Driving environment-based vehicle drag coefficient prediction method, system and device
By using methods based on real-time meteorological and traffic information, combined with wind tunnel testing and computational fluid dynamics, the problem of predicting the drag coefficient of automobiles under real driving conditions was solved, and accurate energy consumption assessment was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- VOYAH AUTOMOBILE TECH CO LTD
- Filing Date
- 2023-05-24
- Publication Date
- 2026-05-12
AI Technical Summary
In existing technologies, the assessment of a car's drag coefficient deviates significantly from that under standard operating conditions and in the consumer's usage environment, making it difficult to predict the drag coefficient in real-time under actual driving conditions.
Based on real-time meteorological, traffic flow, and road information, the drag coefficient of a vehicle is predicted in real time by determining the joint probability distribution of yaw angle, vehicle length, and vehicle distance, combined with wind tunnel tests and computational fluid dynamics.
It enables accurate prediction of drag coefficient under real driving conditions, improving the accuracy of energy consumption assessment.
Smart Images
Figure CN116793630B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle performance parameter prediction technology, and in particular to a method, system and device for predicting the drag coefficient of a car based on the driving environment. Background Technology
[0002] Air resistance significantly impacts vehicle energy consumption. Currently, the drag coefficient of a vehicle is primarily assessed using wind tunnel testing, computational fluid dynamics, and coasting tests. However, the evaluation conditions differ greatly from actual consumer usage environments, mainly due to differences in ambient wind, temperature, humidity, and traffic flow. Consequently, energy consumption under standard operating conditions often deviates significantly from consumer usage. Existing technologies calculate the drag coefficient by repeatedly coasting at varying speeds and distances, but this assumption of a constant drag coefficient during coasting is inconsistent with reality. Therefore, how to predict the drag coefficient of a vehicle in real-time under actual driving conditions is a pressing technical problem that needs to be solved.
[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0004] The main objective of this invention is to provide a method, system, and device for predicting the drag coefficient of a vehicle based on the driving environment, aiming to solve the problem of how to predict the drag coefficient of a vehicle in real time under actual driving conditions.
[0005] To achieve the above objectives, the present invention provides a method for predicting the drag coefficient of a vehicle based on the driving environment, the method comprising:
[0006] Determine the environmental wind speed information corresponding to the predicted itinerary based on real-time meteorological information;
[0007] The yaw angle is determined based on the environmental wind speed information and vehicle speed, and the vehicle length, distance, and drag coefficient under the yaw angle are determined based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle.
[0008] The joint probability distribution of vehicle length and vehicle distance is determined based on the vehicle length and vehicle distance of each road segment.
[0009] The drag coefficients at each moment corresponding to the predicted journey are obtained based on the drag coefficients under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
[0010] Optionally, the step of determining the yaw angle based on the environmental wind speed information and vehicle speed includes:
[0011] Determine the wind speed and wind direction angle in the road area based on the environmental wind speed information;
[0012] Determine the road orientation angle based on information from the transportation department;
[0013] The yaw angle is determined based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle.
[0014] Optionally, before the step of determining the drag coefficient of vehicle length, distance, and yaw angle based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle, the method further includes:
[0015] The predicted road segment is divided according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point;
[0016] Vehicle length is determined based on local traffic management data and sales data.
[0017] The distance between vehicles on each road segment is determined based on the estimated traffic flow, the vehicle speed, and the vehicle length.
[0018] Optionally, the step of obtaining the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance, includes:
[0019] Based on wind tunnel tests and computational fluid dynamics, the drag coefficients at each moment corresponding to the predicted journey are determined according to the drag coefficients under the vehicle length, vehicle spacing, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle spacing.
[0020] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drag coefficient prediction system based on driving environment, the vehicle drag coefficient prediction system based on driving environment comprising:
[0021] The determination module is used to determine the environmental wind speed information corresponding to the predicted itinerary based on real-time meteorological information;
[0022] The processing module is used to determine the yaw angle based on the environmental wind speed information, and to determine the vehicle length, vehicle distance, and drag coefficient under the yaw angle based on the vehicle distance between each driving segment, the vehicle length of each driving segment, and the yaw angle.
[0023] The processing module is also used to determine the joint probability distribution of vehicle length and vehicle distance based on the vehicle length of each driving segment and the vehicle distance of each driving segment;
[0024] The prediction module is used to obtain the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
[0025] Optionally, the processing module is further configured to determine the wind speed and wind direction angle in the road area based on the environmental wind speed information;
[0026] The processing module is also used to determine the road orientation angle based on information from the transportation department;
[0027] The processing module is also used to determine the yaw angle based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle.
[0028] Optionally, the processing module is further configured to divide the predicted road segment according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point;
[0029] The processing module is also used to determine the vehicle length based on local traffic management data and sales data.
[0030] The processing module is also used to determine the distance between vehicles on each road segment based on the estimated traffic flow, the vehicle speed, and the vehicle length.
[0031] Optionally, the prediction module is further configured to determine the drag coefficient at each moment corresponding to the predicted journey based on wind tunnel tests and computational fluid dynamics, according to the drag coefficient under the vehicle length, vehicle spacing, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle spacing.
[0032] Furthermore, to achieve the above objectives, the present invention also proposes a vehicle drag coefficient prediction device based on driving environment. The device includes: a memory, a processor, and a vehicle drag coefficient prediction program based on driving environment stored in the memory and executable on the processor. The vehicle drag coefficient prediction program based on driving environment is configured to implement the steps of the vehicle drag coefficient prediction method based on driving environment as described above.
[0033] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a vehicle drag coefficient prediction program based on the driving environment, wherein when the vehicle drag coefficient prediction program based on the driving environment is executed by a processor, the steps of the vehicle drag coefficient prediction method based on the driving environment described above are implemented.
[0034] This invention first determines the environmental wind speed information corresponding to the predicted journey based on real-time meteorological information. Then, it determines the yaw angle based on the environmental wind speed information and vehicle speed. Next, it determines the drag coefficient at each driving segment based on the vehicle distance, vehicle length, and yaw angle. Then, it determines the joint probability distribution of vehicle length and distance based on each driving segment's vehicle length and distance. Finally, it obtains the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient at each driving segment's length, distance, and yaw angle, and the joint probability distribution of yaw angle, vehicle length, and distance. Existing technologies calculate the air drag coefficient by varying the gliding distance, but this assumption of a constant drag coefficient during gliding is inconsistent with reality. This invention predicts the vehicle's drag coefficient under real-world driving conditions based on meteorological, traffic flow, and road information, thereby accurately assessing actual driving energy consumption. Attached Figure Description
[0035] Figure 1 This is a schematic diagram of the structure of a vehicle drag coefficient prediction device based on the driving environment, which is part of the hardware operating environment of the embodiment of the present invention.
[0036] Figure 2 This is a flowchart illustrating the first embodiment of the vehicle drag coefficient prediction method based on driving environment according to the present invention.
[0037] Figure 3 This is a schematic diagram of road segmentation for the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention;
[0038] Figure 4 This is a schematic diagram of the road direction and the ambient wind direction in the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention;
[0039] Figure 5 This is a flowchart illustrating the second embodiment of the vehicle drag coefficient prediction method based on driving environment according to the present invention.
[0040] Figure 6 This is a structural block diagram of the first embodiment of the vehicle drag coefficient prediction system based on driving environment of the present invention.
[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0042] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.
[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a vehicle drag coefficient prediction device based on the driving environment, which is part of the hardware operating environment of the embodiment of the present invention.
[0044] like Figure 1 As shown, the vehicle drag coefficient prediction device based on the driving environment may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage system independent of the aforementioned processor 1001.
[0045] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on a vehicle drag coefficient prediction device based on the driving environment, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0046] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a vehicle drag coefficient prediction program based on the driving environment.
[0047] exist Figure 1 In the vehicle drag coefficient prediction device based on driving environment shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the vehicle drag coefficient prediction device based on driving environment of the present invention can be set in the vehicle drag coefficient prediction device based on driving environment. The vehicle drag coefficient prediction device based on driving environment calls the vehicle drag coefficient prediction program based on driving environment stored in memory 1005 through processor 1001 and executes the vehicle drag coefficient prediction method based on driving environment provided in the embodiment of the present invention.
[0048] This invention provides a method for predicting the drag coefficient of a vehicle based on the driving environment, referring to... Figure 2 , Figure 2This is a flowchart illustrating the first embodiment of the vehicle drag coefficient prediction method based on driving environment according to the present invention.
[0049] In this embodiment, the vehicle drag coefficient prediction method based on driving environment includes the following steps:
[0050] Step S10: Determine the environmental wind speed information corresponding to the predicted itinerary based on real-time meteorological information.
[0051] It is easy to understand that the execution subject of this embodiment can be a vehicle drag coefficient prediction device based on the driving environment with functions such as data processing, network communication and program operation, or other computer devices with similar functions. This embodiment does not limit it.
[0052] It should also be noted that the ambient wind speed information includes data on the magnitude and direction of the ambient wind speed.
[0053] Step S20: Determine the yaw angle based on the environmental wind speed information and vehicle speed, and determine the vehicle length, vehicle distance, and drag coefficient under the yaw angle based on the vehicle distance, vehicle length, and yaw angle of each driving segment.
[0054] It should also be noted that the predicted road segments are divided according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point. Vehicle length information, including the vehicle length range and probability distribution, is determined based on local traffic management data and sales data. Vehicle distance information for each driving segment, including the vehicle distance range and probability distribution, is determined based on the expected traffic flow, vehicle speed, and vehicle length information.
[0055] In the specific implementation, refer to Figure 3 , Figure 3 This is a road segmentation diagram illustrating the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The method for determining the vehicle distance in each road segment is as follows: based on the monitoring points in the traffic management system, the entire vehicle journey is divided into multiple segments, and the expected flow rate q(x) at each monitoring point can be obtained. i ,t), vehicle speed v(x) i In the figure, within a unit time T, the speed of x... i The number of vehicles at point N(x) i ,t)=q(x i Given a time interval t), assume that the speed of the vehicle passing through this point is v(x). i If x ∈ (t, t), then the distance traveled within the time interval tT to t is v(x). i ,t)T. Then the distance between vehicles W=(v(x i ,t)TL(x i ,t)*N(x i ,t)) / N(x i ,t)=v(x i,t) / q(x i ,t)-L(x i ,t), where L(x i ,t) represents the time interval T through which x passes i The average vehicle length.
[0056] It should also be understood that this embodiment can statistically analyze the range and probability distribution of vehicle lengths based on local traffic management data and sales data.
[0057] Furthermore, the method for determining the yaw angle based on environmental wind speed information and vehicle speed is as follows: determine the wind speed and environmental wind direction angle in the road area based on the environmental wind speed information, then determine the road orientation angle based on the information from the transportation department, and finally determine the yaw angle based on the vehicle speed, the wind speed in the road area, the environmental wind direction angle, and the road orientation angle.
[0058] In this embodiment, based on local historical meteorological information, the magnitude and direction of environmental wind speed are statistically analyzed, and the yaw angle distribution range of vehicles traveling on the road is determined accordingly.
[0059] refer to Figure 4 , Figure 4 This is a schematic diagram of the road direction and ambient wind direction in the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The wind speed v in the road area is obtained according to the real-time environmental prediction of the meteorological department. w And the heading angle θ. The heading angle of road segment AB is α, and the direction angle of the ambient wind is θ. Therefore, the angle between the vehicle speed and the wind speed is θ-α, and the yaw angle is:
[0060]
[0061] Meteorological departments typically collect wind speed data at a height of ten meters above the ground. w =v w10 (z / 10) μ v w10 The wind speed is the wind speed published by the meteorological department, z is the altitude, and μ is related to the terrain and can be obtained by looking up a table.
[0062] Step S30: Determine the joint probability distribution of vehicle length and distance based on the vehicle length and distance of each road segment.
[0063] In this embodiment, the independent probability distributions of vehicle length L and vehicle distance W determined in step S20 are used to determine the joint probability distribution P(L,W) of vehicle length L and vehicle distance W for each road segment during the expected journey.
[0064] Step S40: Based on the drag coefficients under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance, obtain the drag coefficients at each moment corresponding to the predicted journey.
[0065] Furthermore, the processing method for obtaining the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient and yaw angle under vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of vehicle length and vehicle distance, is based on wind tunnel tests and computational fluid dynamics to determine the drag coefficient at each moment corresponding to the predicted journey according to the drag coefficient and yaw angle under vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of vehicle length and vehicle distance.
[0066] In practice, the drag coefficient, Cd(L,W,ψ), is determined under different vehicle lengths, distances between vehicles, and yaw angles through wind tunnel tests and computational fluid dynamics. Then, based on the steps described above, Cd=∑Cd(L,W,ψ)P(L,W) is used to obtain the drag coefficient at each moment during the predicted journey.
[0067] In this embodiment, the environmental wind speed information corresponding to the predicted journey is first determined based on real-time meteorological information. Then, the yaw angle is determined based on the environmental wind speed information and vehicle speed. The drag coefficients under vehicle length, distance, and yaw angle are determined based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle. Next, the joint probability distribution of vehicle length and distance is determined based on the vehicle length and distance on each road segment. Finally, the drag coefficients at each moment corresponding to the predicted journey are obtained based on the drag coefficients under vehicle length, distance, and yaw angle, and the joint probability distribution of yaw angle, vehicle length, and distance. Existing technologies calculate the air drag coefficient by varying the gliding distance, but this assumption based on a constant drag coefficient during gliding is inconsistent with reality. This embodiment, however, predicts the vehicle's drag coefficient under real-world driving conditions based on meteorological, traffic flow, and road information, thereby accurately assessing actual driving energy consumption.
[0068] refer to Figure 5 , Figure 5 This is a flowchart illustrating the second embodiment of the vehicle drag coefficient prediction method based on driving environment according to the present invention.
[0069] Based on the first embodiment described above, in this embodiment, before step S20, the method further includes:
[0070] Step S01: Divide the predicted road segment according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point.
[0071] In the specific implementation, refer to Figure 3 , Figure 3 This is a road segmentation diagram of the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The method for determining the vehicle distance range of each road segment is as follows: based on the monitoring points in the traffic management system, the entire vehicle journey is divided into multiple segments, and the expected flow rate q(x) at each monitoring point can be obtained. i ,t), vehicle speed v(x) i ,t).
[0072] Step S02: Determine the vehicle length based on local traffic management data and sales data.
[0073] It should also be understood that this embodiment can be based on local traffic management data and sales data to calculate vehicle length, including the range of vehicle lengths and their probability distribution.
[0074] Step S03: Determine the vehicle distance information for each travel segment based on the estimated traffic flow, the vehicle speed, and the vehicle length.
[0075] It should also be noted that, Figure 3 In the middle, within a unit time T, through x i The number of vehicles at point N(x) i ,t)=q(x i Given a time interval t), assume that the speed of the vehicle passing through this point is v(x). i If x ∈ (t, t), then the distance traveled within the time interval tT to t is v(x). i ,t)T,L(x i ,t) represents the time interval T through which x passes i The average vehicle length. Where the vehicle spacing W = (v(x) i ,t)TL(x i ,t)*N(x i ,t)) / N(x i ,t)=v(x i ,t) / q(x i ,t)-L(x i ,t).
[0076] In this embodiment, the predicted road segment is first divided according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point. Then, the vehicle length is determined based on local traffic management data and sales data. Finally, the vehicle distance for each driving segment is determined based on the expected traffic flow, vehicle speed, and vehicle length, including the vehicle distance range and its probability distribution. In this embodiment, the predicted road segment is divided according to the monitoring points to obtain the corresponding road segment information, so that the driving route can be further planned based on the road segment information.
[0077] Based on the first embodiment described above, in this embodiment, step S20 includes:
[0078] Step S201: Determine the wind speed and wind direction angle in the road area based on the environmental wind speed information.
[0079] In the specific implementation, refer to Figure 4 , Figure 4 This is a schematic diagram of the road direction and ambient wind direction in the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The wind speed v in the road area is obtained according to the real-time environmental prediction of the meteorological department.w and direction angle θ.
[0080] Step S202: Determine the road orientation angle based on information from the transportation department.
[0081] In practice, the orientation angle of road segment AB can be determined as α, and the direction angle of the ambient wind can be determined as θ, based on information from the transportation department.
[0082] Step S203: Determine the yaw angle based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle; and determine the vehicle length, vehicle distance, and drag coefficient under the yaw angle based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle.
[0083] It should also be understood that meteorological departments typically collect wind speed data at a height of ten meters above the ground. w =v w10 (z / 10) μ v w10 The wind speed is as published by the meteorological department, z is the altitude, and μ is related to the terrain and can be obtained from a table. The angle between the vehicle speed and the wind speed is θ-α, and the yaw angle is:
[0084]
[0085] The drag coefficient, Cd(L,W,ψ), under different vehicle lengths, spacings, and yaw angles was determined through wind tunnel tests and computational fluid dynamics, where L is the vehicle length, W is the spacing, and ψ is the yaw angle.
[0086] In this embodiment, the wind speed in the road area, the wind direction angle, and the road orientation angle are determined based on the ambient wind speed information. The yaw angle is determined based on the vehicle speed, the wind speed in the road area, the wind direction angle, and the road orientation angle, thereby obtaining accurate yaw angle information, which facilitates the subsequent determination of the drag coefficient under real driving conditions.
[0087] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the vehicle drag coefficient prediction system based on driving environment of the present invention.
[0088] like Figure 6 As shown, the vehicle drag coefficient prediction system based on driving environment proposed in this embodiment of the invention includes:
[0089] The determination module 6001 is used to determine the environmental wind speed information corresponding to the predicted route based on real-time meteorological information.
[0090] It should also be noted that the ambient wind speed information includes data on the magnitude and direction of the ambient wind speed.
[0091] The processing module 6002 is used to determine the yaw angle based on the environmental wind speed information and vehicle speed, and to determine the drag coefficient of vehicle length, vehicle distance, and yaw angle based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle.
[0092] The processing module 6002 is also used to determine the wind speed and wind direction angle in the road area based on the environmental wind speed information.
[0093] The processing module 6002 is also used to determine the road orientation angle based on information from the transportation department.
[0094] The processing module 6002 is also used to determine the yaw angle based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle.
[0095] The processing module 6002 is also used to divide the predicted road segment according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed of each monitoring point.
[0096] The processing module 6002 is also used to determine the vehicle length based on local traffic management data and sales data.
[0097] The processing module 6002 is also used to determine the distance between vehicles on each road segment based on the estimated traffic flow, the vehicle speed, and the vehicle length.
[0098] It should also be noted that the predicted road segments are divided according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point. The vehicle length is determined based on local traffic management data and sales data. The distance between vehicles on each road segment is determined based on the expected traffic flow, vehicle speed and vehicle length.
[0099] In the specific implementation, refer to Figure 3 , Figure 3 This is a road segmentation diagram of the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The method for determining the vehicle distance range of each road segment is as follows: based on the monitoring points in the traffic management system, the entire vehicle journey is divided into multiple segments, and the expected flow rate q(x) at each monitoring point can be obtained. i ,t), vehicle speed v(x) i In the figure, within a unit time T, the speed of x... i The number of vehicles at point N(x) i ,t)=q(x i Given a time interval t), assume that the speed of the vehicle passing through this point is v(x). i If x ∈ (t, t), then the distance traveled within the time interval tT to t is v(x). i ,t)T. Then the distance between vehicles W=(v(x i ,t)TL(x i ,t)*N(x i,t)) / N(x i ,t)=v(x i ,t) / q(x i ,t)-L(x i ,t), where L(x i ,t) represents the time interval T through which x passes i The average vehicle length.
[0100] It should also be understood that this embodiment can use local traffic management data and sales data to statistically analyze the length of motor vehicles, including the range of vehicle lengths and their probability distribution, to obtain the range of vehicle distances and their probability distribution during road travel.
[0101] Furthermore, the method for determining the yaw angle based on environmental wind speed information and vehicle speed is as follows: determine the wind speed and environmental wind direction angle in the road area based on the environmental wind speed information, then determine the road orientation angle based on the information from the transportation department, and finally determine the yaw angle based on the vehicle speed, the wind speed in the road area, the environmental wind direction angle, and the road orientation angle.
[0102] refer to Figure 4 , Figure 4 This is a schematic diagram of the road direction and ambient wind direction in the first embodiment of the vehicle drag coefficient prediction method based on driving environment of the present invention. The wind speed v in the road area is obtained according to the real-time environmental prediction of the meteorological department. w And the heading angle θ. The heading angle of road segment AB is α, and the direction angle of the ambient wind is θ. Therefore, the angle between the vehicle speed and the wind speed is θ-α, and the yaw angle is:
[0103]
[0104] Meteorological departments typically collect wind speed data at a height of ten meters above the ground. w =v w10 (z / 10) μ v w10 The wind speed is the wind speed published by the meteorological department, z is the altitude, and μ is related to the terrain and can be obtained by looking up a table.
[0105] The processing module 6002 is also used to determine the joint probability distribution of vehicle length and distance based on the vehicle length and distance of each driving segment.
[0106] In this embodiment, the independent probability distributions of vehicle length L and vehicle distance W determined by the processing module are used to determine the joint probability distribution P(L,W) of vehicle length L and vehicle distance W for each road segment during the expected journey.
[0107] The prediction module 6003 is used to obtain the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient under the vehicle length, vehicle distance, and yaw angle and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
[0108] In practice, the drag coefficient, Cd(L,W,ψ), is determined under different vehicle lengths, distances between vehicles, and yaw angles through wind tunnel tests and computational fluid dynamics. Then, based on the steps described above, Cd=∑Cd(L,W,ψ)P(L,W) is used to obtain the drag coefficient at each moment during the predicted journey.
[0109] In this embodiment, the environmental wind speed information corresponding to the predicted journey is first determined based on real-time meteorological information. Then, the yaw angle is determined based on the environmental wind speed information and vehicle speed. The drag coefficients under vehicle length, distance, and yaw angle are determined based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle. Next, the joint probability distribution of vehicle length and distance is determined based on the vehicle length and distance on each road segment. Finally, the drag coefficients at each moment corresponding to the predicted journey are obtained based on the drag coefficients under vehicle length, distance, and yaw angle, and the joint probability distribution of yaw angle and vehicle length and distance. Existing technologies calculate the air drag coefficient by varying the gliding distance, but this assumption based on a constant drag coefficient during gliding is inconsistent with reality. This embodiment, however, predicts the vehicle drag coefficient under real-world driving conditions based on meteorological, traffic flow, and road information, thereby accurately assessing actual driving energy consumption.
[0110] Other embodiments or specific implementations of the vehicle drag coefficient prediction system based on driving environment of the present invention can be referred to the above-described method embodiments, and will not be repeated here.
[0111] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0112] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0114] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A method for predicting the drag coefficient of a vehicle based on the driving environment, characterized in that, The method for predicting the vehicle drag coefficient based on the driving environment includes the following steps: Determine the environmental wind speed information corresponding to the predicted itinerary based on real-time meteorological information; The yaw angle is determined based on the environmental wind speed information and vehicle speed, and the vehicle length, distance, and drag coefficient under the yaw angle are determined based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle. The joint probability distribution of vehicle length and vehicle distance is determined based on the vehicle length and vehicle distance of each road segment. The drag coefficients at each moment corresponding to the predicted journey are obtained based on the drag coefficients under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
2. The method as described in claim 1, characterized in that, The step of determining the yaw angle based on the environmental wind speed information and vehicle speed includes: Determine the wind speed and wind direction angle in the road area based on the environmental wind speed information; Determine the road orientation angle based on information from the transportation department; The yaw angle is determined based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle.
3. The method as described in claim 1, characterized in that, Before the step of determining the drag coefficients of vehicle length, distance, and yaw angle based on the distance between vehicles on each road segment, the vehicle length on each road segment, and the yaw angle, the method further includes: The predicted road segments are divided based on the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed at each monitoring point. Vehicle length is determined based on local traffic management data and sales data. The distance between vehicles on each road segment is determined based on the estimated traffic flow, the vehicle speed, and the vehicle length.
4. The method as described in claim 1, characterized in that, The step of obtaining the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance, includes: Based on wind tunnel tests and computational fluid dynamics, the drag coefficients at each moment corresponding to the predicted journey are determined according to the drag coefficients under the vehicle length, vehicle spacing, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle spacing.
5. A vehicle drag coefficient prediction system based on driving environment, characterized in that, The vehicle drag coefficient prediction system based on driving environment includes: The determination module is used to determine the environmental wind speed information corresponding to the predicted itinerary based on real-time meteorological information; The processing module is used to determine the yaw angle based on the environmental wind speed information and vehicle speed, and to determine the vehicle length, vehicle distance, and drag coefficient under the yaw angle based on the vehicle distance of each driving segment, the vehicle length of each driving segment, and the yaw angle. The processing module is also used to determine the joint probability distribution of vehicle length and vehicle distance based on the vehicle length of each driving segment and the vehicle distance of each driving segment; The prediction module is used to obtain the drag coefficient at each moment corresponding to the predicted journey based on the drag coefficient under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
6. The system as described in claim 5, characterized in that, The processing module is also used to determine the wind speed and wind direction angle in the road area based on the environmental wind speed information; The processing module is also used to determine the road orientation angle based on information from the transportation department; The processing module is also used to determine the yaw angle based on the vehicle speed, the wind speed in the road area, the ambient wind direction angle, and the road orientation angle.
7. The system as described in claim 5, characterized in that, The processing module is also used to divide the predicted road segment according to the monitoring points in the traffic management system to obtain the expected traffic flow and vehicle speed of each monitoring point. The processing module is also used to determine the vehicle length based on local traffic management data and sales data. The processing module is also used to determine the distance between vehicles on each road segment based on the estimated traffic flow, the vehicle speed, and the vehicle length.
8. The system as described in claim 5, characterized in that, The prediction module is also used to determine the drag coefficient at each moment corresponding to the predicted journey based on wind tunnel tests and computational fluid dynamics, according to the drag coefficient under the vehicle length, vehicle distance, and yaw angle, and the joint probability distribution of the yaw angle, vehicle length, and vehicle distance.
9. A vehicle drag coefficient prediction device based on driving environment, characterized in that, The device includes: a memory, a processor, and a driving environment-based vehicle drag coefficient prediction program stored in the memory and executable on the processor, the driving environment-based vehicle drag coefficient prediction program being configured to implement the steps of the driving environment-based vehicle drag coefficient prediction method as described in any one of claims 1 to 4.