Calculation method for fuel consumption saving rate of vehicle platoon based on topological structure and dynamic characteristics

Abstract

This invention discloses a method for calculating the fuel consumption saving rate of a vehicle platoon based on topology and dynamic characteristics. The method includes establishing a vehicle dynamics model, an external flow field model, and a mathematical analysis model of the fuel consumption saving rate of the vehicle platoon. The proposed method conducts simulation experiments for different parameters such as longitudinal vehicle spacing, lateral vehicle offset distance, and vehicle speed. By analyzing the influence of longitudinal vehicle spacing, lateral vehicle offset distance, and vehicle speed on the vehicle's aerodynamic drag coefficient, the functional relationship between the vehicle platoon's aerodynamic drag coefficient and the above parameters is determined. Furthermore, the functional relationship between the aerodynamic drag coefficient and the fuel consumption saving rate is determined to calculate the vehicle platoon's fuel consumption saving rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automotive electronics, in particular to a vehicle queue fuel consumption saving rate calculation method based on topological structure and dynamic characteristics. BACKGROUND

[0002] With the development of intelligent transportation systems, vehicle platoon strategies have developed, that is, a group of vehicles follow each other in a dense and coordinated manner, mainly to increase road traffic and reduce overall energy consumption of the vehicle fleet.

[0003] Current research mainly focuses on the influence of different vehicle body streamline on vehicle aerodynamic characteristics, or discusses the influence factors such as the number of vehicles, longitudinal vehicle spacing and vehicle speed when the vehicle fleet is longitudinally distributed, and then calculates the fuel saving rate of the vehicle fleet. However, in order to consider the influence of vehicle fleet driving on road maintenance cost and service life, the vehicle fleet can have a certain lateral offset distance when arranged.

[0004] Vehicle marshalling helps to improve traffic flow and ensure traffic safety, and has a positive impact on the aerodynamic resistance and fuel economy of vehicles. With the development of vehicle network technology, it has become possible to standardize vehicle marshalling. Studying the energy-saving effect of vehicle platooning can provide a theoretical basis for the construction of vehicle platoon control strategies, and also provide a reference for balancing energy saving and reducing road maintenance costs and extending road service life. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art, and to provide a vehicle queue fuel consumption calculation method based on topological structure and dynamic characteristics, which can solve the problem of calculating the fuel saving rate of the vehicle queue when there is a lateral offset distance between the front and rear vehicles. The influence of topological structure and dynamic characteristics on the aerodynamic characteristics of small car platooning is analyzed, including the change of air resistance of single vehicle and vehicle queue.

[0006] Technical scheme: The vehicle queue fuel consumption saving rate calculation method based on topological structure and dynamic characteristics of the present application comprises the following steps:

[0007] Step (1), single vehicle CFD simulation experiment;

[0008] Step (1.1), establish a single Mira stepback three-dimensional model and a corresponding external flow field model, pretreat the single Mira stepback model (mainly used to ignore the small features of the vehicle body, such as rearview mirror, window, etc., so as to avoid low-quality grids caused by small features in grid division, resulting in complicated calculation and low precision), perform grid division and grid quality optimization processing to obtain a single vehicle grid model;

[0009] Step (1.2), the vehicle grid model obtained in step (1.1) is imported into a CFD solver, and relevant settings are made to obtain the aerodynamic drag coefficient of the single Mira model CFD simulation experiment, including boundary condition setting, turbulence model selection, Reynolds number setting, initial condition setting, etc.

[0010] Step (1.3), the aerodynamic drag coefficient of the single Mira model CFD simulation experiment obtained in step (1.2) is compared with the existing Mira model wind tunnel experiment data. If the comparison data shows inconsistency, then return to step (1.1) to re-establish the outflow field model, and then enter step (1.2) to adjust the relevant settings and re-calculate the aerodynamic drag coefficient of the single vehicle until the two drag coefficients are close. The accuracy of the simulation experiment data is verified by a single vehicle.

[0011] Step (2), establish a vehicle queue outflow field model and a fluid dynamics model;

[0012] Step (2.1), select at least three Mira models to arrange the queue distribution to obtain different topological structure forms of vehicle formation models, and then construct a multi-vehicle Mira queue model and a corresponding outflow field model. The vehicle formation model is subjected to grid division and grid quality optimization processing to obtain a multi-vehicle grid model.

[0013] Step (2.2), the vehicle grid model of step (2.1) is imported into a CFD solver software. First, make relevant settings, including boundary condition setting, turbulence model selection, Reynolds number setting, initial condition setting, etc.

[0014] Then, set different lateral offset distances in the vehicle queue, and sequentially change the longitudinal vehicle spacing and vehicle speed to obtain the drag coefficient of each vehicle in the vehicle queue and the average drag coefficient of the vehicle queue.

[0015] Finally, the obtained data is processed and analyzed to obtain the influence law of different lateral offset distances, longitudinal vehicle spacing and vehicle speed on the aerodynamic drag coefficient of the vehicle queue, and to determine the fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, vehicle speed and lateral offset distance.

[0016] Step (3), according to the fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, vehicle speed and lateral offset distance obtained in step (2), the function relationship between the vehicle queue aerodynamic drag coefficient and the fuel consumption saving rate is determined by using the fuel consumption saving rate mathematical analysis model of the vehicle queue driving.

[0017] Further, in step (1), when the vehicle grid model obtained in step (1.1) is imported into a CFD solver for CFD simulation test, a k-ω model is used as the turbulence model to ensure that the flow field velocity value is consistent with the vehicle speed.

[0018] The complete vehicle is verified by CFD simulation test.

[0019] Further, the specific content of the step (2) of constructing the multi-vehicle Mira queue model and the corresponding outflow field model is:

[0020] First, at least three Mira models are selected for queue distribution arrangement, and the length, width and height of each Mira model are 4165mm, 1625mm and 1420mm respectively;

[0021] Then, the vehicle queue outflow field model is established according to the queue distribution, that is, the size of the outflow field is reasonably selected according to the requirements of the CFD simulation test, so that the blockage ratio is less than 5% during the simulation test;

[0022] Next, the vehicle fluid dynamics model is established according to the obtained vehicle queue outflow field model by using the following three equations:

[0023] ①, the law of conservation of mass:

[0024]

[0025] ρ represents the air density; t represents the time; u, v, w respectively represent the components of the velocity vector u in the x, y, z directions;

[0026] ②, the law of conservation of momentum:

[0027]

[0028]

[0029]

[0030] In equations (2), (3), (4): p represents the pressure on the air fluid element; u represents the velocity; τ xx , τ yx , τ zx respectively represent the components of the viscous stress τ in the x direction; τ xy , τ yy , τ zy respectively represent the components of the viscous stress τ in the y direction; τ xz , τ yz , τ zz respectively represent the components of the viscous stress τ in the z direction; F x , F y , F z respectively represent the volume force on the element;

[0031] ③, the law of conservation of energy:

[0032]

[0033] T represents temperature.

[0034] Further, the specific method of griding the vehicle platoon model in step (2) is:

[0035] Firstly, the grid size of the cuboid external flow field in the vehicle platoon model is set to 1-100 mm, the grid size of the vehicle is set to 5-20 mm, and the grid growth rate is 1.2; and the grid is encrypted around the vehicle, and the density box size is set to 5-100 mm; a plurality of boundary layers are taken on the surface of the vehicle body to better capture the airflow flow state of the surface of the vehicle body; then, the boundary conditions of the calculation domain are set: the inlet is set as a velocity inlet, the outlet is set as a pressure outlet, the wall surface is set as a slidable wall surface, and the ground is set as an immovable wall surface.

[0036] Further, the specific content of step (2) is that the vehicle grid model is imported into the CFD solver for CFD simulation test:

[0037] The k-ε model is used as the turbulent flow model for simulation, and the specific expression equation is as follows:

[0038] ①, k equation:

[0039]

[0040] ②, ε equation:

[0041]

[0042] Gk in formula (6) and (7) represents the generation term of turbulent kinetic energy due to the average velocity gradient, G k represents the generation term of turbulent kinetic energy due to the lift, Y b represents the fluctuation generated by excessive diffusion in compressible turbulent flow, C m , C 1ε , C 2ε , C 3ε are empirical constants, σk and σε are the turbulent Prandtl numbers of the k equation and the ε equation, S k , S e are user-defined source terms.

[0043] Further, in the CFD simulation test process in step (2), the lateral offset distances of the middle vehicle of the queue are selected as 0W, 1 / 6W, 1 / 3W, 1 / 2W and 1W, to simulate the influence of the lateral offset distance on the air resistance of the single vehicle and the whole vehicle platoon queue, and W is the width of the single vehicle; the specific method is:

[0044] Step (2.1), when the lateral offset distance is 0W, the front and rear vehicle spacing is respectively taken as 0.1L, 0.5L, 1L, 1.5L, 2L, 2.5L and 3L, and then 7 groups of the aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained to simulate the influence of the longitudinal vehicle spacing on the air resistance of the single vehicle and the whole in the queue, L being the length of the single vehicle;

[0045] Step (2.2), when the lateral offset distance is respectively 1 / 6W, 1 / 3W, 1 / 2W and 1W, the corresponding front and rear vehicle spacing is also respectively taken as 0.1L, 0.5L, 1L, 1.5L, 2L, 2.5L and 3L, and thus 28 groups of the aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained;

[0046] Step (2.3), according to the aerodynamic drag coefficients obtained in steps (2.1) and (2.2), the influence of the longitudinal vehicle spacing and the lateral offset distance on the air resistance of the single vehicle and the whole in the queue is simulated when the vehicle speed is taken as any fixed speed in the interval of 50km / h-100km / h, and the function relationship between the aerodynamic drag coefficient (C D1 ) of the vehicle queue and the longitudinal vehicle spacing (L) and the lateral offset distance (W1) is constructed by the polynomial fitting method as follows:

[0047]

[0048] Wherein the value range of a1 is [-0.036, 0.012], the value range of b1 is [-0.0278, 0.008], the value range of c1 is [-0.05, 0.0752], the value range of d1 is [-0.002, 0.126], the value range of e1 is [-0.068, 0.236], and the value range of f1 is [0.180, 0.276];

[0049] Step (2.4), when the lateral offset distance is 0W, the vehicle speed is taken as 50km / h, 60km / h, 70km / h, 80km / h and 90km / h in turn, and 5 groups of the aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained;

[0050] Step (2.5), when the lateral offset distance is respectively 1 / 6W, 1 / 3W, 1 / 2W and 1W, the corresponding vehicle speed is also respectively taken as 50km / h, 60km / h, 70km / h, 80km / h and 90km / h, and thus 20 groups of the aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained;

[0051] Step (2.6), according to the aerodynamic drag coefficient obtained in step (2.4) and step (2.5), simulate the influence of vehicle speed and lateral offset distance on the air resistance of single vehicle and the whole vehicle queue when the fixed vehicle spacing is randomly selected in the interval of 0.1L-3L, and construct the function relationship between the aerodynamic drag coefficient (C D2 ) of vehicle queue and vehicle speed (V) and lateral offset distance (W2) by polynomial fitting method as follows:

[0052]

[0053] wherein the value range of a2 is [3.126, 7.557], the value range of b2 is [-2.86, 10], the value range of c2 is [-0.01, 0.5]; the value range of d2 is [-0.0056, 0], the value range of e2 is [-0.018, 0.10], and the value range of f2 is [0.3, 0.4];

[0054] Step (2.7), after determining the function relationship between the aerodynamic drag coefficient of vehicle queue and longitudinal vehicle spacing, vehicle lateral offset distance and vehicle speed, a mathematical model of vehicle aerodynamic drag coefficient and fuel consumption saving rate is established.

[0055] I. Under the influence of lateral offset distance and longitudinal vehicle spacing, the function relationship between fuel consumption saving rate (Q1) and aerodynamic drag coefficient (C D1 ) of vehicle queue is established as follows:

[0056]

[0057] wherein the value range of a3 is [-0.0037, -0.0026], the value range of b3 is [-0.0008, 0.0192], and the value range of c3 is [0.0364, 0.1357];

[0058] II. Under the influence of lateral offset distance and vehicle speed, the function relationship between fuel consumption saving rate (Q2) and aerodynamic drag coefficient (C D2 ) of vehicle queue is established as follows:

[0059]

[0060] wherein the value range of a4 is [-0.0096, -0.0045], the value range of b4 is [0.0192, 0.0248], and the value range of c4 is [0.0599, 0.2475].

[0061] Further, the step (2.7) establishes a mathematical model of vehicle aerodynamic drag coefficient and fuel consumption saving rate as follows:

[0062] The vehicle dynamics equation is as follows:

[0063]

[0064]

[0065] where F t is the vehicle driving force, F w is the air resistance, F f is the rolling resistance, m is the vehicle mass, g is the air acceleration, f is the rolling resistance coefficient, C d is the air resistance coefficient, A is the vehicle frontal area, p is the air density, and v r is the driving speed.

[0066] The influence of natural wind on driving and unit speed change (v r converted to v a ) is ignored in the CFD simulation test, and the air resistance of the driving vehicle is represented by the following formula:

[0067] The vehicle engine power is:

[0068]

[0069] where η t is the transmission system mechanical power.

[0070] The fuel consumption per 100 kilometers of the vehicle at a constant speed is calculated by the following formula:

[0071]

[0072] where m is the total fuel consumption, and p is the fuel density.

[0073] The reduction rate of the air resistance coefficient ΔC d and the fuel saving rate ΔQ s are defined by the following two formulas:

[0074]

[0075]

[0076] In formulas (16) and (17), C d0 is the single vehicle resistance coefficient, C d is the average resistance coefficient of the vehicle fleet, and Q s0 is the fuel consumption per 100 kilometers of a vehicle.

[0077] From formulas (12)-(17), the relationship between the fuel consumption saving rate and the aerodynamic resistance coefficient reduction rate is shown in formula (18), where

[0078]

[0079] Beneficial effects: the present application uses CFD simulation to simulate vehicle queue driving with lateral offset distance, obtains single vehicle drag coefficient and queue average drag coefficient in the queue, and uses automobile aerodynamics to build a functional relationship between drag coefficient and fuel consumption saving rate, so that the corresponding drag coefficient can be found more conveniently and quickly through the driving state of the vehicle, and the fuel consumption saving rate of the vehicle is obtained, and suitable vehicle formation topology structure is provided for how to save fuel consumption when the vehicle drives on the road. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 is a three-vehicle queue driving and external flow field schematic diagram in the embodiment of the present application;

[0081] Figure 2 is a CFD simulation overall flowchart of the present application;

[0082] Figure 3 is the drag coefficient of a single vehicle obtained by the present application;

[0083] Figure 4 is a vehicle air resistance coefficient change graph obtained by the embodiment under different vehicle spacing parameters;

[0084] Figure 5 is a vehicle air resistance coefficient change graph obtained by the embodiment under different vehicle speed parameters;

[0085] Figure 4 (a) refers to the air resistance coefficient change graph of the leading vehicle, Figure 4 (b) refers to the air resistance coefficient change graph of the middle vehicle, Figure 4 (c) refers to the air resistance coefficient change graph of the tail vehicle, Figure 5 (a) refers to the air resistance coefficient change graph of the leading vehicle, Figure 5 (b) refers to the air resistance coefficient change graph of the middle vehicle, Figure 5 (c) refers to the air resistance coefficient change graph of the tail vehicle. DETAILED DESCRIPTION

[0086] The technical scheme of the present application will be described in detail below, but the protection scope of the present application is not limited to the described embodiments.

[0087] The vehicle queue fuel consumption saving rate calculation method based on topology structure and dynamic characteristics of the present embodiment comprises the following steps:

[0088] Step (1), single vehicle CFD simulation experiment;

[0089] Step (1.1), a single Mira stepback three-dimensional model and a corresponding external flow field model are established, a single Mira model is pretreated, grid division and grid quality optimization processing are carried out, and a single vehicle grid model is obtained;

[0090] Step (1.2), the vehicle grid model obtained in step (1.1) is imported into a CFD solver, and relevant settings are made to obtain the aerodynamic drag coefficient of the single Mira model CFD simulation experiment, including boundary condition setting, turbulence model selection, Reynolds number setting, initial condition setting, etc.

[0091] Step (1.3), the aerodynamic drag coefficient of the single Mira model CFD simulation experiment obtained in step (1.2) is compared with the existing Mira model wind tunnel experiment data to verify the accuracy of the single simulation experiment data.

[0092] Step (2), a vehicle external flow field model and a queue fluid dynamics model are established.

[0093] Step (2.1), three Mira models are selected to form a vehicle formation model, then a multi-vehicle Mira queue model and a corresponding external flow field model are constructed, the vehicle formation model is subjected to grid division and grid quality optimization processing, and a multi-vehicle grid model is obtained.

[0094] Step (2.2), the vehicle grid model of step (2.1) is imported into a CFD solver software, and relevant settings are made, including boundary condition setting, turbulence model selection, Reynolds number setting, initial condition setting, etc.

[0095] Then, different lateral offset distances are set in the vehicle formation, and the longitudinal vehicle spacing and the vehicle speed are changed in turn, to obtain the drag coefficient of each vehicle in the vehicle formation and the average drag coefficient of the vehicle formation.

[0096] Finally, the obtained data is processed and analyzed to obtain the influence law of changing the longitudinal vehicle spacing and the vehicle speed on the aerodynamic drag coefficient of the vehicle formation on the basis of different lateral offset distances, and a fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, the vehicle speed and the lateral offset distance is determined.

[0097] Step (3), according to the fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, the vehicle speed and the lateral offset distance obtained in step (2), a vehicle queue driving fuel consumption saving rate mathematical analysis model is used to determine the function relationship between the vehicle queue aerodynamic drag coefficient and the fuel consumption saving rate.

[0098] The aerodynamic force coefficient involved in the application includes air resistance (F W ), air resistance coefficient (C d), aerodynamic lift coefficient (C L ) :

[0099]

[0100]

[0101]

[0102] As shown in Figure 1 and Figure 2 , the embodiment is used to build a three-dimensional vehicle model and an external flow field model of a vehicle fleet, and to pre-process the model;

[0103] The following standards are referred to in the mesh partitioning: the cuboid external flow field mesh size is set to 0-100mm, the vehicle mesh size is set to 5-20mm, the mesh growth rate is 1.2, and the density box size is set to 0-100mm.

[0104] After the mesh partitioning is completed, the CFD solver is imported to perform relevant settings and external flow field solving processing. The boundary conditions are set, the external flow field inlet is set as a velocity inlet, the external flow field outlet is set as a pressure outlet, the wall surface is a slippable wall surface, and the ground is an unslippable wall surface.

[0105] The standard k-ε model is selected as the turbulent flow model in the present application, which can obtain satisfactory results for the boundary layer and improve the efficiency and accuracy of the flow field calculation.

[0106] The Reynolds number is set, which is an adimensional number group for judging the flow state of viscous fluid, and a suitable Reynolds number needs to be set in the experiment.

[0107] Since the present application studies the influence of the vehicle fleet topological structure on the air resistance coefficient, the lateral offset distance of the middle vehicle in the queue is taken as 0W, 1 / 6W, 1 / 3W, 1 / 2W and 1W for various topological distribution structures.

[0108] Under the above vehicle fleet topological structure distribution, the influence of the longitudinal vehicle spacing on the aerodynamic characteristic parameters of the vehicle fleet is studied, the longitudinal vehicle spacing values are taken as 0.1L, 0.5L, 1L, 1.5L, 2L, 2.5L and 3L, respectively, the air resistance coefficient values of the three vehicles in the vehicle fleet under different longitudinal vehicle spacings are obtained, and the air resistance coefficient under the single vehicle state is compared with the resistance coefficient values of each vehicle in the vehicle fleet, to obtain the influence law of the change of the longitudinal vehicle spacing on the air resistance coefficient of the head vehicle, the middle vehicle and the tail vehicle, as shown in the resistance coefficient values of the three vehicles in the vehicle fleet. Figure Four

[0109] The single vehicle resistance coefficient value is shown in Figure 3

[0110] ​​Secondly, under the above vehicle fleet topology distribution, the influence of vehicle speed on aerodynamic characteristics is analyzed, the vehicle speed is valued at 50km / h, 60km / h, 70km / h, 80km / h, 90km / h, and the air resistance coefficient values of the three vehicles in the vehicle fleet under different vehicle speeds are obtained in turn. Similarly, the air resistance coefficient of a single vehicle is compared with the coefficient values of each vehicle in the vehicle fleet, and the influence of vehicle speed change on the air resistance coefficient of the head vehicle, the middle vehicle and the tail vehicle is obtained, as shown in Figure 5 The resistance coefficient values of the three vehicles in the vehicle fleet are shown in Table 1.

[0111] Through the above series of simulation experiments under different working conditions, the functional relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, vehicle speed, and lateral offset distance is determined by polynomial fitting method.

[0112] Under the influence of lateral offset distance and longitudinal vehicle spacing, the functional relationship between vehicle queue aerodynamic drag coefficient (C D1 ) and longitudinal vehicle spacing (L) and lateral offset distance (W1) is as follows:

[0113]

[0114] The value range of a1 is [-0.036, 0.012], the value range of b1 is [-0.0278, 0.008], the value range of c1 is [-0.05, 0.0752], the value range of d1 is [-0.002, 0.126], the value range of e1 is [-0.068, 0.236], and the value range of f1 is [0.180, 0.276];

[0115] Under the influence of lateral offset distance and vehicle speed, the functional relationship between vehicle queue aerodynamic drag coefficient (C D2 ) and vehicle speed (V) and lateral offset distance (W2) is as follows:

[0116]

[0117] The value range of a2 is [3.126, 7.557], the value range of b2 is [-2.86, 10], the value range of c2 is [-0.01, 0.5], the value range of d2 is [-0.0056, 0], the value range of e2 is [-0.018, 0.10], and the value range of f2 is [0.3, 0.4].

[0118] Finally, the vehicle energy economy is analyzed by the data obtained, and a mathematical model of vehicle air resistance coefficient and fuel consumption saving rate needs to be established, and the main expression equation is as follows:

[0119] According to the automobile theory, the vehicle motion equation is as follows:

[0120]

[0121] where F t is the vehicle driving force, F w is the air resistance, F f is the rolling resistance, m is the vehicle mass, g is the air acceleration, f is the rolling resistance coefficient, C d is the air resistance coefficient, A is the vehicle frontal area, p is the air density, and v r is the running speed.

[0122] In the present CFD simulation test, the influence of natural wind on the running and the unit speed change (v r is converted to v a ) is ignored. The air resistance F W of the driving vehicle, the engine power P, and the fuel consumption per 100 kilometers at a constant speed Q s are respectively expressed by the following three parameters:

[0123]

[0124]

[0125]

[0126] where η t is the transmission system mechanical power, m is the total fuel consumption, and p is the fuel density.

[0127] The reduction rate AC d of the air resistance coefficient and the fuel saving rate AQ s are defined by the following two formulas.

[0128]

[0129]

[0130] In formulas (12) and (13), C d0 is the single vehicle resistance coefficient, C d is the average resistance coefficient of the vehicle fleet, and Q s0 is the fuel consumption per 100 kilometers of a vehicle.

[0131] From formulas (9)-(13), the relationship between the fuel consumption saving rate and the aerodynamic resistance coefficient reduction rate is shown in formula (14), where

[0132] At this time,

[0133] Based on the above derivation and the functional relationship between the aerodynamic drag coefficient of the vehicle platoon and the longitudinal vehicle spacing, the lateral offset distance of the vehicle and the vehicle speed, the functional relationship between the aerodynamic drag coefficient of the vehicle platoon and the fuel consumption saving rate is further determined by the polynomial fitting method, as shown in formulas (15) and (16).

[0134] Under the influence of lateral offset distance and longitudinal vehicle spacing, the aerodynamic drag coefficient (C) of the vehicle platoon is established. D1 The functional relationship between (Q1) and fuel saving rate (Q1) is as follows:

[0135]

[0136] The values ​​of a3 range from [-0.0037, -0.0026], the values ​​of b3 range from [-0.0008, 0.0192], and the values ​​of c3 range from [0.0364, 0.1357].

[0137] Under the influence of lateral offset distance and vehicle speed, establish the aerodynamic drag coefficient (C) of the vehicle platoon. D2 The functional relationship between Q1 and Q2 is as follows:

[0138]

[0139] The values ​​of a4 range from [-0.0096, -0.0045], the values ​​of b4 range from [0.0192, 0.0248], and the values ​​of c4 range from [0.0599, 0.2475].

[0140] like Figure 4 As shown, after increasing the lateral offset distance of the middle car, it can be seen that the drag coefficients of the leading car and the middle car gradually increase with the increase of the car spacing. When the lateral offset distance of the middle car reaches 1 / 2W and 1W, the drag coefficient of the middle car exceeds that of the leading car, while the drag coefficient of the tail car first decreases and then increases.

[0141] like Figure 5 As shown, with the increase of vehicle speed, the drag coefficient of the lead car first decreases and then increases between 0W and 1W in lateral distance, reaching its minimum at 1 / 6W. When the lateral distance is 1W, the drag coefficient of the lead car approaches the drag coefficient of a single car. The drag coefficient of the middle car first decreases and then increases with the change of lateral distance, and when the lateral distance reaches 1 / 2W, the drag coefficient of the middle car exceeds that of the lead car and approaches the drag coefficient of a single car. The drag coefficient of the tail car continues to increase with the increase of vehicle speed.

[0142] Before the wind tunnel experiment, the vehicle model is scaled down by 3D printing; the measurement points are marked on the surface of the vehicle body; and the scaled vehicle model is analyzed at the same Reynolds number to ensure that the wind tunnel experiment measurement is consistent with the actual situation. Finally, the data obtained from the wind tunnel experiment is compared with the CFD simulation data to ensure that the data is within the allowable error range.

Claims

1. A method for calculating fuel consumption saving rate of a vehicle platoon based on topological structure and dynamic characteristics, characterized in that: The method comprises the following steps: Step (1), performing a single vehicle CFD simulation experiment; Step (1.1), establishing a single vehicle Mira stepback three-dimensional model and a corresponding external flow field model, preprocessing the single vehicle Mira stepback model, performing grid division and grid quality optimization processing, and obtaining a single vehicle grid model; Step (1.2), importing the vehicle grid model obtained in step (1.1) into a CFD solver, and performing relevant settings to obtain the aerodynamic drag coefficient of the single vehicle Mira model CFD simulation experiment, wherein the relevant settings include boundary condition setting, turbulence model selection, Reynolds number setting, and initial condition setting; Step (1.3), comparing the aerodynamic drag coefficient of the single vehicle Mira model CFD simulation experiment obtained in step (1.2) with the existing Mira model wind tunnel experiment data, if the comparison data are inconsistent, returning to step (1.1) to reestablish the external flow field model, then entering step (1.2) to adjust the relevant settings and recalculate the aerodynamic drag coefficient of the single vehicle, until the two drag coefficients are close; Step (2), establishing a vehicle queue external flow field model and a fluid dynamics model; Step (2.1), selecting at least three Mira models to arrange a vehicle queue to obtain a vehicle formation model with different topological structures, then constructing a multi-vehicle Mira queue model and a corresponding external flow field model, performing grid division and grid quality optimization processing on the vehicle formation model, and obtaining a multi-vehicle grid model; Step (2.2), importing the vehicle grid model of step (2.1) into a CFD solver software, first performing relevant settings, including boundary condition setting, turbulence model selection, Reynolds number setting, and initial condition setting; Then, setting different lateral offset distances in the vehicle queue, and sequentially changing the longitudinal vehicle spacing and the vehicle speed to obtain the drag coefficient of each vehicle in the vehicle queue and the average drag coefficient of the vehicle queue; Finally, processing and analyzing the obtained data to obtain the influence law of different lateral offset distances, longitudinal vehicle spacing, and vehicle speed on the aerodynamic drag coefficient of the vehicle queue, and determining the fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, vehicle speed, and lateral offset distance; Step (3), according to the fitting function relationship between the vehicle queue aerodynamic drag coefficient and the longitudinal vehicle spacing, vehicle speed, and lateral offset distance obtained in step (2), using a fuel consumption saving rate mathematical analysis model of vehicle queue driving, determining the function relationship between the vehicle queue aerodynamic drag coefficient and the fuel consumption saving rate.

2. The method of claim 1, wherein: In step (1), when the vehicle grid model obtained in step (1.1) is imported into the CFD solver for CFD simulation experiment, the k-omega model is used as the turbulence model, and the complete single vehicle is verified through the CFD simulation experiment.

3. The method of claim 1, wherein: In step (2), the specific content of constructing a multi-vehicle Mira queue model and a corresponding external flow field model is as follows: First, at least three Mira models are selected to arrange a queue; Then, the vehicle queue outflow field model is established according to the queue distribution: the size of the outflow field is reasonably selected according to the requirements of the CFD simulation experiment, so that the blockage ratio in the simulation CFD simulation experiment is less than 5%; Then, the vehicle fluid dynamics model is established by using the following three equations according to the obtained vehicle queue outflow field model: ①, the law of conservation of mass: ρ represents the air density; t represents time; u, v, w respectively represent the components of the velocity vector u in the x, y, z directions; ②, the law of conservation of momentum: In formulas (2), (3), (4): p represents the pressure on the air fluid microelement; u represents the velocity; τ xx , τ yx , τ zx respectively represent the components of the viscous stress τ in the x direction; τ xy , τ yy , τ zy respectively represent the components of the viscous stress τ in the y direction; τ xz , τ yz , τ zz respectively represent the components of the viscous stress τ in the z direction; F x , F y , F z respectively represent the volume forces on the microelement; ③, the law of conservation of energy: T represents temperature.

4. The method according to claim 1 or 3, wherein: The specific method for meshing the vehicle formation model in step (2) is: Firstly, the rectangular cuboid outflow field mesh size in the vehicle formation model is set to 1-100mm, the vehicle mesh size is set to 5-20mm, and the mesh growth rate is 1.2; and the mesh is encrypted around the vehicle, and the density box size is set to 5-100mm; a plurality of boundary layers are taken on the surface of the vehicle body to better capture the airflow flow state on the surface of the vehicle body; then, the boundary conditions of the calculation domain are set: the inlet is set as a velocity inlet, the outlet is set as a pressure outlet, the wall is set as a slippable wall, and the ground is set as an unslippable wall.

5. The method according to claim 1 or 3, wherein: The specific content of importing the vehicle mesh model into the CFD solver for CFD simulation test in step (2) is: The k-ε model is used as the simulation turbulent flow model, and the specific expression equation is as follows: ①, k equation: ②, ε equation: G in equations (6) and (7) k represents a generation term of turbulent kinetic energy due to the average velocity gradient, G b represents a generation term of turbulent kinetic energy due to the lift, Y m represents a fluctuation generated by over diffusion in compressible turbulence, C 1ε , C 2ε , C 3ε is an empirical constant, σ k , σ ε are turbulent Prandtl numbers of k-equation and ε-equation, respectively, S k , S e is a user-defined source term.

6. The method according to claim 1 or 3, wherein: In the CFD simulation test process in step (2), the lateral offset distance of the middle vehicle of the queue is selected as 0W, 1 / 6W, 1 / 3W, 1 / 2W and 1W to simulate the influence of the lateral offset distance on the air resistance of the single vehicle in the queue and the whole vehicle formation queue, and W is the single vehicle width; the specific method is: Step (2.1), when the lateral offset distance is 0W, the front and rear vehicle spacing is taken as 0.1L, 0.5L, 1L, 1.5L, 2L, 2.5L and 3L, and then 7 groups of aerodynamic drag coefficients of the single vehicle in the queue and the whole queue are obtained to simulate the influence of the longitudinal vehicle spacing on the air resistance of the single vehicle in the queue and the whole queue, and L is the single vehicle length; Step (2.2), when the lateral offset distance is 1 / 6W, 1 / 3W, 1 / 2W and 1W respectively, the corresponding front and rear vehicle spacing is also taken as 0.1L, 0.5L, 1L, 1.5L, 2L, 2.5L and 3L, so as to obtain 28 groups of aerodynamic drag coefficients of the single vehicle in the queue and the whole queue; Step (2.3), according to the aerodynamic drag coefficients obtained in steps (2.1) and (2.2), simulate the effects of longitudinal inter-vehicle distance and lateral offset distance on the aerodynamic drag of individual vehicles and the whole vehicle queue when the vehicle speed is randomly selected within the range of 50-100 km / h, and construct the functional relationship between the aerodynamic drag coefficient (C D1 ) of the vehicle queue and the longitudinal inter-vehicle distance (L) and the lateral offset distance (W1) by polynomial fitting method as follows: The value range of a1 is [-0.036, 0.012], the value range of b1 is [-0.0278, 0.008], the value range of c1 is [-0.05, 0.0752], the value range of d1 is [-0.002, 0.126], the value range of e1 is [-0.068, 0.236], and the value range of f1 is [0.180, 0.276]. Step (2.4), when the lateral offset distance is 0W, the vehicle speed is taken as 50km / h, 60km / h, 70km / h, 80km / h and 90km / h in turn, and 5 groups of aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained; Step (2.5), when the lateral offset distance is 1 / 6W, 1 / 3W, 1 / 2W and 1W in turn, the corresponding vehicle speed is taken as 50km / h, 60km / h, 70km / h, 80km / h and 90km / h in turn, and 20 groups of aerodynamic drag coefficients of the single vehicle and the whole in the queue are obtained; Step (2.6), according to the aerodynamic drag coefficients obtained in step (2.4) and step (2.5), simulate the influence of vehicle speed and lateral offset distance on the aerodynamic drag of single vehicle and the whole vehicle platoon when the fixed vehicle spacing is randomly selected in the interval of 0.1L-3L, and construct the function relationship between the aerodynamic drag coefficient (C D2 ) of vehicle platoon and vehicle speed (V) and lateral offset distance (W2) by polynomial fitting method as follows: The value range of a2 is [3.126, 7.557], the value range of b2 is [-2.86, 10], the value range of c2 is [-0.01, 0.5]; the value range of d2 is [-0.0056, 0], the value range of e2 is [-0.018, 0.10], and the value range of f2 is [0.3, 0.4]; Step (2.7), after determining the functions of the aerodynamic drag coefficient of the vehicle queue and the longitudinal vehicle spacing, the vehicle lateral offset and the vehicle speed, a mathematical model of the aerodynamic drag coefficient of the vehicle and the fuel consumption saving rate is established; I. Under the influence of lateral offset distance and longitudinal inter-vehicle distance, the function relationship between fuel consumption saving rate (Q1) and vehicle platoon aerodynamic drag coefficient (C D1 ) is as follows: The value range of a3 is [-0.0037, -0.0026], the value range of b3 is [-0.0008, 0.0192], and the value range of c3 is [0.0364, 0.1357]; II. The function relationship between fuel consumption saving rate (Q2) and vehicle platoon aerodynamic drag coefficient (C D2 ) under the influence of lateral offset distance and vehicle speed is as follows: The value range of a4 is [-0.0096, -0.0045], the value range of b4 is [0.0192, 0.0248], and the value range of c4 is [0.0599, 0.2475].

7. The method of claim 6, wherein: The process of establishing the mathematical model of the aerodynamic drag coefficient of the vehicle and the fuel consumption saving rate in step (2.7) is as follows: The automobile driving dynamics equation is as follows: where F t is the vehicle driving force, F w is the air resistance, F f is the rolling resistance, m is the vehicle mass, g is the air acceleration, f is the rolling resistance coefficient, C d is the air resistance coefficient, A is the vehicle frontal area, p is the air density, v r is the running speed; The vehicle engine power is: wherein η t is the mechanical power of the transmission system; The fuel consumption per 100 kilometers of the automobile at constant speed is calculated by the following formula: Wherein, m is the total fuel consumption, and p is the fuel density; The reduction rate AC of the air resistance coefficient is defined by the following two equations d and the fuel saving rate AQ s : In equations (16) and (17), C d0 is the single vehicle drag coefficient, C d is the average drag coefficient of the vehicle platoon, Q s0 is the fuel consumption per 100 km of a vehicle; From equations (12) - (17), the relationship between the fuel consumption saving rate and the aerodynamic drag coefficient reduction rate is derived as shown in equation (18), where

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