A vehicle performance testing method and device, electronic equipment and storage medium

Abstract

The application relates to the technical field of vehicles and discloses a vehicle performance testing method and device, an electronic device and a storage medium. The testing method comprises the following steps: in the process that a test vehicle is tested based on original test parameters, generating different types of test results and optimization suggestions corresponding to each type of test result according to working condition data of the test vehicle; wherein the optimization suggestions comprise adjustment instructions for adjusting vehicle control parameters; determining an optimization strategy according to the different types of test results and an adjustment instruction set to optimize the original test parameters; wherein the adjustment instruction set comprises at least one selected adjustment instruction. The application guarantees the timeliness of test parameter adjustment, improves the test efficiency while improving the test effect of vehicle performance testing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle performance testing method and device, an electronic device and a storage medium. BACKGROUND

[0002] Vehicle performance testing (such as acceleration, braking, energy consumption, handling stability, etc.) is a key link in vehicle development and verification. Vehicle performance testing relies on manual testing, and the related technology is to control the vehicle to test by using fixed test parameters. However, in the vehicle testing process, the test effect may not be ideal due to the change of vehicle working condition parameters or other factors, and multiple manual adjustments of test parameters are required, resulting in low vehicle performance testing efficiency. SUMMARY

[0003] In view of the above problems, the present application provides a vehicle performance testing method and device, an electronic device and a storage medium, which are used to automatically optimize the original test parameters in real time during the testing of a test vehicle based on original test parameters, so as to improve the test effect and test efficiency of vehicle performance testing.

[0004] According to one aspect of the present application, a vehicle performance testing method is provided, which includes: during the testing of a test vehicle based on original test parameters, generating different types of test results and corresponding optimization suggestions for each type of test result according to working condition data of the test vehicle; wherein the optimization suggestions include adjustment instructions for adjusting vehicle control parameters; determining an optimization strategy according to the different types of test results and a set of adjustment instructions to optimize the original test parameters; wherein the set of adjustment instructions includes at least one selected adjustment instruction.

[0005] In an optional manner, the original test parameters include original constraint conditions and corresponding original test targets; generating different types of test results and corresponding optimization suggestions for each type of test result according to the working condition data of the test vehicle includes: collecting working condition data of the test vehicle running under the original constraint conditions to achieve the original test target; wherein the working condition data is data representing the working conditions of different components in the test vehicle; inputting the processed data of each component working state into a corresponding preset text template to obtain multiple input texts; inputting the multiple input texts into a preset model to make the preset model output different types of test results and corresponding optimization suggestions for each type of test result; wherein the optimization suggestions include a first suggestion for adjusting the original constraint conditions to achieve the original test target, and a second suggestion for maintaining the original constraint conditions unchanged and adjusting the original test target.

[0006] In an optional manner, the processed data of the working states of the components is input into the respective preset text template to obtain a plurality of input texts, including: performing feature extraction on the data of the working states of the components to obtain feature data corresponding to the data of the working states of the components; inputting the feature data, the original constraint condition and the original test target into the respective preset text template to obtain the plurality of input texts.

[0007] In an optional manner, the plurality of input texts include a first input text, a second input text and a third input text; the plurality of input texts are input into the preset model to make the preset model output different types of test results and optimization suggestions corresponding to each type of test result, including: inputting the plurality of input texts into the preset model to make the preset model generate a first type of test result and a first optimization suggestion according to the first input text, generate a second type of test result and a second optimization suggestion according to the first type of test result and the second input text, and generate a third type of test result and a third optimization suggestion according to the second type of test result and the third input text.

[0008] In an optional manner, the original test parameters include an original constraint condition and an original test target corresponding to the original constraint condition; an optimization strategy is determined according to the different types of test results and the adjustment instruction set to optimize the original test parameters, including: determining a target adjustment instruction according to the original constraint condition, a repeated adjustment instruction group in the adjustment instruction set and the different types of test results; the repeated adjustment instruction group is a combination of instructions for adjusting the same adjustment object; and determining an optimization strategy according to the original test target, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group to optimize the original test parameters.

[0009] In an optional manner, the optimization strategy is determined according to the original test target, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group, including: generating a plurality of initial strategies according to the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group; different initial strategies have different performance optimization targets; and an initial strategy with a performance optimization target matching the original test target in the plurality of initial strategies is taken as the optimization strategy.

[0010] In an optional mode, the test method further comprises: if there is a vehicle model change suggestion in the optimization suggestion corresponding to the different types of test results, updating the original test parameters based on the test parameters of the vehicle model specified in the vehicle model change suggestion, and / or updating the original test parameters according to the preset test parameters corresponding to the coding of the test vehicle; if there is a driving condition change suggestion in the optimization suggestion corresponding to the different types of test results, updating the original test parameters based on the test parameters of the driving condition specified in the driving condition change suggestion.

[0011] According to another aspect of the present application, a test device for vehicle performance is provided, comprising: a test module configured to generate different types of test results and corresponding optimization suggestions for adjusting vehicle control parameters during testing of a test vehicle based on original test parameters according to working condition data of the test vehicle; and an optimization module configured to determine an optimization strategy according to the different types of test results and a set of adjustment instructions to optimize the original test parameters, wherein the set of adjustment instructions comprises at least one selected adjustment instruction.

[0012] According to an aspect of the present application, an electronic device is provided, comprising: a controller; a memory configured to store one or more programs, which, when executed by the controller, perform the test method described above.

[0013] According to an aspect of the present application, a computer readable storage medium having computer readable instructions stored thereon is also provided, which, when executed by a processor of a computer, causes the computer to perform the test method described above.

[0014] According to an aspect of the present application, a computer program product or computer program is also provided, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the test method described above.

[0015] The present application generates different types of test results and corresponding optimization suggestions based on real-time working condition parameters of a test vehicle during testing of the test vehicle based on original test parameters, so that a test personnel can select adjustment instructions in the corresponding optimization suggestions to obtain a set of adjustment instructions, and then optimize the original test parameters according to the different types of test results and the set of adjustment instructions, thereby ensuring the timeliness of test parameter adjustment, improving the test efficiency while improving the test effect of vehicle performance testing.

[0016] The above description is only a summary of the technical solutions of the present application. In order to make the technical means of the present application more clear, the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings incorporated into the specification and forming part of the specification, show embodiments consistent with the present application, and together with the specification serve to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0018] Figure 1 is a flowchart of a vehicle performance test method according to an exemplary embodiment of the present application.

[0019] Figure 2 is a flowchart of another vehicle performance test method according to an exemplary embodiment of the present application. Figure 1

[0020] Figure 3 is a schematic diagram of a preset model according to an exemplary embodiment of the present application.

[0021] Figure 4 is a flowchart of another vehicle performance test method according to an exemplary embodiment of the present application. Figure 1 Figure 2

[0022] Figure 5 is a schematic diagram of the application scenario of the vehicle performance test method of the present application.

[0023] Figure 6 is a flowchart of a test method executed by a performance test system according to an exemplary embodiment of the present application.

[0024] Figure 7 is a structural diagram of a vehicle performance test device according to an exemplary embodiment of the present application.

[0025] Figure 8 is a structural diagram of a computer system of an electronic device according to an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0026] ​​​The exemplary embodiments will be described in detail below with reference to the drawings. In the following description, the same numbers are used to denote the same elements throughout the several views. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0027] The block diagrams shown in the drawings are merely functional entities, and do not necessarily have to correspond to physically independent entities. That is, the functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] The flowcharts shown in the drawings are merely exemplary illustrations, and do not necessarily include all contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0029] In the present application, "multiple" means two or more. The association relationship of "and / or" describes the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0030] As described in the background, vehicle performance testing relies heavily on manual testing, and the test parameters for vehicle performance testing are relatively fixed. Even if the test parameters are modified, the test personnel will adjust them based on test experience after the vehicle performance test is completed, and then test again based on the adjusted test parameters. This process is repeated, and the test parameters need to be manually adjusted multiple times, resulting in low efficiency of vehicle performance testing.

[0031] To this end, one aspect of the present application provides a vehicle performance testing method, which automatically optimizes the original test parameters in real time during the testing of the vehicle based on the original test parameters, to improve the test effect and test efficiency of vehicle performance testing. For details, please refer to Figure 1 , Figure 1 is a flowchart of a vehicle performance testing method according to an exemplary embodiment of the present application. The testing method at least includes S110 to S120, which are described in detail as follows:

[0032] S110: In the process of testing the test vehicle based on the original test parameters, different types of test results are generated according to the working condition data of the test vehicle, and the optimization suggestions corresponding to each type of test result; wherein the optimization suggestions include adjustment instructions for adjusting the vehicle control parameters.

[0033] The test vehicle is a vehicle in the process of performance testing (acceleration, braking, energy consumption, handling stability, etc.), and the performance testing includes verification (such as verifying whether the relevant performance can reach the test target) testing, exploratory testing (such as exploring the performance threshold under the corresponding working condition), etc.

[0034] The original test parameters are original parameters for vehicle performance testing, including but not limited to vehicle control parameters, constraint conditions, test targets, etc.

[0035] The working condition data is the real-time data generated by the corresponding components of the test vehicle during the test process, such as the relevant state data of the power battery, the relevant state data of the motor, the relevant state data of the range extender, etc. The working condition data includes but is not limited to vehicle speed, running time, power battery discharge power, battery temperature, motor temperature, motor output power and torque curve, etc.

[0036] Different types of test results can be understood as performance test results corresponding to different components, for example, performance test results for power batteries, performance test results for motors. In some embodiments, different types of test results not only include performance test results corresponding to different components, but also include performance test results of different dimensions of the same component, for example, test results of power battery output power and test results of power battery thermal management.

[0037] In this embodiment, the corresponding test results can be test results predicted according to the real-time working condition parameters of the test vehicle, or test results directly determined according to the real-time working condition parameters. For example, the verification performance test is to accelerate the vehicle from zero to 40km / h within 10 seconds while ensuring that the temperature of the power battery does not rise more than 5℃. The temperature rise of the power battery collected at the 8th second is 6℃, and the vehicle speed is 38km / h. Obviously, according to the working condition parameters at this time, it can be directly determined that the temperature test of the power battery is not up to standard, and whether the vehicle speed is up to standard needs to be determined according to the final vehicle speed predicted according to 38km / h (current vehicle speed) and other working condition parameters, so that the final vehicle speed predicted according to the current can be directly judged whether the performance test is up to standard, without waiting for the end of the entire test process.

[0038] Each type of test result corresponds to a corresponding optimization suggestion, for example, exploratory performance test: the test result of the shift timing type is that the first gear to the second gear action occurs at the engine speed of 6250 rpm, but the engine peak power interval is from 6500 rpm to 7000 rpm, and the current shift point causes insufficient power utilization. The corresponding optimization suggestion is to adjust the 1-up 2-down speed threshold to 6800 rpm, that is, the gear adjustment instruction; wherein "6800 rpm" is a vehicle control parameter. The test result of the torque response type is that the response time from the accelerator pedal to the torque reaching 90% peak is 120 ms, and there is optimization space to improve the initial acceleration feeling. The corresponding optimization suggestion is to increase the torque request slope rate by 20%, that is, the torque adjustment instruction; wherein "20%" is a vehicle control parameter. The test result of the traction control type is that during the acceleration starting stage, a short drive wheel slip is monitored, and the TCS (Traction Control System) intervenes, causing the torque to be limited for about 50 ms. The corresponding optimization suggestion is to increase the slip rate intervention threshold of the traction control by 2%, that is, the traction torque adjustment instruction; wherein "2%" is a vehicle control parameter.

[0039] S120: determining an optimization strategy according to the test results of different types and the adjustment instruction set to optimize the original test parameters; wherein the adjustment instruction set includes at least one selected adjustment instruction.

[0040] The adjustment instruction set is a collection of adjustment instructions, and the adjustment instruction set can include only one adjustment instruction corresponding to one optimization suggestion. The adjustment instruction set can also be understood as including multiple adjustment instructions corresponding to multiple optimization suggestions. The adjustment instructions in the adjustment instruction set can be one or more adjustment instructions selected by the tester from the adjustment instructions corresponding to the optimization suggestions in S110.

[0041] The optimization strategy is a strategy for optimizing the vehicle test process, and the optimization strategy includes but is not limited to the test parameters that need to be optimized to update the original test parameters.

[0042] In the verification performance test scenario, the optimized test parameters include optimized constraints and test targets, and the original test parameters include original constraints and original test targets corresponding to the original constraints. Illustratively, according to the original constraints, the repeated adjustment instruction group in the adjustment instruction set and the test results of different types determine the target adjustment instruction; wherein the repeated adjustment instruction group is a combination of instructions for adjusting the same adjustment object; according to the original test target, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group determine the optimization strategy to optimize the original test parameters.

[0043] The repeated adjustment instruction group refers to a combination of adjustment instructions that have conflicts. For example, adjustment instruction A in the adjustment instruction set is to increase the output power of the motor by 10%. Adjustment instruction B is to reduce the output power of the motor by 5%. Adjustment instruction C is to reduce the output power of the motor by 2%. Here, the three adjustment instructions are all instructions for adjusting the same adjustment object (the motor), and if each adjustment instruction in the repeated adjustment instruction group is executed together, there will be conflicts. The reason for the conflict is that each type of test result is determined based on the local components or local parameters of the test vehicle, and the adjustment instructions in the corresponding optimization recommendations are also determined based on the local components or local parameters, and do not fully consider the impact of other components or other parameters, resulting in conflicts in the corresponding adjustment instructions. Because some conflicts do not appear on the surface, the adjustment instructions with hidden conflicts are selected into the adjustment instruction set during the selection of adjustment instructions. In some embodiments, the adjustment instructions with conflicts in the adjustment instruction set are determined by a conflict analysis model to determine one or more corresponding repeated adjustment instruction groups. The present application does not limit the specific type and composition of the conflict analysis model, which can be an AI (Artificial Intelligence) model or other analysis model. The AI model has pre-set related hardware parameters and software parameters of the test vehicle, and can simulate a virtual test vehicle to enable the AI model to output an optimization strategy that adapts to the test vehicle based on the input different types of test results and adjustment instruction set.

[0044] This example analyzes the adjustment instructions with conflicts in the adjustment instruction set, and determines the target adjustment instruction based on the original constraint conditions and different types of test results, similar to filtering, fusing, and other processing of adjustment instructions with conflicts, so that the determined target adjustment instruction is an instruction for adjusting the target object, which is the adjustment object corresponding to the same repeated adjustment instruction group. Then, the optimization strategy is determined based on the original test target, the target adjustment instruction, and the adjustment instructions in the adjustment instruction set other than the repeated adjustment instruction group, and the original test parameters are modified, replaced, added, and other optimization operations are performed based on the optimization strategy.

[0045] This example illustrates how to determine the optimization strategy based on the original test target, the target adjustment instruction, and the adjustment instructions in the adjustment instruction set other than the repeated adjustment instruction group: generate a plurality of initial strategies based on the target adjustment instruction and the adjustment instructions in the adjustment instruction set other than the repeated adjustment instruction group; different initial strategies have different performance optimization targets; and the initial strategy with a performance optimization target that matches the original test target is used as the optimization strategy.

[0046] Because the target adjustment instruction has been filtered and fused from the adjustment instructions with contradictory conflicts in the pre-step, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group obviously have no contradictory conflicts. In the process of generating the initial strategy, the corresponding adjustment instruction can be filtered according to different performance optimization targets. For example, the performance optimization target is to reduce the working temperature of the power battery; the corresponding adjustment instruction is to increase the power of the power battery, which will cause the temperature of the power battery to rise. Therefore, in the case of taking the working temperature of the power battery as the performance optimization target, the adjustment instruction of increasing the power of the power battery will be filtered in the process of generating the corresponding initial strategy. Different initial strategies have different performance optimization targets. For example, the first initial strategy is a strategy determined based on an acceleration performance optimization target. The second initial strategy is a strategy determined based on an energy consumption optimization target. The third initial strategy is a strategy determined based on a safety performance optimization target.

[0047] In order to make the determined optimization strategy more suitable for the original test conditions of the test vehicle, the performance optimization target of each initial strategy is matched with the original test target in this example, and the matched initial strategy is taken as the optimization strategy to determine the optimization strategy suitable for the original test target. In the case where there are multiple matched initial strategies, the matching degrees between them and the original test target are compared, and the initial strategy with the highest matching degree is taken as the optimization strategy. In the case where the matching is unsuccessful (i.e., both the original test target and the initial strategy fail to match), the matching degrees of the performance optimization targets of each initial strategy and the original test target are calculated, and the initial strategy with the highest matching degree is taken as the optimization strategy.

[0048] In the process of testing the test vehicle based on the original test parameters, different types of test results and corresponding optimization suggestions are generated according to the real-time working condition parameters of the test vehicle in this embodiment, so that the test personnel can select the adjustment instruction in the corresponding optimization suggestion to obtain the adjustment instruction set, so as to optimize the original test parameters according to different types of test results and adjustment instruction sets, thereby ensuring the timeliness of the test parameter adjustment, improving the test effect of the vehicle performance test, and improving the test efficiency.

[0049] In another exemplary embodiment of the present application, how to generate different types of test results according to the working condition data of the test vehicle and the optimization suggestions corresponding to each type of test result are described in detail. For details, please refer to Figure 2 , Figure 2 is another flowchart of a vehicle performance test method shown in the example of Figure 1 . The test method is as shown in Figure 1The S110 shown includes S210 to S230; wherein the original test parameters include original constraints, and original test targets corresponding to the original constraints, which are described in detail as follows:

[0050] S210: Collecting working condition data of the test vehicle running under the original constraints to achieve the original test target; wherein the working condition data is data representing the working conditions of different components in the test vehicle.

[0051] For example, the temperature of the vehicle's power battery is limited to above 30°C (i.e., the original constraint), and the vehicle is accelerated to 100km / h (i.e., the original test target) to collect data of the working conditions of each component in the vehicle during the acceleration process, such as the vehicle speed, power battery temperature, output power, motor output power and speed, etc. working condition parameters during the acceleration process of the vehicle under the condition of limiting the temperature of the power battery.

[0052] S220: Inputting the processed data of the working conditions of each component into the respective corresponding preset text template to obtain multiple input texts.

[0053] The original data of the working conditions of each component may have invalid data, inconsistent formats, etc., and need to be preprocessed, including but not limited to feature extraction, data cleaning and format unification, etc. processing methods.

[0054] The preset text template is a text template adapted to the preset model, which can facilitate the preset model to quickly and accurately extract the corresponding features from the preset text template for analysis. The preset text template not only includes the processed data of the working conditions of each component, but also includes template instructions, so that the preset model can quickly know the related analysis requirements, purposes, etc.

[0055] The related art is to directly input the original data into the preset model without any processing of the original data, which requires higher intelligence of the preset model. Since the original data is rough, the preset model cannot accurately extract key data from it, and also cannot perceive the analysis focus.

[0056] Therefore, the present application not only performs feature extraction on the data, but also introduces a preset text template adapted to the preset model to improve the analysis accuracy of the preset model. For example, the data of the working conditions of each component is extracted to obtain feature data corresponding to the data of the working conditions of each component; each feature data, original constraint and original test target are input into the respective corresponding preset text template to obtain multiple input texts.

[0057] The characteristic data can be a key value of a key parameter, such as the temperature of the power battery, the output power of the motor, etc. The characteristic data can be a value such as 5℃, 10kw, etc., or a binary string or other binary string, etc. The characteristic data can be understood as missing key data in the preset text template, and the extracted characteristic data is filled into the corresponding position in the preset text template to complete the semantics of the preset text template. At the same time, the original constraint condition and the original test target are input into the corresponding preset text template, so that the obtained input text can better adapt to the original test scene, and the analysis result of the preset model will not deviate greatly from the original test scene, and the input text can indicate the preset model to better perform data analysis and processing. For example, the accuracy of the AI model output result will differ with different input inquiry texts, in order to make the AI model output accurate test results corresponding to each performance test task, different inquiry templates (i.e., preset text templates) are configured for different performance test tasks to generate different inquiry texts in combination with the characteristic data in the corresponding performance test task, so that the AI model can more accurately perceive the emphasis and requirements of different performance test tasks, thereby outputting more accurate test results. Among them, the AI model is preloaded with related hardware parameters and software parameters of the test vehicle, which can simulate a virtual test vehicle to enable the AI model to output test results and corresponding optimization suggestions that adapt to the test vehicle in combination with the input multiple input texts.

[0058] For example, the original performance test is to limit the temperature of the power battery of the vehicle to above 30℃ (i.e., the original constraint condition) and accelerate the vehicle to 100km / h (i.e., the original test target). The characteristic data extracted from the collected working condition parameters includes the power battery temperature of 28℃, the vehicle speed of 90km / h, and the motor output power of 10kw, which are filled into the preset text template to obtain the input text: "The current power battery temperature is 28℃, the current vehicle speed is 90km / h, and the current motor output power is 10kw. Please limit the temperature of the power battery of the vehicle to above 30℃ and accelerate the vehicle to 100km / h. Can you achieve the test target? If so, how to control and adjust the parameters of the related components of the vehicle at present." In some input texts, real-time collected working condition data logs, historical test reports, etc. are also added to enable the preset model to perceive the real-time status of the vehicle to improve the analysis accuracy.

[0059] S230: inputting the multiple input texts into the preset model to enable the preset model to output different types of test results and optimization suggestions corresponding to each type of test result; wherein the optimization suggestions include a first suggestion of adjusting the original constraint condition to achieve the original test target, and a second suggestion of adjusting the original test target while maintaining the original constraint condition unchanged.

[0060] The preset model is a specially trained intelligent system with multi-task output and multi-strategy decision-making capability. The preset model can understand the complex mapping relationship between the test target, the constraint condition and the test result, and provide a dialectical optimization path. The preset model can be an AI model, such as a hybrid model based on a pre-trained language model based on a Transformer architecture for fine-tuning, which has the following architecture: an encoder responsible for encoding and analyzing multiple input texts; a multi-task decoding head: the model backend connects multiple parallel "output heads", each of which is responsible for a specific type of test result and its corresponding optimization suggestion. For example, the performance output head: responsible for outputting test results and optimization suggestions related to power performance, economy, etc. The durability output head: responsible for outputting test results and optimization suggestions related to fatigue and wear. The safety output head: responsible for outputting test results and optimization suggestions related to braking and handling stability.

[0061] Optimization suggestions are generally made to achieve the original test target while maintaining the original constraint condition, but in cases where the actual working conditions are not allowed, the related technology will directly display a test failure and will not provide corresponding optimization suggestions. However, in cases where the actual working conditions are not allowed, this embodiment will make trade-offs between the original constraint condition and the original test target. The optimization suggestions after the trade-offs are generally divided into two types: the first suggestion is to maintain the original test target unchanged and adjust the original constraint condition, and the second suggestion is to maintain the original constraint condition unchanged and adjust the original test target. This improvement measure can determine optimization suggestions that exceed the expectations of the test personnel (i.e., the original constraint condition and / or the original test target) to provide more optimization suggestions for the test personnel to choose from. For example, the test result: "Under the current configuration, the peak power of the electric drive system cannot meet the 5-second acceleration target, and the tire grip is the bottleneck."

[0062] The first suggestion (adjusting the constraint condition): "To achieve the original test target (5 seconds), it is recommended to adjust the original constraint condition: temporarily increase the maximum discharge power of the battery to 450kW, and replace it with a 265 / 40 R19 high-performance tire with better grip."

[0063] The second suggestion (adjusting the test target): "If the original constraint condition remains unchanged (battery power ≤ 400kW, tire specifications remain unchanged), it is recommended to adjust the original test target to a more realistic value, such as: 0-100km / h acceleration time ≤ 5.5 seconds."

[0064] In some embodiments, in order to make the test results and optimization suggestions more accurate, the preset model corrects the post-test task based on the test results obtained from the pre-test task processing when handling multiple test tasks, so that the test results and optimization suggestions obtained from the post-test task processing are more comprehensive and accurate.

[0065] Exemplarily, the plurality of input texts comprises a first input text, a second input text and a third input text; the plurality of input texts are input into the preset model, so that the preset model generates a first type of test result and a first optimization suggestion according to the first input text, generates a second type of test result and a second optimization suggestion according to the first type of test result and the second input text, and generates a third type of test result and a third optimization suggestion according to the second type of test result and the third input text.

[0066] The architecture of the preset model is shown in a serial architecture as Figure 3 shown, wherein each sub-model is connected in sequence, and the output of a preceding sub-model can be used as the input of a subsequent sub-model, and each sub-model comprises a separate analysis algorithm to generate a corresponding test result and optimization suggestion. For example, the first input text is determined by a first algorithm in a first sub-model of the preset model to obtain the temperature of the power battery cell (i.e. the first type of test result) and the first optimization suggestion; the temperature of the power battery cell and the second input text are input into a second sub-model to determine whether the power generation of the power battery meets the standard (i.e. the second type of test result) and the second optimization suggestion by combining a second algorithm in the second sub-model; and the threshold of the power generation of the power battery (a parameter contained in the second type of test result) and the third input text are input into a third sub-model to generate the maximum power and torque of the motor (i.e. the third type of test result) and the third optimization suggestion by combining a third algorithm in the third sub-model.

[0067] In this example, there is an association between the corresponding test results, and the data of different components (i.e. different types) are combined to avoid the limitation of a single type of data on the test result, so that the test result is limited to a single factor and the accuracy of the test result cannot be improved.

[0068] In another exemplary embodiment of the present application, how to timely adjust the original test parameters is described in detail, please refer to Figure 4 , Figure 4 based on Figure 1 or Figure 2 the flowchart of another vehicle performance test method shown in the exemplary embodiment shown. The test method further comprises S410 to S420, which are described in detail as follows:

[0069] S410: if there is a vehicle model change suggestion in the optimization suggestions corresponding to the different types of test results, the original test parameters are updated based on the test parameters of the specified vehicle model in the vehicle model change suggestion, and / or the original test parameters are updated according to the preset test parameters corresponding to the coding of the test vehicle.

[0070] The specified vehicle type is a vehicle type specified in the vehicle type change suggestion, and the specified vehicle type is a vehicle type corresponding to the current test vehicle predicted according to relevant parameters of the current test vehicle, that is, a vehicle type predicted according to relevant parameters when the vehicle type change suggestion is generated.

[0071] For the vehicle type change suggestion, a large number of updates need to be made to the original test parameters to update the test parameters corresponding to the specified vehicle type. The original test parameters can be updated according to the test parameters of the specified vehicle type in the vehicle type change suggestion, and the original test parameters can also be updated according to the preset test parameters corresponding to the coding of the test vehicle, such as the VIN (Vehicle-Identification-Number, vehicle identification code). Of course, the two updating methods can also be combined, for example, the test parameters of the specified vehicle type are not complete, and the preset test parameters corresponding to the coding of the test vehicle need to be supplemented to ensure that the original test parameters are updated without omission.

[0072] S420: If there is a driving condition change suggestion in the optimization suggestion corresponding to the test result of different types, update the original test parameters based on the test parameters of the specified driving condition in the driving condition change suggestion.

[0073] If there is a driving condition change suggestion, it indicates that the driving condition in the current test scene is not suitable for the test vehicle, and the test vehicle cannot obviously complete the test under the current driving condition. The driving condition needs to be adjusted in time to update the original test parameters based on the test parameters of the specified driving condition in the driving condition change suggestion.

[0074] The way of updating the original test parameters described in S410 and S420 is applicable to the scene where the original test parameters need to be adjusted in time, that is, the original test parameters obviously cannot adapt to the current test vehicle, and do not need to be determined and audited by humans. The original test parameters need to be automatically adjusted in time to ensure the normal test of the current test vehicle.

[0075] The application scenarios of the above-mentioned multiple test methods are exemplarily described in another exemplary embodiment of the present application, please refer to Figure 5 , Figure 5 is a schematic diagram of the application scenario of the test method of the vehicle performance of the present application. It includes a test vehicle 100 and a server 200, and the two ends can be connected through wireless communication. The present application does not limit the connection mode between them.

[0076] The server 200 can collect data of different components in the test vehicle 100 in real time, including but not limited to data collected by different sensors installed on the test vehicle 100. The server 200 can serve as the execution subject of any of the above-mentioned test methods to execute any of the above-mentioned test methods, which are exemplarily described as follows:

[0077] In the process of testing the test vehicle 100 based on the original test parameters, the server 200 generates different types of test results according to the working condition data of the test vehicle 100, and the optimization suggestions corresponding to each type of test result; wherein the optimization suggestions include adjustment instructions for adjusting the vehicle control parameters; the server 200 determines the optimization strategy according to the different types of test results and the adjustment instruction set to optimize the original test parameters; wherein the adjustment instruction set includes at least one selected adjustment instruction.

[0078] The server 200 can be placed in the test vehicle 100 as shown in Figure 5 , it can also be a physical server independent of the test vehicle 100, or a server cluster or distributed system composed of multiple physical servers, wherein multiple servers can form a block chain, and the server is a node on the block chain. The server 200 can also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network, Content Delivery Network), and big data and artificial intelligence platforms, etc. Basic cloud computing services, which are not limited herein.

[0079] The execution subject of any of the above test methods can be understood as a performance test system in the server 200, as shown in Figure 6 , Figure 6 is a flowchart of the test method executed by the performance test system of an exemplary embodiment of the present application. The performance test system includes a collection module 610, a processing module 620, a control module 630, a display module 640, and an optimization module 650.

[0080] The acquisition module 610 acquires the working condition data of different components in the test process of the test vehicle 100 for transmission to the processing module 620 for processing. The processing module 620 generates different types of test results and optimization suggestions corresponding to each type of test result according to the working condition data of the test vehicle 100. The display module 640 is used to display different types of test results generated by the processing module 620 and optimization suggestions corresponding to each type of test result, so that the tester can select adjustment instructions for adjusting vehicle control parameters from the relevant optimization suggestions to obtain a set of adjustment instructions. The optimization module 650 determines an optimization strategy according to different types of test results and the set of adjustment instructions to optimize the original test parameters, so that the test vehicle 100 tests based on the optimized test parameters. The processing module 620 and the optimization module 650 can be internally built or loaded with an AI model, so that the processing module 620 generates more accurate different types of test results and optimization suggestions corresponding to each type of test result according to the working condition data of the test vehicle 100. The optimization module 650 can determine a more accurate optimization strategy according to different types of test results and the set of adjustment instructions.

[0081] Another aspect of the present application also provides a vehicle performance testing device, as shown in Figure 7 Figure 7 is a structural schematic diagram of a vehicle performance testing device according to an example embodiment of the present application. The testing device 700 comprises:

[0082] The test module 710 is configured to generate different types of test results and optimization suggestions corresponding to each type of test result according to the working condition data of the test vehicle during the test of the test vehicle based on the original test parameters. The optimization suggestions include adjustment instructions for adjusting vehicle control parameters.

[0083] The optimization module 730 is configured to determine an optimization strategy according to different types of test results and a set of adjustment instructions to optimize the original test parameters. The set of adjustment instructions includes at least one selected adjustment instruction.

[0084] In another example embodiment, the original test parameters include original constraint conditions and original test targets corresponding to the original constraint conditions. The test module 710 comprises:

[0085] The acquisition unit is configured to acquire working condition data of the test vehicle running under the original constraint conditions to achieve the original test targets. The working condition data is data representing the working conditions of different components in the test vehicle.

[0086] The input text unit is configured to input the processed data of each component working state into a respective corresponding preset text template to obtain a plurality of input texts. ​

[0087] a test unit configured to input the plurality of input texts into the preset model to cause the preset model to output different types of test results and corresponding optimization suggestions for each type of test result; wherein the optimization suggestions include a first suggestion of adjusting the original constraint condition to achieve the original test target, and a second suggestion of adjusting the original test target while maintaining the original constraint condition unchanged.

[0088] In another exemplary embodiment, the input text unit includes:

[0089] a feature extraction block configured to perform feature extraction on the data of the working states of the components to obtain feature data corresponding to the data of the working states of the components.

[0090] an input text block configured to input each of the feature data, the original constraint condition and the original test target into a corresponding preset text template to obtain a plurality of input texts.

[0091] In another exemplary embodiment, the plurality of input texts include a first input text, a second input text and a third input text; and the test unit includes:

[0092] a test block configured to input the plurality of input texts into the preset model to cause the preset model to generate a first type of test result and a first optimization suggestion according to the first input text, a second type of test result and a second optimization suggestion according to the first type of test result and the second input text, and a third type of test result and a third optimization suggestion according to the second type of test result and the third input text.

[0093] In another exemplary embodiment, the original test parameters include an original constraint condition and an original test target corresponding to the original constraint condition; and the optimization module 730 includes:

[0094] a target adjustment instruction determination unit configured to determine a target adjustment instruction according to the original constraint condition, a repeated adjustment instruction group in the instruction set and different types of test results; wherein the repeated adjustment instruction group is a combination of instructions for adjusting the same adjustment object.

[0095] an optimization unit configured to determine an optimization strategy according to the original test target, the target adjustment instruction and adjustment instructions in the instruction set other than the repeated adjustment instruction group, to optimize the original test parameters.

[0096] In another exemplary embodiment, the optimization unit includes:

[0097] an initial strategy generation block configured to generate a plurality of initial strategies according to the target adjustment instruction and the adjustment instructions in the instruction set other than the repeated adjustment instruction group; wherein different initial strategies have different performance optimization targets.

[0098] The optimization plate is used to match the initial strategy with the performance optimization target in the plurality of initial strategies to the original test target as the optimization strategy.

[0099] In another exemplary embodiment, the test device 700 further comprises:

[0100] The first updating module is configured to update the original test parameters based on the test parameters of the specified vehicle model in the vehicle model change suggestion and / or update the original test parameters according to the preset test parameters corresponding to the coding of the test vehicle, if the vehicle model change suggestion exists in the optimization suggestions corresponding to the different types of test results.

[0101] The second updating module is configured to update the original test parameters based on the test parameters of the specified driving condition in the driving condition change suggestion, if the driving condition change suggestion exists in the optimization suggestions corresponding to the different types of test results.

[0102] The test device of the present application generates different types of test results and corresponding optimization suggestions according to the real-time working condition parameters of the test vehicle during the test of the test vehicle based on the original test parameters, so as to obtain an adjustment instruction set by selecting the adjustment instructions in the corresponding optimization suggestions by the test personnel, and to optimize the original test parameters according to the different types of test results and the adjustment instruction set, thereby ensuring the timeliness of the test parameter adjustment, improving the test effect of the vehicle performance test, and improving the test efficiency.

[0103] It should be noted that the test device provided in the above embodiments and the test method provided in the foregoing embodiments belong to the same concept, and the specific operation manner of each module and unit has been described in detail in the method embodiments, which will not be described here.

[0104] Another aspect of the present application further provides an electronic device, comprising: a controller; a memory for storing one or more programs, when the one or more programs are executed by the controller, to execute the test method described above.

[0105] Please refer to Figure 8 , Figure 8 is a structural schematic diagram of a computer system of an electronic device according to an exemplary embodiment of the present application, which shows the structural schematic diagram of the computer system of the electronic device suitable for implementing the embodiments of the present application.

[0106] It should be noted that Figure 8 The computer system 800 of the electronic device shown is only an example, and should not limit the functions and use range of the embodiments of the present application.

[0107] As Figure 8As shown, the computer system 800 includes a central processing unit (CPU) 801 which can perform various suitable actions and processes in accordance with programs stored in a read-only memory (ROM) 802 or loaded from a storage section 808 into a random access memory (RAM) 803, such as performing the methods in the above-described embodiments. Various programs and data required for system operation are also stored in the RAM 803. The CPU 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0108] Connected to the I / O interface 805 are an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as necessary. A removable recording medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 810 as necessary, so that a computer program read therefrom is installed into the storage section 808 as necessary.

[0109] In particular, in accordance with embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 809, and / or installed from the removable recording medium 811. When the computer program is executed by the central processing unit (CPU) 801, various functions defined in the system of the present application are performed.

[0110] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium that contains or stores a program used by an instruction execution system, apparatus or device, and can be used or combined with the same. In this application, the computer-readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer-readable computer programs. Such a propagated data signal can take various forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, transmit, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. The computer programs contained in the computer-readable medium can be transmitted by any suitable medium, including, but not limited to, wireless, wired, or the like, or any suitable combination thereof.

[0111] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order from that noted in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for implementing the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0112] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can also be located in a single processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0113] Another aspect of the present application provides a computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the test method as described above. The computer readable storage medium can be included in the electronic device as described in the embodiments above, or can exist separately and not be assembled into the electronic device.

[0114] Another aspect of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the test method provided in the embodiments above.

[0115] According to an aspect of the embodiments of the present application, a computer system is also provided, which includes a central processing unit (CPU) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) or loaded from a storage section into a random access memory (RAM), such as executing the methods in the embodiments above. Various programs and data required for system operation are also stored in the RAM. The CPU, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0116] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as necessary. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive as necessary, so that a computer program read therefrom is installed into the storage section as necessary.

[0117] The above merely provides preferred exemplary embodiments of the present application, and is not intended to limit the implementation of the present application. Based on the main concept and spirit of the present application, the person skilled in the art can easily make corresponding changes or modifications, and the protection scope of the present application should be subject to the protection scope required by the claims.

Claims

1. A method of testing vehicle performance, characterized by, The test method comprises: In the process of testing the test vehicle based on the original test parameters, different types of test results and corresponding optimization suggestions for each type of test result are generated according to the working condition data of the test vehicle; wherein the optimization suggestions include adjustment instructions for adjusting vehicle control parameters; the original test parameters include original constraint conditions and original test targets corresponding to the original constraint conditions; An optimization strategy is determined according to the different types of test results and the adjustment instruction set to optimize the original test parameters, comprising: according to the original constraint conditions, the target adjustment instruction is determined by the repeated adjustment instruction group in the adjustment instruction set and the different types of test results; the repeated adjustment instruction group is a combination of instructions for adjusting the same adjustment object; according to the original test target, the optimization strategy is determined by the target adjustment instruction and the adjustment instruction in the adjustment instruction set except the repeated adjustment instruction group to optimize the original test parameters; wherein the adjustment instruction set includes at least one selected adjustment instruction; the target adjustment instruction is an instruction for adjusting the same adjustment object.

2. The test method of claim 1, wherein, The original test parameters include original constraint conditions and original test targets corresponding to the original constraint conditions; Different types of test results and corresponding optimization suggestions for each type of test result are generated according to the working condition data of the test vehicle, comprising: Collecting the working condition data of the test vehicle running under the original constraint conditions to achieve the original test target; wherein the working condition data is data representing the working condition of different components in the test vehicle; Input the processed data of each component working state into the corresponding preset text template to obtain a plurality of input texts; Input the plurality of input texts into the preset model to make the preset model output different types of test results and corresponding optimization suggestions for each type of test result; wherein the optimization suggestions include a first suggestion for adjusting the original constraint conditions to achieve the original test target, and a second suggestion for maintaining the original constraint conditions unchanged and adjusting the original test target.

3. The test method of claim 2, wherein, Input the processed data of each component working state into the corresponding preset text template to obtain a plurality of input texts, comprising: Feature extraction is performed on the data of each component working state to obtain feature data corresponding to the data of each component working state; Input each feature data, the original constraint condition and the original test target into the corresponding preset text template to obtain a plurality of input texts.

4. The test method of claim 2, wherein, The plurality of input texts include first input texts, second input texts and third input texts; Input the plurality of input texts into the preset model to make the preset model output different types of test results and corresponding optimization suggestions for each type of test result, comprising: The plurality of input texts are input into a preset model, so that the preset model generates a first type of test result and a first optimization suggestion according to the first input text, a second type of test result and a second optimization suggestion according to the first type of test result and the second input text, and a third type of test result and a third optimization suggestion according to the second type of test result and the third input text.

5. The test method of claim 1, wherein, According to the original test target, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group, an optimization strategy is determined, including: According to the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group, a plurality of initial strategies are generated; different initial strategies have different performance optimization targets; The initial strategy with the performance optimization target matching the original test target in the plurality of initial strategies is taken as the optimization strategy.

6. The test method according to any one of claims 1 to 5, characterized in that, The test method further includes: If there is a vehicle model change suggestion in the optimization suggestions corresponding to the different types of test results, the original test parameters are updated based on the test parameters of the specified vehicle model in the vehicle model change suggestion, and / or the original test parameters are updated according to the preset test parameters corresponding to the coding of the test vehicle; If there is a driving condition change suggestion in the optimization suggestions corresponding to the different types of test results, the original test parameters are updated based on the test parameters of the specified driving condition in the driving condition change suggestion.

7. A device for testing the performance of a vehicle, characterized in that The test device includes: A test module is configured to generate different types of test results and optimization suggestions corresponding to each type of test result according to the working condition data of the test vehicle during the test of the test vehicle based on the original test parameters; the optimization suggestions include adjustment instructions for adjusting vehicle control parameters; the original test parameters include original constraint conditions and original test targets corresponding to the original constraint conditions; An optimization module is configured to determine an optimization strategy according to the different types of test results and an adjustment instruction set to optimize the original test parameters, including: determining a target adjustment instruction according to the original constraint conditions, a repeated adjustment instruction group in the adjustment instruction set and different types of test results; the repeated adjustment instruction group is a combination of instructions for adjusting the same adjustment object; determining an optimization strategy according to the original test target, the target adjustment instruction and the adjustment instructions in the adjustment instruction set except the repeated adjustment instruction group to optimize the original test parameters; wherein the adjustment instruction set includes at least one selected adjustment instruction; the target adjustment instruction is an instruction for adjusting the same adjustment object.

8. An electronic device, comprising: including: a controller; a memory for storing one or more programs, when the one or more programs are executed by the controller, the controller implements the test method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, a computer readable instruction stored thereon, when the computer readable instruction is executed by a processor of a computer, the computer executes the test method of any one of claims 1 to 6.

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