A vehicle controller-in-the-loop test management system and method

Abstract

This invention provides a vehicle controller-in-the-loop test management system and method, relating to the field of electronic control technology. The method includes: calculating the efficiency changes of the battery and motor based on set low-temperature environmental parameters, performing coupling analysis, and outputting corrected actual battery usable performance parameters and actual motor output performance parameters; inputting the corrected performance parameters into a real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response, and tire rolling resistance under low-temperature conditions, obtaining a simulation signal of the battery and motor's collaborative working state. This invention forms a closed-loop test management architecture through bidirectional data bus communication.

Description

Technical Field

[0001] This invention relates to the field of electronic control technology, and in particular to a vehicle controller-in-the-loop test management system and method. Background Technology

[0002] With the rapid development of the new energy vehicle industry, the requirements for vehicle adaptability to extreme environments are becoming increasingly stringent. As the core control hub of new energy vehicles, the vehicle control unit (VCU) is responsible for coordinating the collaborative work of the power battery, drive motor, and chassis system. The effectiveness of its control strategy directly determines the vehicle's power performance, safety, and reliability. In extreme environments such as low temperature and high altitude, the capacity of the power battery decreases and the efficiency of the drive motor declines. Traditional normal temperature testing environments can no longer meet the comprehensive verification requirements of the VCU control strategy.

[0003] Hardware-in-the-loop (HIL) testing technology, with its advantages of virtual simulation and physical verification, has become a core method for the development of automotive electronic controllers. However, existing HIL testing systems have several technical bottlenecks: First, batteries often use simplified equivalent circuits, failing to consider electrochemical characteristics such as sudden changes in SEI film impedance and nonlinear decay of ion conduction rate at low temperatures, resulting in significantly insufficient voltage simulation accuracy in low-temperature environments. Second, the dynamic coupling effect between the motor and the battery is ignored, failing to establish a closed-loop feedback mechanism between motor efficiency loss and battery discharge load, and thus unable to reproduce the risk accumulation process when the two work together. Third, test case design suffers from a lack of scenario diversity, insufficient coverage of complex extreme conditions such as rapid acceleration on icy and snowy roads and cold starts at low temperatures, and traditional testing systems have shortcomings in their ability to identify failure modes under extreme environments. Fourth, control command evaluation uses fixed threshold judgments, lacking intelligent analysis of parameter distribution density and dynamic correlation, making it difficult to capture potential risks from instantaneous torque fluctuations. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a vehicle controller-in-the-loop test management system and method, which forms a closed-loop test management architecture through bidirectional communication via a data bus.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A first aspect includes a method for managing vehicle controller-in-the-loop testing, the method comprising:

[0007] Based on the set low-temperature environment parameters, the efficiency changes of the battery and motor are calculated, and a coupling analysis is performed to output the corrected actual usable energy parameters of the battery and the actual output performance parameters of the motor.

[0008] The corrected performance parameters are input into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and to obtain the simulation signal of the battery and motor working together.

[0009] A hardware-in-the-loop test environment is constructed based on simulated signals. The actual vehicle VCU hardware is connected to the test platform, and simulated signals are sent to the VCU while simulated driver operation signals are injected.

[0010] The VCU receives simulation signals and operation signals, and generates motor torque distribution commands, battery energy management commands, and drive anti-slip control commands.

[0011] The collected command sequence is subjected to multi-dimensional parameterized analysis and feature extraction. Combined with the dynamic response characteristics of the motor controller to torque commands, a convex optimized geometric envelope structure in a high-dimensional feature space is constructed based on all command data points. The spatial mapping relationship and topological connection between each command parameter and the actual response of the motor are established. Adaptive dynamic weight adjustment coefficients are generated according to parameter distribution density, regional adjacency relationship and motor control dynamic performance.

[0012] The comprehensive performance evaluation index of the control command is weighted and compensated and corrected based on the adaptive dynamic weight adjustment coefficient. It is determined whether there is a risk of power interruption, abnormal torque or battery performance exceeding the limit during low temperature acceleration, and outputs a verification conclusion on the synergistic effectiveness of the VCU control strategy.

[0013] Furthermore, based on the set low-temperature environmental parameters, the efficiency changes of the battery and motor are calculated, and a coupling analysis is performed to output corrected actual usable battery performance parameters and actual output performance parameters of the motor, including:

[0014] Based on the set low-temperature environment parameters, the influence of low temperature on the internal electrochemical characteristics of the battery is analyzed, and the efficiency change characteristics caused by the decrease in ion conduction rate and the increase in internal resistance of the battery active material are calculated. At the same time, the influence of low temperature on the electromagnetic characteristics of the motor is analyzed, and the efficiency change characteristics caused by the change in motor winding resistance, the change in permanent magnet flux intensity, and the increase in lubricating viscosity are calculated.

[0015] By comprehensively analyzing the characteristics of battery efficiency variation and motor efficiency variation, and based on the limiting relationship between battery output power attenuation due to low temperature and the maximum torque output capability of the motor, and the feedback relationship between the increase in battery discharge load caused by the motor efficiency loss due to low temperature, dynamic coupling calculation is performed to obtain the results of dynamic coupling calculation.

[0016] Based on the results of dynamic coupling calculations, the corrected actual usable energy parameters of the battery and the corrected actual output performance parameters of the motor are output.

[0017] Furthermore, the corrected performance parameters are input into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response, and tire rolling resistance under low-temperature conditions, obtaining simulation signals of the battery and motor working in tandem, including:

[0018] Based on the corrected actual usable energy parameters of the battery, the changes in the electrochemical characteristics of the battery under low temperature environment are simulated by a real-time simulation system, and the output voltage and current signals reflecting the decay process of the actual usable capacity of the battery are dynamically generated.

[0019] Using the battery output voltage and current signals as the power input conditions for motor operation, combined with the corrected actual output performance parameters of the motor, the electromagnetic and mechanical characteristics of the motor under low temperature environment and actual power supply conditions are simulated by a real-time simulation system, and motor speed and torque signals including torque output delay, fluctuation and limiting characteristics are dynamically generated.

[0020] Based on the vehicle driving characteristics reflected by the motor speed and torque signals, and combined with the change in rolling resistance coefficient caused by tire rubber hardening in low temperature environments, the driving resistance data of the vehicle under different driving conditions are calculated through a real-time simulation system.

[0021] By comprehensively integrating battery output voltage and current signals, motor speed and torque signals, and vehicle driving resistance data, and through real-time vehicle dynamics simulation calculations, a simulation signal is generated that fully reflects the collaborative working state of the battery, motor, and vehicle in a low-temperature environment.

[0022] Furthermore, a hardware-in-the-loop test environment is constructed based on simulated signals. The actual vehicle's VCU hardware is connected to the test platform, and simulated signals are sent to the VCU. Simultaneously, simulated driver operation signals are injected, including:

[0023] Based on the simulation signal set reflecting the collaborative working state of the battery, motor and vehicle, a real-time simulation test environment is constructed, which includes simulation nodes for the battery management system, motor controller and vehicle dynamics.

[0024] In a real-time simulation test environment, the actual vehicle VCU controller is electrically connected to each simulation node through a hardware interface to form a complete hardware-in-the-loop test platform.

[0025] The battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals contained in the simulation signal set are transmitted in real time to the corresponding input interfaces of the VCU controller through the hardware-in-the-loop test platform.

[0026] Based on the preset test conditions, simulated driver operation signals are injected into the VCU controller, including accelerator pedal opening signal, brake pedal signal and gear status signal, to form complete vehicle operation input conditions.

[0027] Furthermore, the VCU receives simulation signals and operation signals, and generates motor torque distribution commands, battery energy management commands, and drive anti-slip control commands, including:

[0028] The VCU receives transmitted battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals through the input interface, while also receiving simulated driver operation signals.

[0029] Based on the received battery output current and voltage signals, the VCU analyzes and obtains the actual operating state parameters of the battery. According to the battery's performance characteristics in low-temperature environments, it generates corresponding battery energy management instructions, including charge and discharge power limit instructions and SOC protection instructions.

[0030] Based on the power limit conditions determined in the generated battery energy management instructions, and combined with the received motor speed and torque signals, vehicle speed and acceleration signals, and driver operation signals, the VCU calculates the torque requirements of each motor and generates the corresponding motor torque allocation instruction sequence.

[0031] Based on the output torque of each motor determined by the motor torque distribution command, and combined with the vehicle driving state reflected by the vehicle speed acceleration signal, the VCU obtains the corresponding drive anti-slip control command by analyzing the slip ratio characteristics of each drive wheel.

[0032] Furthermore, the collected command sequences undergo multi-dimensional parameterized analysis and feature extraction. Combining this with the dynamic response characteristics of the motor controller to torque commands, a convex optimized geometric envelope structure is constructed in a high-dimensional feature space based on all command data points. This establishes a spatial mapping relationship and topological connection between each command parameter and the actual motor response. Adaptive dynamic weight adjustment coefficients are generated based on parameter distribution density, regional adjacency relationships, and the dynamic performance of motor control, including:

[0033] The generated motor torque distribution command sequence, battery energy management command sequence, and drive anti-slip control command sequence are time-aligned and standardized preprocessed to extract the time domain, frequency domain, and statistical dimension features of the command parameters.

[0034] Based on the extracted dimensional features, the actual torque response data fed back by the motor controller is received, and the torque response data is matched and associated with the corresponding torque command to obtain the dynamic response characteristics of the motor controller to the torque command and construct a high-dimensional feature space. A geometric envelope structure covering all data points is generated through a convex optimization algorithm.

[0035] The geometric envelope structure is divided into multi-level meshes to divide the instruction feature subspace that reflects different control states, and the spatial mapping relationship and topological connection between VCU instruction parameters and motor actual response parameters are established.

[0036] Based on spatial mapping relationships and topological connections, adaptive dynamic weight adjustment coefficients are generated according to the distribution density of data points, adjacency relationships, and dynamic performance indicators of motor control in each subspace.

[0037] Furthermore, based on the adaptive dynamic weight adjustment coefficient, the comprehensive performance evaluation index of the control command is weighted, compensated, and corrected to determine whether there is a risk of power interruption, abnormal torque, or battery performance exceeding limits during low-temperature acceleration. The result is a verification conclusion on the effectiveness of the VCU control strategy, including:

[0038] Based on the generated adaptive dynamic weight adjustment coefficient, the comprehensive performance evaluation index of the control command sequence is dynamically weighted and compensated to obtain the corrected comprehensive performance evaluation index.

[0039] Based on the revised comprehensive performance evaluation index, the power system response characteristics of the vehicle under low temperature acceleration conditions are analyzed to determine whether there is a risk of power interruption due to battery output power limitation, a risk of drive torque fluctuation due to abnormal motor torque distribution, or a risk of battery performance protection being triggered due to battery charging and discharging power exceeding the limit, so as to obtain the judgment results of each risk.

[0040] Based on the assessment results of various risks, a verification conclusion is generated on the synergistic effectiveness of the VCU control strategy in low-temperature environments.

[0041] Secondly, a vehicle controller-in-the-loop test management system includes:

[0042] The acquisition module is used to calculate the efficiency changes of the battery and motor based on the set low temperature environment parameters, perform coupled analysis, and output the corrected actual usable energy parameters of the battery and the actual output performance parameters of the motor.

[0043] The simulation module is used to input the corrected performance parameters into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and obtain the simulation signal of the battery and motor working together. Based on the simulation signal, a hardware-in-the-loop test environment is constructed, the actual vehicle VCU hardware is connected to the test platform, and the simulation signal is sent to the VCU, while the simulated driver operation signal is injected.

[0044] The adjustment module is used by the VCU to receive simulation signals and operation signals, and generate motor torque distribution commands, battery energy management commands and drive anti-slip control commands. It performs multi-dimensional parameterized analysis and feature extraction on the collected command sequence, and combined with the dynamic response characteristics of the motor controller to torque commands, it constructs a convex optimized geometric envelope structure in a high-dimensional feature space based on all command data points, establishes the spatial mapping relationship and topological connection between each command parameter and the actual motor response, and generates adaptive dynamic weight adjustment coefficients according to parameter distribution density, regional adjacency relationship and motor control dynamic performance.

[0045] The correction module is used to perform weighted compensation and correction on the comprehensive performance evaluation index of the control command based on the adaptive dynamic weight adjustment coefficient, to determine whether there is a risk of power interruption, abnormal torque or battery performance exceeding the limit during low temperature acceleration, and to output a verification conclusion on the effectiveness of the VCU control strategy.

[0046] Thirdly, a computing device, comprising:

[0047] One or more processors;

[0048] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0049] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0050] The above-described solution of the present invention has at least the following beneficial effects:

[0051] Based on the set low-temperature environment parameters, the efficiency changes of the battery and motor are calculated and dynamically coupled to output corrected actual battery usable performance parameters and actual motor output performance parameters. These corrected parameters are then input into a real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response, and tire rolling resistance at low temperatures, generating a battery-motor-vehicle collaborative simulation signal. Based on this simulation signal, a hardware-in-the-loop test environment containing a battery management system, motor controller, and vehicle dynamics simulation nodes is constructed and connected to the actual vehicle's VCU. Simulation signals are transmitted and simulated driver operation signals are injected. After acquiring the control command sequence generated by the VCU, multi-dimensional time-domain and value-domain features are extracted, a high-dimensional feature space convex optimization geometric envelope structure is constructed, and the command feature subspace is divided to generate adaptive dynamic weight adjustment coefficients. Finally, based on these coefficients, the overall efficiency is corrected. This technology combines performance evaluation indicators to determine the risks of power interruption, abnormal torque, and battery performance exceeding limits under low-temperature acceleration conditions, and outputs verification conclusions on the collaborative effectiveness of the VCU control strategy. Therefore, it overcomes the technical problems of existing HIL test systems, such as insufficient accuracy of low-temperature voltage simulation due to the use of simplified equivalent circuits for batteries, failure to reproduce the risk accumulation process due to ignoring the dynamic coupling effect between the motor and battery, limited coverage of complex extreme conditions such as rapid acceleration on icy and snowy roads due to the single test case scenario, and difficulty in capturing the potential risks of instantaneous torque fluctuations due to the reliance on fixed thresholds for control command evaluation. It can then reproduce the real collaborative working state of the battery, motor, and vehicle under low-temperature conditions, comprehensively and accurately identify power system risks under extreme conditions, effectively verify the collaborative effectiveness of the VCU control strategy, and provide reliable test management support for the development and optimization of the vehicle controller. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of a vehicle controller-in-the-loop test management method provided by an embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of a vehicle controller-in-the-loop test management system provided by an embodiment of the present invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] like Figure 1 As shown, an embodiment of the present invention proposes a vehicle controller-in-the-loop test management method, the method comprising the following steps:

[0056] Step 1: Based on the set low-temperature environment parameters, calculate the efficiency changes of the battery and motor, perform coupling analysis, and output the corrected actual usable energy parameters of the battery and the actual output performance parameters of the motor.

[0057] Step 2: Input the corrected performance parameters into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and obtain the simulation signal of the battery and motor working together.

[0058] Step 3: Construct a hardware-in-the-loop test environment based on simulation signals, connect the actual vehicle VCU hardware to the test platform, send simulation signals to the VCU, and simultaneously inject simulated driver operation signals.

[0059] Step 4: The VCU receives simulation signals and operation signals, and generates motor torque distribution commands, battery energy management commands, and drive anti-slip control commands.

[0060] Step 5: Perform multi-dimensional parameterization analysis and feature extraction on the collected command sequence. Combined with the dynamic response characteristics of the motor controller to torque commands, construct a convex optimization geometric envelope structure in the high-dimensional feature space based on all command data points. Establish the spatial mapping relationship and topological connection between each command parameter and the actual response of the motor. Generate adaptive dynamic weight adjustment coefficients based on parameter distribution density, regional adjacency relationship and motor control dynamic performance.

[0061] Step 6: Based on the adaptive dynamic weight adjustment coefficient, the comprehensive performance evaluation index of the control command is weighted, compensated and corrected to determine whether there is a risk of power interruption, abnormal torque or battery performance exceeding limits during low-temperature acceleration, and output the verification conclusion of the collaborative effectiveness of the VCU control strategy.

[0062] In this embodiment of the invention, the efficiency changes of the battery and motor are calculated based on the set low-temperature environment parameters, and dynamic coupling analysis is performed to output corrected actual battery usable performance parameters and actual motor output performance parameters. The corrected parameters are input into a real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response, and tire rolling resistance at low temperatures to generate a battery-motor-vehicle collaborative simulation signal. Based on the simulation signal, a hardware-in-the-loop test environment with multiple system simulation nodes is constructed and connected to the actual vehicle VCU to transmit simulation signals and inject simulated driver operation signals. After acquiring the control command sequence generated by the VCU, multi-dimensional time-domain and value-domain features are extracted, a high-dimensional feature space convex optimization geometric envelope structure is constructed, and the command feature subspace is divided to generate adaptive dynamic weight adjustment coefficients. Then, based on this system... This technology, which involves revising comprehensive performance evaluation indicators, identifying risks of power interruption, abnormal torque, and battery performance exceeding limits under low-temperature acceleration conditions, and outputting verification conclusions on the collaborative effectiveness of the VCU control strategy, overcomes the technical problems in existing HIL testing systems, such as insufficient accuracy of low-temperature voltage simulation due to the use of simplified equivalent circuits for batteries, failure to reproduce the risk accumulation process due to ignoring the dynamic coupling effect between the motor and battery, limited coverage of complex extreme conditions due to the single test case scenario, and difficulty in capturing the potential risks of instantaneous torque fluctuations due to the reliance on fixed thresholds for control command evaluation. It then reproduces the true collaborative working state of the battery, motor, and vehicle under low-temperature conditions, comprehensively identifies power system risks under extreme conditions, effectively verifies the collaborative effectiveness of the VCU control strategy, and provides reliable test management support for the development and optimization of the vehicle controller.

[0063] In a preferred embodiment of the present invention, step 1 above may include:

[0064] Step 1.1: Based on the set low-temperature environment parameters, analyze the impact of low temperature on the internal electrochemical characteristics of the battery, and calculate the efficiency changes caused by the decrease in ion conduction rate and increase in internal resistance of the battery active materials. Simultaneously, analyze the impact of low temperature on the electromagnetic characteristics of the motor, and calculate the efficiency changes caused by changes in motor winding resistance, permanent magnet flux intensity, and lubricant viscosity. Specifically, this includes: determining the required low-temperature environment parameters for testing, including the target low-temperature temperature range, the duration of the low-temperature environment, and the rate of temperature change; constructing an experimental platform including a low-temperature environment chamber, an electrochemical workstation, and a motor performance testing bench; placing the battery sample in the low-temperature environment chamber; controlling the chamber temperature according to the set low-temperature parameters; collecting real-time ion migration rate data of the battery active materials at different low-temperature stages through the electrochemical workstation; and simultaneously measuring the ohmic internal resistance of the battery at different low temperatures. Based on the collected data on the decrease in ion conduction rate and the increase in internal resistance, and combined with the correlation in battery electrochemical theory, the characteristic curves of battery efficiency versus temperature under different low-temperature conditions were calculated. Simultaneously, a motor sample was mounted on a motor performance test bench and placed in a low-temperature environment chamber. The DC resistance change of the motor windings at different low temperatures was measured using a resistance testing device on the bench. The attenuation of magnetic flux intensity of the permanent magnet at different low temperatures was detected using a flux meter. Furthermore, the changes in motor mechanical losses due to the increase in lubricant viscosity were recorded using torque and speed sensors on the bench. Based on the data on changes in winding resistance, magnetic flux intensity, and mechanical losses, and combined with the correlation in the electromagnetic and mechanical characteristics theory of motors, the characteristic curves of motor efficiency versus temperature under different low-temperature conditions were calculated.

[0065] Step 1.2 involves a comprehensive analysis of the battery efficiency variation characteristics and the motor efficiency variation characteristics. Based on the limiting relationship between battery output power degradation due to low temperatures and the motor's maximum torque output capability, and the feedback relationship between the increased battery discharge load caused by low-temperature efficiency loss in the motor, dynamic coupling calculations are performed to obtain the results. Specifically, this includes importing the obtained battery efficiency variation curves and motor efficiency variation curves into coupling analysis software. Within the software, the parameter association logic for the coordinated operation of the battery and motor is established. First, the law of battery output power degradation with low temperatures is analyzed. Based on this law, the maximum output power that the battery can provide at different low temperatures is determined. Then, combined with the power demand characteristics of the motor, the limiting relationship logic between battery output power and the motor's maximum torque output capability is established. That is, when the battery output power decreases... The maximum torque achievable by the motor under the current power input is calculated. Then, the pattern of motor efficiency loss with low temperature is analyzed. Based on the pattern, the efficiency loss ratio of the motor under different low temperatures is determined. Then, combined with the motor's load characteristics, a feedback relationship logic between motor efficiency loss and battery discharge load is established. That is, when the motor efficiency decreases, the increased battery discharge current and discharge time required to maintain the target output torque of the motor are calculated. Then, the battery and motor collaborative working process under different low temperature conditions is simulated using coupling analysis software. During the simulation, the above-mentioned constraint relationship logic and feedback relationship logic are called in real time to dynamically adjust the battery output power and motor torque output. At the same time, the operating parameters of the battery and motor after each adjustment are recorded. Through multiple iterative calculations, the result data of the dynamic coupling working of the battery and motor under different low temperature conditions are finally obtained.

[0066] Step 1.3: Based on the results of the dynamic coupling calculation, output the corrected actual usable battery performance parameters and the corrected actual motor output performance parameters. Specifically, this includes: classifying and organizing the obtained dynamic coupling calculation results data, selecting key operating parameters of the battery under different low-temperature conditions, including the actual usable capacity of the battery under different discharge currents, the maximum charge / discharge power at different temperatures, and the internal resistance change value under different SOC states. Based on these key parameters, compare them with the standard performance parameters of the battery at room temperature, and correct the actual usable capacity, charge / discharge power, and internal resistance performance parameters of the battery to obtain the corrected actual usable battery performance parameters, ensuring that the parameters reflect the true performance state of the battery under low-temperature conditions affected by the motor load; simultaneously, select key operating parameters of the motor under different low-temperature conditions, including the actual output torque of the motor under different input power, the torque response time at different speeds, and the maximum output power at different temperatures. Based on these key parameters, compare them with the standard performance parameters of the motor at room temperature, and correct the output torque, torque response time, and maximum output power performance parameters of the motor to obtain the corrected actual motor output performance parameters.

[0067] In this embodiment of the invention, the influence of low temperature on the internal electrochemical characteristics of the battery is analyzed according to the set low temperature environment parameters to calculate the battery efficiency change characteristics. At the same time, the influence of low temperature on the electromagnetic and mechanical characteristics of the motor is analyzed to calculate the motor efficiency change characteristics. Then, the efficiency change characteristics of the two are combined and dynamically coupled based on the bidirectional relationship between the battery output power limiting the maximum torque of the motor and the increase in battery discharge load due to motor efficiency loss. Finally, the corrected actual usable performance parameters of the battery and the actual output performance parameters of the motor are output. Therefore, this method overcomes the technical problems of traditional vehicle controller loop testing, where the battery model does not consider key electrochemical characteristics such as the decrease in ion conduction rate and the increase in internal resistance at low temperatures, and the motor model does not cover the efficiency changes caused by the change in winding resistance, the change in permanent magnet flux intensity, and the increase in lubricating viscosity at low temperatures. It also ignores the dynamic coupling relationship between the battery and the motor and cannot reflect the mutual constraint feedback between the two. Thus, the battery and motor performance parameters that are close to the actual working conditions under low temperature environment are obtained, avoiding the deviation between the simulation signal and the actual operating state caused by the distortion of performance parameters.

[0068] In a preferred embodiment of the present invention, step 2 above may include:

[0069] Step 2.1: Based on the corrected actual usable battery performance parameters, the electrochemical characteristics of the battery under low-temperature conditions are simulated using a real-time simulation system. This dynamically generates output voltage and current signals reflecting the decay of the battery's actual usable capacity. Specifically, this includes: importing the corrected actual usable battery performance parameters into the battery simulation unit of the real-time simulation system; setting the same low-temperature environment parameters in the simulation unit; simulating the electrochemical characteristics changes of the battery's internal active materials at low temperatures, such as decreased ion conduction rate and increased internal resistance; starting from the battery's full SOC state, simulating the battery discharge process at a preset discharge rate; collecting the battery's output voltage and current values ​​in real-time at different stages of discharge; adjusting the voltage calculation logic based on the corrected internal resistance change value when the battery SOC drops to different thresholds, ensuring the output voltage signal reflects the voltage drop caused by increased internal resistance; controlling the total discharge time based on the corrected actual usable capacity parameters to ensure the current signal reflects the decrease in discharge capacity caused by capacity decay; and finally, dynamically generating battery output voltage and current signals that continuously change with the discharge process and conform to low-temperature electrochemical characteristics.

[0070] Step 2.2: Using the battery output voltage and current signals as the power input conditions for motor operation, and combining them with the corrected actual motor output performance parameters, a real-time simulation system is used to simulate the changes in the electromagnetic and mechanical characteristics of the motor under low-temperature environments and actual power supply conditions. This dynamically generates motor speed and torque signals that include torque output delay, fluctuations, and limiting characteristics. Specifically, this includes: connecting the generated battery output voltage and current signals to the motor simulation unit of the real-time simulation system as the real-time power input source for motor operation; simultaneously importing the corrected actual motor output performance parameters into the motor simulation unit; and setting the same low-temperature environment parameters as in Step 1 in the simulation unit to simulate the increase in motor winding resistance and permanent magnet flux. The electromagnetic characteristics change due to intensity decay, and the mechanical loss changes caused by the increase in the viscosity of the lubricating medium. First, the real-time input power is calculated based on the voltage and current input from the battery. Then, the motor torque output logic is adjusted in combination with the corrected parameters. When receiving power demands for rapid acceleration, the rise of the torque signal is controlled according to the corrected torque output delay time. During the stable torque output stage, a small-fluctuation torque signal is generated according to the corrected fluctuation range. When the input power exceeds the power threshold corresponding to the corrected maximum output torque, the torque signal is limited to the maximum output torque range. At the same time, the corresponding speed signal is generated in combination with the motor speed calculation logic. Finally, a motor speed and torque signal that includes low-temperature characteristics is dynamically generated.

[0071] Step 2.3: Based on the vehicle driving characteristics reflected by the motor speed and torque signals, and combined with the change in rolling resistance coefficient caused by tire rubber hardening in low-temperature environments, the vehicle's driving resistance data under different driving conditions is calculated through a real-time simulation system. Specifically, this includes: inputting the generated motor speed and torque signals into the vehicle resistance simulation unit of the real-time simulation system; calculating the vehicle's real-time driving force using the speed and torque signals; determining the tire rolling resistance coefficient based on preset low-temperature environment parameters; importing the vehicle's basic parameters into the simulation unit; calculating the driving resistance at start-up based on the driving force and rolling resistance coefficient for starting conditions; calculating the vehicle acceleration based on the real-time speed for acceleration conditions; adjusting the resistance calculation value based on the acceleration and rolling resistance coefficient for acceleration conditions; and calculating the constant driving resistance based on the vehicle speed corresponding to the stable speed and the rolling resistance coefficient for constant speed conditions. When switching between different driving conditions, the resistance calculation parameters are updated in real time to ensure that the resistance data can dynamically adjust with changes in conditions and low-temperature characteristics, ultimately obtaining vehicle driving resistance data that covers multiple driving conditions and conforms to low-temperature resistance characteristics.

[0072] Step 2.4: Integrate battery output voltage and current signals, motor speed and torque signals, and vehicle driving resistance data. Through real-time vehicle dynamics simulation calculations, generate simulation signals that fully reflect the collaborative working state of the battery, motor, and vehicle in a low-temperature environment. Specifically, this includes: integrating battery output voltage and current signals, motor speed and torque signals, and vehicle driving resistance data into the vehicle dynamics simulation unit of the real-time simulation system; importing the basic parameters required for the vehicle dynamics model into the unit, including suspension system characteristics, steering system parameters, and braking system parameters; first calculating the real-time output state of the power system based on battery and motor signals; then calculating the real-time force situation of the vehicle based on driving resistance data; obtaining vehicle state parameters such as real-time vehicle speed, acceleration, and body posture through dynamic calculations; and integrating these vehicle state parameters with the power system signals. When the battery voltage drops, causing a decrease in motor torque, the trend of the vehicle speed and acceleration signals is adjusted synchronously. When driving resistance increases, the vehicle speed is judged based on the power system output capability to determine whether it can be maintained or decreased, ensuring that all signals can collaboratively reflect the interaction between the systems, ultimately generating a complete signal set containing the states of the battery, motor, and vehicle.

[0073] In this embodiment of the invention, based on the corrected actual usable battery performance parameters, a real-time simulation system is used to simulate changes in the battery's electrochemical characteristics at low temperatures to dynamically generate output voltage and current signals reflecting battery capacity decay. These battery signals are used as the motor power input and combined with the corrected actual motor output performance parameters. The real-time simulation system then simulates changes in the motor's electromagnetic and mechanical characteristics under low temperatures and actual power supply conditions to dynamically generate motor speed and torque signals containing torque delay, fluctuation, and limiting characteristics. Based on the driving characteristics reflected by the motor signals and the changes in tire rolling resistance coefficient at low temperatures, the real-time simulation system calculates the vehicle's driving resistance data under different operating conditions. Finally, the battery data is comprehensively integrated. This technology uses data on motor and vehicle resistance, along with real-time vehicle dynamics simulation calculations, to generate a complete simulation signal reflecting the low-temperature collaborative working state of the battery, motor, and vehicle. This overcomes the limitations of traditional vehicle controller-in-the-loop testing simulation systems, which cannot realistically reproduce the battery capacity decay process, abnormal motor torque output characteristics, and tire rolling resistance changes at low temperatures. Furthermore, these systems cannot integrate multi-system signals to generate realistic collaborative working simulation signals, leading to significant deviations between the input signals received by the VCU and actual low-temperature operating conditions. This technology simulates the real operating characteristics of the battery, motor, and vehicle systems under low-temperature conditions, generating a multi-system collaborative simulation signal highly consistent with actual low-temperature operating conditions.

[0074] In a preferred embodiment of the present invention, step 3 above may include:

[0075] Step 3.1: Based on the simulation signal set reflecting the coordinated working state of the battery, motor, and vehicle, construct a real-time simulation test environment including a battery management system simulation node, a motor controller simulation node, and a vehicle dynamics simulation node. Specifically, this includes: compiling a simulation signal set reflecting the coordinated working state of the battery, motor, and vehicle. The signal set must include battery output voltage signals, battery output current signals, motor speed signals, motor torque signals, vehicle speed signals, vehicle acceleration signals, and vehicle driving resistance data. Then, create the battery management system simulation node, motor controller simulation node, and vehicle dynamics simulation node in the real-time simulation software, and configure battery signal processing for the battery management system simulation node. The system is configured with logic to receive and parse battery-related simulation signals, simulating the battery management system's monitoring and preliminary processing functions for battery status. Motor signal processing logic is configured for the motor controller simulation node, enabling it to receive battery signals and generate intermediate motor control signals based on motor parameters, simulating the motor controller's signal conversion function. Vehicle state calculation logic is configured for the vehicle dynamics simulation node, enabling it to integrate motor signals and driving resistance data to generate vehicle speed, acceleration, and other signals. Simultaneously, a real-time data interaction channel is set up between the three nodes to ensure that each node can synchronously transmit signals at a preset frequency, such as 100Hz. Ultimately, a real-time simulation test environment capable of reproducing the collaborative operation of multiple systems is constructed.

[0076] Step 3.2: In the real-time simulation test environment, establish electrical connections between the actual vehicle VCU controller and each simulation node through hardware interfaces to form a complete hardware-in-the-loop test platform. Specifically, this includes: identifying the input / output interface types of the actual vehicle VCU controller, including CAN bus interface, analog input interface, and digital input interface; preparing the corresponding hardware interface devices; connecting one end of the CAN bus interface module to the VCU's CAN bus interface via a shielded cable, and connecting the other end to the CAN communication port of each simulation node in the real-time simulation test environment; connecting the input of the analog signal conversion module to the analog signal output of the simulation node, such as the battery voltage / current signal output, and connecting the output to the VCU's analog input interface; connecting the input of the digital signal conversion module to the digital signal output of the simulation node, such as the gear position signal output, and connecting the output to the VCU's digital input interface. After completing the hardware connections, use a multimeter to measure the power supply voltage and signal grounding of each interface to ensure stable electrical connections. Then, send test signals through the real-time simulation software to check whether the VCU can normally receive the signals transmitted by each simulation node. After confirming that there is no signal loss or distortion, a complete hardware-in-the-loop test platform is formed.

[0077] Step 3.3: Using a hardware-in-the-loop test platform, transmit the battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals contained in the simulation signal set to the corresponding input interfaces of the VCU controller in real time. Specifically, this includes: first, in the signal configuration software of the hardware-in-the-loop test platform, associating the battery output current and voltage signals from the simulation signal set with the analog input interfaces of the VCU; setting signal conversion rules to match the numerical range of the simulation signals with the receiving range of the VCU interface, such as converting the battery voltage signal into a 0-5V analog voltage signal for transmission to the corresponding VCU interface; and then transmitting the motor speed and torque signals in real time to the corresponding input interfaces of the VCU controller. The vehicle speed signal and vehicle acceleration signal are associated with the VCU's CAN bus interface. The signal ID, data length, and byte allocation are configured according to the CAN message format supported by the VCU. The signal transmission frequency is set to 100Hz to ensure real-time performance. Then, the real-time simulation test environment and VCU controller are started. The transmission status of each signal is observed through the platform's signal monitoring interface. It is confirmed that the battery output current and voltage signals can be stably transmitted to the VCU's analog input interface, and that the motor speed and torque signals and vehicle speed and acceleration signals can be accurately transmitted to the VCU's CAN interface through the CAN bus. This ensures that the VCU can obtain simulation signals that reflect the working status of each system in real time.

[0078] Step 3.4: Based on the preset test conditions, inject simulated driver operation signals into the VCU controller, including accelerator pedal opening signals, brake pedal signals, and gear status signals, to form complete vehicle operation input conditions. Specifically, this includes: based on the composite extreme conditions to be covered, preset low-temperature cold start condition, icy and snowy road rapid acceleration condition, low-temperature constant speed driving condition, and low-temperature braking condition; design driver operation procedures for each preset condition, such as the operation procedure for the low-temperature cold start condition being to switch gear from P to D and maintain accelerator pedal opening at 5%; and the operation procedure for the icy and snowy road rapid acceleration condition being to maintain gear in D and gradually increase accelerator pedal opening from 0% to 70%. Then, use a signal generator to generate... The corresponding simulated driver operation signals are as follows: for the accelerator pedal opening signal, an opening change curve is set according to the operation process to generate a continuous analog voltage signal; for the brake pedal signal, a digital signal corresponding to the depressed or released state is set according to the working condition requirements; for the gear status signal, different digital encoding signals are set for P, D, and R gears. The generated accelerator pedal opening signal is injected into the accelerator pedal signal input interface of the VCU through the analog interface, and the brake pedal signal and gear status signal are injected into the corresponding input interface of the VCU through the digital interface. At the same time, the time and value of the injected signals are recorded in the test platform to ensure that the operation signal and the simulation signal are synchronized to form complete vehicle operation input conditions.

[0079] In this embodiment of the invention, a real-time simulation test environment is constructed based on a simulation signal set reflecting the coordinated working state of the battery, motor, and vehicle. This environment includes simulation nodes for the battery management system, motor controller, and vehicle dynamics. Within this environment, the actual vehicle's VCU controller is electrically connected to each simulation node via a hardware interface to form a complete hardware-in-the-loop test platform. Using this platform, the battery output current and voltage, motor speed and torque, and vehicle speed and acceleration signals from the simulation signal set are transmitted in real-time to the corresponding input interface of the VCU. Simultaneously, based on preset test conditions, the VCU is injected with signals including accelerator pedal opening, brake pedal position, and gear status. This technology uses simulated driver operation signals to overcome the technical problems of traditional hardware-in-the-loop testing, such as the single simulation node, inability to reproduce multi-system collaborative operation scenarios, non-standard connection between VCU and simulation environment leading to signal transmission delay or distortion, and lack of real driver operation signal injection resulting in incomplete input conditions for VCU and difficulty in simulating actual vehicle operation. It constructs a test environment that closely resembles the real vehicle system architecture, ensuring that VCU can receive collaborative simulation signals from multiple systems and operation signals that conform to actual driving scenarios in real time, providing complete and realistic input conditions for VCU to execute control logic and output reasonable control commands.

[0080] In a preferred embodiment of the present invention, step 4 above may include:

[0081] Step 4.1: The VCU receives the transmitted battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals through the input interface. Simultaneously, it receives simulated driver operation signals. Specifically, the VCU first receives the transmitted motor speed and torque signals and vehicle speed and acceleration signals through its own CAN bus interface, receives the battery output current and voltage signals through the analog input interface, and receives the simulated driver operation signals (accelerator pedal opening, brake pedal, and gear status) through the digital input interface. During the reception process, the VCU's built-in signal preprocessing unit performs low-pass filtering on all input signals to remove high-frequency noise signals caused by hardware interface interference. Simultaneously, it timestamps and calibrates the signals received from different types of interfaces to ensure that the battery output signals, motor operation signals, vehicle status signals, and driver operation signals remain synchronized in the time dimension.

[0082] Step 4.2: Based on the received battery output current and voltage signals, the VCU parses the actual battery operating state parameters and generates corresponding battery energy management instructions according to the battery's performance characteristics in low-temperature environments. These instructions include charge / discharge power limiting instructions and SOC protection instructions. Specifically, the VCU extracts the battery output current and voltage signals from its local data buffer, and through its built-in battery state parsing program, calculates the battery's current SOC value, actual operating internal resistance, and remaining usable capacity, among other actual operating state parameters, based on the increased internal resistance and decreased ion conduction rate of the battery in low-temperature environments. This is then compared to the battery's safe operating parameter range in low-temperature environments. The safe operating parameter range is based on a correction... The system then sets the actual usable battery performance parameters. When the current discharge current of the battery is detected to be close to the over-discharge current threshold, a charge / discharge power limit instruction is generated. This instruction specifies the maximum allowable discharge power and maximum charging power of the battery under the current low-temperature conditions. For example, based on the battery capacity decay at low temperatures, the maximum discharge power is limited to 65%~80% of that at room temperature. At the same time, a SOC protection instruction is set based on the calculated SOC value. For example, in a low-temperature environment, the minimum SOC protection threshold is set to 18%~22%, which is higher than the SOC protection threshold at room temperature, to prevent the actual usable power from being depleted prematurely due to low-temperature capacity decay. Finally, the charge / discharge power limit instruction and the SOC protection instruction are integrated into a complete battery energy management instruction.

[0083] Step 4.3: Based on the power limitations determined in the generated battery energy management instructions, and combined with the received motor speed and torque signals, vehicle speed and acceleration signals, and driver operation signals, the VCU calculates the torque requirements of each motor and generates a corresponding motor torque allocation instruction sequence. Specifically, the VCU first extracts the battery energy management instructions from the instruction output buffer to determine the maximum charge and discharge power limitations under the current low-temperature environment. Then, it retrieves the motor speed and torque signals, vehicle speed and acceleration signals, and driver operation signals from the local data buffer, analyzes the trend of accelerator pedal opening changes in the driver operation signals, and determines the driver's power demand level. For example, a rapid increase in accelerator pedal opening from 30% to 70% corresponds to high power. The system determines the current driving conditions by combining the vehicle speed and acceleration signals. For example, if the vehicle speed increases from 5 km / h to 30 km / h and the acceleration stabilizes at 0.8 m / s², it corresponds to a low-temperature acceleration condition. The system also refers to the motor speed and torque signals to understand the current operating efficiency of the motor, such as whether the current motor speed is in the high-efficiency operating range at low temperatures. Based on the power limit conditions, the upper limit of the current total torque output is calculated. Then, according to the torque distribution logic corresponding to the vehicle's drive type, such as the torque distribution based on the front and rear wheel load ratio for four-wheel drive vehicles, the total torque demand is allocated to each drive motor. This generates a motor torque distribution instruction sequence that includes the real-time torque output value of each motor, the torque adjustment interval (e.g., updated every 10 ms), and the torque output protection threshold.

[0084] Step 4.4: Based on the output torque of each motor determined by the motor torque distribution command, and combined with the vehicle driving state reflected by the vehicle speed acceleration signal, the VCU obtains the corresponding drive anti-slip control command by analyzing the slip ratio characteristics of each drive wheel. Specifically, the VCU obtains the output torque of the drive wheel corresponding to each drive motor from the motor torque distribution command in the command output buffer, and combines it with the vehicle speed acceleration signal in the local data buffer to determine the current vehicle driving state. For example, if the vehicle speed is below 20km / h and the acceleration is greater than 1m / s², corresponding to the slippery condition on low-temperature icy and snowy roads, the VCU uses the built-in slip ratio analysis program to convert the actual rotational speed of each drive wheel from the motor speed to the transmission system speed ratio to obtain the actual vehicle speed. The system directly obtains and compares the data, calculating the real-time slip ratio of each drive wheel. When the slip ratio of a drive wheel exceeds the anti-slip threshold in low-temperature environments (e.g., the slip ratio threshold for icy and snowy roads is set at 12%~18%), it analyzes the correlation between the current torque output of the drive wheel and the slip ratio, generating drive anti-slip control commands. These commands may include reducing the torque output value of the motor corresponding to the drive wheel, with the reduction amount determined by the degree to which the slip ratio exceeds the threshold, or adjusting the torque distribution ratio to transfer some torque to other drive wheels with lower slip ratios. This ensures the vehicle's power stability when driving on low-temperature, slippery roads, avoiding power waste or driving safety risks caused by excessive drive wheel slippage. Finally, the drive anti-slip control commands are added to the command output buffer to form a complete VCU control command set.

[0085] In this embodiment of the invention, the battery output current and voltage, motor speed and torque, vehicle speed and acceleration signals, and simulated driver operation signals are received completely through the input interface. Based on the battery signal analysis of the actual working state parameters and combined with the low-temperature battery performance characteristics, a battery energy management command containing charge and discharge power limits and SOC protection is generated. According to the power limit conditions of the command and the received motor, vehicle, and driver signals, the torque requirements of each motor are calculated and a torque distribution command sequence is generated. Then, based on the motor output torque determined by the torque distribution command and the vehicle driving state, the slip ratio characteristics of each drive wheel are analyzed to obtain the drive anti-slip control command. Therefore, this method overcomes the technical problems of traditional VCUs in low-temperature testing, such as difficulty in generating energy management commands in combination with the actual working state of the battery, insufficient correlation between torque distribution and battery power limits and vehicle driving needs, and insufficient coordination between drive anti-slip control, torque distribution, and energy management, which leads to control commands that cannot adapt to the coordinated working needs of the battery, motor, and vehicle at low temperatures. This ensures that the three types of commands generated by the VCU—battery energy management, motor torque distribution, and drive anti-slip control—are coordinated with each other and can match the actual performance of the battery, the output capacity of the motor, and the driving state of the vehicle in low-temperature environments, avoiding power waste, battery damage, or driving safety risks caused by command disconnection.

[0086] In a preferred embodiment of the present invention, step 5 above may include:

[0087] Step 5.1 involves performing time alignment and standardization preprocessing on the generated motor torque distribution command sequence, battery energy management command sequence, and drive anti-slip control command sequence, extracting the time domain, frequency domain, and statistical dimension features of the command parameters. Specifically, this includes: obtaining the motor torque distribution command sequence, battery energy management command sequence, and drive anti-slip control command sequence output by the VCU; performing time alignment processing based on the timestamp of the VCU command output, uniformly adjusting the three command sequences to the same time interval, such as 10 milliseconds / data point, to ensure that the three types of commands at the same time point can correspond and match; subsequently, standardization preprocessing is performed. For the motor torque distribution command, the torque value is converted into a percentage relative to the maximum rated torque of the motor to eliminate the dimensional differences between different motor models; for the battery energy management command... The charging and discharging power limits are converted into percentages relative to the battery's maximum charging and discharging power at room temperature. For the drive anti-slip control command, the torque adjustment range is converted into a percentage relative to the original torque command. After preprocessing, multi-dimensional features of the command parameters are extracted. Time-domain features include the response delay of the motor torque command, the update interval between adjacent commands, the power limit update frequency of the battery energy management command, and the trigger delay of the drive anti-slip control command. Frequency-domain features include the number of fluctuations in the motor torque command per unit time and the fluctuation frequency of the battery power limit value. Statistical features include the maximum, minimum, average, and fluctuation range of the motor torque command, the maximum deviation of the battery power limit, and the torque adjustment range of the drive anti-slip control command. Finally, the data is organized to form an original dataset containing multi-dimensional features.

[0088] Step 5.2: Based on the extracted dimensional features, receive the actual torque response data fed back by the motor controller, and match and associate the torque response data with the corresponding torque commands to obtain the dynamic response characteristics of the motor controller to the torque commands and construct a high-dimensional feature space. Generate a geometric envelope structure covering all data points through a convex optimization algorithm. Specifically, this includes: receiving the actual torque response data through the CAN bus interface of the motor controller, the data including the real-time output torque value of the motor in a low-temperature environment, the torque response time, and the torque fluctuation deviation; matching and associating the motor torque allocation command at the same time point with the corresponding actual torque response data one by one according to the time-aligned command sequence, for example, binding the VCU torque command value at time t with the actual motor torque value at time t and the torque response time at time t, and calculating the torque response time at each time point. The torque response deviation rate is the percentage of the deviation value to the command value. Based on the extracted multi-dimensional features and key parameters in the actual torque response data of the motor, such as the actual torque value and response time, a high-dimensional feature space is constructed. Each dimension corresponds to a feature parameter, such as dimension 1 being the torque command response delay, dimension 2 being the torque fluctuation frequency, dimension 3 being the torque response deviation rate, and dimension 4 being the battery power limit value. All matched and associated command and response data are converted into data points in the high-dimensional feature space. A convex optimization calculation method is adopted, with the goal of covering all effective data points and minimizing the boundary, to iteratively calculate the convex set boundary in the high-dimensional space, forming a geometric envelope structure that can surround all data points. The boundary of the structure is used to define the normal operating range of VCU command and motor response matching under low temperature conditions, ensuring that subsequent evaluation can distinguish between normal fluctuations and abnormal deviations.

[0089] Step 5.3 involves multi-level meshing of the geometric envelope structure to divide it into instruction feature subspaces reflecting different control states, and establishing spatial mapping relationships and topological connections between VCU instruction parameters and actual motor response parameters. Specifically, this includes: based on the physical meaning and control priority of parameters in each dimension of the high-dimensional feature space, multi-level meshing of the geometric envelope structure is performed. The first layer is divided according to the motor torque response deviation rate, such as a low deviation layer for deviation rate ≤ 5%, a medium deviation layer for deviation rate ≤ 15%, and a high deviation layer for deviation rate > 15%. The second layer is divided according to the battery power limit value, such as a low power layer for power limit ≤ 30% at room temperature, a medium power layer for power limit ≤ 70% at room temperature, and a high power layer for power limit > 70% at room temperature. The third layer is divided according to the motor torque response delay, such as a delay ≤ 2... 0 milliseconds is the short delay layer, 20 milliseconds < delay ≤ 50 milliseconds is the medium delay layer, and delay > 50 milliseconds is the long delay layer. The geometric envelope structure is divided into multiple independent instruction feature subspaces through three-layer decomposition, with each subspace corresponding to a specific control state. A spatial mapping relationship between VCU instruction parameters and actual motor response parameters is established, clarifying the range of actual motor response parameters corresponding to VCU instruction parameters in each subspace. At the same time, the topological relationships between each subspace are analyzed. For example, the medium deviation, medium power and medium delay subspaces are adjacent to the low deviation, medium power and short delay subspaces, and the high deviation, medium power and long delay subspaces. This shows that when the VCU instruction or motor response changes slightly, the control state may switch between adjacent subspaces. The adjacency relationship and switching frequency of each subspace are recorded to form a complete topological connection network.

[0090] Step 5.4: Based on spatial mapping and topological connections, generate adaptive dynamic weight adjustment coefficients according to the distribution density, adjacency relationship, and dynamic performance indicators of motor control within each subspace. Specifically, this includes: calculating the distribution density of data points within each instruction feature subspace, i.e., the proportion of high-dimensional feature data points within the subspace to the total number of data points. For example, the proportion of data points in the low-deviation, medium-power, and short-delay subspaces is 35%, while the proportion in the medium-deviation, medium-power, and medium-delay subspaces is 28%. Higher density indicates that the control state occurs more frequently in low-temperature testing, and has a greater impact on overall control performance; and analyzing each subspace based on the established topological connection network. The degree of adjacency is determined by calculating the proportion of data point conversions between adjacent subspaces to the total number of conversions. Dynamic performance indicators for motor control are introduced, including torque response speed, torque output stability, and power matching degree. The performance indicators of each subspace are scored, with scores above 80 being excellent, 60-80 being average, and below 60 being poor. Based on the above data, the adaptive dynamic weight adjustment coefficient of each subspace is calculated according to the rule of density weight (proportion × 0.5) + association strength weight (proportion × 0.3) + performance score weight (score ÷ 100 × 0.2), and finally the adaptive weight coefficients of all subspaces are generated.

[0091] In this embodiment of the invention, the motor torque distribution, battery energy management, and drive anti-slip control command sequences are preprocessed with time alignment and standardization to extract time-domain, frequency-domain, and statistical dimensional features. A high-dimensional feature space is constructed by matching and associating the actual torque response data fed back by the motor controller with the corresponding torque commands. A geometric envelope structure covering all data points is generated using a convex optimization algorithm. The geometric envelope structure is then multi-level meshed to divide the command feature subspaces for different control states, and a spatial mapping relationship and topological connection between VCU command parameters and actual motor response parameters are established. Based on the mapping relationship and topological connection, combined with the data point distribution density, adjacency relationship, and dynamic performance indicators of each subspace, an adaptive dynamic weight adjustment coefficient is generated. This overcomes the technical problems in existing VCU control command analysis, such as ignoring the correlation between commands and actual motor responses, lacking multi-dimensional feature comprehensive characterization, failing to highlight key control states using fixed weights, and struggling to capture instantaneous deviations and potential risks between commands and actual responses. Furthermore, it establishes a correlation mapping between VCU control commands and the actual operating state of the motor, and the dynamic characteristics of control commands under multi-dimensional low-temperature environments. By using adaptive weights to focus on key control subspaces and performance indicators, the accuracy of identifying command and response deviation risks is improved.

[0092] In a preferred embodiment of the present invention, step 6 above may include:

[0093] Step 6.1: Based on the generated adaptive dynamic weight adjustment coefficients, the comprehensive performance evaluation index of the control command sequence is dynamically weighted and compensated to obtain the corrected comprehensive performance evaluation index. Specifically, this includes: determining the comprehensive performance evaluation index of the control command sequence, including the power response delay time under low-temperature acceleration conditions (interval from accelerator pedal signal input to actual torque output), the motor torque output fluctuation rate (proportion of the difference between the maximum and minimum torque values ​​per unit time to the average torque), the battery charging and discharging power control accuracy (deviation rate between actual power and command-limited power), and the drive anti-slip control trigger accuracy (correct trigger count to the required trigger count). The ratio is based on the acquired adaptive dynamic weight adjustment coefficient, in which indicators that affect low-temperature acceleration are assigned higher weights. For example, the weight of subspace correlation indicators with high distribution density is set to 0.85, and the weight of indicators with low correlation degree is set to 0.35. The original score of each indicator is calculated by multiplying the score from 0 to 10 according to the preset standard with the corresponding weight to obtain the weighted score. Then, the characteristic deviation under low temperature environment is compensated and corrected. For example, when the torque fluctuation rate is systematically high due to poor low-temperature lubrication, the original score is reduced by the deviation ratio to reflect the real impact. Finally, all the corrected weighted scores are summarized to obtain the corrected comprehensive performance evaluation index.

[0094] Step 6.2: Based on the revised comprehensive performance evaluation index, analyze the power system response characteristics of the vehicle under low-temperature acceleration conditions to determine whether there is a risk of power interruption due to battery output power limitation, a risk of drive torque fluctuation due to abnormal motor torque distribution, or a risk of battery performance protection triggering due to exceeding battery charging and discharging power limits. This involves extracting power system response data under low-temperature acceleration conditions based on the revised comprehensive performance evaluation index, including the battery output power limitation curve, motor torque distribution command sequence, and actual battery charging and discharging power curve. When analyzing the risk of power interruption, compare the real-time difference between the power corresponding to the motor torque demand and the battery output power limitation. If the motor power demand exceeds the battery output power limitation for more than 50 milliseconds without a torque reduction command, the risk is considered. When analyzing the risk of power interruption due to battery output power limitation, the system determines that there is a risk of power interruption due to battery output power limitation. When analyzing the risk of drive torque fluctuation, the system counts the torque change within 10 milliseconds in the torque distribution command sequence. If the change exceeds the maximum allowable fluctuation value of the motor at low temperature, and this occurs more than 5 times within 1 minute based on the corrected motor performance parameters, the system determines that there is a risk of drive torque fluctuation due to abnormal motor torque distribution. When analyzing the risk of battery performance protection triggering, the system tracks the deviation between the actual battery charging and discharging power and the power limit in the energy management command. When the actual power exceeds the limit for 30 consecutive milliseconds and triggers the overcurrent protection signal of the battery management system, the system determines that there is a risk of battery performance protection triggering due to battery charging and discharging power exceeding the limit. Finally, the system records the occurrence time, duration, and associated parameters of each risk to form a clear risk judgment result.

[0095] Step 6.3: Based on the risk assessment results, generate a verification conclusion on the effectiveness of the VCU control strategy coordination in low-temperature environments. This includes: summarizing the risk assessment results, statistically analyzing the total number of risks occurring under low-temperature acceleration conditions, and the proportion of high-risk items (e.g., power interruption risk is a high risk, and its proportion is the percentage of the total number of risks); when assessing the effectiveness of the VCU control strategy coordination, if all risks are low-risk and the total number of occurrences is less than 3, it is considered effective; if there are 1-2 medium-risk items and no high-risk items, it is considered basically effective but requires local optimization; if there are high-risk items or more than 3 medium-risk items, it is considered ineffective and requires comprehensive optimization. Based on the assessment results, generate a verification conclusion, clearly indicating the specific manifestations of effective coordination, such as good matching between torque distribution and battery power limit, existing problems such as battery power limit adjustment lagging behind torque demand during rapid low-temperature acceleration, and improvement directions such as optimizing the linkage response logic between battery power limit and torque demand; and compiling the verification conclusion, risk assessment results, corrected comprehensive performance evaluation indicators, and test process data into a standardized report.

[0096] In this embodiment of the invention, the comprehensive performance evaluation index of the control command sequence is dynamically weighted and compensated based on the generated adaptive dynamic weight adjustment coefficient to obtain the corrected comprehensive performance evaluation index. The power system response characteristics of the vehicle under low-temperature acceleration conditions are analyzed based on the index to determine various risks such as power interruption, drive torque fluctuation, and battery performance protection triggering. Based on the risk judgment results, a verification conclusion on the synergistic effectiveness of the VCU control strategy under low-temperature conditions is generated to complete the technical means of vehicle controller-in-the-loop test management. Therefore, this overcomes the technical problems of traditional control command evaluation using fixed thresholds, which leads to insufficient identification of potential risks under complex extreme conditions such as low-temperature acceleration, and the inability to assess the synergy of the VCU control strategy and support closed-loop management of in-the-loop testing. Furthermore, it captures various risks of the power system under low-temperature acceleration conditions, comprehensively verifies the synergistic effectiveness of the VCU control strategy under extreme environments, provides scientific and complete verification conclusions for vehicle controller-in-the-loop testing, and effectively improves the management efficiency of VCU control strategy testing under low-temperature conditions.

[0097] like Figure 2 As shown, embodiments of the present invention also provide a vehicle controller-in-the-loop test management system, comprising:

[0098] The acquisition module is used to calculate the efficiency changes of the battery and motor based on the set low temperature environment parameters, perform coupled analysis, and output the corrected actual usable energy parameters of the battery and the actual output performance parameters of the motor.

[0099] The simulation module is used to input the corrected performance parameters into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and obtain the simulation signal of the battery and motor working together. Based on the simulation signal, a hardware-in-the-loop test environment is constructed, the actual vehicle VCU hardware is connected to the test platform, and the simulation signal is sent to the VCU, while the simulated driver operation signal is injected.

[0100] The adjustment module is used by the VCU to receive simulation signals and operation signals, and generate motor torque distribution commands, battery energy management commands and drive anti-slip control commands. It performs multi-dimensional parameterized analysis and feature extraction on the collected command sequence, and combined with the dynamic response characteristics of the motor controller to torque commands, it constructs a convex optimized geometric envelope structure in a high-dimensional feature space based on all command data points, establishes the spatial mapping relationship and topological connection between each command parameter and the actual motor response, and generates adaptive dynamic weight adjustment coefficients according to parameter distribution density, regional adjacency relationship and motor control dynamic performance.

[0101] The correction module is used to perform weighted compensation and correction on the comprehensive performance evaluation index of the control command based on the adaptive dynamic weight adjustment coefficient, to determine whether there is a risk of power interruption, abnormal torque or battery performance exceeding the limit during low temperature acceleration, and to output a verification conclusion on the effectiveness of the VCU control strategy.

[0102] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for managing vehicle controller-in-the-loop testing, characterized in that, The method includes: Based on the set low-temperature environment parameters, the efficiency changes of the battery and motor are calculated, and a coupled analysis is performed to output corrected actual usable energy parameters of the battery and actual output performance parameters of the motor, including: Based on the set low-temperature environment parameters, the influence of low temperature on the internal electrochemical characteristics of the battery is analyzed, and the efficiency change characteristics caused by the decrease in ion conduction rate and the increase in internal resistance of the battery active material are calculated. At the same time, the influence of low temperature on the electromagnetic characteristics of the motor is analyzed, and the efficiency change characteristics caused by the change in motor winding resistance, the change in permanent magnet flux intensity, and the increase in lubricating viscosity are calculated. By comprehensively analyzing the characteristics of battery efficiency variation and motor efficiency variation, and based on the limiting relationship between battery output power attenuation due to low temperature and the maximum torque output capability of the motor, and the feedback relationship between the increase in battery discharge load caused by the motor efficiency loss due to low temperature, dynamic coupling calculation is performed to obtain the results of dynamic coupling calculation. Based on the results of dynamic coupling calculation, the corrected actual usable energy parameters of the battery and the corrected actual output performance parameters of the motor are output. The corrected performance parameters are input into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and to obtain the simulation signal of the battery and motor working together. A hardware-in-the-loop test environment is constructed based on simulated signals. The actual vehicle VCU hardware is connected to the test platform, and simulated signals are sent to the VCU while simulated driver operation signals are injected. The VCU receives simulation signals and operation signals, and generates motor torque distribution commands, battery energy management commands, and drive anti-slip control commands. The acquired command sequence undergoes multi-dimensional parameterized analysis and feature extraction. Combining this with the dynamic response characteristics of the motor controller to torque commands, a convex optimized geometric envelope structure is constructed in a high-dimensional feature space based on all command data points. This establishes a spatial mapping relationship and topological connection between each command parameter and the actual motor response. Adaptive dynamic weight adjustment coefficients are generated based on parameter distribution density, regional adjacency relationships, and the dynamic performance of motor control, including: The generated motor torque distribution command sequence, battery energy management command sequence, and drive anti-slip control command sequence are time-aligned and standardized preprocessed to extract the time domain, frequency domain, and statistical dimension features of the command parameters. Based on the extracted dimensional features, the actual torque response data fed back by the motor controller is received, and the torque response data is matched and associated with the corresponding torque command to obtain the dynamic response characteristics of the motor controller to the torque command and construct a high-dimensional feature space. A geometric envelope structure covering all data points is generated through a convex optimization algorithm. The geometric envelope structure is divided into multi-level meshes to divide the instruction feature subspace that reflects different control states, and the spatial mapping relationship and topological connection between VCU instruction parameters and motor actual response parameters are established. Based on spatial mapping relationships and topological connections, adaptive dynamic weight adjustment coefficients are generated according to the distribution density of data points, adjacency relationships, and dynamic performance indicators of motor control in each subspace. The comprehensive performance evaluation index of the control command is weighted and compensated and corrected based on the adaptive dynamic weight adjustment coefficient. It is determined whether there is a risk of power interruption, abnormal torque or battery performance exceeding the limit during low temperature acceleration, and outputs a verification conclusion on the synergistic effectiveness of the VCU control strategy.

2. The vehicle controller-in-the-loop test management method according to claim 1, characterized in that, The corrected performance parameters are input into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response, and tire rolling resistance under low-temperature conditions, obtaining simulation signals of the battery and motor working together, including: Based on the corrected actual usable energy parameters of the battery, the changes in the electrochemical characteristics of the battery under low temperature environment are simulated by a real-time simulation system, and the output voltage and current signals reflecting the decay process of the actual usable capacity of the battery are dynamically generated. Using the battery output voltage and current signals as the power input conditions for motor operation, combined with the corrected actual output performance parameters of the motor, the electromagnetic and mechanical characteristics of the motor under low temperature environment and actual power supply conditions are simulated by a real-time simulation system, and motor speed and torque signals including torque output delay, fluctuation and limiting characteristics are dynamically generated. Based on the vehicle driving characteristics reflected by the motor speed and torque signals, and combined with the change in rolling resistance coefficient caused by tire rubber hardening in low temperature environments, the driving resistance data of the vehicle under different driving conditions are calculated through a real-time simulation system. By comprehensively integrating battery output voltage and current signals, motor speed and torque signals, and vehicle driving resistance data, and through real-time vehicle dynamics simulation calculations, a simulation signal is generated that fully reflects the collaborative working state of the battery, motor, and vehicle in a low-temperature environment.

3. The vehicle controller-in-the-loop test management method according to claim 2, characterized in that, A hardware-in-the-loop test environment is constructed based on simulated signals. The actual vehicle's VCU hardware is connected to the test platform, and simulated signals are sent to the VCU. Simultaneously, simulated driver operation signals are injected, including: Based on the simulation signal set reflecting the collaborative working state of the battery, motor and vehicle, a real-time simulation test environment is constructed, which includes simulation nodes for the battery management system, motor controller and vehicle dynamics. In a real-time simulation test environment, the actual vehicle VCU controller is electrically connected to each simulation node through a hardware interface to form a complete hardware-in-the-loop test platform. The battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals contained in the simulation signal set are transmitted in real time to the corresponding input interfaces of the VCU controller through the hardware-in-the-loop test platform. Based on the preset test conditions, simulated driver operation signals are injected into the VCU controller, including accelerator pedal opening signal, brake pedal signal and gear status signal, to form complete vehicle operation input conditions.

4. The vehicle controller-in-the-loop test management method according to claim 3, characterized in that, The VCU receives simulation signals and operation signals, and generates motor torque distribution commands, battery energy management commands, and drive anti-slip control commands, including: The VCU receives transmitted battery output current and voltage signals, motor speed and torque signals, and vehicle speed and acceleration signals through the input interface, while also receiving simulated driver operation signals. Based on the received battery output current and voltage signals, the VCU analyzes and obtains the actual operating state parameters of the battery. According to the battery's performance characteristics in low-temperature environments, it generates corresponding battery energy management instructions, including charge and discharge power limit instructions and SOC protection instructions. Based on the power limit conditions determined in the generated battery energy management instructions, and combined with the received motor speed and torque signals, vehicle speed and acceleration signals, and driver operation signals, the VCU calculates the torque requirements of each motor and generates the corresponding motor torque allocation instruction sequence. Based on the output torque of each motor determined by the motor torque distribution command, and combined with the vehicle driving state reflected by the vehicle speed acceleration signal, the VCU obtains the corresponding drive anti-slip control command by analyzing the slip ratio characteristics of each drive wheel.

5. The vehicle controller-in-the-loop test management method according to claim 4, characterized in that, The comprehensive performance evaluation index of the control command is weighted and corrected based on the adaptive dynamic weight adjustment coefficient to determine whether there is a risk of power interruption, abnormal torque, or battery performance exceeding limits during low-temperature acceleration. The result is a verification conclusion on the effectiveness of the VCU control strategy, including: Based on the generated adaptive dynamic weight adjustment coefficient, the comprehensive performance evaluation index of the control command sequence is dynamically weighted and compensated to obtain the corrected comprehensive performance evaluation index. Based on the revised comprehensive performance evaluation index, the power system response characteristics of the vehicle under low temperature acceleration conditions are analyzed to determine whether there is a risk of power interruption due to battery output power limitation, a risk of drive torque fluctuation due to abnormal motor torque distribution, or a risk of battery performance protection being triggered due to battery charging and discharging power exceeding the limit, so as to obtain the judgment results of each risk. Based on the assessment results of various risks, a verification conclusion is generated on the synergistic effectiveness of the VCU control strategy in low-temperature environments.

6. A vehicle controller-in-the-loop test management system, wherein the system implements the method as described in any one of claims 1 to 5, characterized in that, include: The acquisition module is used to calculate the efficiency changes of the battery and motor based on the set low temperature environment parameters, perform coupled analysis, and output the corrected actual usable energy parameters of the battery and the actual output performance parameters of the motor. The simulation module is used to input the corrected performance parameters into the real-time simulation system to simulate the combined effects of battery capacity decay, motor torque response and tire rolling resistance under low temperature conditions, and obtain the simulation signal of the battery and motor working together. A hardware-in-the-loop test environment is constructed based on simulated signals. The actual vehicle VCU hardware is connected to the test platform, and simulated signals are sent to the VCU while simulated driver operation signals are injected. The adjustment module is used by the VCU to receive simulation signals and operation signals, and to generate motor torque distribution commands, battery energy management commands, and drive anti-slip control commands. The collected command sequence is subjected to multi-dimensional parameterized analysis and feature extraction. Combined with the dynamic response characteristics of the motor controller to torque commands, a convex optimized geometric envelope structure in a high-dimensional feature space is constructed based on all command data points. The spatial mapping relationship and topological connection between each command parameter and the actual response of the motor are established. Adaptive dynamic weight adjustment coefficients are generated according to parameter distribution density, regional adjacency relationship and motor control dynamic performance. The correction module is used to perform weighted compensation and correction on the comprehensive performance evaluation index of the control command based on the adaptive dynamic weight adjustment coefficient, to determine whether there is a risk of power interruption, abnormal torque or battery performance exceeding the limit during low temperature acceleration, and to output a verification conclusion on the effectiveness of the VCU control strategy.

7. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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