Pure electric vehicle data integration and test method based on VCU control

Abstract

The invention discloses a VCU control-based pure electric vehicle data integration and test method, and particularly relates to the technical field of vehicle control testing, the VCU control strategy function, the VCU control safety, the VCU control performance and the VCU control intelligent level are marked as test targets, the test requirements are analyzed, the test requirements are decomposed to generate test points, and the test points are integrated and tested. Test cases are written based on test points, multi-type test environments under different working conditions are established based on test targets, and the test cases are applied to test a VCU control strategy function, VCU control safety, VCU control performance and a VCU control intelligent level in different test environments. According to the method, the VCU control function completion degree coefficient, the VCU control safety coefficient, the VCU control performance coefficient and the VCU control intelligence coefficient are calculated, then the VCU control quality index is calculated, the function safety of the vehicle control unit can be further guaranteed, the fault probability is reduced, and meanwhile the performance and the intelligence level of the vehicle control unit can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control testing, and more specifically, to a method for data integration and testing of pure electric vehicles based on VCU control. Background Art

[0002] With the rapid development of pure electric vehicles, the complexity of the vehicle control unit (VCU) of pure electric vehicles has been continuously increasing, and the failure problems of vehicle functional safety have also increased accordingly. How to ensure the functional safety of the vehicle control unit to the greatest extent and reduce the failure probability has become the focus of the application of the vehicle control unit.

[0003] The existing data testing method for pure electric vehicles based on VCU control monitors various control instructions generated by VCU control. First, it judges whether the range of the generated control instructions meets the VCU control function standard, then judges whether the generated control instructions conform to the set control logic, and finally judges whether the actual execution situation of the control instructions matches the content of the generated control instructions. If all match, it is determined that the function of the vehicle control unit meets the requirements, which ensures the function coverage rate of the vehicle control unit to a certain extent.

[0004] However, there are still some problems with the existing method: the existing method can only test the integrity of the function implementation of the vehicle control unit and the accuracy of the execution of the control strategy, but does not involve the functional safety of the vehicle control unit and the related capabilities of fault protection after the injection of fault types. It is necessary to further ensure the functional safety of the vehicle control unit and reduce the failure probability. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention marks the VCU control strategy function, VCU control safety, VCU control performance, and VCU control intelligence level as test objectives and then writes test cases for testing, which ensures the functional safety of the vehicle control unit to a greater extent and effectively reduces the failure probability.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for data integration and testing of pure electric vehicles based on VCU control, comprising the following steps: S1. Confirm the test objective: Mark the VCU control strategy function, VCU control safety, VCU control performance, and VCU control intelligence level as test objectives; S2. Write test cases: Analyze the test requirements, decompose the test requirements to generate test points, and write test cases based on the test points; S3. Build a test environment: Build multi-type test environments under different working conditions based on the test objectives; S4. Test the target through test cases: Apply test cases to test the VCU control strategy function, VCU control security, VCU control performance, and VCU control intelligence level in different test environments, and record the test results; S5. Preprocess the test results: Cluster the test results according to the test item type keywords, set the format, integrate the test result information, and automatically back it up; S6. Analyze the test results: Analyze the preprocessed test results, and calculate the VCU control strategy function index, VCU control security index, VCU control performance index, and VCU control intelligence index respectively; S7. Control quality evaluation: Compare the calculated VCU control-related indexes with the expected VCU control-related indexes, calculate the VCU control function completion coefficient, VCU control safety coefficient, VCU control performance coefficient, and VCU control intelligence coefficient respectively, and then calculate the VCU control quality index; S8. Transmit the control quality evaluation result: Transmit the calculated control quality index to the pure electric vehicle operation management terminal.

[0007] The technical effects and advantages of the present invention: The present invention marks the VCU control strategy function, VCU control security, VCU control performance, and VCU control intelligence level as test objectives; analyzes the test requirements, decomposes the test requirements to generate test points, and writes test cases based on the test points; builds multi-type test environments under different working conditions based on the test objectives; applies the test cases to test the VCU control strategy function, VCU control security, VCU control performance, and VCU control intelligence level in different test environments, and records the test results; clusters the test results according to the test item type keywords and then arranges them automatically according to the test timestamp and test case name, and then integrates the test results to obtain the number of correct and incorrect control instructions generated, the number of matching and non-matching executions of correct control instructions, the number of times the VCU protection mechanism is triggered and not triggered for the i-th fault type, the trigger duration of the k-th protection mechanism for the j-th fault type that triggers the VCU protection mechanism, the number of VCU control failures occurring within the i-th continuous operation preset period, the number of cyclic simulated power-on and power-off times, the number of abnormal system restarts during cyclic simulated power-on and power-off, the i-th zero-to-hundred-kilometer acceleration duration, the i-th braking energy recovery efficiency, the extreme working condition stability coefficient, the number of successful and failed upgrade requests, and the response delay of the i-th instruction packet sent in the 4G / 5G network; analyzes the preprocessed test results, calculates the VCU control strategy function index, VCU control security index, VCU control performance index, and VCU control intelligence index respectively; compares the calculated VCU control-related indexes with the expected VCU control-related indexes, calculates the VCU control function completion coefficient, VCU control safety coefficient, VCU control performance coefficient, and VCU control intelligence coefficient respectively, and further calculates the VCU control quality index, which can further ensure the functional safety of the vehicle controller, reduce the failure probability, and at the same time ensure the performance and intelligence level of the vehicle controller. Brief Description of the Drawings

[0008] Figure 1 It is a method step diagram of the present invention.

[0009] Figure 2 It is a system structure block diagram of the present invention. Detailed Embodiment

[0010] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0011] Such as Figure 1The present embodiment provides a method for integrating and testing pure electric vehicle data based on VCU control, including the following steps: S1. Confirm the test objectives: Mark the VCU control strategy function, VCU control security, VCU control performance, and VCU control intelligence level as the test objectives; Furthermore, the VCU control strategy includes the vehicle power-on and power-off control strategy, gear management control strategy, torque management control strategy, and energy management control strategy. The indicators corresponding to the VCU control strategy function are the generation accuracy rate of the control strategy and the execution matching rate of the control strategy; the indicators corresponding to the VCU control security are the fault protection coverage rate, the processing speed of fault types, the continuous operation failure rate, and the abnormal rate of the power-on and power-off cycle simulation; the indicators corresponding to the VCU control performance are the acceleration time for 100 kilometers, the braking energy recovery efficiency, and the stability coefficient under extreme conditions; the indicators corresponding to the VCU control intelligence level are the OTA upgrade success rate and the vehicle-cloud collaboration delay.

[0012] Specifically in this embodiment, the process of vehicle power-on and power-off refers to a series of operations for supplying power or cutting off the power to the high-voltage and low-voltage systems of the vehicle. The high-voltage switch of the vehicle mainly issues commands to the BMS through the vehicle-mounted controller, coordinates other components according to the vehicle's fault status and the driver's operation conditions, and issues corresponding commands to ensure the high-voltage safety of the vehicle. The VCU power-on control logic is as follows: the VCU sleeps; the VCU wakes up and starts low-voltage power-on; the key ON signal wakes up the BMS / MCU / DCDC for self-check; if there is no fault in the self-check, it waits for the low-voltage power-on to complete, and if there is a fault in the self-check, it enters the VCU sleep state; after the low-voltage power-on is completed, if the key START signal, no charging, and there is no fault above level three in the system, then high-voltage power-on is started; after the main positive relay and the main negative relay are closed, it waits for the high-voltage power-on to complete; after the high-voltage power-on is completed, it maintains the high-voltage READY state. The VCU power-off control logic is as follows: in the high-voltage READY state; if there is no key signal or a level-three fault lasts for a preset time, then high-voltage power-off is started; after the vehicle speed is less than the calibrated value and the motor speed is less than the calibrated value, the high-voltage relay is disconnected; the DCDC disables the enable, and the motor actively discharges; after the motor actively discharges, the DCDC stops working; the VCU sleeps.

[0013] Specifically in this embodiment, compared with traditional fuel vehicles, pure electric vehicles cancel the traditional gearshift lever control and instead issue shift commands by the controller. For a pure electric vehicle with four signal handle positions including the parking gear (P), drive gear (D), reverse gear (R), and neutral gear (N), the control strategy logic for processing the shift handle position signal is as follows: when the shift handle is placed in P, the vehicle speed is less than the maximum shift speed; when the shift handle is placed in D, the motor speed is greater than the minimum speed for shifting into D; when the shift handle is placed in R, the motor speed is less than the maximum speed for shifting into R; when the shift handle is placed in N, the brake pedal is depressed.

[0014] Specifically in this embodiment, the torque management strategy includes the analysis of the driver's throttle and brake pedal openings and the torque of the powertrain. Combining with the calculation of the battery motor output value of the energy management strategy, the minimum value of the calculation result is used as the final vehicle torque demand. The sum of the driving torque demand of the accelerator pedal and the braking torque demand of the brake pedal is the driver's torque demand. The driving torque demand is the demand torque based on economy or power of the driver at the current vehicle speed and acceleration opening obtained by looking up the table according to the driver's current pedal opening and vehicle speed; the braking torque demand is the braking torque distributed to the motor during the braking process by the driver, and the braking recovery torque of the driver at the current vehicle speed gear and brake pedal opening is obtained by looking up the table according to the driver's current brake pedal opening and vehicle speed. During the vehicle movement, the driver's driving intention is converted into the current torque demand, which needs to be calculated and output by combining the accelerator pedal opening signal and the current vehicle speed, and finally the driver's driving intention is truly analyzed and executed. When the current vehicle speed is 0 and the accelerator pedal is greater than a certain opening, the vehicle control unit (VCU) judges the driver's starting intention. At this time, the vehicle starting torque is given according to the torque look-up function in the software combined with the current vehicle speed, and the size of the throttle opening indicates the driver's acceleration intention and torque demand; after the generated torque control command exceeds the vehicle starting torque demand, the vehicle smoothly enters the starting state. Extract the minimum value of the maximum transmission torque of the powertrain and the driver's torque demand, compare the extraction result with the maximum output torque of the motor, extract the minimum value between the two, and then compare it with the minimum transmission torque of the powertrain and the minimum torque that the motor can transmit in the current state, and extract the maximum value among the three as the output torque of the motor.

[0015] Specifically in this embodiment, the vehicle energy management mainly includes the calculation of the battery output capacity and the calculation of the motor output capacity. The output capacity indicators of the battery are the maximum output power and the maximum charging power at the current voltage. The maximum output power at the current voltage can be expressed as the product of the battery allowable discharge coefficient, the maximum allowable discharge current, the average single-cell voltage of the battery, and the total number of battery single cells under the current vehicle state; the maximum charging power at the current voltage can be expressed as the product of the battery allowable charging coefficient, the maximum allowable charging current, the average single-cell voltage of the battery, and the total number of battery single cells under the current vehicle state. The calculation of the motor output capacity is mainly manifested as the calculation of the torque that the motor can output, which is divided into two parts: the maximum output torque and the maximum braking torque. The calculation of the motor maximum output torque is divided into three items: the maximum torque calculated from the maximum power allowed by the vehicle electrical energy for the motor, the torque value obtained from the motor temperature, and the maximum output torque of the motor calculated by the MCU. The minimum value of the three values is taken as the maximum torque output of the motor, aiming to ensure that the motor will not operate overload and the operation of the motor will not affect the work of other components; when calculating the maximum braking torque of the motor, it is divided into three items: the maximum charging torque calculated from the maximum charging power allowed by the battery, the temperature correction torque obtained from the current temperature of the motor, and the maximum generating torque of the motor calculated by the MCU. The maximum value of the three values is taken as the maximum braking torque of the motor, aiming to ensure that the motor will not operate overload and the operation of the motor will not affect the work of other components.

[0016] In this embodiment, it should be specifically noted that the accuracy rate of control strategy generation refers to the accuracy rate of control instructions generated under the set scenario control logic. For example, in the torque management strategy, when calculating the driver's torque demand, it is necessary to first calculate the driving torque demand of the pedal and the braking torque demand of the brake pedal and then sum them. If only the driving torque demand instruction of the pedal or the braking torque demand instruction of the brake pedal is generated, it is regarded as an incorrect control instruction. The execution matching rate of the control strategy refers to the execution situation of the correct control instruction. For example, if the generated control instruction is to calculate the driver's torque demand by summing the driving torque demand of the pedal and the braking torque demand of the brake pedal, and only the driving torque demand of the pedal or the braking torque demand of the brake pedal is calculated during actual execution and the calculation result is marked as the driver's torque demand, it is regarded as a mismatch in the execution of the correct control instruction. The fault protection coverage rate refers to the probability that the VCU triggers the protection mechanism after injecting the fault type. The fault type processing speed refers to the maximum trigger duration of the protection mechanism corresponding to different fault types. The continuous operation failure rate refers to the control failure rate of the VCU during continuous operation for a preset period. The abnormal rate of power-on and power-off cycle simulation refers to the probability of abnormal system restart during cycle simulation of power-on and power-off. The acceleration duration for 100 kilometers refers to the acceleration duration of the pure electric vehicle from a speed of 0 to 100 km / h. The braking energy recovery efficiency refers to the ratio of the energy converted from kinetic energy to electrical energy by the motor and stored in the battery system during vehicle braking to the total kinetic energy generated during vehicle braking. The stability coefficient αw under extreme conditions is related to the overrun coefficient βt of the low-temperature cold start duration and the overrun coefficient βp of the high-temperature climbing output power, and is specifically expressed as: , The overrun coefficient βt of the low-temperature cold start duration is specifically the ratio of the difference between the average value tae of the low-temperature cold start duration of the pure electric vehicle and the preset start duration tb to the preset start duration tb. If the difference is positive, it is directly calculated. If the difference is negative or 0, the calculated value is output as 0. The corresponding calculation formula is: , The overrun coefficient βp of the high-temperature climbing output power is specifically the ratio of the difference between the average value pae of the high-temperature climbing output power and the preset high-temperature climbing output power pbe to the preset high-temperature climbing output power pbe. If the difference is positive, it is directly calculated. If the difference is negative or 0, the calculated value is output as 0. The corresponding calculation formula is: , The low temperature for low-temperature cold start is -30°C, and the high temperature for high-temperature climbing is 45°C. The OTA upgrade success rate refers to the success rate when simulating 1000 upgrade requests. The vehicle-cloud collaboration delay refers to the average response delay of sending 1000 instruction packets under 4G / 5G networks.

[0017] S2. Write test cases: Analyze the test requirements, decompose the test requirements to generate test points, and write test cases based on the test points; Specifically, in this embodiment, what needs to be explained is that in the power-on and power-off control strategy, the two processes of power-on and power-off need to be tested to ensure that there are no faults in each sub-component during power-on and the input quantity meets the power-off conditions during power-off. The power-on process is divided into two parts: low-voltage power-on and high-voltage power-on. During the low-voltage power-on process, the combinations of key signals and charging signals in different states need to be considered. High-voltage power-on is mainly to verify whether the MCU voltage can make correct responses according to the control strategy at different input values; the power-off process involves power-off in case of faults and whether the motor speed, vehicle speed, and current will enter the power-off mode at different inputs during power-off.

[0018] Specifically, in this embodiment, what needs to be explained is that the shift lever position signal processing control strategy needs to test the recognition of the target lever position signal and the mutations between various signals. The mutation processing of P / R / N / D signals includes the mutation processing of P / R signals, P / N signals, P / D signals, N / D signals, R / N signals, and R / D signals.

[0019] Specifically, in this embodiment, what needs to be explained is that the torque management control strategy needs to determine the output of the driver's required torque and the vehicle's required torque. Inaccurate recognition of the driver's intention will lead to too large or too small torque output, resulting in safety problems.

[0020] Specifically, in this embodiment, what needs to be explained is that the functional safety level of the energy management control strategy is QM, and it is necessary to test whether the battery output ability and the motor output ability can be achieved according to the control strategy.

[0021] Specifically, in this embodiment, what needs to be explained is that the VCU control safety test needs to test whether injecting different fault types triggers the fault protection mechanism, the trigger duration of the corresponding protection mechanism for injecting different fault types, the failure rate during continuous operation for a preset period, and the probability of system abnormal restart during cyclic simulation of power-on and power-off; the VCU control performance test needs to test the 0-100 km / h acceleration duration, the braking energy recovery efficiency, and the stability under extreme conditions; the VCU control intelligence level test needs to test the OTA upgrade success rate and the vehicle-cloud collaboration delay.

[0022] Specifically in this embodiment, it should be noted that the designed test cases include normal working conditions and abnormal working conditions. Generally, test cases consist of basic elements such as case ID, case name, preconditions, execution conditions, and expected results. There are many different methods for designing test cases. From the perspective of testing techniques, they can be divided into black-box testing design methods, white-box testing design methods, and gray-box testing methods. Black-box testing technology is a technique for designing or selecting test cases based on system function or non-functional specifications without involving the internal structure of the software. White-box testing technology is a type of testing that focuses on the internal structure and logic of the software. It is a method for designing test cases by delving into the internal structure of the software. Gray-box testing is usually used in the integration testing phase. The application of this technology depends on certain background information, that is, understanding part of the internal structure of the software product or application program.

[0023] S3. Set up the test environment: Set up multiple types of test environments under different working conditions based on the test objectives; Specifically in this embodiment, it should be noted that a real vehicle test environment can be set up, and a VBOX device is used to record the acceleration curve of the pure electric vehicle and output the acceleration duration from 0 to 100 km / h of the pure electric vehicle for several times. A bench test environment can be set up to simulate the braking condition through a chassis dynamometer and measure the ratio of the feedback power to the theoretical value.

[0024] S4. Test the target through test cases: Apply the test cases to test the VCU control strategy function, VCU control security, VCU control performance, and VCU control intelligence level in different test environments, and record the test results; Furthermore, the test results include the correct or incorrect judgment generated for each control instruction, the matching or non-matching judgment for each correctly executed control instruction, whether the VCU protection mechanism is triggered after injecting different fault types, the protection mechanism trigger duration for each fault type that triggers the VCU protection mechanism, the number of VCU control failures occurring within each continuously operating preset cycle, whether there is a system abnormal restart during each cycle of simulated power-on and power-off, the acceleration duration for each 100 kilometers, the braking energy recovery efficiency for each group, the stability coefficient under extreme working conditions, the judgment result of whether each upgrade request is successful, and the response delay of each instruction packet sent under 4G / 5G networks.

[0025] S5. Preprocess the test results: Cluster the test results according to the keywords of the test item types, integrate the test result information after setting the format, and automatically back it up; Specifically in this embodiment, the project type keywords include whether the control instruction generation is correct or incorrect, whether the execution of the correct control instruction is matched or not, whether the VCU protection mechanism of the i-th fault type is triggered or not, the k-th protection mechanism trigger duration of the j-th fault type that triggers the VCU protection mechanism, the number of VCU control failures occurring in the preset cycle of continuous operation, whether the cyclic simulation of power-on and power-off has abnormal restart, the acceleration duration per 100 kilometers, the braking energy recovery efficiency, the stability coefficient under extreme conditions, whether the upgrade request is successful or failed, and the response delay of the 4G / 5G network instruction packet transmission.

[0026] Furthermore, after clustering the test results according to the test project type keywords, they are automatically arranged according to the test timestamp and test case name, and then the test results are integrated to obtain the number of correct and incorrect control instruction generations, the number of matches and non-matches in the execution of the correct control instruction, the number of times and non-trigger times of the VCU protection mechanism triggered by the i-th fault type, the k-th protection mechanism trigger duration of the j-th fault type that triggers the VCU protection mechanism, the number of VCU control failures occurring within the i-th preset cycle of continuous operation, the number of cyclic simulations of power-on and power-off, the number of abnormal restarts of the cyclic simulation of power-on and power-off system, the i-th acceleration duration per 100 kilometers, the i-th braking energy recovery efficiency, the stability coefficient under extreme conditions, the number of successful and failed upgrade requests, and the response delay of the i-th 4G / 5G network instruction packet transmission.

[0027] S6. Analysis of test results: Analyze the preprocessed test results, and calculate the VCU control strategy function index, VCU control security index, VCU control performance index, and VCU control intelligence index respectively; Furthermore, the steps of the test result analysis are as follows: S61. Retrieve the preprocessed test result information; S62. Calculate the control strategy generation accuracy rate ηsa, which is expressed as the ratio of the number of correct control instruction generations to the sum of the number of correct and incorrect control instruction generations, and calculate the control strategy execution matching rate ηpa, which is expressed as the ratio of the number of matches in the execution of the correct control instruction to the sum of the number of matches and non-matches in the execution of the correct control instruction; S63. Calculate the fault protection coverage rate ηfa, which is expressed as the sum of the ratios of the number of times the VCU protection mechanism is triggered for the i-th fault type to the sum of the number of times the VCU protection mechanism is triggered and not triggered for the i-th fault type, and then calculate the average value. Calculate the processing speed ηtaj for the j-th fault type, which is expressed as the average value of the k protection mechanism trigger durations for the j-th fault type that triggers the VCU protection mechanism. Calculate the continuous operation failure rate ηga, which is expressed as the sum of the ratios of the number of VCU control failures occurring within the i-th continuous operation preset period to the continuous operation preset period, and then calculate the average value. Calculate the power-on and power-off cycle simulation anomaly rate ηha, which is expressed as the ratio of the number of abnormal restarts of the power-on and power-off cycle simulation system to the number of power-on and power-off cycle simulations; S64. Calculate the zero-to-hundred-kilometer acceleration duration tca, which is expressed as the sum of the zero-to-hundred-kilometer acceleration durations for the i-th case and then calculate the average value. Calculate the regenerative braking energy recovery efficiency ηea, which is expressed as the sum of the regenerative braking energy recovery efficiencies for the i-th case and then calculate the average value. Calculate the stability coefficient αwa under extreme conditions; S65. Calculate the OTA upgrade success rate ηca, which is expressed as the ratio of the number of successful upgrade requests to the sum of the number of successful and failed upgrade requests. Calculate the vehicle-cloud collaboration delay ηxa, which is expressed as the sum of the response delays of the i-th instruction packet sent over the 4G / 5G network and then calculate the average value; S66. Automatically store the calculated VCU control strategy function indicators, VCU control safety indicators, VCU control performance indicators, and VCU control intelligence indicators.

[0028] In this embodiment, it should be specifically noted that in the analysis steps of the VCU control strategy function indicators, VCU control safety indicators, VCU control performance indicators, and VCU control intelligence indicators, the written expressions of the calculation processes for each indicator have been given. In the calculation processes, the relevant applications of the average value formula and the summation formula are involved, and the two can directly apply the existing formulas for calculation, so the specific formulas are not given here.

[0029] S7. Control quality assessment: Compare the calculated VCU control-related indicators with the expected VCU control-related indicators, calculate the VCU control function completion coefficient, VCU control safety coefficient, VCU control performance coefficient, and VCU control intelligence coefficient respectively, and then calculate the VCU control quality index; Furthermore, the specific steps of the control quality assessment are as follows: S71. Retrieve the expected control strategy generation accuracy rate ηsr, control strategy execution matching rate ηpr, fault protection coverage rate ηfr, processing speed ηtrj for the j-th type of fault, continuous operation failure rate ηgr, abnormal rate ηhr of power-on and power-off cycle simulation, acceleration time tcr for 100 kilometers, braking energy recovery efficiency ηer, stability coefficient αwr under extreme conditions, OTA upgrade success rate ηcr, and vehicle-cloud collaboration delay ηxr; Specifically, it should be noted in this embodiment that the expected values are set based on the functional safety standards, performance requirements, and intelligent requirements of the pure electric vehicle VCU control.

[0030] S72. Calculate the VCU control completion degree coefficient Xg, which is expressed as the square root of the product of the ratio of the calculated value ηsa of the control strategy generation accuracy rate to the expected value ηsr and the ratio of the calculated value ηpa of the control strategy execution matching rate to the expected value ηpa. The specific formula is: ; S73. Calculate the VCU control safety coefficient Xa, which is expressed as the average value of the sum of the ratio of the calculated value ηfa of the fault protection coverage rate to the expected value ηfr and the ratio of the expected value ηtrj of the processing speed for the j-th type of fault to the calculated value ηtaj, the ratio of the expected value ηgr of the continuous operation failure rate to the calculated value ηga, and the ratio of the expected value ηhr of the abnormal rate of power-on and power-off cycle simulation to the calculated value ηha. The specific formula is: , where na is the number of fault types that can trigger the VCU protection mechanism; Specifically, it should be noted in this embodiment that the VCU control safety coefficient is positively correlated with the fault protection coverage rate. The higher the fault protection coverage rate, the safer the VCU control. Therefore, calculate the ratio of the calculated value of the fault protection coverage rate to the expected value. The larger the corresponding ratio, the better the expected effect. The VCU control safety coefficient is positively correlated with the fault processing speed, and the fault processing speed is negatively correlated with the trigger duration of the fault protection mechanism. Therefore, the VCU safety coefficient is negatively correlated with the trigger duration of the fault protection mechanism. Therefore, calculate the ratio of the expected value of the fault type processing speed to the calculated value. The larger the ratio, the better the expected effect. The VCU control safety coefficient is also negatively correlated with the continuous operation failure rate and the abnormal rate of power-on and power-off cycle simulation. The formula design principle is the same as the formula design principle of the above fault processing speed.

[0031] S74. Calculate the VCU control performance coefficient Xe, which is expressed as the cube root of the product of the ratio of the expected value tcr of the acceleration time for 100 kilometers to the calculated value tca, the ratio of the calculated value ηea of the braking energy recovery efficiency to the expected value ηer, and the ratio of the calculated value αwa of the stability coefficient under extreme conditions to the expected value αwr. The specific formula is: ; S75. Calculate the intelligent coefficient Xz of VCU control, which is expressed as the square root of the product of the ratio of the calculated value ηca of the OTA upgrade success rate to the expected value ηcr and the ratio of the expected value ηxr of the vehicle-cloud collaboration delay to the calculated value ηxa. The specific formula is: ; S76. Calculate the control quality index YC of VCU control, which is expressed as the weighted average of the VCU control function completion coefficient Xg, the VCU control safety coefficient Xa, the VCU control performance coefficient Xe, and the VCU control intelligent coefficient Xz. The specific formula is: , where λ1, λ2, λ3, and λ4 are the weight coefficients of the VCU control function completion coefficient Xg, the VCU control safety coefficient Xa, the VCU control performance coefficient Xe, and the VCU control intelligent coefficient Xz in sequence.

[0032] Specifically, in this embodiment, it should be noted that the weight coefficients are set based on actual needs, and no specific value limitations are made here.

[0033] S8. Transmission of control quality assessment results: Transmit the calculated control quality index to the pure electric vehicle operation management terminal.

[0034] As Figure 2 shown, this embodiment provides a pure electric vehicle data integration and testing system based on VCU control, including a test target confirmation module, a test case writing module, a test environment construction module, a test module, a test result preprocessing module, a test result analysis module, a control quality assessment module, a human-computer interaction module, and a database. The test target confirmation module is connected to the test case writing module and the test module. The test case writing module is connected to the test module. The test environment construction module, the test module, the test result preprocessing module, the test result analysis module, the control quality assessment module, and the human-computer interaction module are connected in sequence. All modules in the system are connected to the database.

[0035] The test target confirmation module marks the VCU control strategy function, VCU control safety, VCU control performance, and VCU control intelligent level as test targets; The test case writing module analyzes the test requirements, decomposes the test requirements into test points, and writes test cases based on the test points; The test environment construction module constructs multi-type test environments under different working conditions based on the test targets; The test module applies the test cases to test the VCU control strategy function, VCU control safety, VCU control performance, and VCU control intelligent level in different test environments and records the test results; The test result preprocessing module clusters the test results according to the keywords of the test item types, integrates the test result information after setting the format, and automatically backs it up; The test result analysis module analyzes the preprocessed test results, and calculates the VCU control strategy function index, the VCU control security index, the VCU control performance index, and the VCU control intelligence index respectively; The control quality evaluation module compares the calculated VCU control-related indexes with the expected VCU control-related indexes, calculates the VCU control function completion coefficient, the VCU control safety coefficient, the VCU control performance coefficient, and the VCU control intelligence coefficient respectively, and then calculates the VCU control quality index; The human-machine interaction module transmits the calculated control quality index to the pure electric vehicle operation management terminal; The database is used to store the data information of all modules in the system.

[0036] Finally: The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A pure electric vehicle data integration and testing method based on VCU control, characterized by: The following steps are involved: S1. Confirm the test objectives: mark the VCU control strategy function, VCU control security, VCU control performance and VCU control intelligence level as test objectives; S2. Write test cases: Analyze test requirements, decompose test requirements into test points, and write test cases based on the test points; S3. Build test environment: Build multiple types of test environments under different working conditions based on test objectives; S4. Test objectives through test cases: Apply test cases to test VCU control strategy functions, VCU control security, VCU control performance, and VCU control intelligence level in different test environments, and record the test results; S5. Preprocess test results: cluster the test results according to the test item type keywords, set the format, integrate the test result information and automatically back up; S6. Test result analysis: Analyze the pre-processed test results and calculate the VCU control strategy function index, VCU control safety index, VCU control performance index, and VCU control intelligence index respectively; S7. Control quality assessment: Compare the analyzed and calculated VCU control related indicators with the expected VCU control related indicators to calculate the VCU control function completion coefficient, VCU control safety factor, VCU control performance coefficient and VCU control intelligence coefficient respectively, and then calculate the VCU control quality index; S8. Transmission of control quality evaluation results: Transmitting the calculated control quality index to the pure electric vehicle operation management terminal.

2. The data integration and testing method for a pure electric vehicle based on VCU control according to claim 1, wherein: The VCU control strategy in step S1 includes the vehicle power-on and power-off control strategy, gear management control strategy, torque management control strategy and energy management control strategy. The indicators corresponding to the VCU control strategy function in step S1 are the control strategy generation accuracy and the control strategy execution matching rate; the indicators corresponding to the VCU control safety in step S1 are the fault protection coverage, fault type processing speed, continuous operation failure rate and power-on and power-off cycle simulation abnormality rate; the indicators corresponding to the VCU control performance in step S1 are the acceleration time per 100 kilometers, the braking energy recovery efficiency and the extreme working condition stability coefficient; the indicators corresponding to the VCU control intelligence level in step S1 are the OTA upgrade success rate and the vehicle-cloud collaboration delay.

3. A method for integrating and testing pure electric vehicle data based on VCU control according to claim 1, characterized in that: The test results in step S4 include the correct or incorrect judgment of each control instruction generation, the match or mismatch judgment of each correct control instruction execution, whether the VCU protection mechanism is triggered after injecting different fault types, the triggering time of the protection mechanism for each fault type that triggers the VCU protection mechanism, the number of VCU control faults that occur within each preset continuous operation cycle, whether the system abnormally restarts during each cycle simulation of power on and off, the acceleration time of each group of 100 kilometers, the braking energy recovery efficiency of each group, the extreme working condition stability coefficient, the judgment result of whether each upgrade request is successful, and the response delay of each instruction packet sent under the 4G / 5G network.

4. A method for integrating and testing pure electric vehicle data based on VCU control according to claim 1, characterized in that: In the preprocessing test results of step S5, the test results are clustered according to the test item type keywords and then automatically arranged according to the test timestamp and test case name. The test results are then integrated to obtain the correct and incorrect control instruction generation times, the correct control instruction execution matching and mismatching times, the VCU protection mechanism triggering times and non-triggers for the i-th fault type, the k-th protection mechanism triggering duration for the j-th fault type that triggers the VCU protection mechanism, the number of VCU control faults occurring within the i-th continuous operation preset period, the number of cyclically simulated power-on and power-off times, the number of cyclically simulated abnormal restarts of the power-on and power-off system, the i-th 100km / h acceleration time, the i-th braking energy recovery efficiency, the extreme working condition stability coefficient, the number of successful and failed upgrade requests, and the response delay of the i-th instruction packet sent by the 4G / 5G network.

5. A method for integrating and testing pure electric vehicle data based on VCU control according to claim 1, characterized in that: The test result analysis steps in step S6 are as follows: S61, retrieve the pre-processed test result information; S62. Calculate the control strategy generation accuracy rate ηsa, which is expressed as the ratio of the number of correct control instruction generation times to the sum of the number of correct and incorrect control instruction generation times; calculate the control strategy execution matching rate ηpa, which is expressed as the ratio of the number of correct control instruction execution matching times to the sum of the number of correct control instruction execution matching times and mismatching times; S63. Calculate the fault protection coverage rate ηfa, which is expressed as the sum and average of the ratio of the number of times the VCU protection mechanism is triggered for the i-th fault type to the sum of the number of times the VCU protection mechanism is triggered and not triggered for the i-th fault type. Calculate the j-th fault processing speed ηtaj, which is expressed as the average of the k-time protection mechanism triggering duration for the j-th fault type that triggers the VCU protection mechanism. Calculate the continuous operation failure rate ηga, which is expressed as the sum and average of the ratio of the number of VCU control failures that occur within the i-th preset continuous operation period to the preset continuous operation period. Calculate the power cycle simulation abnormality rate ηha, which is expressed as the ratio of the number of abnormal restarts of the power cycle simulation system to the number of power cycles simulation. S64. Calculate the 100 km / h acceleration time tca, expressed as the summed and averaged result of the i-th 100 km / h acceleration time; calculate the braking energy recovery efficiency ηea, expressed as the summed and averaged result of the i-th braking energy recovery efficiency; and calculate the extreme operating condition stability coefficient αwa; S65. Calculate the OTA upgrade success rate ηca, which is the ratio of the number of successful upgrade requests to the sum of the number of successful and failed upgrade requests. Calculate the vehicle-cloud collaboration delay ηxa, which is the sum and average of the response delays sent by the i-th instruction packet on the 4G / 5G network. S66. Automatically store the calculated VCU control strategy function index, VCU control safety index, VCU control performance index, and VCU control intelligence index.

6. A method for integrating and testing pure electric vehicle data based on VCU control according to claim 1, characterized in that: The specific steps of controlling the quality assessment in step S7 are as follows: S71. Retrieve the expected control strategy to generate the accuracy rate ηsr of control strategy generation, the matching rate ηpr of control strategy execution, the fault protection coverage rate ηfr, the processing speed ηtrj of the j-th type of fault, the continuous operation failure rate ηgr, the abnormal rate ηhr of up and down power cycle simulation, the acceleration time tcr for 100 kilometers, the braking energy recovery efficiency ηer, the stability coefficient αwr under extreme conditions, the success rate ηcr of OTA upgrade, and the vehicle-cloud collaboration delay ηxr; S72. Calculate the VCU control function completion coefficient Xg, which is expressed as the square root of the product of the ratio of the calculated value ηsa of the control strategy generation accuracy rate to the expected value ηsr and the ratio of the calculated value ηpa of the control strategy execution matching rate to the expected value ηpa. The specific formula is as follows: ; S73. Calculate the VCU control safety factor Xa, which is the sum of the ratio of the calculated value ηfa to the expected value ηfr of the fault protection coverage rate, the ratio of the expected value ηtrj to the calculated value ηtaj of the j-th fault type processing speed, the ratio of the expected value ηgr to the calculated value ηga of the continuous operation failure rate, and the average value of the ratio of the expected value ηhr to the calculated value ηha of the power cycle simulation abnormality rate. The specific formula is: , where na is the number of fault types that can trigger the VCU protection mechanism; S74. Calculate the VCU control performance coefficient Xe, which is expressed as the cube root of the product of the ratio of the expected value tcr of the 0-100 km / h acceleration duration to the calculated value tca, the ratio of the calculated value ηea of the braking energy recovery efficiency to the expected value ηer, and the ratio of the calculated value αwa of the extreme condition stability coefficient to the expected value αwr. The specific formula is as follows: ; S75. Calculate the VCU control intelligence coefficient Xz, which is expressed as the square root of the product of the ratio of the calculated value ηca of the OTA upgrade success rate to the expected value ηcr and the ratio of the expected value ηxr of the vehicle-cloud collaboration delay to the calculated value ηxa. The specific formula is: ； S76. Calculate the VCU control quality index YC, which is expressed as the weighted average of the VCU control function completion coefficient Xg, the VCU control safety coefficient Xa, the VCU control performance coefficient Xe, and the VCU control intelligence coefficient Xz. The specific formula is: ， λ1, λ2, λ3, λ4 are the weight coefficients of the VCU control function completion coefficient Xg, the VCU control safety coefficient Xa, the VCU control performance coefficient Xe, and the VCU control intelligence coefficient Xz in sequence.

Images