Vehicle user road and test site road equivalent method based on Internet of Vehicles

Abstract

The invention discloses a vehicle user road and test site road equivalent method based on the Internet of Vehicles, and belongs to the technical field of new energy vehicles. Comprising the steps of obtaining driving information of a vehicle on a user road; according to the driving information of the vehicle on the user road, mileage and vehicle speed analysis is carried out on the vehicle; obtaining user road load data and test site road load data of the vehicle based on the mileage and vehicle speed analysis result and a preset vehicle load acquisition point; and based on the user road load data, the test site road load data and the S-N curve of the material of the vehicle load acquisition point, obtaining an equivalent result of the user road and the test site road of the vehicle. According to the method, real-time collection and calling of user data are rapidly completed by means of the vehicle networking platform, a user and test field association equivalent scheme conforming to the new energy vehicle is constructed on the basis of real vehicle driving information and road information, the reliability cycle mileage of a test field road is compressed, and the test period is shortened.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy vehicles and relates to a method for equating vehicle user roads with test field roads based on vehicle networking. Background Art

[0002] Proving ground road tests are the main verification method in the current commercial vehicle durability and reliability development and design stage. How to accurately reproduce the damage to key parts of the vehicle under actual user conditions in the proving ground and achieve the correlation between the proving ground and user damage is an important challenge faced by the entire commercial vehicle industry in conducting proving ground vehicle durability road tests. Traditional user surveys are conducted in the form of questionnaires, user visits, and telephone consultations. They are usually limited by time and resources and are difficult to fully cover all user groups, resulting in biased results. In addition, the survey process often relies on the user's subjective recollection, which poses the risk of information distortion and misunderstanding. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles, which utilizes the Internet of Vehicles platform to obtain user vehicle usage information in real time, efficiently and accurately, which not only saves manpower and material costs, but also avoids data deviations.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] The present invention provides a method for equivalence between a vehicle user road and a test field road based on an Internet of Vehicles, comprising:

[0006] Obtain the driving information of the vehicle on the user's road;

[0007] Analyze the mileage and speed of the vehicle based on the vehicle's driving information on the user's road;

[0008] Based on the results of the mileage and speed analysis and the preset vehicle load collection points, obtaining user road load data and test site road load data of the vehicle;

[0009] Based on the user road load data and the test field road load data, and the SN curve of the material at the vehicle load collection point, the user road and the test field road equivalence result of the vehicle is obtained.

[0010] Furthermore, the driving information of the vehicle on the user roads includes GPS data, vehicle speed and mileage of the vehicle on various user roads.

[0011] Furthermore, the driving information of the vehicle on the user's road is obtained through the Internet of Vehicles platform.

[0012] Furthermore, based on the driving information of the vehicle on the user's road, the mileage and speed of the vehicle are analyzed, including:

[0013] Based on the GPS data, speed and mileage of the vehicle on various user roads, the mileage ratio and speed distribution of the vehicle on each user road are obtained by taking the median of the data;

[0014] Based on the mileage proportion and speed distribution of the vehicle on each user road and the mandatory scrapping age of the vehicle, the target mileage of the vehicle on each user road is obtained.

[0015] Furthermore, based on the results of the mileage and speed analysis and the preset vehicle load collection points, the user road load data of the vehicle is obtained, including:

[0016] According to the mileage proportion and speed distribution of vehicles on each user road, set the collection mileage for each user road;

[0017] Taking the user road proportion and the user road speed distribution as the working conditions, load collection is performed at the preset vehicle load collection points to obtain the collected mileage load data of the user road;

[0018] According to the statistical law of the collected mileage load data of the user road, the target mileage load data of the user road is obtained through the frequency domain load extrapolation method.

[0019] Furthermore, the vehicle load collection points include: cab, axle head, rear battery frame and frame.

[0020] Furthermore, the test site roads include: cobblestone roads, type C twisted roads, type B twisted roads, short wave roads, long wave roads, oblique twisted roads, straight twisted roads and Belgian roads.

[0021] Further, based on the user road load data and the test field road load data, and the SN curve of the material at the vehicle load collection point, an equivalent result of the correlation between the user road and the test field road of the vehicle is obtained, including:

[0022] Based on the SN curve of the material at the vehicle load collection point, the damage calculation is performed on the user road load data and the test field road load data, and then the damage correlation matrix of the user road and the test field road is constructed;

[0023] Taking the best proving ground road cycle ranking and the shortest proving ground cycle mileage as constraint boundaries, the non-core or replaceable roads in the proving ground roads are eliminated, and the number of cycles for each proving ground road is solved by the least squares method.

[0024] Furthermore, it also includes: performing burr removal, filtering and detrending processing on the collected user road load data and test field road load data.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The method for equivalence of user roads and test field roads of a vehicle provided by the present invention can quickly complete the real-time collection and call of user data with the help of a vehicle networking platform, thus saving a lot of manpower and material costs; based on real vehicle driving information and road information, an equivalent model for the association of user roads and test field roads that conforms to the vehicle is constructed; by using a frequency domain load extrapolation method, the load spectrum of the entire life mileage is predicted through the statistical law of the collected partial load spectrum, thus shortening the time for collecting user road loads and saving test costs; using the best test field road cycle sorting and the shortest test field cycle mileage as constraint boundaries, not only the rationality of the constructed equivalent association scheme of user roads and test field roads is ensured, but also the reliability cycle mileage of the test field roads is compressed, thus shortening the test cycle and saving test costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a method for equivalence of vehicle user roads and test field roads based on Internet of Vehicles provided in an embodiment of the present invention;

[0028] Figure 2 Another flow chart of a method for equivalence of a vehicle user road and a test field road based on the Internet of Vehicles provided in an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of vehicle driving information on a vehicle networking platform in an embodiment of the present invention;

[0030] Figure 4 Schematic diagram of the mileage ratio of vehicles on various user roads in an embodiment of the present invention;

[0031] Figure 5 Schematic diagram of vehicle speed distribution on various user roads in an embodiment of the present invention;

[0032] Figure 6 Schematic diagram of target mileage distribution of vehicles in an embodiment of the present invention;

[0033] Figure 7 It is a schematic diagram of a rain flow matrix of collected mileage load data of a user road and a rain flow matrix of extrapolated target mileage load data in an embodiment of the present invention;

[0034] Figure 8 Schematic diagram of the damage correlation matrix between user roads and test field roads in an embodiment of the present invention;

[0035] Fig. 9 Schematic diagram of damage ratio of load collection points of a user road and a test field road in an embodiment of the present invention;

[0036] Fig.10 A schematic diagram of a road cycle that is equivalent to a user road and a test field road in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. The embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0038] The term "and / or" herein is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " herein generally indicates that the front and back associated objects are in an "or" relationship. The orientation or position relationship indicated by the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "inside", "outside", etc. is based on the orientation or position relationship shown in the accompanying drawings, and is only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. It is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present disclosure / application.

[0039] In addition, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0040] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0041] like Figures 1 to 10As shown, an embodiment of the present invention provides a method for equivalence of vehicle user roads and test field roads based on Internet of Vehicles. Figure 1 and Figure 2 The flowchart is a flow chart of the method for equivalence between vehicle user roads and test field roads based on the vehicle network. The flowchart only shows the logical sequence of the method described in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different Figure 1 or Figure 2 The steps shown or described are accomplished in the order shown.

[0042] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps:

[0043] Step 1: Obtain the vehicle's driving information on the user's road.

[0044] like Figure 3 As shown, in the embodiment of the present invention, the driving information of the vehicle on the user road is obtained through the Internet of Vehicles platform, and the driving information of the vehicle on the user road includes GPS data, vehicle speed and mileage of the vehicle on various user roads. Specifically, the present invention imports the acquisition platform with the help of DBC files, and the data acquisition is completed through the acquisition function in the firmware of the vehicle-mounted data acquisition terminal (T-BOX) or a self-written acquisition program. The DBC files of different types and series of models need to be written in a unified manner.

[0045] Step 2: Analyze the mileage and speed of the vehicle based on the vehicle's driving information on the user's road.

[0046] To ensure that the extracted data is representative, the embodiment of the present invention extracts vehicle driving information of 12 typical users in six cities in six regions of East China, North China, Central China, South China, Northeast China and Southwest China for one month based on the Internet of Vehicles platform, and divides the user roads into urban roads, expressways and general highways. Figure 4 As shown in the figure, the collected driving information is summarized and analyzed, and the mileage proportion of vehicles of 12 typical users on each user road is obtained by taking the median method. Among them, the mileage of urban roads accounts for 15%, the mileage of expressways accounts for 10%, and the mileage of general roads (national / provincial / county roads) accounts for 75%.

[0047] like Figure 5As shown in the figure, the vehicle speed of the user with the largest monthly mileage is analyzed to obtain the speed-mileage ratio of the vehicle on each user road. The driving speed of urban road conditions is mainly concentrated in the [30km / h, 50km / h] range, and the mileage accounts for 80% of the urban road mileage. The driving speed of expressway conditions is mainly concentrated in the [70km / h, 90km / h] range, and the mileage accounts for 85% of the expressway mileage. The driving speed of general highway (national / provincial / county roads) conditions is mainly concentrated in the [40km / h, 60km / h] range, and the mileage accounts for 80% of the general highway mileage.

[0048] like Figure 6 As shown, based on the monthly mileage of 12 typical users, 12 months of operation per year, and the mandatory scrapping age of commercial vehicles is 15 years, the target mileage of the vehicle is 600,000 kilometers obtained by taking the median method.

[0049] Step 3: Combine the results of the mileage and speed analysis and the preset vehicle load collection points to obtain the user road load data and the test site road load data of the vehicle.

[0050] Among them, based on the results of the mileage and speed analysis and the preset vehicle load collection points, the user road load data of the vehicle is obtained, including: taking the vehicle target mileage of 600,000 kilometers as the mileage boundary, taking the user road proportion and the user road speed distribution as the working conditions, formulating a collection plan, and collecting loads at the preset vehicle load collection points. The vehicle load collection points of this embodiment include key vehicle components such as the vehicle cab, axle head, back battery frame and frame.

[0051] Since collecting user road loads for the entire mileage requires a lot of manpower and material costs, the present invention introduces a calculation and analysis method for frequency domain load extrapolation to collect load data for some user roads, and use the statistical laws of the collected load spectra to predict the uncollected load spectra, and obtain the load data for the target mileage of user roads. Specifically, according to the mileage proportion and speed distribution of vehicles on each user road, the collection mileage of each user road is set, the collection mileage of urban roads is set to 90km, the collection mileage of highways is set to 60km, and the collection mileage of general highways (national / provincial / county roads) is set to 450km. Collect 90km of urban road loads and extrapolate to 90,000km of urban road loads; collect 60km of highway loads and extrapolate to 60,000km of highway loads; collect 450km of general highway (national / provincial / county roads) loads and extrapolate to 450,000km of general highway (national / provincial / county roads) loads, Figure 7 The collected mileage load data rain flow matrix of the user road and the extrapolated target mileage load data rain flow matrix are shown.

[0052] Obtain the vehicle's test site road load data, including: based on the Xiangyang test site's cobblestone road, type C twisted road, type B twisted road, short wave road, long wave road, inclined road, straight road, Belgian road and other reliability roads, and complete the vehicle's cab, axle head, rear battery frame, frame and other key components load collection.

[0053] Step 4: Based on the user road load data and the test field road load data, and the SN curve of the material at the vehicle load collection point, obtain the equivalent results of the user road and the test field road of the vehicle.

[0054] The SN curve is a curve that uses the fatigue strength of a material standard specimen as the ordinate and the logarithm of the fatigue life as the abscissa. It represents the relationship between the fatigue strength and fatigue life of a standard specimen under certain cycle characteristics. It is also called a stress-life curve.

[0055] The collected user road load data and test site road load data generally have defects such as glitches and trend items, and the load data needs to be preprocessed. The preprocessing software can adopt software such as GlyphWorks known in the art, so it will not be described here.

[0056] Step 4 specifically includes the following steps:

[0057] Step 401: Based on the SN curve of the material at the vehicle load collection point, damage calculation is performed on the user road load data and the test field road load data, and then a damage correlation matrix of the user road and the test field road is constructed.

[0058] Among them, Figure 8 As shown, a damage correlation matrix between the user road and the test field road is constructed, including: taking the damage of the vehicle load collection point under the characteristic road condition of the test field as the independent variable, taking the damage of the vehicle load collection point of the user road as the target, taking the number of cycles of each road condition as the unknown quantity to be solved, and constructing the damage correlation matrix between the user road and the test field road.

[0059] Step 402: Using the optimal proving ground road cycle ranking and the shortest proving ground cycle mileage as constraint boundaries, non-core or replaceable roads such as long-wave roads and short-wave roads in the proving ground roads are eliminated, and the number of cycles for each proving ground road is solved by the least squares method.

[0060] Among them, it is necessary to ensure that the damage ratio of key focus areas between the test site road and the user road is between 0.5 and 2.5, and the damage ratio of secondary focus areas is between 0.3 and 3.5. Fig. 9The damage ratio of the user road and the test field road at the load collection points such as the cab, axle head, rear battery frame, and frame is shown. Finally, a user road equivalent scheme with 450 cycles as the period was constructed, with the test field cobblestone road, type C twisted road, type B twisted road, inclined road, straight road, and Belgian road as the reliability test sections. Fig.10 The equivalent road cycle of the user road and the proving ground road is shown.

[0061] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for equivalence between vehicle user roads and test field roads based on Internet of Vehicles, characterized in that: include: Obtain the driving information of the vehicle on the user's road; Analyze the mileage and speed of the vehicle based on the vehicle's driving information on the user's road; Combining the results of the mileage and speed analysis with the preset vehicle load collection points, obtaining user road load data and test site road load data of the vehicle; Based on the user road load data and the test field road load data, and the SN curve of the material at the vehicle load collection point, the user road and the test field road equivalence result of the vehicle is obtained.

2. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 1 is characterized in that: The vehicle's driving information on user roads, including the vehicle's GPS data, speed and mileage on various user roads.

3. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 1 is characterized in that: The vehicle's driving information on the user's road is obtained through the Internet of Vehicles platform.

4. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 2 is characterized in that: According to the driving information of the vehicle on the user's road, the vehicle's mileage and speed are analyzed, including: Based on the GPS data, speed and mileage of the vehicle on various user roads, the mileage ratio and speed distribution of the vehicle on each user road are obtained by taking the median of the data; Based on the mileage proportion and speed distribution of the vehicle on each user road and the mandatory scrapping age of the vehicle, the target mileage of the vehicle on each user road is obtained.

5. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 4 is characterized in that: Based on the results of the mileage and speed analysis and the preset vehicle load collection points, the user road load data of the vehicle is obtained, including: According to the mileage proportion and speed distribution of vehicles on each user road, set the collection mileage for each user road; Taking the user road proportion and the user road speed distribution as the working conditions, load collection is performed at the preset vehicle load collection points to obtain the collected mileage load data of the user road; According to the statistical law of the collected mileage load data of the user road, the target mileage load data of the user road is obtained through the frequency domain load extrapolation method.

6. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 1 is characterized in that: Vehicle load collection points include: cab, axle head, rear battery frame and frame.

7. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 1 is characterized in that: The test site roads include: cobblestone road, type C twisted road, type B twisted road, short wave road, long wave road, oblique road, straight road and Belgian road.

8. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 1 is characterized in that: Based on the user road load data and the test field road load data, and the SN curve of the material at the vehicle load collection point, an equivalent result of the correlation between the user road and the test field road of the vehicle is obtained, including: Based on the SN curve of the material at the vehicle load collection point, the damage calculation is performed on the user road load data and the test field road load data, and then the damage correlation matrix of the user road and the test field road is constructed; Taking the optimal proving ground road cycle ranking and the shortest proving ground cycle mileage as constraint boundaries, non-core and / or replaceable roads in the proving ground roads are eliminated, and the number of cycles for each proving ground road is solved by the least squares method.

9. The method for equivalence of vehicle user roads and test field roads based on the Internet of Vehicles according to claim 8, characterized in that: It also includes: deburring, filtering and detrending the collected user road load data and test field road load data.

Images