electric vehicles, hybrid electric vehicles (HEVs) have become the mainstream choice in the market. Among them, HEVs with the generator located at the engine crankshaft and the drive motor at the transmission output are widely used in range-extended and plug-in hybrid electric vehicles due to their compact structure, flexible mode switching, and high energy recovery efficiency. The vehicle controller needs to coordinate multiple power sources, including the engine, generator, drive motor, and battery, in real time to achieve smooth switching between pure electric, series, and parallel modes. The correctness of its control logic directly affects the vehicle's performance and safety. Therefore, during the development of the vehicle controller, hardware-in-the-loop simulation testing is necessary to fully verify its control logic.

[0003] However, existing hardware-in-the-loop simulation testing methods lack accurate calculation methods for powertrain torque and speed for the above architecture. In parallel mode, they cannot accurately reflect the superposition relationship between the combined engine torque and generator torque and the drive motor torque, and do not consider the influence of clutch engagement and disengagement on engine speed. This leads to inaccurate dynamic simulation of the coupling between the engine and dual motors during multi-mode switching, reducing the confidence level of vehicle controller simulation testing.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a hardware-in-the-loop simulation test method, apparatus, device, and storage medium for hybrid electric vehicle controllers, aiming to solve the technical problem of how to improve the simulation accuracy of engine and dual-motor coupling dynamics under multi-mode switching in the hardware-in-the-loop simulation test of hybrid electric vehicle controllers.

[0006] To achieve the above objectives, the present invention provides a hardware-in-the-loop simulation test method for a hybrid electric vehicle controller, the method comprising the following steps: Acquire mode commands sent by the vehicle controller and clutch status signals of the hybrid vehicle; The torque calculation rules for the powertrain model in the vehicle dynamics simulation model are determined according to the mode instructions. The speed calculation rules for the powertrain model are determined based on the clutch status signal; Based on the torque calculation rules and the speed calculation rules, the vehicle dynamics simulation model is simulated in real time to obtain vehicle operating status data. The vehicle operating status data is sent to the vehicle controller for hardware-in-the-loop simulation testing, and the hardware-in-the-loop simulation test results of the vehicle controller are obtained.

[0007] In one embodiment, the step of determining the torque calculation rules of the powertrain model in the vehicle dynamics simulation model according to the mode instruction includes: The mode command is parsed to obtain the operating modes of the hybrid vehicle, which include pure electric mode, series mode and parallel mode. When the operating mode is pure electric mode or series mode, the product of the drive motor torque of the drive motor located at the output end of the gearbox and the speed ratio from the drive motor to the wheel end is determined as the powertrain wheel end torque. When the operating mode is parallel mode, the generator torque of the generator located at the crankshaft end of the engine is added to the product of the generator torque and the speed ratio from the generator to the engine, and the engine torque of the engine to obtain the engine-side composite torque. The product of the combined torque on the engine side and the engine-to-wheel speed ratio is added to the product of the torque on the drive motor and the drive motor-to-wheel speed ratio to obtain the powertrain wheel-end torque. The corresponding torque calculation rule is determined based on the wheel-end torque of the powertrain.

[0008] In one embodiment, the step of determining the corresponding torque calculation rule based on the powertrain wheel-end torque includes: The powertrain wheel-end torque of the hybrid electric vehicle under different vehicle speeds, different accelerator pedal openings, and different battery charge states was obtained through real vehicle calibration. A multi-dimensional mapping table is established based on the wheel-end torque of the powertrain, with vehicle speed, accelerator pedal opening and battery state of charge as inputs. During real-time simulation, the current vehicle speed, current accelerator pedal opening, and current battery state of charge are obtained. The current powertrain wheel-end torque is obtained by querying the multidimensional mapping table based on the current vehicle speed, the current accelerator pedal opening, and the current battery state of charge. The corresponding torque calculation rule is determined based on the current powertrain wheel-end torque.

[0009] In one embodiment, the step of determining the speed calculation rule of the powertrain model based on the clutch state signal includes: The clutch status signal is analyzed to obtain the clutch status, which includes an engaged state and a disengaged state. When the clutch is in the engaged state, the product of the tire speed of the hybrid vehicle and the engine-to-wheel speed ratio is determined as the engine speed. When the clutch is in the disengaged state, the crankshaft speed of the engine of the hybrid vehicle is determined as the engine speed; The corresponding speed calculation rules are determined based on the method for determining the engine speed.

[0010] In one embodiment, the step of determining the engine speed by the product of the tire speed of the hybrid vehicle and the engine-to-wheel speed ratio when the clutch is in the engaged state includes: Obtain the current gear position of the transmission of the hybrid vehicle; The engine-to-wheel speed ratio is obtained by querying the preset gear ratio correspondence based on the current gear position. When the transmission is a continuously variable transmission (CVT), the engine-to-wheel speed ratio is calculated in real time based on the vehicle speed of the hybrid vehicle. The engine speed is determined by multiplying the tire speed of the hybrid vehicle by the engine-to-wheel speed ratio.

[0011] In one embodiment, the step of performing real-time simulation of the vehicle dynamics simulation model based on the torque calculation rule and the speed calculation rule to obtain vehicle operating state data includes: The driver model in the vehicle dynamics simulation model outputs accelerator pedal gear signal and brake pedal gear signal based on the test condition data; The road surface and environmental information are provided through the road model and traffic scene model in the vehicle dynamics simulation model; The powertrain model calculates the wheel-end torque and engine speed according to the torque calculation rules and the speed calculation rules. The vehicle dynamics simulation model is simulated in real time based on the powertrain wheel-end torque, the engine speed, the road surface information, and the environmental information to obtain vehicle operating status data.

[0012] In one embodiment, the step of sending the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation testing and obtaining the hardware-in-the-loop simulation test results of the vehicle controller includes: Design test cases that cover typical driving cycle conditions, mode switching transient conditions, and battery state of charge boundary conditions. Inject clutch jamming fault and motor failure fault into the test cases; Execute the test cases to obtain the model response data of the vehicle dynamics simulation model; Real-time acquisition of control commands fed back by the vehicle controller and model response data; The control commands and the model response data are compared to obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a hardware-in-the-loop simulation test device for a hybrid electric vehicle controller, the device comprising: The signal acquisition module is used to acquire mode commands sent by the vehicle controller and clutch status signals of the hybrid vehicle. The torque rule module is used to determine the torque calculation rules of the powertrain model in the vehicle dynamics simulation model according to the mode command. The speed rule module is used to determine the speed calculation rules of the powertrain model based on the clutch status signal; The real-time simulation module is used to perform real-time simulation of the vehicle dynamics simulation model based on the torque calculation rules and the speed calculation rules to obtain vehicle operating status data. The closed-loop test module is used to send the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation test and obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a hardware-in-the-loop simulation test device for a hybrid electric vehicle controller. The device includes: a memory, a processor, and a hardware-in-the-loop simulation test program for a hybrid electric vehicle controller stored in the memory and executable on the processor. The hardware-in-the-loop simulation test program for a hybrid electric vehicle controller is configured to implement the steps of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a hardware-in-the-loop simulation test program for a hybrid electric vehicle controller. When the hardware-in-the-loop simulation test program for a hybrid electric vehicle controller is executed by a processor, it implements the steps of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: By using mode commands and dynamic torque and speed calculation rules for clutch state switching, the coupling dynamics between the engine and dual motors during multi-mode switching of hybrid electric vehicles are accurately reproduced, improving the confidence of the vehicle controller hardware-in-the-loop simulation test. At the same time, by combining real-time simulation of driver, road and traffic scenarios with test cases of multiple working conditions and fault injection, the realism of the simulation environment and test coverage are improved. Furthermore, by using lookup tables and variable gear ratios, both the real-time performance of the simulation and the vehicle model adaptability are taken into account. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller of this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller provided in this application. Figure 3 This is a schematic diagram of the module structure of the hardware-in-the-loop simulation test device for the hybrid electric vehicle controller according to an embodiment of this application; Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller in this embodiment of the application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a hardware-in-the-loop simulation test device for a hybrid electric vehicle controller. The following description uses a hardware-in-the-loop simulation test device for a hybrid electric vehicle controller as an example to illustrate this embodiment and the subsequent embodiments.

[0025] Based on this, embodiments of this application provide a hardware-in-the-loop simulation test method for a hybrid electric vehicle controller, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller of this application.

[0026] In this embodiment, the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller includes steps S10 to S50: Step S10: Obtain the mode command sent by the vehicle controller and the clutch status signal of the hybrid vehicle; It should be understood that the vehicle dynamics model includes: driver model, engine model, transmission model, clutch model, drive axle model, tire model, suspension model, body dynamics model, steering system model, air brake model, road model, and traffic scene model. The signal transmission relationships between the models are as follows: The driver model receives experimental operating condition data (accelerator pedal gear position, brake pedal gear position) and outputs control commands; the engine model receives the accelerator pedal gear position signal and outputs engine speed; the transmission model receives the accelerator pedal gear position signal and outputs torque; the clutch model connects the engine model and the transmission model, controlling the on / off state of power transmission; the drive axle model transmits the transmission output torque to the tire model; the tire model receives road surface coordinates, friction coefficient, and slope information; the suspension model receives vehicle body forces and wheel coordinates; the body dynamics model receives suspension output and outputs vehicle coordinates; the air brake model receives brake pedal signals and outputs braking information; the road model and traffic scene model provide road surface and environmental information.

[0027] It should be noted that the vehicle controller is the controller in a hybrid electric vehicle that is responsible for coordinating multiple power sources such as the engine, generator, drive motor and power battery. It is used to realize the switching control of pure electric, series and parallel working modes. In this embodiment, the vehicle controller is the object under test, that is, the real controller whose control logic needs to be verified in the hardware-in-the-loop simulation test.

[0028] Hybrid electric vehicles are vehicles that simultaneously use an engine and an electric motor as power sources. In this embodiment, the generator of the hybrid electric vehicle is located at the crankshaft end of the engine, and the drive motor is located at the output end of the gearbox. The engine and the wheel end can be mechanically coupled and decoupled through a clutch.

[0029] The mode command is a command issued by the vehicle controller based on the vehicle's operating status to indicate the current operating mode of the hybrid electric vehicle. The operating modes include pure electric mode, series mode, and parallel mode. In this embodiment, the mode command is issued by the vehicle controller through the bus interface.

[0030] The clutch status signal reflects whether the clutch is currently engaged or disengaged. The clutch connects the engine and the transmission and is used to control the on / off state of power transmission between the engine and the wheel ends. In this embodiment, the clutch status signal can be output from the clutch model in the vehicle dynamics simulation model.

[0031] Understandably, the system receives mode commands from the vehicle controller via the bus interface and collects clutch status signals to provide input for determining the torque and speed calculation rules of the powertrain model.

[0032] The beneficial effect of this step is that it introduces the actual mode commands issued by the vehicle controller into the simulation process, so that the switching of subsequent calculation rules is synchronized with the actual control behavior of the vehicle controller, thus ensuring the closed-loop authenticity of the simulation test.

[0033] Step S20: Determine the torque calculation rules for the powertrain model in the vehicle dynamics simulation model according to the mode instructions; It should be noted that the whole vehicle dynamics simulation model is a simulation model used to simulate the dynamic characteristics of real vehicles, including driver model, engine model, gearbox model, clutch model, drive half shaft model, tire model, suspension model, vehicle body dynamics model, steering system model, air brake model, road model and traffic scene model. In this embodiment, the whole vehicle dynamics simulation model is built in simulation modeling software, compiled and deployed to run on a real-time simulator.

[0034] The powertrain model is the part of the vehicle dynamics simulation model used to simulate the dynamic characteristics of the powertrain. It includes the engine model, generator model, drive motor model, gearbox model, and clutch model. In this embodiment, the engine model adopts the mean value model, and the generator model and drive motor model adopt the lookup table model.

[0035] The torque calculation rule is used to calculate the wheel-end torque of the powertrain. It corresponds to the working mode. In pure electric mode and series mode, the wheel-end torque of the powertrain is obtained only by converting the drive motor torque through the speed ratio. In parallel mode, the wheel-end torque of the powertrain is obtained by superimposing the drive motor torque with the combined torque of the engine and generator.

[0036] Understandably, the current operating mode of the hybrid vehicle is obtained by parsing the mode command, and the torque calculation rule corresponding to this operating mode is selected as the torque calculation rule of the powertrain model for use in the real-time simulation process.

[0037] The beneficial effect of this step is that the torque calculation rules dynamically switch with the working mode, accurately reflecting the power transmission relationship between the engine and the wheel end in pure electric and series modes, and the engine and the wheel end in parallel mode, thus improving the torque simulation accuracy during the multi-mode switching process.

[0038] Understandably, the torque calculation rules are encapsulated as an independent module and embedded into the vehicle dynamics simulation model. During real-time simulation, the calculation method within the independent module is called and switched according to the mode command.

[0039] Step S30: Determine the speed calculation rules for the powertrain model based on the clutch status signal; It should be noted that the speed calculation rule is a rule used to determine the engine speed, which corresponds to the clutch state. That is, when the clutch is engaged, the engine speed is determined based on the tire speed, and when the clutch is disengaged, the engine speed is determined based on the crankshaft speed.

[0040] Understandably, the clutch status signal is analyzed to obtain the current state of the clutch, and the speed calculation rule corresponding to this state is selected as the speed calculation rule of the powertrain model for use in the real-time simulation process.

[0041] The beneficial effect of this step is that the source of engine speed calculation is dynamically distinguished according to the clutch engagement and disengagement state, avoiding the distortion of engine speed calculation caused by not considering the clutch state, and improving the speed simulation accuracy in parallel mode and transient mode switching.

[0042] In one feasible implementation, step S30 includes steps A11 to A14: Step A11: Analyze the clutch status signal to obtain the clutch status, which includes the engagement state and the disengagement state; It should be noted that the clutch status refers to the current working state of the clutch, including the engaged state and the disengaged state.

[0043] The engaged state is when the clutch is engaged and the power transmission between the engine and the transmission is connected.

[0044] The disengaged state is when the clutch is released and the power transmission between the engine and the transmission is disconnected.

[0045] Understandably, the value of the clutch status signal is read and mapped to the actual state of the clutch to obtain the determination result of the engaged or disengaged state.

[0046] The beneficial effect of this step is that it transforms the clutch status signal into a clear clutch status, providing a basis for subsequent calculations that differentiate engine speeds based on their status.

[0047] Step A12: When the clutch is engaged, the product of the tire speed of the hybrid vehicle and the engine-to-wheel speed ratio is determined as the engine speed. It should be noted that the tire speed is the rotational speed of the wheel tires of the hybrid vehicle. In this embodiment, the tire speed is output by the tire model in the vehicle dynamics simulation model.

[0048] The engine-to-wheel speed ratio is the transmission speed ratio between the engine output end and the wheel edge. In this embodiment, the engine-to-wheel speed ratio can be a fixed speed ratio. When the gearbox has multiple gears or is a continuously variable transmission, it can be determined according to the current gear or vehicle speed.

[0049] Engine speed is the rotational speed of the engine output shaft. It is a key parameter reflecting the engine's operating status and an important basis for the vehicle controller to perform mode coordination control.

[0050] Understandably, when the clutch is engaged, the engine and the wheel are mechanically coupled, and there is a definite speed ratio between the tire speed and the engine speed. By multiplying the tire speed by the speed ratio from the engine to the wheel, the engine speed can be derived from the tire speed.

[0051] The beneficial effect of this step is that, in the combined state, the engine speed is obtained by inversely calculating the tire speed through the speed ratio, which is consistent with the motion relationship between the engine and the mechanical connection at the wheel end in a real vehicle, thus ensuring the accuracy of the engine speed simulation.

[0052] Step A13: When the clutch is in the disengaged state, determine the crankshaft speed of the hybrid vehicle's engine as the engine speed; It should be noted that the crankshaft speed is the rotational speed of the engine crankshaft. In this embodiment, the crankshaft speed is output by the engine model based on the accelerator pedal gear signal, reflecting the engine's own operating status.

[0053] Understandably, when the clutch is disengaged, the engine and wheel ends are mechanically decoupled, and there is no longer a speed ratio between the tire speed and the engine speed. At this time, the crankshaft speed of the engine is directly obtained and determined as the engine speed.

[0054] The beneficial effect of this step is that the engine speed in the disengaged state is directly determined by the engine's own operating state, avoiding the speed calculation error caused by still calculating the speed by the tire speed, and accurately reflecting the true physical relationship of the engine and wheel end decoupling when the clutch is disengaged.

[0055] In one feasible implementation, step A13 includes steps B11-B14: Step B11: Obtain the current gear of the hybrid vehicle's transmission; It should be noted that the current gear is the current operating gear of the transmission. In this embodiment, the current gear can be determined by the transmission model according to the shift control command of the vehicle controller.

[0056] Understandably, when the clutch is engaged, the current gear of the transmission is obtained to provide input for determining the currently effective engine-to-wheel speed ratio.

[0057] The benefit of this step is that it ensures that the determination of the engine-to-wheel speed ratio corresponds to the actual gear position of the transmission, providing an accurate speed ratio input for the speed simulation of multi-gear vehicles.

[0058] Step B12: Based on the current gear position, query the preset gear ratio correspondence to obtain the engine-to-wheel speed ratio; It should be noted that the preset gear ratio correspondence is a pre-set correspondence between each gear of the transmission and the speed ratio from the engine to the wheel. In this embodiment, the preset gear ratio correspondence can be obtained through actual vehicle calibration or high-precision simulation.

[0059] Understandably, by using the current gear as input, a preset gear ratio correspondence is queried to obtain the engine-to-wheel speed ratio corresponding to the current gear.

[0060] The beneficial effect of this step is that it replaces the fixed speed ratio with a speed ratio that varies with the gear, so that multi-gear vehicles can obtain an accurate engine-to-wheel speed ratio in different gears, thereby improving the accuracy of speed calculation.

[0061] Step B13: When the transmission is a continuously variable transmission (CVT), calculate the engine-to-wheel speed ratio in real time based on the vehicle speed of the hybrid vehicle; It should be noted that a continuously variable transmission (CVT) is a transmission whose gear ratio can be continuously varied within a certain range. Its gear ratio changes continuously with the vehicle speed and there are no fixed gear divisions.

[0062] Vehicle speed refers to the driving speed of a hybrid electric vehicle. In this embodiment, the vehicle speed can be output from the vehicle body dynamics model in the whole vehicle dynamics simulation model.

[0063] Understandably, when the transmission is a continuously variable transmission (CVT), there are no fixed gears available for query. The speed ratio is calculated in real time based on the vehicle speed of the hybrid vehicle to obtain a continuously changing engine-to-wheel speed ratio.

[0064] The beneficial effect of this step is that it adopts a speed ratio that is continuously calculated with vehicle speed for continuously variable transmission (CVT) vehicles, so that the speed calculation rules can be adapted to CVT hybrid vehicles, thus expanding the applicability of the simulation test method.

[0065] Step B14: Determine the engine speed by multiplying the tire speed of the hybrid vehicle by the engine-to-wheel speed ratio.

[0066] Understandably, the engine speed is obtained by multiplying the tire speed of the hybrid vehicle by the engine-to-wheel speed ratio determined in steps B11 to B13.

[0067] The beneficial effect of this step is that the engine-to-wheel speed ratio is dynamically determined according to the gearbox type and the current gear, so that the reverse calculation result of the engine speed under the engagement state is consistent with the actual transmission state of the gearbox, further improving the accuracy of speed simulation.

[0068] Step A14: Determine the corresponding speed calculation rules based on the method of determining engine speed.

[0069] Understandably, the engine speed calculation method determined by the clutch state is solidified into a speed calculation rule. That is, in the engaged state, the calculation method derived from the tire speed is called, and in the disengaged state, the calculation method that directly obtains the crankshaft speed is called, thus obtaining the speed calculation rule corresponding to the clutch state.

[0070] The beneficial effect of this step is that the speed calculation rules dynamically switch with the clutch state, ensuring the continuity and accuracy of engine speed simulation during the transient process of mode switching.

[0071] In one feasible implementation, the switching between torque calculation rules and speed calculation rules can be achieved using a finite state machine.

[0072] It should be noted that a finite state machine is a model that divides the operation process into several discrete states and transitions between states according to triggering conditions. Sensor signals are signals collected by various sensors that reflect the vehicle's operating state.

[0073] Understandably, the clutch state, pure electric mode, series mode, parallel mode, and energy recovery mode are defined as the various states of a finite state machine. Each state corresponds to a set of torque calculation submodules and speed calculation submodules. The transition between states is triggered by mode commands issued by the vehicle controller and sensor signals. After the state transition, the torque calculation submodule and speed calculation submodule corresponding to the current state are called for calculation.

[0074] The beneficial effects of this implementation method are that the model has a high degree of structure, clear logic and is easy to debug, and it is convenient to manage the multi-mode switching process by state.

[0075] Step S40: Based on the torque calculation rules and speed calculation rules, perform real-time simulation of the vehicle dynamics simulation model to obtain vehicle operating status data; It should be noted that real-time simulation is a simulation method in which the computation process of the simulation model proceeds synchronously with real time. In this embodiment, the vehicle dynamics simulation model is compiled into real-time executable code and then downloaded to the real-time simulator for execution to meet the real-time requirements of closed-loop interaction with the real vehicle controller.

[0076] Vehicle operating status data is the data reflecting the vehicle's operating status output during the real-time simulation of the vehicle dynamics simulation model. In this embodiment, the vehicle operating status data includes vehicle coordinates output by the vehicle body dynamics model, tire rotation speed and vehicle speed output by the tire model, etc.

[0077] Understandably, the currently effective torque calculation rules and speed calculation rules are invoked to drive the vehicle dynamics simulation model to perform calculations synchronized with real time, simulate the dynamic response of a real vehicle, and obtain vehicle operating status data.

[0078] The beneficial effect of this step is that the vehicle dynamics simulation model runs in real time according to dedicated calculation rules, providing the vehicle controller with an operating environment consistent with the real vehicle, thus ensuring the confidence level of the hardware-in-the-loop simulation test.

[0079] In one feasible implementation, step S40 includes steps A21 to A24: Step A21: The driver model in the vehicle dynamics simulation model outputs the accelerator pedal gear signal and brake pedal gear signal based on the test condition data; It should be noted that the driver model is a model used in the whole vehicle dynamics simulation model to simulate the driver's driving operation and to output driving operation commands according to the test conditions.

[0080] The test condition data are pre-set condition data used to simulate the driving process. In this embodiment, the test condition data includes the accelerator pedal gear and the brake pedal gear.

[0081] The accelerator pedal gear signal is a signal that represents the driver's acceleration demand, i.e., the accelerator pedal opening. In this embodiment, the accelerator pedal gear signal is output to the engine model and the transmission model.

[0082] The brake pedal position signal is a signal that represents the driver's braking demand, i.e., the brake pedal opening. In this embodiment, the brake pedal position signal is output to the air pressure braking model.

[0083] Understandably, the driver model receives test condition data, simulates the driver's operation of the accelerator and brake pedals, and outputs accelerator pedal gear signal and brake pedal gear signal as input to the powertrain model and air brake model.

[0084] The beneficial effect of this step is that by reproducing real driving operation inputs through a driver model, the simulation process covers the interaction between the driver and the vehicle, thus improving the completeness of the simulation environment.

[0085] Step A22: Provide road surface and environmental information through the road model and traffic scene model in the vehicle dynamics simulation model; It should be noted that the road model is a model used to provide road conditions in the whole vehicle dynamics simulation model. In this embodiment, the road model provides road surface coordinates, friction coefficient, and slope information.

[0086] Traffic scene model is a model used in vehicle dynamics simulation model to provide traffic scene information around the vehicle.

[0087] Road surface information reflects the road conditions under which vehicles travel, including road surface coordinates, friction coefficient, and slope information.

[0088] Environmental information is information that reflects the traffic scene around a vehicle.

[0089] Understandably, the road model provides the tire model with road surface coordinates, friction coefficient, and slope information, while the traffic scene model provides environmental information about the vehicle's surroundings, all of which participate in the real-time calculation of the vehicle dynamics simulation model.

[0090] The beneficial effect of this step is that the simulation process reproduces the road conditions and traffic scenarios in real vehicle operation, improving the realism of the simulation environment and the confidence level of the test.

[0091] Step A23: Calculate the powertrain wheel-end torque and engine speed using the powertrain model according to the torque calculation rules and speed calculation rules; It should be noted that the powertrain wheel-end torque is the torque transmitted from the powertrain to the wheel ends via the drive chain, which is the effective torque that drives the vehicle.

[0092] Understandably, the transmission model receives the gear position signal from the accelerator pedal and outputs torque, while the clutch model controls the on / off state of power transmission between the engine and transmission models. The powertrain model calculates the powertrain wheel torque according to the torque calculation rules corresponding to the current operating mode, and calculates the engine speed according to the speed calculation rules corresponding to the current clutch state, thus achieving dynamic switching between the two calculation rules based on mode commands and clutch states.

[0093] The benefit of this step is that the calculation of powertrain wheel-end torque and engine speed is consistent with the power transmission relationship of the real architecture, ensuring the accuracy of powertrain simulation during multi-mode switching.

[0094] Step A24: Perform real-time simulation of the vehicle dynamics simulation model based on the powertrain wheel-end torque, engine speed, road surface information, and environmental information to obtain vehicle operating status data.

[0095] Understandably, the drive half-shaft model transmits the powertrain wheel-end torque to the tire model. The tire model combines road information to calculate the force between the wheel and the road surface. The suspension model receives the vehicle body force and wheel coordinates and outputs them to the vehicle dynamics model. The vehicle dynamics model solves for the vehicle coordinates. At the same time, the air pressure braking model outputs braking information based on the brake pedal gear signal. All of these are combined to obtain the vehicle's operating status data.

[0096] The beneficial effect of this step is that the vehicle operating status data is obtained by solving the complete vehicle dynamics chain, covering drive, braking, suspension and vehicle body response, so that the output vehicle operating status data is consistent with the actual vehicle operating status.

[0097] Step S50: Send the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation test, and obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0098] It should be noted that hardware-in-the-loop simulation testing is a testing method that connects the real vehicle controller to the simulation closed-loop loop. That is, the controller hardware is in the simulation loop and interacts with the real-time running vehicle dynamics simulation model through real physical signals. In this embodiment, the vehicle dynamics simulation model is deployed on a real-time simulator and connected to the vehicle controller through a bus interface and a hardwired interface, and a signal mapping table is configured to complete the signal docking.

[0099] Controller Area Network (CAN) is a bus in a vehicle used for data communication between controllers. In this embodiment, vehicle operating status data is sent to the vehicle controller via the CAN bus interface. A real-time emulator is a simulation device used to run real-time executable code. In this embodiment, the real-time emulator can be either the dSPACE Scalexio real-time emulator or the NI VeriStand real-time simulation platform; both are trade names for real-time simulation devices.

[0100] Hardware-in-the-loop simulation test results are obtained after verifying the control logic of the vehicle controller, and are used to reflect the correctness and robustness of the vehicle controller's control strategy.

[0101] Understandably, vehicle operating status data is sent to the vehicle controller according to the signal mapping table. The vehicle controller calculates the data based on the vehicle operating status data and feeds back control commands. The control commands are fed back into the vehicle dynamics simulation model to form a closed-loop interaction, complete the hardware-in-the-loop simulation test, and obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0102] The beneficial effect of this step is that the real vehicle controller and the high-fidelity vehicle dynamics simulation model form a closed loop, which allows the control logic of the vehicle controller to be fully verified before installation on the vehicle, reducing the cost and risk of real vehicle testing.

[0103] In one feasible implementation, the vehicle dynamics simulation model can be run using a co-simulation approach. It should be noted that co-simulation is a simulation method that connects and collaboratively runs models built by different simulation software through a standard interface. The Functional Mock-up Interface (FMI) is a standard interface for the encapsulation and interaction of simulation models; the model file encapsulated according to this interface is the Functional Mock-up Unit (FMU). Transmission Control Protocol / Internet Protocol (TCP / IP) are protocols used for data communication between network devices.

[0104] Understandably, the driver model, road model, and traffic scenario model are constructed using third-party high-precision simulation software, and interact with the powertrain model running on the real-time simulator through functional mock unit interfaces or transmission control protocols and Internet protocols to jointly complete the real-time operation of the vehicle dynamics simulation model.

[0105] The advantages of this implementation method are that the simulation accuracy of road and traffic scenarios is high, which is suitable for testing needs with high requirements for scenario fidelity. However, it requires additional software licenses, and co-simulation may introduce communication delays, which requires higher real-time performance.

[0106] In one feasible implementation, step S50 includes steps A31 to A35: Step A31: Design test cases that cover typical driving cycle conditions, mode switching transient conditions, and battery state of charge boundary conditions; It should be noted that test cases are a collection of test scenarios and test procedures designed to verify the control logic of the vehicle controller.

[0107] Typical driving cycles are standard vehicle driving cycles, including the New European Driving Cycle (NEDC) and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC), used to simulate the normal driving process of a vehicle.

[0108] The mode switching transient condition is the transient process of a hybrid electric vehicle switching from pure electric mode to series mode, or from series mode to parallel mode.

[0109] The battery state of charge boundary condition is the condition in which the battery state of charge (SOC) is near the preset upper or lower limit boundary. The battery state of charge is the ratio of the remaining power battery capacity to its rated capacity.

[0110] Understandably, test cases are designed to cover typical driving cycle conditions, mode switching transient conditions, and battery state-of-charge boundary conditions. In this embodiment, for example, in a globally unified light vehicle test cycle, the battery state of charge is set to drop from 80% to 20%, and the switching logic and torque response of the vehicle controller between pure electric, series, and parallel modes are observed. This example uses a combination of driving cycle and battery state-of-charge boundary conditions to assess mode switching control.

[0111] The benefit of this step is that the test cases cover scenarios such as normal driving, transient mode switching, and battery boundary, thereby improving the coverage of hardware-in-the-loop simulation testing.

[0112] Step A32: Inject clutch sticking fault and motor failure fault into the test cases; It should be noted that clutch jamming is a type of jamming fault in which the clutch cannot properly engage or disengage.

[0113] Motor failure is a fault in which the generator or drive motor loses its power generation or driving ability.

[0114] Understandably, during the execution of test cases, clutch jamming and motor failure faults are artificially injected into the vehicle dynamics simulation model to simulate vehicle failure scenarios and verify the control logic of the vehicle controller under failure scenarios.

[0115] The beneficial effect of this step is that it reproduces the fault scenario without damaging the real hardware, thus verifying the robustness of the vehicle controller's control strategy under fault scenarios.

[0116] Step A33: Execute the test cases to obtain the model response data of the whole vehicle dynamics simulation model; It should be noted that the model response data is the response data generated by the vehicle dynamics simulation model under the action of test case excitation and control commands fed back by the vehicle controller, that is, the specific output of vehicle operating status data during the test process.

[0117] Understandably, the vehicle dynamics simulation model is driven to run according to the working condition sequence set in the test cases, and the model response data generated during the operation is recorded.

[0118] The benefit of this step is that the model response data fully records the simulation response of the vehicle during the test, providing a data foundation for subsequent evaluation of the vehicle controller's control logic.

[0119] Step A34: Real-time acquisition of control commands and model response data fed back by the vehicle controller; It should be noted that control commands are commands output by the vehicle controller after processing vehicle operating status data. These commands include mode commands and commands for coordinated control of the engine, generator, drive motor, and power battery.

[0120] Understandably, during the execution of test cases, control commands fed back by the vehicle controller are collected in real time, and model response data is collected simultaneously to achieve a corresponding record of control behavior and vehicle response.

[0121] The benefit of this step is that control commands and model response data are collected synchronously, ensuring the temporal correspondence between control behavior and vehicle response during subsequent comparative analysis.

[0122] Step A35: Compare the control commands and model response data to obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0123] Understandably, the collected control commands are compared with the model response data to check whether the control commands issued by the vehicle controller under various operating conditions are consistent with the design expectations and whether the vehicle response is correct. The correctness and robustness of the vehicle controller's control strategy are automatically evaluated to obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0124] The beneficial effect of this step is that by automatically comparing and evaluating control commands and model response data, the test results are automatically interpreted, which improves the efficiency of hardware-in-the-loop simulation testing and the objectivity of the evaluation.

[0125] This embodiment provides a hardware-in-the-loop simulation test method for a hybrid electric vehicle (HEV) controller. Based on mode commands and clutch status signals sent by the vehicle controller, the torque and speed calculation rules of the powertrain model are dynamically switched. In pure electric and series modes, the wheel-end torque of the powertrain is provided solely by the drive motor. In parallel mode, it is obtained by superimposing the torque of the drive motor with the combined torque of the engine and generator. Furthermore, when the clutch is engaged, the engine speed is inferred from the tire speed; when disengaged, the crankshaft speed is directly obtained. This accurately reproduces the coupling dynamics between the engine and dual motors during multi-mode switching in a HEV and the impact of clutch status on power transmission, thus improving the confidence level of the HEV controller hardware-in-the-loop simulation test. Meanwhile, the engine-to-wheel speed ratio can be queried based on the current gear position of the transmission or calculated in real time with the vehicle speed during continuously variable transmission (CVT), enabling the simulation testing method to be adapted to multi-gear and CVT models. In addition, the complete vehicle dynamics link involving the driver model, road model, and traffic scenario model reproduces the interaction process of driver operation, road conditions, and traffic scenarios in real vehicle operation, improving the realism of the simulation environment. Through test cases covering typical driving cycle conditions, mode switching transient conditions, and battery charge state boundary conditions, as well as fault injections such as clutch jamming and motor failure, the correctness and robustness of the vehicle controller's control strategy under boundary conditions and fault scenarios are verified, improving the test coverage.

[0126] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S20 includes steps S201 to S205: Step S201: Parse the mode command to obtain the working mode of the hybrid vehicle. The working modes include pure electric mode, series mode and parallel mode. It should be noted that the operating mode refers to the current power output mode of the hybrid vehicle, including pure electric mode, series mode, and parallel mode.

[0127] Pure electric mode is a working mode in which the wheels are driven solely by the drive motor, and the engine does not participate in the driving.

[0128] In series mode, the engine drives the generator to generate electricity, and the drive motor drives the wheels to move. In this mode, there is no mechanical connection between the engine and the wheel ends.

[0129] Parallel mode is a working mode in which the engine and drive motor jointly drive the wheels. In this mode, the engine and the wheel end are in a state of mechanical coupling.

[0130] Understandably, reading the mode command and parsing the mode type it indicates allows us to determine the current operating mode of the hybrid vehicle, i.e., pure electric mode, series mode, or parallel mode, providing a basis for selecting the corresponding torque calculation method.

[0131] The benefit of this step is that it clarifies the current operating mode of the vehicle controller, providing a basis for determining the calculation method of powertrain wheel-end torque according to the mode.

[0132] Step S202: When the working mode is pure electric mode or series mode, the product of the drive motor torque of the drive motor set at the output end of the gearbox and the speed ratio from the drive motor to the wheel end is determined as the powertrain wheel end torque. It should be noted that the drive motor is a motor located at the output end of the gearbox, used to convert electrical energy into mechanical energy to drive the wheels. In this embodiment, the drive motor model adopts a lookup table model.

[0133] The drive motor torque is the torque output by the drive motor.

[0134] The drive motor to wheel speed ratio is the transmission speed ratio between the output end of the drive motor and the wheel rim.

[0135] A gearbox is a device located between the engine and the drive half-shaft, used to change the transmission speed ratio from the engine to the wheel.

[0136] Understandably, in pure electric mode or series mode, the engine and wheel ends are decoupled, and the powertrain wheel end torque is provided only by the drive motor. The powertrain wheel end torque is obtained by multiplying the drive motor torque by the drive motor speed ratio to the wheel end.

[0137] The beneficial effect of this step is that it accurately reflects the power transmission relationship in pure electric mode and series mode where the engine does not directly output power to the wheel end and the wheel end torque is only provided by the drive motor, thus avoiding the erroneous superposition of torque from multiple power sources.

[0138] Step S203: When the working mode is parallel mode, the generator torque of the generator located at the crankshaft end of the engine is added to the product of the generator speed ratio from the generator to the engine and the engine torque to obtain the engine-side composite torque. It should be noted that the engine is a power device that converts the chemical energy of fuel into mechanical energy and is one of the power sources of hybrid vehicles. In this embodiment, the engine model adopts the mean model.

[0139] Engine torque is the torque output by the engine.

[0140] The generator is a motor installed at the crankshaft end of the engine. In series mode, it is driven by the engine to generate electricity. In parallel mode, it outputs power to the wheel end together with the engine. In this embodiment, the generator model adopts a lookup table model.

[0141] Generator torque is the torque output by the generator.

[0142] The generator-to-engine speed ratio is the transmission speed ratio between the generator output end and the engine output end.

[0143] The engine-side composite torque is the combined torque obtained by superimposing the generator torque, which is converted to the engine output after speed ratio conversion in parallel mode, with the engine torque.

[0144] Understandably, in parallel mode, the engine and wheel ends are in a mechanically coupled state. First, the generator torque is multiplied by the speed ratio from the generator to the engine, and the generator torque is reduced to the engine output. Then, the reduced generator torque is added to the engine torque to obtain the combined torque on the engine side.

[0145] The beneficial effect of this step is that the generator torque, after being reduced by the speed ratio, is combined with the engine torque at the crankshaft end, accurately depicting the torque coupling relationship between the generator and the engine located at the engine crankshaft end.

[0146] Step S204: Add the product of the engine-side composite torque and the engine-to-wheel speed ratio to the product of the drive motor torque and the drive motor-to-wheel speed ratio to obtain the powertrain wheel-end torque. Understandably, the combined torque on the engine side is multiplied by the engine-to-wheel speed ratio to transfer the power from the engine side to the wheel end. At the same time, the torque of the drive motor is multiplied by the drive motor-to-wheel speed ratio to transfer the power from the drive motor to the wheel end. The results of the two calculations are added together to obtain the powertrain wheel end torque.

[0147] The beneficial effect of this step is that, in parallel mode, the power from the engine side and the power from the drive motor side are accurately superimposed at the wheel end after their respective speed ratios are calculated. This is consistent with the transmission relationship of the two power sources converging at the wheel end in the real architecture, thus improving the torque simulation accuracy in parallel mode.

[0148] Step S205: Determine the corresponding torque calculation rule based on the powertrain wheel end torque.

[0149] Understandably, the powertrain wheel-end torque calculation method determined according to the working mode will be solidified into the corresponding torque calculation rules. That is, the pure electric mode and series mode correspond to the rule that the drive motor alone provides the powertrain wheel-end torque, while the parallel mode corresponds to the rule that the combined torque on the engine side and the torque of the drive motor are superimposed at the wheel end.

[0150] The beneficial effect of this step is that the torque calculation rules correspond one-to-one with the working modes and switch dynamically with the mode commands, ensuring the accuracy and continuity of powertrain torque simulation during multi-mode switching.

[0151] In one feasible implementation, step S205 includes steps A41 to A15: Step A41: Obtain the powertrain wheel-end torque of the hybrid vehicle at different vehicle speeds, different accelerator pedal openings, and different battery charge states through real vehicle calibration; It should be noted that vehicle calibration is a process of testing and setting parameters on a real vehicle, and the parameters obtained from calibration are consistent with the actual characteristics of the real vehicle.

[0152] Accelerator pedal opening is the degree to which the accelerator pedal is depressed, used to indicate the driver's acceleration needs.

[0153] It is understandable that calibration tests are conducted on real vehicles to measure the wheel-end torque of the powertrain of hybrid electric vehicles under different combinations of vehicle speed, accelerator pedal opening, and battery state of charge, as a data source for establishing mapping relationships.

[0154] The beneficial effect of this step is that the sample data of the powertrain wheel-end torque comes from actual vehicle measurements, making the subsequent mapping relationship consistent with the torque characteristics of real vehicles.

[0155] Step A42: Establish a multi-dimensional mapping table based on the powertrain wheel-end torque, with vehicle speed, accelerator pedal opening and battery state of charge as inputs; It should be noted that a multidimensional mapping table is a pre-established correspondence table with multiple parameters as input and a target parameter as output. When querying, the corresponding output result is obtained directly based on the value of the input parameters.

[0156] Understandably, with vehicle speed, accelerator pedal opening, and battery state of charge as input dimensions and powertrain wheel-end torque as output, the data obtained from the actual vehicle calibration is organized into a correspondence between multiple inputs and a single output, and a multi-dimensional mapping table is established.

[0157] The benefit of this step is that it pre-defines the complex torque calculation relationship into a multidimensional mapping table, providing a data foundation for quickly obtaining the powertrain wheel-end torque by looking up the table during real-time simulation.

[0158] Step A43: During real-time simulation, obtain the current vehicle speed, current accelerator pedal opening, and current battery state of charge; It should be noted that the current vehicle speed is the speed of the hybrid vehicle at the current moment during the real-time simulation.

[0159] The current accelerator pedal opening is the accelerator pedal opening output by the driver model at the current moment during the real-time simulation.

[0160] The current state of charge of the battery is the ratio of the remaining charge of the power battery to its rated capacity at the current moment during the real-time simulation. In this embodiment, the power battery adopts an equivalent circuit model, and the battery state of charge is output by the power battery model.

[0161] Understandably, during real-time simulation, the current vehicle speed, current accelerator pedal opening, and current battery state of charge are read in real time as inputs for querying the multidimensional mapping table.

[0162] The beneficial effect of this step is that the lookup input is updated synchronously with the simulation process, ensuring the consistency between the lookup results and the current simulation conditions.

[0163] Step A44: Query the multidimensional mapping table based on the current vehicle speed, current accelerator pedal opening, and current battery charge state to obtain the current powertrain wheel-end torque; It should be noted that the current powertrain wheel-end torque is the powertrain wheel-end torque at the current moment during the real-time simulation.

[0164] Understandably, the current vehicle speed, current accelerator pedal opening, and current battery state of charge are used as indexes to query the multidimensional mapping table, directly read the corresponding powertrain wheel-end torque, and obtain the current powertrain wheel-end torque.

[0165] The beneficial effect of this step is that it replaces real-time analytical calculation with a table lookup method to obtain the wheel-end torque of the powertrain. While ensuring that the torque result is consistent with the characteristics of the actual vehicle, it reduces the computational load of real-time simulation and ensures the real-time performance of the simulation.

[0166] Step A45: Determine the corresponding torque calculation rules based on the current powertrain wheel-end torque.

[0167] Understandably, the method of determining the current powertrain wheel-end torque based on multi-dimensional mapping table lookup is solidified into the corresponding torque calculation rule, replacing the method of analytical calculation based on speed ratio, for use in real-time simulation.

[0168] The beneficial effect of this step is that the torque calculation rules can be obtained directly from the calibration data of the real vehicle, which reduces the computational burden of the real-time simulator. Moreover, the calibration data is consistent with the real vehicle, thus balancing simulation accuracy and real-time performance.

[0169] This embodiment provides a hardware-in-the-loop simulation test method for a hybrid electric vehicle (HEV) controller. By parsing mode commands, the operating modes of the HEV are obtained. In pure electric mode and series mode, the drive motor torque is calculated separately as the powertrain wheel-end torque after being reduced by the drive motor-to-wheel speed ratio. In parallel mode, the generator torque is first reduced to the engine output end and combined with the engine torque after being reduced by the generator-to-engine speed ratio. Then, the combined engine-side torque is calculated to the wheel end and superimposed with the drive motor torque after being superimposed by the engine-to-wheel speed ratio. This accurately depicts the power transmission relationship of mechanical coupling and decoupling between the engine and wheel ends under different operating modes, as well as the torque coupling relationship between the generator located at the engine crankshaft end and the engine. This avoids the erroneous superposition of torques from multiple power sources and improves the simulation accuracy and continuity of the powertrain wheel-end torque during multi-mode switching. Furthermore, a multi-dimensional mapping table with vehicle speed, accelerator pedal opening, and battery state of charge as inputs is established through real vehicle calibration. The powertrain wheel-end torque is determined by looking up the table, ensuring that the torque result is consistent with the characteristics of the real vehicle. This reduces the computational load of real-time simulation while balancing simulation accuracy and real-time performance.

[0170] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0171] This application also provides a hardware-in-the-loop simulation test device for the vehicle controller of a hybrid electric vehicle. Please refer to [link / reference]. Figure 3 The hardware-in-the-loop simulation test setup for the hybrid electric vehicle controller includes: The signal acquisition module 10 is used to acquire the mode command sent by the vehicle controller and the clutch status signal of the hybrid vehicle. The torque rule module 20 is used to determine the torque calculation rules of the powertrain model in the vehicle dynamics simulation model according to the mode command. The speed rule module 30 is used to determine the speed calculation rules of the powertrain model based on the clutch status signal; The real-time simulation module 40 is used to perform real-time simulation of the vehicle dynamics simulation model based on torque calculation rules and speed calculation rules to obtain vehicle operating status data. The closed-loop test module 50 is used to send vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation testing and obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0172] The hybrid electric vehicle (HEV) controller hardware-in-the-loop simulation test apparatus provided in this application employs the HEV controller hardware-in-the-loop simulation test method described in the above embodiments. It addresses the technical problem of improving the simulation accuracy of engine and dual-motor coupling dynamics under multi-mode switching in HEV controller hardware-in-the-loop simulation testing. Compared with existing technologies, the beneficial effects of the HEV controller hardware-in-the-loop simulation test apparatus provided in this application are the same as those of the HEV controller hardware-in-the-loop simulation test method provided in the above embodiments. Furthermore, other technical features of the HEV controller hardware-in-the-loop simulation test apparatus are the same as those disclosed in the methods of the above embodiments, and will not be elaborated upon here.

[0173] In one embodiment, the torque rule module 20 is also used to parse the mode command to obtain the operating mode of the hybrid electric vehicle, including pure electric mode, series mode and parallel mode. When the working mode is pure electric mode or series mode, the product of the drive motor torque of the drive motor set at the output end of the gearbox and the speed ratio from the drive motor to the wheel end is determined as the powertrain wheel end torque. When the working mode is parallel mode, the generator torque of the generator located at the crankshaft end of the engine is added to the product of the generator speed ratio to the engine and the engine torque to obtain the engine-side composite torque. The product of the engine-side composite torque and the engine-to-wheel speed ratio is added to the product of the drive motor torque and the drive motor-to-wheel speed ratio to obtain the powertrain wheel-end torque. The corresponding torque calculation rules are determined based on the wheel-end torque of the powertrain.

[0174] In one embodiment, the torque rule module 20 is also used to obtain the powertrain wheel-end torque of the hybrid electric vehicle at different vehicle speeds, different accelerator pedal openings, and different battery charge states through real vehicle calibration. A multi-dimensional mapping table is established based on the powertrain wheel-end torque, with vehicle speed, accelerator pedal opening and battery state of charge as inputs. During real-time simulation, the current vehicle speed, current accelerator pedal opening, and current battery state of charge are obtained. Based on the current vehicle speed, current accelerator pedal opening and current battery state of charge, the multidimensional mapping table is consulted to obtain the current powertrain wheel-end torque; The corresponding torque calculation rules are determined based on the current powertrain wheel-end torque.

[0175] In one embodiment, the speed rule module 30 is also used to parse the clutch state signal to obtain the clutch state, which includes an engaged state and a disengaged state. When the clutch is engaged, the product of the tire speed of the hybrid vehicle and the engine-to-wheel speed ratio is determined as the engine speed. When the clutch is in the disengaged state, the crankshaft speed of the hybrid vehicle's engine is determined as the engine speed; The corresponding speed calculation rules are determined based on the method of determining engine speed.

[0176] In one embodiment, the speed rule module 30 is also used to obtain the current gear of the transmission of the hybrid vehicle; Based on the current gear position, the preset gear ratio correspondence is queried to obtain the engine-to-wheel speed ratio; When the transmission is a continuously variable transmission (CVT), the engine-to-wheel speed ratio is calculated in real time based on the vehicle speed of the hybrid vehicle. The engine speed is determined by multiplying the tire speed of a hybrid electric vehicle by the engine-to-wheel speed ratio.

[0177] In one embodiment, the real-time simulation module 40 is also used to output accelerator pedal gear signal and brake pedal gear signal based on test condition data through the driver model in the vehicle dynamics simulation model; The road surface and environmental information are provided through the road model and traffic scene model in the whole vehicle dynamics simulation model; The powertrain wheel-end torque and engine speed are calculated using the powertrain model according to the torque calculation rules and speed calculation rules. The vehicle dynamics simulation model is used to perform real-time simulation based on the powertrain wheel-end torque, engine speed, road surface information, and environmental information to obtain vehicle operating status data.

[0178] In one embodiment, the closed-loop test module 50 is also used to design test cases covering typical driving cycle conditions, mode switching transient conditions, and battery state of charge boundary conditions. Inject clutch sticking faults and motor failure faults into the test cases; Execute test cases to obtain model response data of the vehicle dynamics simulation model; Real-time acquisition of control commands and model response data fed back from the vehicle controller; By comparing the control commands and model response data, the hardware-in-the-loop simulation test results of the vehicle controller are obtained.

[0179] This application provides a hardware-in-the-loop simulation test device for a hybrid electric vehicle controller. The hardware-in-the-loop simulation test device for a hybrid electric vehicle controller includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller in the above embodiment 1.

[0180] The following is for reference. Figure 4 This document illustrates a schematic diagram of a hardware-in-the-loop simulation test device suitable for implementing the embodiments of this application for a hybrid electric vehicle controller. The hardware-in-the-loop simulation test device for a hybrid electric vehicle controller in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The hardware-in-the-loop simulation test equipment for the hybrid vehicle controller shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0181] like Figure 4As shown, the hybrid electric vehicle controller hardware-in-the-loop simulation test equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the hybrid electric vehicle controller hardware-in-the-loop simulation test equipment. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the hybrid vehicle controller hardware-in-the-loop simulation test equipment to communicate wirelessly or wiredly with other devices to exchange data. Although a hybrid vehicle controller hardware-in-the-loop simulation test equipment with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.

[0182] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0183] The hybrid electric vehicle (HEV) controller hardware-in-the-loop simulation test equipment provided in this application employs the HEV controller hardware-in-the-loop simulation test method described in the above embodiments, and can solve the technical problem of how to improve the simulation accuracy of the dynamic coupling between the engine and dual motors under multi-mode switching in HEV controller hardware-in-the-loop simulation testing. Compared with the prior art, the beneficial effects of the HEV controller hardware-in-the-loop simulation test equipment provided in this application are the same as those of the HEV controller hardware-in-the-loop simulation test method provided in the above embodiments, and other technical features of this HEV controller hardware-in-the-loop simulation test equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0184] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0185] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0186] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the hardware-in-the-loop simulation test method for the hybrid electric vehicle controller in the above embodiments.

[0187] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0188] The aforementioned computer-readable storage medium may be included in the hybrid electric vehicle controller hardware-in-the-loop simulation test equipment; or it may exist independently and not be assembled into the hybrid electric vehicle controller hardware-in-the-loop simulation test equipment.

[0189] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the hardware-in-the-loop simulation test equipment for the hybrid electric vehicle controller, the hardware-in-the-loop simulation test equipment for the hybrid electric vehicle controller performs the following actions: acquires mode commands sent by the vehicle controller and clutch status signals of the hybrid electric vehicle; determines the torque calculation rules for the powertrain model in the vehicle dynamics simulation model based on the mode commands; determines the speed calculation rules for the powertrain model based on the clutch status signals; performs real-time simulation of the vehicle dynamics simulation model based on the torque calculation rules and speed calculation rules to obtain vehicle operating status data; and sends the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation testing to obtain the hardware-in-the-loop simulation test results of the vehicle controller.

[0190] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0191] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0192] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0193] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described hardware-in-the-loop simulation test method for a hybrid electric vehicle controller. This solves the technical problem of improving the simulation accuracy of the engine-dual-motor coupling dynamics under multi-mode switching in the hardware-in-the-loop simulation test of a hybrid electric vehicle controller. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller provided in the above embodiments, and will not be repeated here.

[0194] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller as described above.

[0195] The computer program product provided in this application solves the technical problem of how to improve the simulation accuracy of engine and dual-motor coupling dynamics under multi-mode switching in the hardware-in-the-loop simulation test of the hybrid electric vehicle controller. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the hybrid electric vehicle controller hardware-in-the-loop simulation test method provided in the above embodiments, and will not be repeated here.

[0196] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A hardware-in-the-loop simulation test method for a hybrid electric vehicle controller, characterized in that, The method includes: Acquire mode commands sent by the vehicle controller and clutch status signals of the hybrid vehicle; The torque calculation rules for the powertrain model in the vehicle dynamics simulation model are determined according to the mode instructions. The speed calculation rules for the powertrain model are determined based on the clutch status signal; Based on the torque calculation rules and the speed calculation rules, the vehicle dynamics simulation model is simulated in real time to obtain vehicle operating status data. The vehicle operating status data is sent to the vehicle controller for hardware-in-the-loop simulation testing, and the hardware-in-the-loop simulation test results of the vehicle controller are obtained.

2. The method as described in claim 1, characterized in that, The step of determining the torque calculation rules for the powertrain model in the vehicle dynamics simulation model according to the mode instructions includes: The mode command is parsed to obtain the operating modes of the hybrid vehicle, which include pure electric mode, series mode and parallel mode. When the operating mode is pure electric mode or series mode, the product of the drive motor torque of the drive motor located at the output end of the gearbox and the speed ratio from the drive motor to the wheel end is determined as the powertrain wheel end torque. When the operating mode is parallel mode, the generator torque of the generator located at the crankshaft end of the engine is added to the product of the generator torque and the speed ratio from the generator to the engine, and the engine torque of the engine to obtain the engine-side composite torque. The product of the combined torque on the engine side and the engine-to-wheel speed ratio is added to the product of the torque on the drive motor and the drive motor-to-wheel speed ratio to obtain the powertrain wheel-end torque. The corresponding torque calculation rule is determined based on the wheel-end torque of the powertrain.

3. The method as described in claim 2, characterized in that, The step of determining the corresponding torque calculation rule based on the wheel-end torque of the powertrain includes: The powertrain wheel-end torque of the hybrid electric vehicle under different vehicle speeds, different accelerator pedal openings, and different battery charge states was obtained through real vehicle calibration. A multi-dimensional mapping table is established based on the wheel-end torque of the powertrain, with vehicle speed, accelerator pedal opening and battery state of charge as inputs. During real-time simulation, the current vehicle speed, current accelerator pedal opening, and current battery state of charge are obtained. The current powertrain wheel-end torque is obtained by querying the multidimensional mapping table based on the current vehicle speed, the current accelerator pedal opening, and the current battery state of charge. The corresponding torque calculation rule is determined based on the current powertrain wheel-end torque.

4. The method as described in claim 1, characterized in that, The step of determining the speed calculation rule of the powertrain model based on the clutch state signal includes: The clutch status signal is analyzed to obtain the clutch status, which includes an engaged state and a disengaged state. When the clutch is in the engaged state, the product of the tire speed of the hybrid vehicle and the engine-to-wheel speed ratio is determined as the engine speed. When the clutch is in the disengaged state, the crankshaft speed of the engine of the hybrid vehicle is determined as the engine speed; The corresponding speed calculation rules are determined based on the method for determining the engine speed.

5. The method as described in claim 4, characterized in that, The step of determining the engine speed by multiplying the tire speed of the hybrid vehicle by the engine-to-wheel speed ratio when the clutch is in the engaged state includes: Obtain the current gear position of the transmission of the hybrid vehicle; The engine-to-wheel speed ratio is obtained by querying the preset gear ratio correspondence based on the current gear position. When the transmission is a continuously variable transmission (CVT), the engine-to-wheel speed ratio is calculated in real time based on the vehicle speed of the hybrid vehicle. The engine speed is determined by multiplying the tire speed of the hybrid vehicle by the engine-to-wheel speed ratio.

6. The method as described in claim 1, characterized in that, The step of performing real-time simulation of the vehicle dynamics simulation model based on the torque calculation rule and the speed calculation rule to obtain vehicle operating state data includes: The driver model in the vehicle dynamics simulation model outputs accelerator pedal gear signal and brake pedal gear signal based on the test condition data; The road surface and environmental information are provided through the road model and traffic scene model in the vehicle dynamics simulation model; The powertrain model calculates the wheel-end torque and engine speed according to the torque calculation rules and the speed calculation rules. The vehicle dynamics simulation model is simulated in real time based on the powertrain wheel-end torque, the engine speed, the road surface information, and the environmental information to obtain vehicle operating status data.

7. The method as described in claim 1, characterized in that, The step of sending the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation testing and obtaining the hardware-in-the-loop simulation test results of the vehicle controller includes: Design test cases that cover typical driving cycle conditions, mode switching transient conditions, and battery state of charge boundary conditions. Inject clutch jamming fault and motor failure fault into the test cases; Execute the test cases to obtain the model response data of the vehicle dynamics simulation model; Real-time acquisition of control commands fed back by the vehicle controller and model response data; The control commands and the model response data are compared to obtain the hardware-in-the-loop simulation test results of the vehicle controller.

8. A hardware-in-the-loop simulation test device for a hybrid electric vehicle controller, characterized in that, The device includes: The signal acquisition module is used to acquire mode commands sent by the vehicle controller and clutch status signals of the hybrid vehicle. The torque rule module is used to determine the torque calculation rules of the powertrain model in the vehicle dynamics simulation model according to the mode command. The speed rule module is used to determine the speed calculation rules of the powertrain model based on the clutch status signal; The real-time simulation module is used to perform real-time simulation of the vehicle dynamics simulation model based on the torque calculation rules and the speed calculation rules to obtain vehicle operating status data. The closed-loop test module is used to send the vehicle operating status data to the vehicle controller for hardware-in-the-loop simulation test and obtain the hardware-in-the-loop simulation test results of the vehicle controller.

9. A hardware-in-the-loop simulation test device for a hybrid electric vehicle controller, characterized in that, The device includes: a memory, a processor, and a hardware-in-the-loop simulation test program for a hybrid electric vehicle controller stored in the memory and executable on the processor, wherein the hardware-in-the-loop simulation test program for the hybrid electric vehicle controller is configured to implement the steps of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a hardware-in-the-loop simulation test program for a hybrid electric vehicle controller. When the processor executes the hardware-in-the-loop simulation test program for a hybrid electric vehicle controller, it implements the steps of the hardware-in-the-loop simulation test method for a hybrid electric vehicle controller as described in any one of claims 1 to 7.

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