Truck cab wind resistance parameter iteration method, device, equipment and medium

Abstract

The invention discloses a truck cab wind resistance parameter iteration method, device and equipment and a medium. The method comprises the following steps: setting a quantitative parameter combination and generating a variable parameter combination based on an obtained appearance characteristic parameter of a to-be-analyzed heavy truck; obtaining a numerical calculation grid meeting a preset wind resistance coefficient precision requirement based on the quantitative parameter combination and the at least one group of variable parameter combination, and constructing a wind resistance performance test model based on the numerical calculation grid; and carrying out wind resistance parameter iterative calculation on the at least one group of variable parameter combination by using the current wind resistance parameter until a preset iteration condition is met. According to the method, multi-wheel aerodynamic optimization iterative calculation is carried out on key pneumatic components of a cab, an original vehicle model platform does not need to be reconstructed, an existing vehicle model core structure and an assembly system can be directly used, the purpose of low wind resistance is achieved to reduce the energy consumption of the whole vehicle, meanwhile, the universalization rate of parts and the production line adaptability are guaranteed, and the production efficiency is improved. And the research and development cycle and development cost of new vehicle types are effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of vehicle cab technology, and in particular to a method, apparatus, equipment and medium for iterating wind resistance parameters of a truck cab. Background Technology

[0002] With the rapid development of the automotive industry, the market and users have gradually increased their requirements for vehicle performance. As a key indicator of vehicle performance, the drag coefficient has naturally received more and more attention. When a vehicle is traveling at a speed of 80-90 km / h, 30% of its power needs to be used to overcome wind resistance. When the drag coefficient of a vehicle is reduced by about 10%, energy consumption can be reduced by about 2.5%. Therefore, in the vehicle design stage, reducing the drag coefficient has become an important design goal for reducing energy consumption.

[0003] In existing technologies, traditional cabs for cabs of heavy-duty cabs have a drag coefficient exceeding 0.5 due to their near-vertical front structure and large frontal area. The drag performance of traditional cabs is typically not specifically optimized. The design of cabs for heavy-duty cabs often prioritizes structural safety requirements, and intelligent driving algorithms are optimized based on the drag coefficient and wind pressure distribution. To reduce the drag coefficient to between 0.4 and 0.5, a completely new cab needs to be developed, resulting in low standardization and utilization, thus incurring significant R&D costs. Summary of the Invention The purpose of this invention is to address the shortcomings of existing technologies by proposing an iterative method, apparatus, equipment, and medium for wind resistance parameters in a truck cab.

[0004] To achieve the above objectives, in a first aspect, the present invention provides an iterative method for truck cab drag parameters, comprising: Based on the obtained external characteristic parameters of the heavy truck to be analyzed, quantitative parameter combinations are set and variable parameter combinations are generated; A numerical calculation grid that meets the preset drag coefficient accuracy requirements is obtained based on the quantitative parameter combination and the at least one set of variable parameter combinations, and a drag performance test model is constructed based on the numerical calculation grid. The drag parameters are iteratively calculated on the at least one set of variable parameter combinations using the current drag parameters until the preset iteration conditions are met, and the variable parameter combination value corresponding to the current drag performance measurement sample is taken as the vehicle drag coefficient of the heavy truck.

[0005] In some embodiments, before performing the step of setting quantitative parameter combinations and generating variable parameter combinations based on the obtained external feature parameter model of the heavy truck to be analyzed, the method further includes: Obtain the engineering data model of the heavy truck and limit the range of values ​​for the external feature parameters; The external shape parameters are parameterized to obtain the external shape parameters of the heavy truck to be analyzed.

[0006] In some embodiments, the step of setting a set of quantitative parameter combinations and generating at least one set of variable parameter combinations based on the acquired external characteristic parameters of the heavy truck to be analyzed includes: Based on the obtained external feature parameter model of the heavy truck to be analyzed, a set of quantitative parameter combinations is set, and at least one set of variable parameter combinations is generated based on the obtained external feature parameter model of the heavy truck to be analyzed.

[0007] In some embodiments, the quantitative parameter combination includes the original vehicle platform variable parameter combination, which includes the front windshield variable parameter combination, the side door variable parameter combination, and the rear roof variable parameter combination.

[0008] In some embodiments, the original vehicle platform variable parameter combination includes vehicle engineering parameters corresponding to the combination of front door, windshield, tires, side skirts, body-in-white and cargo box; the front windshield variable parameter combination is the vehicle engineering parameters corresponding to the combination of front grille and bumper; the side door variable parameter combination is the vehicle engineering parameters corresponding to the combination of door trim, step plate, wheel cover and mudguard; and the rear roof variable parameter combination is the vehicle engineering parameters corresponding to the fairing combination.

[0009] In some embodiments, obtaining a numerical calculation grid that meets the preset drag coefficient accuracy requirements based on the quantitative parameter combination and the at least one set of variable parameter combinations, and constructing a drag performance test model based on the numerical calculation grid, includes: Based on the quantitative parameter combination and the at least one set of variable parameter combinations, a main constraint coordinate system is constructed, and the coordinate values ​​of the cab structure nodes and the node connection vector values ​​are determined. Based on the quantitative parameter combination and the at least one set of variable parameter combinations, constraint variables and constraint conditions are set, and a numerical calculation grid that meets the preset wind resistance coefficient accuracy requirements is obtained based on the main constraint coordinate system, constraint variables and constraint conditions. A wind resistance performance test model is constructed based on the numerical calculation grid, and aerodynamic optimization design characterization parameters are generated based on the combination of variable parameters.

[0010] In some embodiments, the drag parameters of the at least one set of variable parameter combinations are iteratively calculated using the current drag parameters until a preset iteration condition is met, and the variable parameter combination value corresponding to the current drag performance measurement sample is used as the vehicle drag coefficient of the heavy truck, including: Input the test values ​​of the aerodynamic optimization design characterization parameters, and perform iterative calculation of wind resistance parameters on one or more sets of the variable parameter combinations based on the preset gradient step size until the preset iteration conditions are met. Read the variable parameter combination values ​​corresponding to the current wind resistance performance measurement sample, and output them as the reference value of the vehicle wind resistance coefficient of the heavy truck.

[0011] In a second aspect, the present invention also provides a truck cab drag parameter iteration device for running the iteration method as described in the first aspect, the iteration device comprising: The parameter determination module is used to set quantitative parameter combinations and generate variable parameter combinations based on the obtained external feature parameters of the heavy truck to be analyzed; The parameter acquisition module is used to obtain a numerical calculation grid that meets the preset drag coefficient accuracy requirements based on the quantitative parameter combination and the at least one set of variable parameter combinations, and to construct a drag performance test model based on the numerical calculation grid. The parameter calculation module is used to perform iterative calculation of the wind resistance parameters on the at least one set of variable parameter combinations with the current wind resistance parameters until the preset iteration conditions are met, and to use the value of the variable parameter combination corresponding to the current wind resistance performance measurement sample as the vehicle wind resistance coefficient of the heavy truck.

[0012] Thirdly, the present invention also provides an electronic device, comprising: One or more processors, memory; and One or more computer programs stored in the memory and configured to be executed by the processor, wherein the one or more computer programs are stored in the memory, the memory is coupled to the processor, and when the processor executes the computer programs, it implements the steps in the iterative method as described in any of the above technical solutions.

[0013] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program for running an iterative method, wherein the computer program causes a computer to perform the iterative method as described in any of the above technical solutions.

[0014] The present invention has the following beneficial effects: This invention targets key aerodynamic components such as the front fascia, side panels, roof fairing, side deflectors, and chassis skirts. It employs multiple rounds of aerodynamic optimization calculations focusing on the installation location, shape, and curvature of these components. This achieves refined aerodynamic optimization of the vehicle's exterior while effectively reducing the cab's drag coefficient. This optimization approach does not require restructuring the existing vehicle platform; it can directly utilize the core structure and assembly system of the existing vehicle's body-in-white, doors, and various accessories. While achieving low drag to reduce overall vehicle energy consumption, it also ensures the commonality of parts and production line compatibility, effectively reducing the development cycle and costs of new models. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the iterative method for truck cab drag parameters proposed in this invention. Figure 1 ; Figure 2 This is a flowchart illustrating the iterative method for truck cab drag parameters proposed in this invention. Figure 2 ; Figure 3 This is a flowchart illustrating the iterative method for truck cab drag parameters proposed in this invention. Figure 3 ; Figure 4 This is a flowchart illustrating the iterative method for truck cab drag parameters proposed in this invention. Figure 4 ; Figure 5 This is a flowchart illustrating the iterative method for truck cab drag parameters proposed in this invention. Figure 5 ; Figure 6 This is a schematic diagram of the truck cab wind resistance parameter iteration device proposed in this invention; Figure 7 This is a schematic diagram of the structure of the truck cab proposed in this invention. Figure 1 ; Figure 8 This is a schematic diagram of the structure of the truck cab proposed in this invention. Figure 2 ; Figure 9 This is a simulation diagram of the truck cab proposed in this invention. Figure 1 ; Figure 10 This is a schematic diagram of the structure of the truck cab proposed in this invention. Figure 3 ; Figure 11 This is a simulation diagram of the truck cab proposed in this invention. Figure 2 ; Figure 12 This is a schematic diagram of the structure of the truck cab proposed in this invention. Figure 4 ; Figure 13 This is a schematic diagram of the structure of the truck cab proposed in this invention. Figure 5 ; Figure 14 This is a simulation diagram of the truck cab proposed in this invention. Figure 3 ; Figure 15 This is a graph showing the speed-energy consumption curve after the change in the drag coefficient of the truck cab in this invention.

[0016] Legend: 1. Windshield; 2. Body-in-white; 3. Front door; 4. Front grille; 5. Bumper; 6. Steps; 7. Door trim panels; 8. Wheel covers; 9. Mudguards; 10. Fairing; 11. Tires; 12. Side skirts; 13. Cargo box. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Understandably, the formula for calculating the drag coefficient is: ; in, This is the drag coefficient. For frontal air resistance, Let v be the projected area of ​​the vehicle's front, and v be the vehicle speed. air density; When the vehicle speed and air density are constant, air resistance depends on the drag coefficient and the vehicle's frontal projected area. The smaller the drag coefficient, the smaller the air resistance. The magnitude of the drag coefficient depends on the vehicle's frontal projected area, while the vehicle's projected area depends on the vehicle's shape.

[0019] This application provides a method, apparatus, device, and medium for iterative calculation of wind resistance parameters in truck cabs. It addresses the problem that the wind resistance performance of traditional cab-over heavy-duty trucks in the prior art is often not specifically optimized. When designing cabs for cabs of cab-over heavy-duty trucks, the design is often based on the safety requirements of structural components, and intelligent driving algorithms are optimized according to the drag coefficient and wind pressure distribution. To reduce the drag coefficient to between 0.4 and 0.5, a completely new cab needs to be developed, resulting in low standardization and utilization, thus incurring huge R&D costs. This application, however, performs multi-round aerodynamic optimization and iterative calculations on key aerodynamic components of the cab. It does not require reconstructing the original vehicle platform and can directly utilize the core structure and assembly system of existing models. While achieving the goal of low wind resistance to reduce overall vehicle energy consumption, it ensures the standardization rate of parts and the adaptability of production lines, effectively reducing the R&D cycle and development costs of new models.

[0020] Please refer to the following examples for details: Reference Figures 1-5 An embodiment of the iterative method for truck cab drag parameters provided by the present invention includes the following steps: S100: Based on the obtained external characteristic parameters of the heavy truck to be analyzed, set quantitative parameter combinations and generate variable parameter combinations; S200, based on a combination of quantitative parameters and at least one set of variable parameters, obtains a numerical calculation grid that meets the preset accuracy requirements of the drag coefficient, and constructs a drag performance test model based on the numerical calculation grid; S300 performs iterative calculations of at least one set of variable parameter combinations using the current wind resistance parameters until the preset iteration conditions are met, and uses the variable parameter combination value corresponding to the current wind resistance performance measurement sample as the vehicle wind resistance coefficient of the heavy truck.

[0021] It should be noted that, prior to performing step S100, the following steps are also included: S000: Obtain the engineering data model of the heavy truck and limit the range of values ​​for the external feature parameters; The shape feature parameters are parameterized to obtain the shape feature parameters of the heavy truck to be analyzed.

[0022] For example, firstly, a complete engineering data model of the heavy truck to be analyzed is obtained. This model must cover the three-dimensional design model of the whole vehicle, that is, include the structural dimensions, component assembly relationships, material properties, etc. of the cab, cargo box 13, and key chassis components, to ensure the consistency between the model and the actual vehicle structure. Subsequently, based on the design constraints, manufacturing process boundaries, and aerodynamic optimization objectives of heavy-duty trucks, the range of values ​​for the exterior shape characteristic parameters of the cab was strictly limited; for example, the range of the front fascia tilt angle parameter was limited to 85° to 88° to avoid insufficient cab space due to excessively small angles, and the height adjustment range of the fairing 10 was limited to ±50mm from the top of the cargo box 13 to ensure the continuity of airflow transition, etc. Next, the external shape features of the cab are decomposed and parameterized, that is, the unstructured geometric shape is transformed into quantifiable and adjustable design parameters. Specifically, for key aerodynamic areas such as the front, side, rear, and roof fairing 10 of the cab, the core geometric parameters that affect the flow field distribution are extracted, such as length, angle, radius of curvature, and installation position coordinates, forming a standardized set of external shape feature parameters to be analyzed.

[0023] Please continue reading. Figure 3 In this embodiment, step S100 includes: S110, set a set of quantitative parameter combinations based on the obtained shape feature parameter model of the heavy truck to be analyzed, and generate at least one set of variable parameter combinations based on the obtained shape feature parameter model of the heavy truck to be analyzed.

[0024] For example, the selection of quantitative parameter combinations is based on the principle of using the core components of the original vehicle platform and ensuring the commonality rate. Key component parameters that have a small impact on wind resistance and involve the stability of the overall vehicle structure and assembly compatibility are selected. Specifically, the original vehicle platform variable parameter combinations cover all vehicle engineering parameters corresponding to the combination of front door 3, front windshield 1, tire 11, side skirt 12, body-in-white 2 and cargo box 13.

[0025] In some embodiments, the parameters of the body-in-white 2 are structural parameters such as the spacing between the longitudinal beams of the frame, the coordinates of the cab mounting support point, and the body outline dimensions; the parameters of the front door 3 and the windshield 1 are the door opening angle range, the window glass thickness, the windshield 1 mounting reference tilt angle, and the assembly gap between the door and the side panel; the parameters of the tire 11 and the side skirt 12 are the tire 11 specifications, the coordinates of the side skirt 12 mounting fixing point, and the basic outline dimensions of the side skirt 12; the parameters of the cargo box 13 are the cargo box 13 length, width reference value, and distance reference value between the cargo box 13 and the cab; the purpose of fixing the above parameters is to ensure that the optimized cab can be directly adapted to the assembly process of the original vehicle production line without modifying core components such as the frame, door hinges, and cargo box 13 connection structure, thereby maximizing the commonality rate of parts.

[0026] For example, the selection of variable parameter combinations is based on the principle of aerodynamic sensitivity and large optimization space. The main optimization is on key exterior components in the cab that directly affect airflow separation and vortex generation. Specifically, there are three categories of variable parameter combinations: front windshield, side door, and rear roof. Each combination corresponds to specific optimization components and engineering parameters. In some embodiments, the variable parameter combination for the front windshield is the vehicle engineering parameters corresponding to the combination of the front cover 4 and the bumper 5, including the length, surface radius of curvature, and upper and lower edge tilt angles of the front cover 4, the forward extension, upper and lower section tilt angles of the bumper 5, and the radius of the corner radius connecting the bumper 5 and the front cover 4; the variable parameter combination for the side doors is the vehicle engineering parameters corresponding to the combination of the door trim panel 7, step plate 6, wheel cover 8, and mudguard 9, including the fitting curvature of the door trim panel 7 and the side door, the protruding length and tilt angle of the step plate 6, the outward expansion radius of the wheel cover 8, and the mudguard 9. The installation height and tilt angle of 9, as well as the tilt angle of the side of the vehicle body structure composed of door panel 7, step plate 6, and wheel cover 8 relative to the X-axis axis, and the radius of the rounded corner of the combined structure and the front tangent of the bumper 5, etc.; the variable parameter combination of the rear roof is the vehicle engineering parameter corresponding to the fairing 10 combination, including the transition line length between the side of fairing 10 and body-in-white 2, the step difference between the top of fairing 10 and cargo box 13, the radius of the rounded corner of the side of fairing 10, the horizontal distance between fairing 10 and cargo box 13, and the step difference distance between fairing 10 and cargo box 13 in the Y-axis direction, etc.

[0027] Please continue reading. Figure 4 In this embodiment, step S200 includes: S210, construct the main constraint coordinate system based on the combination of quantitative parameters and at least one set of variable parameters, and determine the coordinate values ​​of the cab structure nodes and the vector values ​​of the node connection vectors; S220 sets constraint variables and constraint conditions based on quantitative parameter combinations and at least one set of variable parameter combinations, and obtains a numerical calculation grid that meets the preset wind resistance coefficient accuracy requirements based on the main constraint coordinate system, constraint variables and constraint conditions. S230 uses a numerical computation grid to build a wind resistance performance test model and generates aerodynamic optimization design characterization parameters based on the combination of variable parameters.

[0028] For example, when constructing the main constraint coordinate system based on the original vehicle platform structure benchmark and the optimization range of the variable parameter combination corresponding to the quantitative parameter combination, the coordinate system takes the vehicle center of gravity as the origin, the X-axis along the vehicle driving direction, the Y-axis along the vehicle vertical direction, and the Z-axis along the vehicle lateral direction; then, combined with the assembly relationship and geometric constraints of each component of the cab, the coordinate values ​​of the key structural nodes of the cab and the vector values ​​of the lines connecting the nodes are determined to ensure the geometric accuracy of the model structure. Subsequently, constraint variables and constraint conditions are set. Constraint variables include the value range of each variable parameter combination, assembly clearance constraints between components, and geometric interference constraints. Constraint conditions include aerodynamic performance constraints and mesh quality constraints. Based on the above main constraint coordinate system, constraint variables, and constraint conditions, the surface of the cab and the flow field region are meshed to form a numerical calculation mesh that meets the preset drag coefficient accuracy requirements. For example, a denser mesh can be used for aerodynamically sensitive areas such as the front bulkhead, fairing 10, and rearview mirrors, while a gradient mesh can be used for flow field regions far from the vehicle body to balance calculation accuracy and calculation efficiency. Next, the generated numerical calculation mesh is imported into computational fluid dynamics (CFD) simulation software, and boundary conditions, turbulence models and solution parameters are set to construct a complete drag performance test model. At the same time, based on the core optimization direction of variable parameter combination, aerodynamic optimization design characterization parameters are generated. This parameter set includes drag coefficient, vehicle frontal projection area, vehicle surface pressure distribution, flow field velocity vector, boundary layer separation point location, eddy intensity, etc., providing a quantitative basis for evaluating the results of subsequent iterative calculations.

[0029] Please continue reading. Figure 5 In this embodiment, step S300 includes: S310: Input the test values ​​of aerodynamic optimization design characterization parameters, and perform iterative calculation of wind resistance parameters on one or more sets of variable parameters based on the preset gradient step size until the preset iteration conditions are met. S320 reads the variable parameter combination values ​​corresponding to the current wind resistance performance calculation sample and outputs them as reference values ​​for the vehicle wind resistance coefficient of heavy trucks.

[0030] For example, the initial test values ​​of the generated aerodynamic optimization design characterization parameters are input into the iterative calculation system, and a preset gradient step size is set. This step size is determined based on a combination of optimization accuracy requirements and computational efficiency. The drag parameters are iteratively calculated for one or more sets of variable parameter combinations. During the iteration process, the values ​​of one or more variable parameters are adjusted each time, and the corresponding aerodynamic characterization parameters are calculated through the drag performance test model. The difference between the current calculation result and the previous result is compared. The calculation stops when the preset iteration conditions are met. The preset iteration conditions include: drag coefficient convergence (e.g., the difference in drag coefficient ≤ 0.005 after 3 consecutive iterations), reaching the preset upper limit of the number of iterations (e.g., 50 times), and the drag coefficient decreasing to the target range (e.g., 0.40 to 0.45). Once the iterative calculation stops, the variable parameter combination value corresponding to the current wind resistance performance measurement sample is read and used as the reference value of the vehicle's wind resistance coefficient. At the same time, the corresponding auxiliary analysis data such as the pressure distribution on the vehicle body surface and the flow field optimization effect are output to provide direct data support for the physical design and prototyping of the cab.

[0031] For example, see Figures 7-15 When setting variables and invariants, the front door 3, windshield 1, tires 11, side skirts 12, body-in-white 2, and cargo box 13 are set as invariants, while the front grille 4, bumper 5, door trim 7, step 6, wheel covers 8, mudguards 9, and fairing 10 are set as variables. Before optimization, the drag coefficient of the base model is 0.55. Figure 7 As shown.

[0032] In some embodiments, the length of the front hood 4 in the front windshield variable parameter combination (in...) Figure 8 The parameter a) is the distance between the front extension of the bumper 5 (in the middle). Figure 8 In the case of parameter b), an equal step-size combination iterative strategy is adopted: the adjustment method of parameter a is based on the initial length, and each step is increased by 10mm as a gradient step; the adjustment method of parameter b is based on the initial forward extension, and each step is increased by 10mm as a gradient step; through orthogonal experimental design, the wind resistance is calculated for all combinations of parameters a and b, and the correspondence between the vehicle's projected area and the wind resistance coefficient under each combination is analyzed. Increasing the length of the front grille 4 allows for a more streamlined transition at the front of the cab, reducing airflow separation at the junction of the front grille 4 and the windshield; proper adjustment of the bumper 5's forward extension optimizes the airflow angle at the front, reducing the frontal impact of airflow on the bumper 5; through multiple iterative calculations, the optimal combination of parameters a and b is finally determined, at which point the vehicle's projected area is optimized to a reasonable range, and the drag coefficient is reduced to 0.422. Figure 9 As shown.

[0033] In some embodiments, the tilt angle between the side of the vehicle body structure composed of the door trim panel 7, step plate 6, and wheel cover 8 in the side door variable parameter combination and the X-axis axis (in...) Figure 10 The body structure, consisting of the door trim panel 7, step panel 6, and wheel cover 8, and the rounded corners of the front tangent of the bumper 5 (where α is the parameter), is similar to that of the bumper 5. Figure 10 The parameter R is used, and an equal-step coupling iteration strategy is adopted: the parameter α is adjusted based on the initial angle, and each step is increased by 5° as a gradient step; the parameter R is adjusted based on the initial radius, and each step is increased by 50mm as a gradient step; wind resistance is calculated for all combinations of parameters α and R, and the boundary layer separation and eddy intensity changes in the side flow field are analyzed in detail. Increasing parameter α causes the side structure composed of door panel 7, step plate 6, and wheel cover 8 to form a wedge-shaped streamline, reducing frictional and induced drag as airflow flows along the side. Increasing parameter R optimizes the airflow transition between the side and the front of the bumper 5, avoiding boundary layer separation caused by sharp edges. Through iterative calculations, the optimal combination of parameters α and R is determined, resulting in a more stable side flow field, a significantly reduced vortex region, and a further reduction in drag coefficient to 0.411. Figure 11 As shown.

[0034] In some embodiments, the length of the transition line between the side of the fairing 10 and the body-in-white 2 in the rear roof variable parameter combination (in...) Figure 12 (Parameter c) The difference between the top of the fairing 10 and the cargo box 13 (in) Figure 13 (Parameter e) and the rounded corners of the fairing 10 side (in) Figure 12 (Parameter R1 is in the middle) and the distance between the fairing 10 and the cargo box 13 (in the middle) Figure 12 (where L is the parameter) and the step distance between the fairing 10 and the cargo box 13 in the Y-axis direction (in Figure 12 (where d is the parameter) adopts a multivariate stepwise iterative strategy: the adjustment method is based on the initial value of each parameter, and each step is increased by 50mm; the stepwise optimization method is adopted, first fixing other parameters, optimizing the optimal value of each individual parameter one by one, and then combining and verifying and fine-tuning the optimal values ​​of all parameters. The core function of the fairing 10 is to connect the airflow between the cab and the cargo box 13. Optimization of parameter c ensures continuous airflow along the surface of the fairing 10. Optimization of parameters e and d eliminates the airflow gap between the cab and the cargo box 13. Optimization of parameter R1 reduces vortex generation at the edge of the fairing 10. Optimization of parameter L controls the airflow transition distance between the fairing 10 and the cargo box 13. Through multi-dimensional combination iterations, one or more optimal combinations are finally determined, allowing the airflow to smoothly transition from the top of the cab to the top of the cargo box 13. The negative pressure drag resistance is significantly reduced, and the drag coefficient is ultimately reduced to 0.402. Figure 14 As shown; Through the above technical solution, the drag coefficient of the basic vehicle model in this application is 0.569, which is reduced to 0.402 after optimization, a reduction of 0.167, successfully entering the target range of 0.4 to 0.5; Real-world testing shows that at a speed of 60 km / h, the optimized model consumes 7.68 kWh less energy per 100 km than the base model; at a speed of 80 km / h, the energy consumption per 100 km is reduced by 13.52 kWh, effectively improving the vehicle's range. During the optimization process, the core components such as the body-in-white 2, doors, and cargo box 13 of the original vehicle platform are fully utilized, and the parts commonality rate reaches more than 80%. There is no need to reconstruct the vehicle platform, develop new molds, or adjust the production line, which greatly reduces the R&D cycle and development costs. Moreover, the optimized parameters can directly guide the design and trial production of physical parts without the need for a large number of additional physical tests, thus reducing R&D risks.

[0035] Understandably, this application focuses on key aerodynamic components such as the front of the cab, side panels, roof fairing 10, side deflectors, and chassis skirts. It conducts multiple rounds of aerodynamic optimization and iteration calculations on their installation positions, shapes, and curvatures to achieve refined aerodynamic optimization of the vehicle's exterior styling while effectively reducing the cab's drag coefficient. This optimization approach does not require restructuring the original vehicle platform and can directly utilize the core structure and assembly system of the existing vehicle's body-in-white 2, doors, and various accessories. The standardization rate can reach over 80%. While achieving the goal of low drag to reduce overall vehicle energy consumption, it also ensures the standardization rate of parts and the adaptability of the production line, effectively reducing the R&D cycle and development costs of new models.

[0036] Reference Figure 6 The present invention also provides an embodiment of a truck cab drag parameter iteration device for running the truck cab drag parameter iteration method in the above embodiment. The iteration device includes: The parameter determination module is used to set quantitative parameter combinations and generate variable parameter combinations based on the obtained external feature parameters of the heavy truck to be analyzed; The parameter acquisition module is used to obtain a numerical calculation grid that meets the preset drag coefficient accuracy requirements based on a combination of quantitative parameters and at least one set of variable parameters, and to build a drag performance test model based on the numerical calculation grid. The parameter calculation module is used to iteratively calculate the drag parameters of at least one set of variable parameter combinations with the current drag parameters until the preset iteration conditions are met, and the variable parameter combination value corresponding to the current drag performance measurement sample is used as the vehicle drag coefficient of the heavy truck.

[0037] The present invention also provides an embodiment of an electronic device, comprising: One or more processors; memory; and One or more programs, wherein the one or more computer programs are stored in memory and configured to be executed by one or more processors, wherein the computer programs are used to perform the following steps: Based on the obtained external characteristic parameters of the heavy truck to be analyzed, quantitative parameter combinations are set and variable parameter combinations are generated; A numerical calculation grid that meets the preset drag coefficient accuracy requirements is obtained based on the quantitative parameter combination and the at least one set of variable parameter combinations, and a drag performance test model is constructed based on the numerical calculation grid. The drag parameters are iteratively calculated on the at least one set of variable parameter combinations using the current drag parameters until the preset iteration conditions are met, and the variable parameter combination value corresponding to the current drag performance measurement sample is taken as the vehicle drag coefficient of the heavy truck.

[0038] For example, the memory is used to store computer programs. The memory is non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, portable hard drive, read-only memory, disk or optical disc, etc.

[0039] The aforementioned memory is internal memory, used to store executable program code, including instructions. Internal memory may include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc. The data storage area may store data created during the use of the electronic device. The processor executes various functional applications and data processing of the electronic device by running instructions stored in the internal memory and / or instructions stored in memory located within the processor.

[0040] The processor executes the computer program stored in the memory to implement the vehicle system operation protection method in the above embodiments. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0041] Optionally, the memory can be either standalone or integrated with the processor. The processor may include one or more processing units, such as an application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). Different processing units can be independent devices or integrated into one or more processors. The controller can generate operation control signals based on the instruction opcode and timing signals to control instruction fetching and execution.

[0042] When memory is a device independent of the processor, the electronic device may also include a bus. This bus is used to connect the memory and the processor. The bus includes hardware, software, or both, that couples components of an online data flow metering device together. The bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0043] It also includes a computer-readable storage medium storing a computer program for running the vehicle system operation protection method, wherein the computer program causes the computer to perform the following steps: Based on the obtained external characteristic parameters of the heavy truck to be analyzed, quantitative parameter combinations are set and variable parameter combinations are generated; A numerical calculation grid that meets the preset drag coefficient accuracy requirements is obtained based on the quantitative parameter combination and the at least one set of variable parameter combinations, and a drag performance test model is constructed based on the numerical calculation grid. The drag parameters are iteratively calculated on the at least one set of variable parameter combinations using the current drag parameters until the preset iteration conditions are met, and the variable parameter combination value corresponding to the current drag performance measurement sample is taken as the vehicle drag coefficient of the heavy truck.

[0044] The computer-readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of a computer program from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the ASIC can reside in a user equipment. Of course, the processor and the computer-readable storage medium can also exist as discrete components in a communication device.

[0045] Specifically, the computer-readable storage medium can be any type of non-volatile storage device, such as electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic random access memory (MRAM), phase-change memory (PCM), resistive random access memory (RRAM), flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0046] It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain portions of the embodiments. In this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An iterative method for truck cab drag parameters, characterized in that, Includes the following steps: Based on the obtained external characteristic parameters of the heavy truck to be analyzed, quantitative parameter combinations are set and variable parameter combinations are generated; A numerical calculation grid that meets the preset drag coefficient accuracy requirements is obtained based on the quantitative parameter combination and the at least one set of variable parameter combinations, and a drag performance test model is constructed based on the numerical calculation grid. The drag parameters are iteratively calculated on the at least one set of variable parameter combinations using the current drag parameters until the preset iteration conditions are met, and the variable parameter combination value corresponding to the current drag performance measurement sample is taken as the vehicle drag coefficient of the heavy truck.

2. The iterative method according to claim 1, characterized in that, Before performing the step of setting quantitative parameter combinations and generating variable parameter combinations based on the obtained external feature parameter model of the heavy truck to be analyzed, the method further includes: Obtain the engineering data model of the heavy truck and limit the range of values ​​for the external feature parameters; The external shape parameters are parameterized to obtain the external shape parameters of the heavy truck to be analyzed.

3. The iterative method according to claim 1, characterized in that, The process involves setting a set of quantitative parameter combinations and generating at least one set of variable parameter combinations based on the acquired external characteristic parameters of the heavy-duty truck to be analyzed, including: Based on the obtained external feature parameter model of the heavy truck to be analyzed, a set of quantitative parameter combinations is set, and at least one set of variable parameter combinations is generated based on the obtained external feature parameter model of the heavy truck to be analyzed.

4. The iterative method according to claim 3, characterized in that, The quantitative parameter combination includes the original vehicle platform variable parameter combination, which includes the front windshield variable parameter combination, the side door variable parameter combination, and the rear roof variable parameter combination.

5. The iterative method according to claim 4, characterized in that, The original vehicle platform variable parameter combination includes the vehicle engineering parameters corresponding to the front door, windshield, tires, side skirts, body-in-white, and cargo box combination. The front windshield variable parameter combination is the vehicle engineering parameters corresponding to the front grille and bumper combination. The side door variable parameter combination is the vehicle engineering parameters corresponding to the door trim panel, step plate, wheel cover, and mudguard combination. The rear roof variable parameter combination is the vehicle engineering parameters corresponding to the fairing combination.

6. The iterative method according to claim 1, characterized in that, The process of obtaining a numerical calculation grid that meets the preset drag coefficient accuracy requirements based on the quantitative parameter combination and the at least one set of variable parameter combinations, and constructing a drag performance test model based on the numerical calculation grid, includes: Based on the quantitative parameter combination and the at least one set of variable parameter combinations, a main constraint coordinate system is constructed, and the coordinate values ​​of the cab structure nodes and the node connection vector values ​​are determined. Based on the quantitative parameter combination and the at least one set of variable parameter combinations, constraint variables and constraint conditions are set, and a numerical calculation grid that meets the preset wind resistance coefficient accuracy requirements is obtained based on the main constraint coordinate system, constraint variables and constraint conditions. A wind resistance performance test model is constructed based on the numerical calculation grid, and aerodynamic optimization design characterization parameters are generated based on the combination of variable parameters.

7. The iterative method according to claim 1, characterized in that, The drag parameters are iteratively calculated using the current drag parameters for at least one set of variable parameter combinations until a preset iteration condition is met. The variable parameter combination value corresponding to the current drag performance measurement sample is taken as the vehicle drag coefficient of the heavy truck, including: Input the test values ​​of the aerodynamic optimization design characterization parameters, and perform iterative calculation of the drag parameters for one or more sets of the variable parameters based on the preset gradient step size until the preset iteration conditions are met. Read the variable parameter combination values ​​corresponding to the current wind resistance performance measurement sample, and output them as the reference value of the vehicle wind resistance coefficient of the heavy truck.

8. A device for iterating the wind resistance parameters of a truck cab, characterized in that, The iterative apparatus is used to run the iterative method as described in any one of claims 1 to 7, the iterative apparatus comprising: The parameter determination module is used to set quantitative parameter combinations and generate variable parameter combinations based on the obtained external feature parameters of the heavy truck to be analyzed; The parameter acquisition module is used to obtain a numerical calculation grid that meets the preset drag coefficient accuracy requirements based on the quantitative parameter combination and the at least one set of variable parameter combinations, and to construct a drag performance test model based on the numerical calculation grid. The parameter calculation module is used to perform iterative calculation of the wind resistance parameters on the at least one set of variable parameter combinations with the current wind resistance parameters until the preset iteration conditions are met, and to use the value of the variable parameter combination corresponding to the current wind resistance performance measurement sample as the vehicle wind resistance coefficient of the heavy truck.

9. An electronic device, characterized in that, include: One or more processors or memories; as well as One or more computer programs stored in the memory and configured to be executed by the processor, wherein the one or more computer programs are stored in the memory, the memory is coupled to the processor, and when the processor executes the computer programs, it implements the steps of the iterative method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program for running an iterative method, wherein the computer program causes a computer to perform the iterative method as described in any one of claims 1-7.

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