New energy automobile HIL double-engine cooperative automatic test method and system

Abstract

The invention discloses a new energy automobile HIL double-engine cooperative automatic test method and system, and relates to the technical field of new energy automobile power testing, and the method comprises the steps: uploading collected configuration data, carrying out the automatic simulation configuration through an in-loop HIL simulation method, generating a test task, and distributing the test task to an HIL rack of each region, battery parameters and double-engine control parameters are obtained; according to the battery parameters and the double-engine control parameters, synchronous simulation of the battery and the double engines is conducted through an in-loop HIL simulation method, and real-time energy interaction data between the battery and the double engines is obtained; summarizing and analyzing according to the double-engine collaborative test data, and generating optimization suggestions of the double-engine collaborative automatic test. Through the dynamic power distribution method, power distribution of the motor and the internal combustion engine can be automatically adjusted according to the battery SOC and the double-engine control parameters collected in real time, cooperative work of the motor and the internal combustion engine is dynamically optimized, energy efficiency is improved in real time, and energy waste is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy vehicle power testing technology, in particular to a new energy vehicle HIL dual-engine cooperative automatic testing method and system. BACKGROUND

[0002] In recent years, with the rapid development of new energy vehicle technology, especially the application of dual-engine cooperative automatic testing, the research on the cooperative control of electric motors and internal combustion engines has been continuously deepened. Based on the hardware-in-the-loop (HIL) simulation method, the performance testing and optimization of new energy vehicles have become a research field in the industry. The HIL simulation method combines control with physical environment, simulates actual operating scenarios, and verifies and optimizes the control of new energy vehicles. Through testing single power, it gradually expands to dual-engine cooperative automatic testing, and through accurate simulation of the energy interaction between the battery and the dual-engine, it improves the efficiency and stability of dual-engine cooperative automatic testing.

[0003] However, the existing HIL simulation technology still has certain limitations in dual-engine cooperative control, which is in real-time scheduling and power distribution optimization under complex working conditions. Although it can provide basic simulation support, it often lacks dynamic real-time adjustment of battery SOC, electric motor and internal combustion engine power distribution, resulting in the inability to fully tap the energy efficiency potential during testing. The cooperative data processing and optimization analysis during testing lags behind, and real-time scheduling cannot provide immediate feedback, affecting testing efficiency and real-time optimization effect. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a new energy vehicle HIL dual-engine cooperative automatic testing method to solve the problems of uneven power distribution and insufficient real-time optimization in dual-engine cooperative automatic testing.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the application provides a new energy vehicle HIL dual-engine cooperative automatic testing method, which comprises uploading collected configuration data and performing automatic simulation configuration through a loop HIL simulation method, generating test tasks and distributing them to HIL benches in each region to obtain battery parameters and dual-engine control parameters; according to the battery parameters and the dual-engine control parameters, performing synchronous simulation of the battery and the dual-engine by using the loop HIL simulation method to obtain real-time energy interaction data between the battery and the dual-engine; according to the cooperative control of the electric motor and the internal combustion engine, performing dynamic adjustment on the real-time energy interaction data by using a dynamic power distribution method to obtain dual-engine cooperative data; performing real-time monitoring and synchronous simulation processing on the dual-engine cooperative data by using the loop HIL simulation method to obtain dual-engine cooperative test data; and performing summarization and analysis according to the dual-engine cooperative test data to generate optimization suggestions for dual-engine cooperative automatic testing.

[0007] As a preferred scheme of the new energy vehicle HIL dual-engine cooperative automatic testing method, the configuration data refers to battery parameters, dual-engine control strategies and test scenarios.

[0008] As a preferred scheme of the new energy vehicle HIL dual-engine cooperative automatic testing method, the loop HIL simulation method refers to combining actual control hardware with actual environment to perform real-time simulation testing.

[0009] As a preferred scheme of the new energy vehicle HIL dual-engine cooperative automatic testing method, the uploading of the collected configuration data and the automatic simulation configuration through the loop HIL simulation method to generate test tasks and distribute them to HIL benches in each region to obtain battery parameters and dual-engine control parameters comprises the following specific steps, performing structured preprocessing on the collected configuration data to obtain a configuration data set; performing automatic simulation configuration on each regional test resource of the configuration data set by using the loop HIL simulation method to generate distributed test task instructions; performing task scheduling and parameter injection processing according to the regional HIL benches of the test task instructions to obtain battery parameters and dual-engine control parameters.

[0010] As a preferred scheme of the new energy vehicle HIL dual-engine cooperative automatic testing method, the synchronous simulation of the battery and the dual-engine by using the loop HIL simulation method according to the battery parameters and the dual-engine control parameters to obtain real-time energy interaction data between the battery and the dual-engine comprises the following specific steps, performing synchronous processing on the battery parameters and the dual-engine control parameters by using the loop HIL simulation method through a real-time simulation interface to obtain initial cooperative running state data; According to the initial cooperative operation state data, the polling scheduling algorithm is used for joint simulation analysis of the battery charging and discharging process and the dual power source output process, and real-time energy interaction data between the battery and the dual engine is obtained.

[0011] As a preferred scheme of the new energy vehicle HIL dual engine cooperative automatic test method, the dynamic power distribution method is used to dynamically adjust the power distribution between the motor and the internal combustion engine according to the battery state and the working load of the dual engine.

[0012] As a preferred scheme of the new energy vehicle HIL dual engine cooperative automatic test method, the dynamic power distribution method is used to dynamically adjust the power distribution between the motor and the internal combustion engine according to the battery state and the working load of the dual engine. According to the cooperative control of the motor and the internal combustion engine, the dynamic power distribution method is used to dynamically track the real-time energy interaction data and perform online correction of parameters, so as to obtain a preliminary optimized power distribution signal. According to the power distribution signal, the dynamic power distribution method is used to dynamically adjust the output power of the motor and the internal combustion engine, so as to realize real-time optimization of the battery SOC and the dual engine efficiency, and obtain dual engine cooperative data.

[0013] As a preferred scheme of the new energy vehicle HIL dual engine cooperative automatic test method, the dynamic power distribution method is used to dynamically adjust the power distribution between the motor and the internal combustion engine according to the battery state and the working load of the dual engine. Based on the in-loop HIL simulation method, high-frequency synchronous acquisition and time alignment processing are performed on the dual engine cooperative data, so as to obtain multi-channel cooperative operation data. According to the multi-channel cooperative operation data, the cooperative operation state of the motor and the internal combustion engine is closed-loop replayed, the response behavior and the energy interaction data of each regional HIL test bench are synchronously simulated, and dual engine cooperative test data are obtained.

[0014] As a preferred scheme of the new energy vehicle HIL dual engine cooperative automatic test method, the dynamic power distribution method is used to dynamically adjust the power distribution between the motor and the internal combustion engine according to the battery state and the working load of the dual engine. The dual engine cooperative test data are distributedly stored and structurally preprocessed, so as to obtain a cooperative test data set. The cooperative test data set is analyzed and evaluated, the performance deviation and the cooperative efficiency evaluation result are obtained by comparing the actual operation data of different test scenes. Based on the performance deviation and collaborative efficiency evaluation results, we analyze the bottlenecks and performance problems in the collaborative test data, identify and optimize uneven power distribution and excessively long response time, and generate optimization suggestions for dual-engine collaborative automated testing by adjusting the dual-engine control strategy.

[0015] Secondly, this invention provides a new energy vehicle HIL dual-engine collaborative automated testing system, comprising: a data acquisition and distribution module, which uploads the acquired configuration data and performs automated simulation configuration using the in-loop HIL simulation method to generate test tasks and distribute them to HIL benches in various areas, thereby obtaining battery parameters and dual-engine control parameters; a simulation analysis module, which performs synchronous simulation of the battery and dual engines using the in-loop HIL simulation method based on the battery parameters and dual-engine control parameters, thereby obtaining real-time energy interaction data between the battery and dual engines; an optimization and adjustment module, which dynamically adjusts the real-time energy interaction data using a dynamic power allocation method based on the coordinated control of the electric motor and internal combustion engine, thereby obtaining dual-engine collaborative data; a synchronization processing module, which uses the in-loop HIL simulation method to perform real-time monitoring and synchronous simulation processing of the dual-engine collaborative data, thereby obtaining dual-engine collaborative test data; and an optimization suggestion module, which summarizes and analyzes the dual-engine collaborative test data to generate optimization suggestions for the automated dual-engine collaborative test.

[0016] The beneficial effects of this invention are as follows: Through a dynamic power allocation method, the power allocation between the electric motor and the internal combustion engine can be automatically adjusted based on real-time collected battery SOC and dual-engine control parameters, dynamically optimizing the coordinated operation of the electric motor and the internal combustion engine, improving energy efficiency in real time, and reducing energy waste. The in-loop HIL simulation method used monitors the performance of dual-engine coordinated automation in real time, ensuring the stability and efficiency of dual-engine coordinated automation are maximized, improving the testing accuracy and efficiency of the dual-engine coordinated automation testing process. Furthermore, it provides scheduling schemes for optimizing power allocation and dual-engine control strategies, enhancing the energy efficiency and stability of new energy vehicles in practical applications. Attached Figure Description

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

[0018] Fig. 1 This is a flowchart of the HIL dual-engine collaborative automated testing method for new energy vehicles.

[0019] Fig. 2 This is a schematic diagram of the HIL dual-engine collaborative automated testing system for new energy vehicles.

[0020] Fig. 3 A flowchart for acquiring real-time energy interaction data.

[0021] Fig. 4 A flowchart generated for dynamic power allocation optimization suggestions. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figs. 1-4 This is one embodiment of the present invention, which provides an automated testing method for HIL dual-engine collaborative operation of new energy vehicles, including the following steps: S1: Upload the collected configuration data and perform automated simulation configuration using the in-loop HIL simulation method to generate test tasks and assign them to HIL benches in various regions to obtain battery parameters and dual-engine control parameters.

[0026] S1.1: Configuration data refers to battery parameters, dual-engine control strategy, and test scenarios.

[0027] S1.2: In-Loop (HIL) simulation refers to combining actual control hardware with the actual environment to conduct real-time simulation testing.

[0028] S1.3: Perform structured preprocessing on the configuration data collected on-site to obtain the configuration dataset.

[0029] Furthermore, the configuration data collected on-site is preprocessed in a structured manner to ensure the uniformity and usability of the configuration data. The configuration data is then classified and organized, and information on battery parameters, dual-engine control strategies, and test scenarios is extracted, scheduled, and categorized into a structured configuration dataset.

[0030] Specifically, classification and organization refers to categorizing the collected battery parameters, dual-engine control strategies, and test scenario data according to different attributes and types.

[0031] The dual-engine control strategy in the configuration data is processed through task scheduling and parameter injection to form dual-engine control parameters.

[0032] It should be noted that structured preprocessing refers to the organization, transformation, classification, cleaning, and standardization of the collected configuration data; Ideally, test data from different sources and of different types (battery parameters, dual-engine control strategies, test scenarios) should be transformed into a structured form for configuration data analysis, storage, and processing, forming a structured dataset for simulation and evaluation.

[0033] The dual-engine control strategy refers to a dynamic adjustment scheme for the coordinated operation of the electric motor and the internal combustion engine, including adjusting the power distribution, operating mode and response behavior of the electric motor and the internal combustion engine according to different operating conditions (load, speed, SOC).

[0034] Categorization refers to grouping the collected battery parameters, dual-engine control strategies, and test scenario data according to type, purpose, and function.

[0035] Task scheduling refers to the rational allocation of test resources in various regions according to test instructions, the execution of test tasks in a predetermined order and time, and the processing of battery parameters and dual-engine control parameters.

[0036] S1.4: Utilize the in-the-loop (HIL) simulation method to automatically simulate and configure test resources in each region of the configuration dataset, generating distributed test task instructions.

[0037] Furthermore, the HIL simulation method is used to automatically identify the available test resources of HIL benches in each region of the configuration dataset for simulation configuration. Tasks are assigned to HIL benches in each region. The HIL simulation method automatically generates test task instructions for each region based on the task assignment results. Each test task instruction automatically configures the simulation parameters of HIL benches in each region, generating distributed test task instructions.

[0038] Test task instructions are a set of instructions generated based on the configuration dataset, which guides test resources in each region to execute test tasks.

[0039] Simulation parameters refer to the various data of operating conditions in the HIL simulation method, including battery parameters, dual-engine control parameters, test scenario settings, and their operating status and behavior in the actual working environment.

[0040] S1.5: Perform task scheduling and parameter injection processing on the HIL test bench within the test task area according to the test task instructions to obtain battery parameters and dual-engine control parameters.

[0041] Furthermore, after receiving the test task instructions generated by the in-loop HIL simulation method, the HIL test bench within the region executes task scheduling according to the instructions. Based on the test scenario, battery parameter requirements, and dual-engine control strategy specified in the test task instructions, the corresponding battery parameters and dual-engine control parameters are injected into the real-time simulation environment of the HIL test bench. By loading and configuring the injected parameters in real time using the in-loop HIL simulation method, the parameters are updated synchronously with the test scenario to obtain battery parameters and dual-engine control parameters consistent with the test task instructions.

[0042] It should be noted that the execution task scheduler distributes the generated test task instructions to the HIL test benches in each region according to the region, and each HIL test bench can execute the corresponding test task.

[0043] Parameter injection processing refers to loading battery parameters and dual-engine control parameters into the HIL test bench for testing, and simulating and executing the test task under the correct parameter configuration.

[0044] Correct parameters refer to the battery parameters and dual-engine control parameters extracted from the configuration dataset and loaded into the HIL bench according to the test task requirements.

[0045] Battery parameters refer to the battery's voltage, current, temperature, state of charge, capacity, and internal resistance; dual-engine control parameters refer to the control strategies, power output characteristics, speed, torque control, and fuel consumption rate parameters of the electric motor and internal combustion engine.

[0046] S2: Based on the battery parameters and dual-engine control parameters, synchronous simulation of the battery and dual engines is performed using the in-loop HIL simulation method to obtain real-time energy interaction data between the battery and dual engines.

[0047] S2.1: Using the in-loop HIL simulation method, the battery parameters and dual-engine control parameters are processed synchronously through a real-time simulation interface to obtain the initial collaborative operation status data.

[0048] Furthermore, by using the HIL simulation method, battery parameters and dual-engine control parameters are input into the real-time simulation interface for synchronous processing. The real-time simulation interface enables real-time interaction and synchronization of the working status information of the battery and dual engines. The battery parameters and dual-engine control parameters are time-aligned and synchronized under a unified time base using timestamps to obtain initial collaborative operation status data.

[0049] Specifically, a timestamp is a time marker used to mark and synchronize the moment when data is exchanged and events occur in different test scenarios.

[0050] A unified time reference refers to the time coordination and consistency of various tests during the HIL simulation process by synchronously processing battery parameters and dual-engine control parameters.

[0051] The real-time simulation interface refers to the interface used in the HIL simulation method to synchronize and process real-time data of battery parameters and dual-engine control parameters, and to interact with the actual test environment.

[0052] S2.2: Based on the initial collaborative operation status data, a polling scheduling algorithm is used to perform joint simulation analysis on the battery charging and discharging process and the dual power source output process to obtain real-time energy interaction data between the battery and the dual engines.

[0053] Furthermore, based on the HIL simulation method, the initial collaborative operation state data is input into the simulation environment through a real-time simulation interface. The state variables of the battery charging and discharging process and the output process of the dual power sources are coordinated by sequentially traversing the battery charging and discharging process through a polling scheduling algorithm. The dynamic calculation of charging and discharging and the power output simulation of the electric motor and internal combustion engine are executed alternately according to a fixed time step. The battery SOC and the output power of the dual engines are updated synchronously in each polling cycle, and the power flow, current, voltage and torque information exchanged between the two in each scheduling cycle are recorded to form continuous real-time energy interaction data between the battery and the dual engines.

[0054] The real-time energy interaction between the battery and the dual engines is expressed as:

[0055] in, This indicates the total energy exchange between the battery and the dual engines. This represents the weighted parameter indicating the energy interaction contribution between the internal combustion engine and the electric motor in each test scenario. The weighted parameter ranges from 0 to 1, and the sum of the coefficients equals 1. It is obtained by round-robin scheduling after standardizing the power interaction values ​​(energy exchange amount, interaction duration, and comprehensive operating condition weight) between the battery and the dual engines in each test scenario. Indicating in the test scenario The power interaction value between the battery, internal combustion engine, and electric motor. This indicates the total number of test scenarios. This indicates the energy interaction process between the battery and the dual engines.

[0056] It should be noted that the round-robin scheduling algorithm is a scheduling method that allocates computing resources and execution time in a fixed order. The round-robin scheduling algorithm coordinates the charging and discharging process of the battery with the output process of the two power sources (electric motor and internal combustion engine) to work together and keep the time and energy exchange synchronized.

[0057] The battery charging / discharging process and the dual-power source output process are simulated alternately with a fixed time step. In each polling cycle, the battery charging / discharging state and the power output state of the dual engines are updated synchronously to obtain the coordination between the battery SOC and the dual engine output power. The parameters of the battery and the dual engines (power flow, current, voltage, torque) are dynamically adjusted and updated to form real-time energy interaction data. The polling scheduling algorithm ensures that each process is executed in sequence in each cycle, keeps the time of energy exchange between the battery and the dual engines consistent, and adjusts the real-time energy interaction data parameters in real time.

[0058] Specifically, battery SOC refers to the ratio of the battery's current charge to its maximum charge, expressed as a percentage.

[0059] Ideally, SOC reflects the remaining energy of the battery and is an indicator for assessing the battery's charge and discharge status. Real-time monitoring of battery SOC is crucial for dynamically adjusting the stability of power distribution.

[0060] The battery SOC ranges from 0% to 100%; 0% indicates that the battery is fully discharged, and 100% indicates that the battery is fully charged.

[0061] The polling period refers to the time interval used for each calculation during the simulation process, which coordinates the synchronous update of the battery charging and discharging process and the dual power source output process, with a range of 1 millisecond.

[0062] A fixed time step refers to the time interval (1 millisecond) used for each calculation during the simulation process. During the simulation, regardless of how the state changes, the frequency and time step of the simulation calculation remain unchanged, and different processes (such as the battery charging and discharging process and the dual power source output process) are synchronized in each polling cycle.

[0063] S3: Based on the coordinated control of the electric motor and the internal combustion engine, a dynamic power distribution method is used to dynamically adjust the real-time energy interaction data to obtain dual-engine coordinated data.

[0064] S3.1: The dynamic power distribution method refers to dynamically adjusting the power distribution between the electric motor and the internal combustion engine based on the battery status and the workload of the two engines.

[0065] S3.2: Based on the coordinated control of the electric motor and the internal combustion engine, a dynamic power distribution method is used to dynamically track real-time energy interaction data and correct parameters online to obtain a preliminary optimized power distribution signal.

[0066] Furthermore, based on the dual-engine control strategy of electric motor and internal combustion engine, real-time energy interaction data is dynamically tracked. By monitoring the battery status and the workload of the dual engines in real time, the power distribution ratio is dynamically adjusted. Based on the real-time updated energy interaction data and battery status, the power distribution signal is further optimized through a dynamic power distribution method to obtain a preliminary optimized power distribution signal.

[0067] Dynamic power allocation, expressed as:

[0068] in, Indicates total power output. This represents the electric motor power allocation coefficient, calculated in real-time during dynamic power allocation using a polling scheduling algorithm based on engine power demand, battery state of charge (SOC), driving conditions, and real-time energy interaction data. It ranges from 0 to 1 and is related to the internal combustion engine's power allocation coefficient. Interdependent This indicates the power output of the electric motor. This represents the internal combustion engine allocation coefficient, calculated in real-time during dynamic power allocation using a polling scheduling algorithm based on engine power demand, battery state of charge (SOC), driving conditions, and real-time energy interaction data. The range is... , This indicates the power output of the internal combustion engine. This represents the total power output of the electric motor and internal combustion engine after being combined using a dynamic power distribution method. Indicates electric motor, It refers to an internal combustion engine.

[0069] The optimal approach combines real-time data on battery state of charge (SOC), vehicle load, and driving conditions to obtain dynamic power distribution between the electric motor and the internal combustion engine. Under different loads and battery conditions, the dynamic power distribution method adjusts the power output ratio, improving the synergistic efficiency of the dual engines and vehicle performance. This enhances the responsiveness of new energy vehicles under various operating conditions, optimizes energy flow and fuel efficiency, effectively extends battery life, and provides control and optimization for the dual-engine synergistic strategy.

[0070] Specifically, the dynamic power distribution method refers to dynamically adjusting the power distribution between the electric motor and the internal combustion engine based on the battery status and the workload of the two engines.

[0071] Efficiency refers to the effectiveness of energy conversion and the optimization of power output during the collaborative operation of the electric motor and the internal combustion engine, as well as the overall vehicle performance and reduced energy consumption.

[0072] The dynamic power distribution method dynamically adjusts the power distribution between the electric motor and the internal combustion engine by monitoring the battery status (SOC, remaining charge, charging status) and the workload of the dual engines (load, speed, torque) in real time. Under different loads and operating conditions, it judges the battery's energy output demand and the dual engine's power output demand by collecting real-time data (vehicle acceleration demand, cruising, and hill climbing status). Based on the battery's remaining charge, the dual engine's power demand, and the vehicle's operating conditions, it adjusts the power ratio between the electric motor and the internal combustion engine in real time.

[0073] For example, under low SOC conditions, the electric motor is used first to provide power, while at high speeds, the internal combustion engine is relied upon to output higher power to optimize fuel consumption and battery life. The dynamic power distribution method monitors real-time data and adjusts the power output ratio to achieve efficient coordinated operation under various operating conditions.

[0074] Workload refers to the load borne by electric motors and internal combustion engines under operating conditions.

[0075] Battery status, including the remaining battery charge and charging status, ranging from 0% to 100%; vehicle driving status, including the vehicle's acceleration needs, cruising, and hill climbing; engine power demand, i.e. the power provided by the electric motor and internal combustion engine when the vehicle is in motion; and torque demand, the torque output of the electric motor and internal combustion engine in different driving modes.

[0076] S3.3: Based on the power distribution signal, the output power of the electric motor and the internal combustion engine is dynamically adjusted using a dynamic power distribution method to optimize the battery SOC and dual-engine efficiency in real time and obtain dual-engine collaborative data.

[0077] Furthermore, based on the dual-engine control strategy of electric motor and internal combustion engine, the power distribution signal is dynamically adjusted using a dynamic power distribution method. According to the battery SOC state and the workload of the dual engines, the ratio of electric power output of electric motor and fuel power output of internal combustion engine is adjusted. While maintaining the battery SOC within the operating range, the operating efficiency of dual engines is improved. The adjusted output power is optimized and verified by the HIL simulation method to obtain the consistency of energy distribution and cooperative control, and obtain dual-engine cooperative data.

[0078] It should be noted that the output ratio is dynamically adjusted according to changes in battery SOC and dual-engine load, with the power distribution between the electric motor and internal combustion engine ranging from 0% to 100%.

[0079] The SOC operating range is 20% to 80%, which can effectively protect battery life and reserve buffer space for energy recovery (regenerative braking) and high-power discharge.

[0080] The ratio of electric power output to fuel power output of the internal combustion engine refers to the dynamic adjustment of the power output ratio of the electric motor and the internal combustion engine according to the state of battery (SOC) and workload during the dual-engine collaborative operation, so as to distribute energy and improve operating efficiency under different operating conditions.

[0081] S4: The in-loop HIL simulation method is used to monitor and simulate the dual-engine collaborative data in real time to obtain dual-engine collaborative test data.

[0082] S4.1: Based on the in-loop HIL simulation method, high-frequency synchronous acquisition and time alignment processing are performed on the dual-engine collaborative data to obtain multi-channel collaborative operation data.

[0083] Furthermore, by utilizing the real-time simulation interface provided by the in-loop (HIL) simulation method, high-frequency synchronous acquisition of dual-engine collaborative data is performed. The operating status data of the electric motor, the operating status data of the internal combustion engine, and the energy interaction status data of the battery are acquired at high frequency and synchronously. Through time alignment processing, the dual-engine collaborative data acquired from each channel are aligned according to the time sequence to obtain the time synchronization of the control signals, feedback signals, and battery charging and discharging signals of the electric motor and the internal combustion engine, forming multi-channel collaborative operation data.

[0084] The superior performance enhances the ability to evaluate the coordinated efficiency of the electric motor and the internal combustion engine. Through closed-loop replay, it reproduces the response behavior and energy interaction process of the HIL test bench in each region. Through high-frequency synchronous acquisition and time-aligned processing, it further optimizes the dual-engine control strategy by evaluating the coordinated performance of the two engines under different test scenarios, and improves the fuel efficiency and battery energy utilization of the vehicle in practical applications.

[0085] Specifically, high-frequency synchronous acquisition refers to the simultaneous collection of dual-engine collaborative data from multiple channels of the electric motor, internal combustion engine, and battery at high speed and accuracy.

[0086] Multi-channel collaborative operation data refers to a time series dataset containing multiple types of signals, including the electric motor output power channel, the internal combustion engine output power channel, the battery SOC status channel, and the energy flow direction channel.

[0087] Time alignment processing refers to synchronizing the dual-engine collaborative data collected from each channel according to the time sequence in the HIL simulation method, so that the data are strictly consistent in the time dimension.

[0088] S4.2: Based on the multi-channel collaborative operation data, the collaborative operation status of the electric motor and the internal combustion engine is replayed in a closed loop. The response behavior and energy interaction data of the HIL test bench in each region are simulated synchronously to obtain the dual-engine collaborative test data.

[0089] Furthermore, by utilizing the in-loop (HIL) simulation method, multi-channel collaborative operation data is used as input to drive each region's HIL test bench to accurately reproduce the control signals, load changes, and energy flow processes of the electric motor and internal combustion engine during the test process, in a state of connection with actual hardware, according to the historical time sequence. During the energy flow process, the real-time simulation interface of the HIL simulation method is used to synchronously collect the response signals of each region's HIL test bench to the coordinated action of the electric motor and internal combustion engine, and combine them with the real-time energy interaction data between the battery and the dual engines to perform closed-loop playback and synchronous simulation, thereby obtaining the dual-engine collaborative test data.

[0090] It should be noted that the response signal refers to the feedback signal of each area HIL test bench to the coordinated action of the electric motor and internal combustion engine, and the real-time response to control commands and load changes during the test.

[0091] Control commands refer to the signals and parameters that regulate the working state and behavior of the electric motor and the internal combustion engine, including power output, speed and torque, and the coordinated control of the electric motor and the internal combustion engine.

[0092] S5: Summarize and analyze the dual-engine collaborative test data to generate optimization suggestions for dual-engine collaborative automated testing.

[0093] S5.1: Distributed storage and structured preprocessing are performed on the dual-engine collaborative test data to obtain the collaborative test dataset.

[0094] Furthermore, the dual-engine collaborative test data is distributed and stored according to different regions. It is classified and stored according to the field structure of test scenario, battery parameters, dual-engine control parameters and real-time energy interaction data. Through the structured preprocessing method in the loop HIL simulation method, the field structure of the distributed interaction data is preprocessed in a structured manner, and the dual-engine collaborative test data is transformed into a structured data table with a unified time base and parameter dimensions to form a collaborative test dataset.

[0095] Specifically, the parameter dimension refers to different types of data fields, such as battery parameters, dual-engine control parameters, test scenarios, and real-time energy interaction data, which describe and distinguish the differences in various indicators and characteristics during the test process.

[0096] A structured data table refers to a table format in which data (battery parameters, dual-engine control parameters, test scenario settings, and real-time energy interaction data) collected during the dual-engine collaborative testing process are organized and stored using a field structure.

[0097] S5.2: Perform data analysis and performance evaluation on the collaborative test dataset. By comparing the actual running data of different test scenarios, obtain the evaluation results of performance deviation and collaborative efficiency.

[0098] Furthermore, the collaborative test dataset is grouped according to test scenario categories, and the battery SOC change curve, electric motor output power sequence, internal combustion engine output power sequence, and real-time energy interaction data of the corresponding dual-engine collaborative test data are extracted. The collaborative efficiency of the electric motor and internal combustion engine and the deviation of power allocation are analyzed, and the actual response delay, energy conversion efficiency, and battery charge-discharge consistency index of the electric motor and internal combustion engine power allocation under each test scenario are calculated. The differences in battery charge-discharge consistency index in different test scenarios are compared to obtain the quantitative performance deviation. Based on the dynamic power allocation method, the adjustment record of the power allocation signal in each test scenario is recorded to evaluate the collaborative efficiency in the dual-engine collaborative operation process, and form the performance deviation and collaborative efficiency evaluation results.

[0099] The superior distributed storage and structured preprocessing mechanism for collaborative test data classifies, stores, and structures test scenarios, battery parameters, dual-engine control strategies, and real-time energy interaction data for different regions. It efficiently analyzes and evaluates changes in battery SOC, differences in power output between the electric motor and internal combustion engine, and energy conversion efficiency performance indicators. This improves the efficiency of data storage and access, optimizes the evaluation of performance deviations and collaborative efficiency in multiple scenarios, and enables dual-engine collaborative automated testing to provide accurate and targeted optimization suggestions under complex operating conditions.

[0100] It should be noted that the battery charge-discharge consistency index refers to the consistency and stability of the battery's charging and discharging processes during the dual-engine coordinated operation, thereby improving battery efficiency and lifespan.

[0101] Quantitative performance deviation refers to the performance difference value obtained by comparing the differences in battery charging and discharging consistency indicators in different test scenarios, calculating the actual response delay and energy conversion efficiency of power distribution between the electric motor and the internal combustion engine. The performance difference value ranges from 0% to 100%, where 0% indicates that there is no difference in performance under different test scenarios and the performance is consistent, while 100% indicates that the performance is completely different under different test scenarios and there are differences in efficiency.

[0102] S5.3: Based on the performance deviation and collaborative efficiency evaluation results, analyze the bottlenecks and performance problems in the collaborative test data, identify and optimize uneven power distribution and excessively long response time, and generate optimization suggestions for dual-engine collaborative automated testing by adjusting the dual-engine control strategy.

[0103] Furthermore, after data analysis and performance evaluation of the collaborative test dataset, based on the comparison results between actual operating data in different test scenarios, the uneven power distribution and excessively long response time phenomena that occurred during the HIL (Hybrid In-Loop) collaborative automation test of new energy vehicles were identified. For uneven power distribution, dynamic power adjustment based on battery status and dual-engine workload was implemented using the dynamic power distribution method, recalibrating the output power ratio of the electric motor and internal combustion engine. For excessively long response time, multi-channel collaborative operating data obtained through high-frequency synchronous acquisition and time alignment processing of dual-engine collaborative data using the in-loop HIL simulation method was used to optimize the timing logic of the electric motor and internal combustion engine collaborative control. Combined with the adjustments, the dual-engine control strategy was updated, generating optimization suggestions for the dual-engine collaborative automation test.

[0104] It should be noted that the comparison results refer to the analysis and evaluation of the synergistic efficiency and performance deviation between the electric motor and the internal combustion engine by comparing actual operating data from different test scenarios.

[0105] For example, in different test scenarios, by comparing the differences in battery SOC change curves, electric motor output power, and internal combustion engine output power sequences, the power distribution response delay between the electric motor and the internal combustion engine can be quantified, and the differences in energy conversion efficiency under different scenarios can be evaluated to obtain the cooperative efficiency and performance deviation in each scenario.

[0106] Different test scenarios: high-speed driving, urban traffic congestion driving, mountain climbing, and flat roads.

[0107] The optimization suggestions refer to the identification and optimization of performance issues such as uneven power distribution and excessively long response time based on the summary and analysis results of dual-engine collaborative test data. By adjusting the dual-engine control strategy, optimization suggestions for dual-engine collaborative automated testing are generated.

[0108] This embodiment also provides a new energy vehicle HIL dual-engine collaborative automated testing system, including: The data acquisition and distribution module uploads the collected configuration data and performs automated simulation configuration using the in-loop HIL simulation method. It generates test tasks and distributes them to HIL benches in various regions to obtain battery parameters and dual-engine control parameters. The simulation analysis module uses the in-loop HIL simulation method to perform synchronous simulation of the battery and dual engines based on battery parameters and dual engine control parameters, and obtains real-time energy interaction data between the battery and dual engines. The optimization and adjustment module dynamically adjusts the real-time energy interaction data based on the coordinated control of the electric motor and the internal combustion engine, using a dynamic power distribution method to obtain dual-engine coordinated data. The synchronization processing module uses the in-loop HIL simulation method to perform real-time monitoring and synchronous simulation processing of the dual-engine collaborative data to obtain dual-engine collaborative test data. The optimization suggestion module summarizes and analyzes the dual-engine collaborative test data to generate optimization suggestions for dual-engine collaborative automated testing.

[0109] In summary, this invention utilizes a dynamic power allocation method to automatically adjust the power distribution between the electric motor and the internal combustion engine based on real-time battery SOC and dual-engine control parameters. This dynamically optimizes the coordinated operation of the electric motor and the internal combustion engine, improving energy efficiency and reducing energy waste in real time. Furthermore, the employing in-loop (HIL) simulation method monitors the performance of dual-engine coordinated automation in real time, ensuring maximum stability and efficiency. This improves the testing accuracy and efficiency of the dual-engine coordinated automation testing process. Finally, it provides scheduling solutions for optimizing power allocation and dual-engine control strategies, enhancing the energy efficiency and stability of new energy vehicles in practical applications.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated testing method for HIL dual-engine collaborative operation in new energy vehicles, characterized in that: include, The collected configuration data is uploaded, and automated simulation configuration is performed using the in-loop HIL simulation method to generate test tasks and allocate them to HIL benches in various regions to obtain battery parameters and dual-engine control parameters. Based on battery parameters and dual-engine control parameters, synchronous simulation of the battery and dual engines is performed using the in-loop HIL simulation method to obtain real-time energy interaction data between the battery and dual engines. Based on the coordinated control of the electric motor and the internal combustion engine, a dynamic power distribution method is used to dynamically adjust the real-time energy interaction data to obtain dual-engine coordinated data. The in-loop HIL simulation method is used to monitor and synchronously simulate the dual-engine collaborative data in real time, and obtain dual-engine collaborative test data. Based on the data collected and analyzed from the dual-engine collaborative testing, optimization suggestions for dual-engine collaborative automated testing are generated.

2. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 1, characterized in that: The configuration data refers to battery parameters, dual-engine control strategy, and test scenarios.

3. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 2, characterized in that: The HIL simulation method refers to combining actual control hardware with the actual environment to conduct real-time simulation testing.

4. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 3, characterized in that: The process involves uploading the collected configuration data and performing automated simulation configuration using the HIL (Hybrid In-Loop) simulation method. Test tasks are then generated and assigned to HIL benches in various regions to obtain battery parameters and dual-engine control parameters. The specific steps are as follows: The configuration data collected on-site is preprocessed in a structured manner to obtain a configuration dataset; The in-the-loop (HIL) simulation method is used to automatically simulate and configure test resources in each region of the configuration dataset, generating distributed test task instructions. According to the test task instructions, the HIL test bench within the area performs task scheduling and parameter injection processing to obtain battery parameters and dual-engine control parameters.

5. The automated testing method for HIL dual-engine collaboration of new energy vehicles as described in claim 4, characterized in that: Based on battery parameters and dual-engine control parameters, synchronous simulation of the battery and dual engines is performed using the in-loop HIL simulation method to obtain real-time energy interaction data between the battery and dual engines. The specific steps are as follows. Using the in-loop HIL simulation method, the battery parameters and dual-engine control parameters are processed synchronously through a real-time simulation interface to obtain the initial collaborative operation status data. Based on the initial collaborative operation status data, a polling scheduling algorithm is used to conduct joint simulation analysis of the battery charging and discharging process and the dual power source output process to obtain real-time energy interaction data between the battery and the dual engines.

6. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 5, characterized in that: The dynamic power distribution method refers to dynamically adjusting the power distribution between the electric motor and the internal combustion engine based on the battery status and the workload of the dual engines.

7. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 6, characterized in that: The method involves dynamically adjusting real-time energy interaction data based on the coordinated control of the electric motor and the internal combustion engine, using a dynamic power distribution method to obtain dual-engine coordinated data. The specific steps are as follows. Based on the coordinated control of the electric motor and the internal combustion engine, a dynamic power distribution method is adopted to dynamically track real-time energy interaction data and correct parameters online, thereby obtaining a preliminary optimized power distribution signal. Based on the power distribution signal, a dynamic power distribution method is used to dynamically adjust the output power of the electric motor and the internal combustion engine, optimize the battery SOC and dual-engine efficiency in real time, and obtain dual-engine collaborative data.

8. The automated testing method for HIL dual-engine collaborative operation of new energy vehicles as described in claim 7, characterized in that: The method employs in-loop (HIL) simulation to perform real-time monitoring and synchronous simulation processing of dual-engine collaborative data, obtaining dual-engine collaborative test data. The specific steps are as follows. Based on the in-loop HIL simulation method, high-frequency synchronous acquisition and time alignment processing are performed on the dual-engine collaborative data to obtain multi-channel collaborative operation data. Based on multi-channel collaborative operation data, the collaborative operation status of the electric motor and internal combustion engine is replayed in a closed loop. The response behavior and energy interaction data of each region's HIL test bench are simulated synchronously to obtain dual-engine collaborative test data.

9. The automated testing method for HIL dual-engine collaboration of new energy vehicles as described in claim 8, characterized in that: The process of summarizing and analyzing the dual-engine collaborative test data to generate optimization suggestions for dual-engine collaborative automated testing involves the following steps: The dual-engine collaborative test data is distributed and preprocessed in a structured manner to obtain the collaborative test dataset; Data analysis and performance evaluation are performed on the collaborative test dataset. By comparing the actual running data of different test scenarios, the performance deviation and collaborative efficiency evaluation results are obtained. Based on the performance deviation and collaborative efficiency evaluation results, we analyze the bottlenecks and performance problems in the collaborative test data, identify and optimize uneven power distribution and excessively long response time, and generate optimization suggestions for dual-engine collaborative automated testing by adjusting the dual-engine control strategy.

10. A new energy vehicle HIL dual-engine collaborative automated testing system, based on the new energy vehicle HIL dual-engine collaborative automated testing method according to any one of claims 1 to 9, characterized in that: include, The data acquisition and distribution module uploads the collected configuration data and performs automated simulation configuration using the in-loop HIL simulation method. It generates test tasks and distributes them to HIL benches in various regions to obtain battery parameters and dual-engine control parameters. The simulation analysis module uses the in-loop HIL simulation method to perform synchronous simulation of the battery and dual engines based on battery parameters and dual engine control parameters, and obtains real-time energy interaction data between the battery and dual engines. The optimization and adjustment module dynamically adjusts the real-time energy interaction data based on the coordinated control of the electric motor and the internal combustion engine, using a dynamic power distribution method to obtain dual-engine coordinated data. The synchronization processing module uses the in-loop HIL simulation method to perform real-time monitoring and synchronous simulation processing of the dual-engine collaborative data to obtain dual-engine collaborative test data. The optimization suggestion module summarizes and analyzes the dual-engine collaborative test data to generate optimization suggestions for dual-engine collaborative automated testing.

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