Vehicle automation simulation method, system, vehicle simulation device and storage medium

Abstract

The present invention provides an autonomous driving operation evaluation method, system, autonomous driving vehicle and storage medium, the method comprising: obtaining vehicle data, and constructing a vehicle model database based on the vehicle data; obtaining a target time period, and determining road spectrum data based on the target time period; obtaining a power chain model of each power component of the vehicle, and constructing a power chain model database based on the power chain model; obtaining a vehicle model of a vehicle to be simulated, and selecting a matching power chain model from the power chain model database based on the vehicle model, and filling data in the power chain model based on the vehicle model database to obtain a target power chain model; simulating the target power chain model based on the road spectrum data to obtain simulation data. The present invention simplifies the construction process of the power chain model, eliminates the feedback time between personnel, improves the construction efficiency of the power chain model, and thereby improves the simulation efficiency of the vehicle to be simulated.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle automation simulation method, system, vehicle simulation equipment and storage medium. Background Art

[0002] In order to improve the safety of vehicles during operation, simulation verification is required before the vehicles are officially put into use.

[0003] In the prior art, the process of simulating a vehicle is as follows: first, the vehicle model requirements of market feedback personnel are obtained, and then the corresponding simulation technicians are asked to build a corresponding model based on the vehicle model requirements, and then the constructed model is simulated, that is, one model is required for each vehicle model. The prior art has the following technical problems: (1) Each time a corresponding model is built according to the vehicle model requirements, the modeling process is cumbersome, resulting in low simulation efficiency; (2) The simulation process relies on personnel feedback, which further reduces the simulation efficiency.

[0004] Therefore, there is an urgent need to propose a vehicle automation simulation method, system, vehicle simulation device and storage medium to simplify the simulation process, realize automated simulation, and thus improve simulation efficiency. Summary of the invention

[0005] In view of this, it is necessary to provide a vehicle automation simulation method, system, vehicle simulation device and storage medium to solve the technical problem of low vehicle simulation efficiency in the prior art.

[0006] In one aspect, the present invention provides a vehicle automation simulation method, comprising:

[0007] Acquire vehicle data, and build a vehicle model database based on the vehicle data;

[0008] Acquire a target time period, and determine road spectrum data based on the target time period;

[0009] Acquire a power chain model of each power component of the vehicle, and construct a power chain model database based on the power chain model;

[0010] Acquire a vehicle model of a vehicle to be simulated, select a matching power chain model from the power chain model database based on the vehicle model, and fill the power chain model with data based on the vehicle model database to obtain a target power chain model;

[0011] The target power chain model is simulated based on the road spectrum data to obtain simulation data.

[0012] In some possible implementations, the vehicle data includes engine-related data, transmission-related data, and additional data. The engine-related data includes engine universal characteristic data, friction work test data at different coolant temperatures, engine accessory specifications, and accessory work; the transmission-related data includes efficiency universal data at each gear of the transmission, speed ratio at each gear, no-load loss at each gear, and drive axle efficiency universal data; the additional data includes vehicle model, tonnage, no-load loss from the rear auxiliary box to the wheel side, tire specifications, and rear axle speed ratio.

[0013] In some possible implementations, determining the road spectrum data based on the target time period includes:

[0014] Determining a target running section of the vehicle to be simulated based on the target time period, and acquiring initial road spectrum data of the target running section based on the vehicle to be simulated;

[0015] Determine at least one similar road spectrum data having a degree of overlap with the target running road section within the target time period that is greater than a degree of overlap threshold;

[0016] The road spectrum data is determined based on the at least one similar road spectrum data and the initial road spectrum data.

[0017] In some possible implementations, the determining the road spectrum data based on the at least one similar road spectrum data and the initial road spectrum data includes:

[0018] Determining whether there is missing data and / or data to be corrected in the initial road spectrum data;

[0019] When there are missing data and / or data to be corrected in the initial road spectrum data, the initial road spectrum data is optimized based on the at least one similar road spectrum data to obtain the road spectrum data.

[0020] In some possible implementations, the simulation data includes accessory power consumption, engine energy consumption, and gear distribution; and the vehicle automation simulation method further includes:

[0021] The abnormal energy flow direction of the vehicle to be simulated is determined based on the accessory power consumption, the engine energy consumption and the gear distribution.

[0022] In some possible implementations, the vehicle automation simulation method further includes:

[0023] The accessory power consumption, the engine energy consumption and the gear distribution are visually displayed.

[0024] In some possible implementations, the vehicle automation simulation method further includes:

[0025] Acquire vehicle update data based on a preset update cycle, and determine whether the vehicle model database needs to be updated based on the vehicle update data and the vehicle data;

[0026] When the vehicle model database needs to be updated, the vehicle model database is updated based on the vehicle update data.

[0027] On the other hand, the present invention also provides a vehicle automation simulation system, comprising:

[0028] A vehicle model database construction unit, used to obtain vehicle data and construct a vehicle model database based on the vehicle data;

[0029] A road spectrum data acquisition unit, used to acquire a target time period and determine road spectrum data based on the target time period;

[0030] A power chain model database construction unit, used to obtain a power chain model of each power component of the vehicle, and to construct a power chain model database based on the power chain model;

[0031] A power chain model generating unit, used for acquiring a vehicle model of a vehicle to be simulated, selecting a matching power chain model from the power chain model database based on the vehicle model, and filling data into the power chain model based on the vehicle model database to obtain a target power chain model;

[0032] A vehicle simulation unit is used to simulate the target power chain model based on the road spectrum data to obtain simulation data.

[0033] On the other hand, the present invention also provides an autonomous driving vehicle, including a memory and a processor, wherein:

[0034] The memory is used to store programs;

[0035] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the vehicle automation simulation method described in any one of the possible implementations above.

[0036] On the other hand, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the vehicle automation simulation method described in any of the above possible implementations.

[0037] The beneficial effect of adopting the above-mentioned implementation method is: the vehicle automation simulation method provided by the present invention constructs a vehicle model database based on the acquired vehicle data, and constructs a power chain model database based on the power chain models of each power component of the vehicle. When the vehicle model of the vehicle to be simulated is obtained, a matching power chain model is selected from the power chain model database based on the vehicle model, and the power chain model is filled with data based on the vehicle model database to obtain the target power chain model, that is: there is no need for the staff to model according to the vehicle model, and it is only necessary to select a matching power chain model from the power chain model database according to the vehicle model, which simplifies the construction process of the power chain model, improves the construction efficiency of the power chain model, and thereby improves the simulation efficiency of the vehicle to be simulated.

[0038] Furthermore, the present invention only needs to obtain the vehicle model and can automatically complete the simulation of the vehicle to be simulated according to the vehicle model, thereby eliminating the feedback time between personnel and further improving the simulation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A schematic flow chart of an embodiment of the vehicle automation simulation method provided by the present invention;

[0041] Figure 2 For the present invention Figure 1 A schematic flow chart of an embodiment of step S102;

[0042] Figure 3 For the present invention Figure 2 A schematic flow chart of an embodiment of step S203;

[0043] Figure 4 A schematic diagram of a flow chart of an embodiment of updating a vehicle model database provided by the present invention;

[0044] Figure 5 A schematic diagram of the structure of an embodiment of the vehicle automation simulation system provided by the present invention;

[0045] Figure 6 A schematic structural diagram of an embodiment of a vehicle simulation device provided by the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0047] It should be understood that the schematic drawings are not drawn to scale. The flow chart used in the present invention shows the operations implemented according to some embodiments of the present invention. It should be understood that the operations of the flow chart can be implemented out of order, and the steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flow chart under the guidance of the content of the present invention, and can also remove one or more operations from the flow chart.

[0048] Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0049] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] In the description of the embodiments of the present application, "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0051] The embodiments of the present invention provide a vehicle automation simulation method, system, vehicle simulation device and storage medium, which are described below respectively.

[0052] Figure 1 A schematic diagram of an embodiment of the vehicle automation simulation method provided by the present invention is shown in FIG. Figure 1 As shown, the vehicle automation simulation method includes:

[0053] S101, acquiring vehicle data, and building a vehicle model database based on the vehicle data;

[0054] S102, obtaining a target time period, and determining road spectrum data based on the target time period;

[0055] S103, obtaining a power chain model of each power component of the vehicle, and building a power chain model database based on the power chain model;

[0056] S104, obtaining a vehicle model of a vehicle to be simulated, and selecting a matching power chain model from a power chain model database based on the vehicle model, and filling data in the power chain model based on the vehicle model database to obtain a target power chain model;

[0057] S105: Simulate the target power chain model based on the road spectrum data to obtain simulation data.

[0058] Compared with the prior art, the vehicle automation simulation method provided by the embodiment of the present invention builds a vehicle model database based on the acquired vehicle data, and builds a power chain model database based on the power chain models of various power components of the vehicle. When the vehicle model of the vehicle to be simulated is obtained, a matching power chain model is selected from the power chain model database based on the vehicle model, and the power chain model is filled with data based on the vehicle model database to obtain a target power chain model, that is: there is no need for staff to model according to the vehicle model, and only needs to select a matching power chain model from the power chain model database according to the vehicle model, which simplifies the construction process of the power chain model, improves the construction efficiency of the power chain model, and thereby improves the simulation efficiency of the vehicle to be simulated.

[0059] Furthermore, the embodiment of the present invention only needs to obtain the vehicle model and can automatically complete the simulation of the vehicle to be simulated according to the vehicle model, thereby eliminating the feedback time between personnel and further improving the simulation efficiency.

[0060] The vehicle data in step S101 may be obtained by collecting the vehicle data from an actually running vehicle or by testing the vehicle on a test bench.

[0061] It should be noted that: in order to ensure the accuracy of the power chain model in the power chain model database, in some embodiments of the present invention, after step S103, it also includes: checking and debugging the power chain model, including but not limited to constant speed driving, gliding with gear, gliding in neutral, and operating fuel consumption test. When the above inspections and debugging are passed, step S104 is executed.

[0062] The accuracy of the power chain model in the power chain model database can be ensured through the above-mentioned inspection and debugging.

[0063] It should be understood that: before step S105, the obtained target power chain model needs to be packaged and compiled to make the target power chain model executable.

[0064] Specifically, define the environment variables of the target power chain model in the Simulink interface, establish input and output windows, and introduce connections for the packaged model, and output the compiled C code and DLL model.

[0065] In some embodiments of the present invention, vehicle data includes engine-related data, transmission-related data and additional data, wherein the engine-related data includes engine universal characteristic data, friction work test data at different coolant temperatures, engine accessory specifications and accessory work; the transmission-related data includes efficiency universal data of the transmission at each gear, speed ratio at each gear, no-load loss at each gear and drive axle efficiency universal data; the additional data includes vehicle model, tonnage, no-load loss from the rear auxiliary box to the wheel side, tire specifications and rear axle speed ratio.

[0066] Among them, engine-related data can be obtained by testing on an engine test bench, and transmission-related data can be obtained by testing on an integrated test bench.

[0067] In a specific embodiment of the present invention, vehicle models include but are not limited to traditional energy vehicles ICE, electric vehicles EV and plug-in hybrid electric vehicles PHEV. The power chain models of the above three different vehicle models are:

[0068] In traditional energy vehicles (ICE), power is provided by a generator, whose power source is fuel. The number of power chains is one. According to the preset power chain, the power components are arranged in sequence between the front axle and the generator. The power chain model is: front axle-differential-reducer-gearbox-generator.

[0069] In an electric vehicle EV, the power system of the electric vehicle includes four parts, namely, power battery, motor, transmission system and control system. The power is provided by the motor, and its power source is the power battery. The number of power chains is one. According to the preset power chain, the power components are arranged in sequence between the front axle and the motor. The power chain model is: front axle-differential-reduction gearbox-motor.

[0070] In a plug-in hybrid electric vehicle (PHEV), power is provided by a generator and a motor, the power sources are power batteries and fuel, and there are two power chains. The power components are arranged in sequence between the front axle and the generator according to the preset power chain. The power chain model is divided into two: one is: front axle-differential-reducer-gearbox-motor-clutch-generator; the other is: rear axle-reducer-clutch-motor.

[0071] It should also be noted that: in order to avoid the technical problem of simulation error when the road spectrum data jumps, the road spectrum data is directly input into the target power chain model for simulation, in some embodiments of the present invention, before step S105, the following is further included:

[0072] Pre-process the road spectrum data.

[0073] The specific process of pre-processing is: when the data difference between two adjacent data points in the road spectrum data is too large, the two data points are smoothed to reduce the data difference between the two data points, thereby avoiding the technical problem of simulation errors caused by excessive changes in the input road spectrum data, and ensuring the simulation success rate when the road spectrum data is input into the target power chain model for simulation.

[0074] Since the simulation of the vehicle to be simulated depends on accurate road spectrum data, that is, accurate road spectrum data will improve the reliability of the simulation result of the vehicle to be simulated, therefore, in order to improve the accuracy of the determined road spectrum data, in some embodiments of the present invention, Figure 2 As shown, step S102 includes:

[0075] S201, determining a target running section of the vehicle to be simulated based on a target time period, and acquiring initial road spectrum data of the target running section based on the vehicle to be simulated;

[0076] S202, determining at least one similar road spectrum data having a degree of overlap with the target running road section within the target time period greater than a degree of overlap threshold;

[0077] S203: Determine road spectrum data based on at least one similar road spectrum data and initial road spectrum data.

[0078] The embodiment of the present invention determines road spectrum data based on the initial road spectrum data and at least one similar road spectrum data whose overlap with the target operating section within the target time period is greater than the overlap threshold, rather than directly using the initial road spectrum data as the road spectrum data. This can reduce the uncertainty of the initial road spectrum data obtained for a single vehicle to be simulated, thereby improving the accuracy of the determined road spectrum data, thereby improving the accuracy and reliability of the simulation of the vehicle to be simulated.

[0079] It should be understood that the overlap threshold can be set or adjusted according to actual application scenarios. In a specific embodiment of the present invention, the overlap threshold is 90%.

[0080] It should be noted that the specific process of step S202 is: searching for vehicles on the same route within the target time period based on multiple trajectory points of the target operating section, and outputting a list of vehicle chassis numbers, overlaps and times, and screening vehicles with overlaps greater than the overlap threshold within the target time period and target operating section, obtaining data on vehicles with overlaps greater than the overlap threshold from the Internet of Vehicles platform, and then determining at least one similar road spectrum data.

[0081] In some embodiments of the present invention, Figure 3 As shown, step S203 includes:

[0082] S301, determining whether there is missing data and / or data to be corrected in the initial road spectrum data;

[0083] S302: When there are missing data and / or data to be corrected in the initial road spectrum data, the initial road spectrum data is optimized based on at least one similar road spectrum data to obtain road spectrum data.

[0084] The embodiment of the present invention improves the accuracy of the obtained road spectrum data by optimizing the initial road spectrum data based on at least one similar road spectrum data when there are confirmed data and / or data to be corrected in the initial road spectrum data.

[0085] In a specific embodiment of the present invention, the specific process of determining the data to be corrected in step S301 is: judging whether the longitude difference and / or latitude difference between two adjacent data points in the initial road spectrum data is too large; when the longitude difference and / or latitude difference is too large, the data of the data point with the longitude difference and / or latitude difference being too large is the data to be corrected.

[0086] In a specific embodiment of the present invention, the simulation data includes accessory power consumption, engine energy consumption and gear distribution.

[0087] Since the purpose of simulating the simulated vehicle is to find unreasonable places in the driving process of the simulated vehicle, in some embodiments of the present invention, the vehicle automation simulation method further includes:

[0088] The abnormal energy flow direction of the vehicle to be simulated is determined based on the accessory power consumption, engine energy consumption and gear distribution.

[0089] Take the gear distribution as an example to illustrate the trend of energy abnormality: determine whether the gear distribution is the same as the reference gear distribution. When the gear distribution is different from the reference gear distribution and the engine energy consumption is obviously higher in the section where the gear distribution is different from the reference gear distribution, it is considered that the energy abnormality is caused by unreasonable gear distribution.

[0090] The trend of energy abnormality is explained by engine energy consumption: engine energy consumption includes driving energy consumption and braking energy consumption. When the braking energy consumption is high, it is considered that the energy abnormality is caused by the driver's driving behavior.

[0091] The energy anomaly trend is explained by the accessory power consumption: when the accessory power consumption ratio of a certain accessory is greater than the power consumption ratio threshold, it is considered that the energy anomaly is caused by a fault in the corresponding accessory.

[0092] It should be noted that: after determining the abnormal energy flow of the vehicle to be simulated, subsequent improvement plans and optimization suggestions can be proposed based on expert knowledge or a pre-built optimization suggestion library.

[0093] In order to enable the staff to intuitively know the simulation results, in some embodiments of the present invention, the vehicle automation simulation method further includes:

[0094] Visualize accessory power consumption, engine energy consumption, and gear distribution.

[0095] Specifically, when performing visual display, you can select a matching display format for display. For example, the engine energy consumption and accessory power consumption can be displayed in the form of a fan chart, that is, the power consumption ratio of each accessory and the energy consumption ratio of the engine energy consumption can be determined through the fan chart. The gear distribution is displayed in the form of a line chart, that is, the relationship between the gear position, time and distance can be determined through the line chart.

[0096] Since the calculation of simulation data depends on the vehicle data in the vehicle model database, as the number of vehicles and the number of models increase, the vehicle data will also increase accordingly. Therefore, in order to avoid the technical problem that the vehicle data in the vehicle model database is not updated in time, resulting in errors or inaccuracies in the simulation of the simulated vehicle, in some embodiments of the present invention, such as Figure 4 As shown, the vehicle automation simulation method also includes:

[0097] S401, acquiring vehicle update data based on a preset update cycle, and determining whether a vehicle model database needs to be updated based on the vehicle update data and vehicle data;

[0098] S402: When the vehicle model database needs to be updated, the vehicle model database is updated based on the vehicle update data.

[0099] The embodiment of the present invention ensures that the vehicle data in the vehicle model database is the latest data by regularly updating the vehicle model database, thereby ensuring the simulation accuracy of the simulated vehicle and expanding the application scope of the database.

[0100] In order to better implement the vehicle automation simulation method in the embodiment of the present invention, based on the vehicle automation simulation method, the embodiment of the present invention also provides a vehicle automation simulation system, such as Figure 5 As shown, the vehicle automation simulation system 500 includes:

[0101] A vehicle model database construction unit 501 is used to obtain vehicle data and construct a vehicle model database based on the vehicle data;

[0102] A road spectrum data acquisition unit 502, used to acquire a target time period and determine road spectrum data based on the target time period;

[0103] A power chain model database construction unit 503 is used to obtain a power chain model of each power component of the vehicle and to construct a power chain model database based on the power chain model;

[0104] The power chain model generating unit 504 is used to obtain the vehicle model of the vehicle to be simulated, select a matching power chain model from the power chain model database based on the vehicle model, and fill the power chain model with data based on the vehicle model database to obtain a target power chain model;

[0105] The vehicle simulation unit 505 is used to simulate the target power chain model based on the road spectrum data to obtain simulation data.

[0106] The vehicle automation simulation system 500 provided in the above embodiment can implement the technical solution described in the above vehicle automation simulation method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above vehicle automation simulation method embodiment, which will not be repeated here.

[0107] like Figure 6 As shown, the present invention also provides a vehicle simulation device 600. The vehicle simulation device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some of the components of the vehicle simulation device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0108] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 602, such as the vehicle automation simulation method of the present invention.

[0109] In some embodiments, the processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 601 may be local or remote. In some embodiments, the processor 601 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0110] In some embodiments, the memory 602 may be an internal storage unit of the vehicle simulation device 600, such as a hard disk or memory of the vehicle simulation device 600. In other embodiments, the memory 602 may also be an external storage device of the vehicle simulation device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the vehicle simulation device 600.

[0111] Furthermore, the memory 602 may include both an internal storage unit of the vehicle simulation device 600 and an external storage device. The memory 602 is used to store application software installed in the vehicle simulation device 600 and various data.

[0112] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information on the vehicle simulation device 600 and to display a visual user interface. The components 601-603 of the vehicle simulation device 600 communicate with each other via a system bus.

[0113] In some embodiments of the present invention, when the processor 601 executes the vehicle automation simulation program in the memory 602, the following steps may be implemented:

[0114] Obtain vehicle data and build a vehicle model database based on the vehicle data;

[0115] Obtain a target time period, and determine road spectrum data based on the target time period;

[0116] Obtaining a power chain model of each power component of the vehicle, and building a power chain model database based on the power chain model;

[0117] Obtaining the vehicle model of the vehicle to be simulated, and selecting a matching power chain model from a power chain model database based on the vehicle model, and filling the power chain model with data based on the vehicle model database to obtain a target power chain model;

[0118] The target power chain model is simulated based on the road spectrum data to obtain simulation data.

[0119] It should be understood that: when the processor 601 executes the autonomous driving operation evaluation program in the memory 602, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the previous related method embodiments.

[0120] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions in the vehicle automation simulation method provided in the above-mentioned method embodiments can be implemented.

[0121] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0122] The above is a detailed introduction to a vehicle automation simulation method, system, vehicle simulation device and storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A vehicle automation simulation method, characterized in that: include: Acquire vehicle data, and build a vehicle model database based on the vehicle data; Acquire a target time period, and determine road spectrum data based on the target time period; Acquire a power chain model of each power component of the vehicle, and construct a power chain model database based on the power chain model; Acquire a vehicle model of a vehicle to be simulated, select a matching power chain model from the power chain model database based on the vehicle model, and fill the power chain model with data based on the vehicle model database to obtain a target power chain model; Simulating the target power chain model based on the road spectrum data to obtain simulation data; The determining of the road spectrum data based on the target time period includes: Determining a target running section of the vehicle to be simulated based on the target time period, and acquiring initial road spectrum data of the target running section based on the vehicle to be simulated; Searching for vehicles on the same route within a target time period according to multiple trajectory points of a target running section, screening vehicles with a coincidence greater than a coincidence threshold within the target time period and the target running section, obtaining data of vehicles with a coincidence greater than the coincidence threshold from the Internet of Vehicles platform, and obtaining at least one similar road spectrum data; Determining whether there is missing data and / or data to be corrected in the initial road spectrum data; When there are missing data and / or data to be corrected in the initial road spectrum data, the initial road spectrum data is optimized based on the at least one similar road spectrum data to obtain the road spectrum data.

2. The vehicle automation simulation method according to claim 1, characterized in that: The vehicle data includes engine-related data, gearbox-related data and additional data, wherein the engine-related data includes engine universal characteristic data, friction work test data at different coolant temperatures, engine accessory specifications and accessory work; The gearbox related data include the efficiency universal data of the gearbox at each gear, the speed ratio at each gear, the no-load loss at each gear and the drive axle efficiency universal data; the additional data include the vehicle model, tonnage, no-load loss from the rear auxiliary box to the wheel side, tire specifications and rear axle speed ratio.

3. The vehicle automation simulation method according to claim 1, characterized in that: The simulation data includes accessory power consumption, engine energy consumption and gear distribution; The vehicle automation simulation method further comprises: The abnormal energy flow direction of the vehicle to be simulated is determined based on the accessory power consumption, the engine energy consumption and the gear distribution.

4. The vehicle automation simulation method according to claim 3, characterized in that: The vehicle automation simulation method further comprises: The accessory power consumption, the engine energy consumption and the gear distribution are visually displayed.

5. The vehicle automation simulation method according to claim 1, characterized in that: The vehicle automation simulation method further comprises: Acquire vehicle update data based on a preset update cycle, and determine whether the vehicle model database needs to be updated based on the vehicle update data and the vehicle data; When the vehicle model database needs to be updated, the vehicle model database is updated based on the vehicle update data.

6. A vehicle automation simulation system, characterized in that: include: A vehicle model database construction unit, used to obtain vehicle data and construct a vehicle model database based on the vehicle data; A road spectrum data acquisition unit, used to acquire a target time period and determine road spectrum data based on the target time period; A power chain model database construction unit, used to obtain a power chain model of each power component of the vehicle, and to construct a power chain model database based on the power chain model; A power chain model generating unit, used for acquiring a vehicle model of a vehicle to be simulated, selecting a matching power chain model from the power chain model database based on the vehicle model, and filling data into the power chain model based on the vehicle model database to obtain a target power chain model; A vehicle simulation unit, used for simulating the target power chain model based on the road spectrum data to obtain simulation data; The determining of the road spectrum data based on the target time period includes: Determining a target running section of the vehicle to be simulated based on the target time period, and acquiring initial road spectrum data of the target running section based on the vehicle to be simulated; Searching for vehicles on the same route within a target time period according to multiple trajectory points of a target running section, screening vehicles with a coincidence greater than a coincidence threshold within the target time period and the target running section, obtaining data of vehicles with a coincidence greater than the coincidence threshold from the Internet of Vehicles platform, and obtaining at least one similar road spectrum data; Determining whether there is missing data and / or data to be corrected in the initial road spectrum data; When there are missing data and / or data to be corrected in the initial road spectrum data, the initial road spectrum data is optimized based on the at least one similar road spectrum data to obtain the road spectrum data.

7. A vehicle simulation device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the vehicle automation simulation method described in any one of claims 1 to 5 above.

8. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the vehicle automation simulation method described in any one of claims 1 to 5.

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