Vehicle testing method and device, vehicle and storage medium

Abstract

The invention relates to the technical field of engineering, in particular to a vehicle testing method and device, a vehicle and a storage medium. The vehicle testing method comprises the following steps: according to at least one of vehicle load, vehicle acceleration and parameters of a road where the vehicle is located, carrying out clustering analysis on historical operation data of a vehicle of which the type is the same as that of a to-be-tested vehicle, and determining a plurality of testing working conditions; and testing the to-be-tested vehicle according to the plurality of test working conditions, and determining the energy conversion efficiency of the to-be-tested vehicle under each test working condition.

Description

Technical Field

[0001] This disclosure relates to the field of engineering technology, and in particular to a vehicle testing method and apparatus, a vehicle, and a storage medium. Background Technology

[0002] During vehicle development, testing is necessary to evaluate key performance aspects such as power, energy consumption, range, and emissions. The results of vehicle testing can identify potential defects in the vehicle, providing a basis for product development optimization and technological upgrades.

[0003] However, in related technologies, the testing conditions for vehicles are too limited to accurately reproduce the actual driving scenarios, resulting in poor test results. Summary of the Invention

[0004] In view of this, this disclosure provides a vehicle testing method and apparatus, a vehicle, and a storage medium. By performing cluster analysis on historical operating data of vehicles of the same type through vehicle parameters, multiple test conditions are determined and the energy conversion efficiency of the vehicle under these test conditions is tested, covering multiple operating scenarios of the vehicle and improving the effectiveness of vehicle testing.

[0005] According to a first aspect of this disclosure, a vehicle testing method is provided, comprising: performing cluster analysis on historical operating data of vehicles of the same type as the vehicle under test based on at least one of vehicle load, vehicle acceleration, and road parameters, to determine multiple test conditions; and testing the vehicle under test based on the multiple test conditions to determine the energy conversion efficiency of the vehicle under test under each test condition.

[0006] In some embodiments, cluster analysis is performed on historical operating data of vehicles of the same type as the vehicle under test based on at least one of parameters including vehicle load, vehicle acceleration, and the road where the vehicle is located, to determine multiple test conditions. This includes: determining multiple test conditions corresponding one-to-one with the multiple cluster centers obtained from the cluster analysis, wherein the test condition corresponding to each of the multiple cluster centers includes multiple of the following: the range of vehicle load, the range of slope of the road where the vehicle is located, and the range of standard deviation of vehicle acceleration.

[0007] In some embodiments, testing the vehicle under test according to the plurality of test conditions includes: for each test condition, randomly selecting parameter values ​​from the vehicle load range, the slope range of the road where the vehicle is located, and the standard deviation range of vehicle acceleration included in the test condition to generate a test parameter combination; and testing the vehicle under test according to the test parameter combination.

[0008] In some embodiments, the test includes multiple test cycles. The vehicle under test is tested according to the multiple test conditions. Determining the energy conversion efficiency of the vehicle under test under each test condition includes: for each test cycle, determining the energy conversion efficiency of the vehicle under test under at least one test condition corresponding to that test cycle based on the vehicle speed and vehicle resistance corresponding to that test cycle. The vehicle speed and vehicle resistance are different for different test cycles.

[0009] In some embodiments, testing the vehicle under test according to the plurality of test conditions and determining the energy conversion efficiency of the vehicle under test under each test condition further includes: determining the priority of the test condition based on the proportion of data corresponding to each test condition in the historical operating data; determining the test condition corresponding to each test round in the plurality of test rounds based on the priority of each test condition, wherein the frequency of testing each test condition in the plurality of test rounds corresponds to the priority of the test condition.

[0010] In some embodiments, the vehicle testing method further includes: testing the vehicle under test under a baseline operating condition to determine the baseline energy conversion efficiency of the vehicle under test.

[0011] In some embodiments, testing the vehicle under test according to the plurality of test conditions and determining the energy conversion efficiency of the vehicle under test under each test condition includes: for each test condition, acquiring the performance parameters of the vehicle under test, the performance parameters including multiple of mechanical parameters, electrical parameters, and thermal parameters; and determining the energy conversion efficiency of the vehicle under test under that test condition based on the performance parameters.

[0012] In some embodiments, determining the energy conversion efficiency of the vehicle under test under the test condition based on the performance parameters includes: synchronizing different types of performance parameters over time; and analyzing the mechanical energy, electrical energy, and thermal energy of the vehicle under test based on the time-synchronized performance parameters to determine the energy conversion efficiency of the vehicle under test.

[0013] In some embodiments, the vehicle testing method further includes: using a machine learning model to predict the energy conversion efficiency of the vehicle under test under other operating conditions based on the energy conversion efficiency of the vehicle under test under the plurality of test operating conditions.

[0014] In some embodiments, the vehicle testing method further includes: determining a control strategy for the vehicle under test based on the change in energy conversion efficiency of the vehicle under test among the plurality of test conditions.

[0015] According to a second aspect of this disclosure, a vehicle testing apparatus is provided, comprising: a working condition determination module configured to perform cluster analysis on historical operating data of a vehicle of the same type as the vehicle under test based on at least one of parameters including vehicle load, vehicle acceleration, and the road on which the vehicle is located, to determine multiple testing working conditions; and an efficiency determination module configured to test the vehicle under test based on the multiple testing working conditions to determine the energy conversion efficiency of the vehicle under test under each testing working condition.

[0016] According to a third aspect of this disclosure, a vehicle testing apparatus is provided, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor being configured to execute a testing method as described in any embodiment of this disclosure based on instructions stored in the at least one memory.

[0017] According to a fourth aspect of this disclosure, a vehicle is provided, including a vehicle testing apparatus as described in any embodiment of this disclosure.

[0018] According to a fifth aspect of this disclosure, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the test method as described in any embodiment of this disclosure.

[0019] According to a sixth aspect of this disclosure, a computer program product is provided that, when run on a computer, causes the computer to implement the testing method as described in any embodiment of this disclosure. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0021] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein: Figure 1 A flowchart illustrating a vehicle testing method according to some embodiments of the present disclosure is shown; Figure 2 A flowchart illustrating a test performed according to some embodiments of the present disclosure is shown; Figure 3 A flowchart illustrating the determination of energy conversion efficiency according to some embodiments of the present disclosure is shown; Figure 4 A block diagram of a vehicle testing apparatus according to some embodiments of the present disclosure is shown; Figure 5 A block diagram of a vehicle testing apparatus according to other embodiments of the present disclosure is shown; Figure 6 This is a block diagram illustrating a computer system for implementing some embodiments of the present disclosure.

[0022] It should be understood that the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale. Furthermore, the same or similar reference numerals denote the same or similar components. Detailed Implementation

[0023] Various embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The descriptions of the embodiments are merely illustrative and are in no way intended to limit the scope of the disclosure or its application or use. The present disclosure may be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully express the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps set forth in these embodiments should be interpreted as merely illustrative and not as limiting.

[0024] The terms “first,” “second,” and similar words used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different parts. Words such as “including” mean that the element preceding the word covers the element listed after the word, and do not exclude the possibility of covering other elements as well.

[0025] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0026] All terms used in this disclosure (including technical or scientific terms) have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in a general dictionary, such as a dictionary, should be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or highly formalized meaning, unless expressly defined herein.

[0027] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0028] Traditional vehicle testing methods often involve testing vehicles under limited conditions, which fails to accurately reproduce real-world driving scenarios and results in poor test outcomes.

[0029] In view of this, this disclosure proposes a vehicle testing method that uses vehicle parameters to perform cluster analysis on historical operating data of vehicles of the same type, determines multiple test conditions, and tests the energy conversion efficiency of the vehicle under these test conditions, covering multiple operating scenarios of the vehicle and improving the effectiveness of vehicle testing.

[0030] First, combined Figure 1 The vehicle testing methods described in this disclosure are explained. Figure 1 A flowchart illustrating a vehicle testing method according to some embodiments of the present disclosure is shown.

[0031] like Figure 1 As shown, the vehicle testing method may include: Step S1, performing cluster analysis on historical operating data of vehicles of the same type as the vehicle under test based on at least one of the parameters of vehicle load, vehicle acceleration, and the road where the vehicle is located, to determine multiple test conditions; Step S2, testing the vehicle under test based on the multiple test conditions to determine the energy conversion efficiency of the vehicle under test under each test condition.

[0032] In step S1, historical vehicle data can be collected, and cluster analysis can be used to determine multiple test conditions that need to be tested.

[0033] Historical data for vehicles can include data from other vehicles of the same type that are actually in operation. For example, if the vehicle under test is a commercial vehicle, data from other commercial vehicles can be collected as historical data for clustering. If the vehicle under test is a tractor, data from other tractors can be collected as historical data for clustering. By using historical data from similar vehicles to determine test conditions, the determined test conditions can correspond to the operating scenarios of that type of vehicle, improving the testing effectiveness of the vehicle under test.

[0034] After collecting historical data, the historical data can be clustered based on at least one of the parameters of vehicle load, acceleration, and the road on which the vehicle is located.

[0035] Vehicle load refers to the total weight of the vehicle acting on its wheels in the vertical direction. This can include the vehicle's own weight, as well as the weight of passengers or cargo. Vehicle load can be characterized, for example, by the ratio between the vehicle's actual load and its rated load. Considering the vehicle's weight, a load factor of 0.2 can approximate an unloaded vehicle, while a load factor of 1.0 represents a fully loaded vehicle.

[0036] Vehicle acceleration can include, for example, the average value and standard deviation of vehicle acceleration. Acceleration can be used to construct the vehicle's velocity profile for each test condition.

[0037] The standard deviation of acceleration can characterize the degree of driving intensity. For example, the more frequent the sudden braking and acceleration, the greater the standard deviation of the vehicle's acceleration. The degree of driving intensity can be characterized, for example, by the ratio between the vehicle's current standard deviation of acceleration and a preset baseline standard deviation of acceleration, which could be the average of the standard deviations of acceleration during vehicle operation. An acceleration coefficient greater than 1 indicates an aggressive driving style.

[0038] Parameters of the road on which the vehicle is located may include, for example, the road friction coefficient and the road slope. The road friction coefficient can be determined, for example, by the road surface type acquired by a camera. The road slope can be determined, for example, by a slope sensor built into the vehicle. The road slope can be characterized, for example, by the ratio between the slope angle and the angle amplitude. For example, an angle amplitude of 10° and a slope coefficient of -0.5 can represent a downhill slope of -5°, while a slope coefficient of 1.5 can represent an uphill slope of 15°.

[0039] Using the parameters mentioned above, force analysis can be performed on the vehicle. For example, by considering rolling resistance, air resistance, gravity, and acceleration, the required driving or braking force can be determined. By identifying the core force state of the vehicle, actual vehicle operation can be simulated within clustered test conditions.

[0040] Clustering by vehicle parameters, rather than by parameters of the motor or engine, allows the clustered test conditions to directly correspond to the actual operating scenarios of the vehicle, enabling direct testing of the vehicle and improving the testing results.

[0041] The aforementioned historical data can be data related to the vehicle's parameters, such as speed profiles, load distribution, road gradient, and braking frequency. By using historical data, the vehicle's parameters can be determined, and cluster analysis can be performed on the data.

[0042] In some embodiments, cluster analysis is performed on historical operating data of vehicles of the same type as the vehicle under test based on at least one of parameters including vehicle load, vehicle acceleration, and the road where the vehicle is located, to determine multiple test conditions. This includes: determining multiple test conditions corresponding one-to-one with the multiple cluster centers obtained from the cluster analysis, wherein the test condition corresponding to each of the multiple cluster centers includes multiple of the following: the range of vehicle load, the range of slope of the road where the vehicle is located, and the range of standard deviation of vehicle acceleration.

[0043] Cluster analysis of historical data can identify multiple clusters. Each cluster is a set of data points with similar characteristics and close proximity after clustering. The cluster center is a representative point calculated by the clustering algorithm for each cluster, representing the overall characteristics of all data points in that cluster.

[0044] For each cluster center, a corresponding test condition can be determined. Using the load coefficient, acceleration coefficient, and slope coefficient mentioned above as examples of the clustered data, the load coefficient range for a cluster center obtained through clustering can be 0.2 to 0.4, the acceleration coefficient range can be 0.6 to 0.9, and the slope coefficient range can be -0.2 to 0.2. This cluster center can then be used to determine a test condition, and the aforementioned coefficient ranges can serve as the vehicle load range, the road slope range, and the vehicle acceleration standard deviation range for that test condition, used for subsequent vehicle testing within these data ranges.

[0045] Furthermore, for each test condition, the clustered data range can be analyzed to generate corresponding labels for each test condition. Using the data range in the example above, we can determine that the vehicle is nearly unloaded, driving on a flat road with frequent starts and stops, which is similar to the conditions when a vehicle is driving in an urban area. Therefore, it can be labeled as an unloaded urban test condition. These labels help determine the importance of each test condition for subsequent testing.

[0046] After determining the data range corresponding to each test condition, testing the vehicle under test according to the multiple test conditions may include: for each test condition, randomly selecting parameter values ​​from the vehicle load range, the slope range of the road where the vehicle is located, and the standard deviation range of vehicle acceleration included in the test condition to generate a test parameter combination; and testing the vehicle under test according to the test parameter combination.

[0047] By randomly selecting parameters within the range corresponding to the test conditions and conducting tests based on parameter combinations, the correlation between parameters can be preserved during the determination of the test conditions. In other words, the vehicle load, gradient coefficient, and standard deviation of vehicle acceleration are interrelated in each test condition. For example, to determine that driving is generally smoother under high load and high gradient, the standard deviation of vehicle acceleration should be low. Clustering multiple parameters together, compared to clustering each test parameter individually, makes the determined test conditions more consistent with the complexities of actual vehicle driving.

[0048] The previous section detailed how to cluster historical operational data to determine test conditions. Now, we will return to... Figure 1 Next, we will introduce how to test the vehicle under test according to the test conditions in step S2.

[0049] In some embodiments, the test may include multiple test cycles. The vehicle under test is tested according to the multiple test conditions to determine the energy conversion efficiency of the vehicle under test under each test condition. This includes: for each test cycle, determining the energy conversion efficiency of the vehicle under test under at least one test condition corresponding to that test cycle based on the vehicle speed and vehicle resistance corresponding to that test cycle, wherein the vehicle speed and vehicle resistance are different for different test cycles.

[0050] In the above embodiments, the vehicle test can be divided into multiple test rounds, and different vehicle resistance equations and vehicle speeds can be used in each test round, thereby conducting more diverse tests on the vehicle.

[0051] For example, in the first test round, an average vehicle speed of 50 kilometers per hour can be used to construct the speed curve required for vehicle testing, while in the second test round, an average vehicle speed of 60 kilometers per hour can be used to construct the speed curve required for vehicle testing, thus realizing the testing of the vehicle at different speeds. As another example, different air drag coefficients can be used in different test rounds to simulate the performance of vehicles with different shapes under the test conditions.

[0052] Other test parameters of the vehicle, such as ambient temperature and air density, can also be adjusted between different test rounds to increase the diversity of the test.

[0053] Below, we will combine Figure 2 This section introduces an example of a test. Figure 2 A flowchart illustrating a test performed according to some embodiments of the present disclosure is shown.

[0054] like Figure 2 As shown, testing the vehicle under test according to the multiple test conditions and determining the energy conversion efficiency of the vehicle under test under each test condition may include: Step S21A, determining the priority of each test condition based on the proportion of data corresponding to each test condition in the historical operating data; Step S22A, determining the test condition corresponding to each test round in the multiple test rounds based on the priority of each test condition, wherein the frequency of testing each test condition in the multiple test rounds corresponds to the priority of the test condition; Step S23A, for each test round, determining the energy conversion efficiency of the vehicle under test under at least one test condition corresponding to that test round based on the vehicle speed and vehicle resistance corresponding to that test round.

[0055] In step S21A, the priority of each test condition can be determined by the relationship between the proportions of data in each test condition during clustering.

[0056] For example, data percentage can be understood as the proportion of the data cluster represented by the cluster center corresponding to the test condition in all historical running data. The higher the data percentage, the higher the frequency of this test condition in the historical running process, and therefore it can be assigned a higher priority so that a more comprehensive test can be conducted on this test condition in subsequent tests.

[0057] In addition to data percentage, the priority of test conditions can also be adjusted according to testing needs. For example, when testing the performance of a vehicle under full load is required, the priority of test conditions with a vehicle load factor of 0.8 or higher can be increased to meet the testing requirements.

[0058] In step S22A, the combination of test conditions to be tested in each round of testing can be determined according to the priority of the test conditions. In each test round, all test conditions obtained from the clustering can be tested, or only a portion of the test conditions can be tested.

[0059] The frequency of testing a test condition corresponds to its priority. In other words, the higher the priority of a test condition, the more times it will be tested in multiple rounds of testing.

[0060] For example, for more common vehicle speed scenarios, such as a test round with an average speed of 60 kilometers per hour, more test conditions can be tested, meaning both high-priority and low-priority test conditions can be tested. However, for more extreme vehicle speed scenarios, such as a test round with an average speed of 90 kilometers per hour, only high-priority test conditions can be tested to ensure that the vehicle performs as required under these extreme conditions.

[0061] By using the above methods, comprehensive testing of vehicles can be conducted under various test conditions and speeds while conserving test resources, thereby improving test efficiency.

[0062] In step S23A, the vehicle can be tested in multiple rounds according to the test conditions included in each test round as determined above.

[0063] Between different test conditions in each test cycle, dynamic testing of the vehicle's transition state can also be performed. For example, if the load factor corresponding to one test condition is 0.3 and the load factor corresponding to the next test condition is 0.5, the vehicle's load factor can be changed from 0.3 to 0.8 and then back to 0.5, and the energy consumption during the change of vehicle state can be tested.

[0064] It should be understood that the above values ​​are merely illustrative. The step changes described above can make the vehicle testing more comprehensive and improve the testing results.

[0065] The above text combines Figure 2 This section introduced how to test vehicles according to test rounds. Below, we will combine... Figure 3 This section will elaborate on how to determine the energy conversion efficiency of a vehicle. Figure 3 A flowchart illustrating the determination of energy conversion efficiency according to some embodiments of the present disclosure is shown.

[0066] like Figure 3 As shown, testing the vehicle under test according to the multiple test conditions and determining the energy conversion efficiency of the vehicle under test under each test condition may include: step S21B, for each test condition, obtaining the performance parameters of the vehicle under test, the performance parameters including multiple of mechanical parameters, electrical parameters, and thermal parameters; step S22B, determining the energy conversion efficiency of the vehicle under test under that test condition based on the performance parameters.

[0067] The aforementioned mechanical parameters may include, for example, the torque and speed of the vehicle's wheel axles, used to determine the vehicle's output mechanical energy. The aforementioned electrical parameters may include, for example, the voltage and current input to the drive system, used to determine the vehicle's input electrical energy. Furthermore, electrical parameters may also include the vehicle's battery voltage and energy recovery current, used to determine the vehicle's energy recovery energy. The aforementioned thermal parameters may include, for example, the vehicle's winding temperature and oil temperature, used to determine the thermal state of the vehicle system.

[0068] After obtaining the above performance parameters, the energy flow within the vehicle can be analyzed to determine the vehicle's energy conversion efficiency. For example, the formula for determining the vehicle's energy conversion efficiency can be: .

[0069] In the above formula, This refers to the vehicle's energy conversion efficiency. This refers to the vehicle's output mechanical energy. This refers to the energy recovered by the vehicle. This refers to the electrical energy input to the vehicle. This refers to the vehicle's auxiliary energy consumption, such as the energy consumption of the cooling system and controllers.

[0070] The above formula allows for energy analysis of vehicles from the perspectives of mechanical and electrical energy, determining the energy conversion efficiency of converting the total input electrical energy into the mechanical energy for vehicle operation.

[0071] It should be understood that the above formula is merely an example, and other formulas, such as the energy conservation formula, can also be used to analyze the energy of a vehicle and determine its energy conversion efficiency.

[0072] In some embodiments, determining the energy conversion efficiency of the vehicle under test under the test condition based on the performance parameters may include: synchronizing different types of performance parameters over time; and analyzing the mechanical energy, electrical energy, and thermal energy of the vehicle under test based on the time-synchronized performance parameters to determine the energy conversion efficiency of the vehicle under test.

[0073] In the above embodiments, before determining the energy conversion efficiency of the vehicle, the different physical parameters of the vehicle can be synchronized in time.

[0074] For example, when acquiring parameters, multiple physical quantity measurements can be synchronized, and data from all channels can be recorded at the same timestamp to achieve time synchronization between different performance parameters.

[0075] For example, performance parameters from different sampling systems can be unified to the same time base using a linear interpolation method, thereby achieving time synchronization between different performance parameters.

[0076] The above methods allow for a comprehensive testing of the vehicle's energy conversion process from multiple physical dimensions, quantifying the contribution of different physical parameters to the vehicle's energy conversion efficiency under different operating conditions, and improving the effectiveness of vehicle testing.

[0077] In some embodiments, the vehicle testing method may further include: testing the vehicle under test under a baseline operating condition to determine the baseline energy conversion efficiency of the vehicle under test.

[0078] In the above embodiments, the vehicle can also be tested under a preset benchmark condition. The benchmark condition can be, for example, the overall average value of parameters such as load and slope in the vehicle's historical operating data, thereby determining the vehicle's benchmark energy conversion efficiency for reference in subsequent tests.

[0079] In addition, the baseline operating condition can also include a preset ambient temperature. For example, the energy conversion efficiency of the vehicle at normal temperature can be used as a baseline, and the ratio of the actual energy conversion efficiency of the vehicle at each test temperature to the baseline energy conversion efficiency can be determined as the temperature influence coefficient of the vehicle.

[0080] In some embodiments, the vehicle testing method may further include: using a machine learning model to predict the energy conversion efficiency of the vehicle under test under other operating conditions based on the energy conversion efficiency of the vehicle under test under the plurality of test operating conditions.

[0081] In the above embodiments, machine learning models, such as Long Short-Term Memory (LSTM) networks, can be used to extend the vehicle's test results based on its energy conversion efficiency under test conditions, thereby determining the vehicle's energy conversion efficiency under other conditions. Since there are many vehicle operating conditions and the vehicle's driving process is often non-linear, using an LSTM model can provide a more accurate prediction of the energy conversion efficiency under other conditions.

[0082] After determining the energy conversion efficiency of the vehicle, the vehicle testing method may further include: determining the control strategy of the vehicle under test based on the change in energy conversion efficiency of the vehicle under test among the multiple test conditions.

[0083] In other words, the vehicle's control strategy can be optimized based on the energy conversion efficiency obtained from the test. For example, the vehicle's overall energy conversion efficiency, energy efficiency stability in different scenarios, adaptability to environmental changes, and dynamic energy changes can be used to evaluate the vehicle's test results and determine the direction for vehicle optimization.

[0084] For example, the vehicle motor efficiency map (MAP) can be determined based on the test results. Furthermore, by combining the distribution of the motor's operating points with the efficiency map, the control strategy for shift points can be optimized to increase the proportion of operating points in the high-efficiency, low-energy-consumption range, thereby improving the overall operating efficiency of the vehicle.

[0085] For example, by testing the energy consumption of vehicles in low-temperature environments, the control strategies of the thermal management system can be adjusted accordingly, including control strategies such as the heating temperature points of the battery and electric drive system, and the fan start and speed set points during low-temperature driving.

[0086] After optimizing the vehicle's control strategy, machine learning models can be used to predict the optimization results, determine the optimization outcome, and improve the vehicle's control efficiency.

[0087] The above describes the vehicle testing method provided in this disclosure. This testing method uses vehicle parameters to perform cluster analysis on historical operating data of vehicles of the same type, determines multiple test conditions, and tests the energy conversion efficiency of the vehicle under these test conditions, covering various vehicle operating scenarios and improving the effectiveness of vehicle testing.

[0088] The following is for reference. Figure 4 and Figure 5 The present disclosure describes a vehicle testing apparatus for performing any of the above-described testing methods. Figure 4 A block diagram of a vehicle testing apparatus according to some embodiments of the present disclosure is shown.

[0089] like Figure 4 As shown, the first vehicle testing device 4 includes: a working condition determination module 41, configured to perform cluster analysis on historical operating data of vehicles of the same type as the vehicle under test based on at least one of the parameters of vehicle load, vehicle acceleration, and the road where the vehicle is located, to determine multiple testing working conditions; and an efficiency determination module 42, configured to test the vehicle under test based on the multiple testing working conditions, and determine the energy conversion efficiency of the vehicle under test under each testing working condition.

[0090] The operating condition determination module 41 of the first vehicle testing device 4 can, for example, be used to perform... Figure 1 Step S1. The efficiency determination module 42 of the first vehicle testing device 4 can, for example, be used to perform... Figure 1 Step S2.

[0091] The vehicle testing device disclosed herein performs cluster analysis on historical operating data of similar vehicles using vehicle parameters, determines multiple test conditions, and tests the energy conversion efficiency of the vehicle under these test conditions, covering various vehicle operating scenarios and improving the effectiveness of vehicle testing.

[0092] Figure 5 A block diagram of a vehicle testing apparatus according to other embodiments of the present disclosure is shown.

[0093] like Figure 5 As shown, the second vehicle testing apparatus 5 includes: at least one memory 51; and at least one processor 52 coupled to the at least one memory 51, the at least one processor 52 being configured to execute the testing method as described in any of the foregoing embodiments based on instructions stored in the at least one memory 51.

[0094] Memory 51 is used to store one or more computer-readable instructions. Memory 51 may include any combination of various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory, including but not limited to random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory. Memory 51 may, for example, store operating systems, application programs, bootloaders, databases, and other programs, as well as various application programs and various data.

[0095] The processor 52 is configured to execute computer-readable instructions to implement the testing method described in any of the foregoing embodiments. Specific implementations of each step of the method can be found in the above embodiments, for example... Figures 1 to 3 The steps involved are repeated here, so the details will not be repeated.

[0096] The vehicle testing device disclosed herein performs cluster analysis on historical operating data of similar vehicles using vehicle parameters, determines multiple test conditions, and tests the energy conversion efficiency of the vehicle under these test conditions, covering various vehicle operating scenarios and improving the effectiveness of vehicle testing.

[0097] The processor 52 can be various processing devices, such as a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The central processing unit (CPU) can be based on x86 or ARM architectures, etc.

[0098] The processor 52 and the memory 51 can communicate with each other directly or indirectly. For example, the processor 52 and the memory 51 can communicate via a network. The network can include a wireless network, a wired network, and / or any combination of wireless and wired networks. The processor 52 and the memory 51 can also communicate with each other via a system bus, which is not limited in this disclosure.

[0099] It should be noted that Figure 5 The components of the second vehicle testing apparatus 5 shown are merely exemplary and not limiting. The second vehicle testing apparatus 5 may also have other components depending on the actual application requirements. The processor 52 can control other components in the second vehicle testing apparatus 5 to perform desired functions.

[0100] The second vehicle testing device 5 can be implemented by software, firmware and / or hardware, and can be integrated into a device with the relevant application installed.

[0101] This disclosure also provides a vehicle, including the vehicle testing apparatus of any of the foregoing embodiments. As before, by performing cluster analysis on historical operating data of vehicles of the same type using vehicle parameters, multiple test conditions are determined, and the energy conversion efficiency of the vehicle under these test conditions is tested, covering various operating scenarios of the vehicle and improving the effectiveness of vehicle testing.

[0102] Figure 6 This is a block diagram illustrating a computer system for implementing some embodiments of the present disclosure.

[0103] like Figure 6 As shown, computer system 6 can be represented in the form of a general computing device. Computer system 6 includes memory 51, processor 52, and bus 60 connecting different system components.

[0104] The memory 51 can be various forms of computer-readable storage media, such as system memory, non-volatile storage media, etc. System memory may store, for example, an operating system, application programs, a bootloader, and other programs. System memory may include volatile storage media, such as random access memory (RAM) and / or cache memory. Non-volatile storage media may store, for example, instructions for executing corresponding embodiments of the test methods. Non-volatile storage media include, but are not limited to, disk storage, optical storage, flash memory, etc.

[0105] The processor 52 can be implemented using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete hardware components such as discrete gates or transistors. Accordingly, each module can be implemented by executing instructions in the central processing unit (CPU) memory to perform the corresponding steps, or by implementing dedicated circuitry to perform the corresponding steps.

[0106] Bus 60 can use any of the various bus architectures. For example, bus architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.

[0107] Computer system 6 may also include input / output interface 63, network interface 64, storage interface 65, etc. These interfaces 63, 64, 65, as well as memory 51 and processor 52, can be connected via bus 60. Input / output interface 63 provides a connection interface for input / output devices such as monitors, mice, and keyboards. Network interface 64 provides a connection interface for various networked devices. Storage interface 65 provides a connection interface for external storage devices such as floppy disks, USB flash drives, and SD cards.

[0108] According to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product that, when run on a computer, causes the computer to implement the testing methods described in any of the foregoing embodiments. The computer program product includes computer instructions carried on a computer-readable medium, the computer instructions containing program code for performing the methods shown in the flowcharts.

[0109] Various embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0110] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A vehicle testing method, comprising: Based on at least one of the parameters of vehicle load, vehicle acceleration, and the road where the vehicle is located, perform cluster analysis on the historical operating data of vehicles of the same type as the vehicle under test to determine multiple test conditions. The vehicle under test is tested according to the multiple test conditions to determine the energy conversion efficiency of the vehicle under test under each test condition.

2. The vehicle testing method according to claim 1, wherein, Based on at least one of the parameters of vehicle load, vehicle acceleration, and the road where the vehicle is located, cluster analysis is performed on the historical operating data of vehicles of the same type as the vehicle under test to determine multiple test conditions, including: Based on the multiple cluster centers obtained from the cluster analysis, a plurality of test conditions corresponding one-to-one with the multiple cluster centers are determined. The test conditions corresponding to each of the multiple cluster centers include multiple of the following: the vehicle load range, the slope range of the road where the vehicle is located, and the standard deviation range of vehicle acceleration.

3. The vehicle testing method according to claim 2, wherein, The testing of the vehicle under test, based on the aforementioned multiple test conditions, includes: For each test condition, parameter values ​​are randomly selected from the vehicle load range, the slope range of the road where the vehicle is located, and the standard deviation range of vehicle acceleration included in the test condition to generate a combination of test parameters. The vehicle under test is tested according to the combination of test parameters.

4. The vehicle testing method according to claim 1, wherein, The test includes multiple test rounds. Based on the multiple test conditions, the vehicle under test is tested to determine its energy conversion efficiency under each test condition, including: For each test round, the energy conversion efficiency of the vehicle under test is determined based on the vehicle speed and vehicle resistance corresponding to that test round, under at least one test condition corresponding to that test round. The vehicle speed and vehicle resistance are different for different test rounds.

5. The vehicle testing method according to claim 4, wherein, The determination of the energy conversion efficiency of the vehicle under test under various test conditions, based on the multiple test conditions, further includes: The priority of each test condition is determined based on the proportion of the data corresponding to each test condition in the historical operation data. Based on the priority of each test condition, the test condition corresponding to each of the plurality of test rounds is determined, wherein the frequency of testing each test condition in the plurality of test rounds corresponds to the priority of that test condition.

6. The vehicle testing method according to claim 1 further includes: The vehicle under test is tested under baseline operating conditions to determine the baseline energy conversion efficiency of the vehicle under test.

7. The vehicle testing method according to claim 1, wherein, Based on the multiple test conditions, the vehicle under test is tested to determine its energy conversion efficiency under each test condition, including: For each test condition, the performance parameters of the vehicle under test are obtained, including multiple parameters such as mechanical parameters, electrical parameters, and thermal parameters. Based on the performance parameters, the energy conversion efficiency of the vehicle under test under the test conditions is determined.

8. The vehicle testing method according to claim 7, wherein, Determining the energy conversion efficiency of the vehicle under test under this test condition based on the performance parameters includes: Synchronize different types of performance parameters over time; Based on the performance parameters after time synchronization, the mechanical energy, electrical energy, and thermal energy of the vehicle under test are analyzed to determine the energy conversion efficiency of the vehicle under test.

9. The vehicle testing method according to claim 1, further comprising: Using a machine learning model, the energy conversion efficiency of the vehicle under test under other operating conditions is predicted based on the energy conversion efficiency of the vehicle under test under the multiple test conditions.

10. The vehicle testing method according to claim 1, further comprising: The control strategy of the vehicle under test is determined based on the change in energy conversion efficiency of the vehicle under test among the multiple test conditions.

11. A vehicle testing apparatus, comprising: The working condition determination module is configured to perform cluster analysis on historical operating data of vehicles of the same type as the vehicle under test based on at least one of the parameters of vehicle load, vehicle acceleration, and the road where the vehicle is located, to determine multiple test working conditions. The efficiency determination module is configured to test the vehicle under test according to the multiple test conditions and determine the energy conversion efficiency of the vehicle under test under each test condition.

12. A vehicle testing apparatus, comprising: At least one memory; as well as At least one processor coupled to the at least one memory, the at least one processor being configured to execute the test method as described in any one of claims 1 to 10 based on instructions stored in the at least one memory.

13. A vehicle comprising the vehicle testing apparatus as described in claim 11 or 12.

14. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the test method as described in any one of claims 1 to 10.

15. A computer program product, when run on a computer, causes the computer to implement the test method as described in any one of claims 1 to 10.

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