JP7408258B2 - Development device and method for mode combining slope and speed - Google Patents

Description

The present invention belongs to the field of transportation, and particularly relates to a development device and method for a mode that combines slope and speed.

Vehicle driving modes are an important common fundamental technology in the automotive industry and are the basis of vehicle energy consumption/emissions testing methods and limit standards. However, in the conventional vehicle driving mode, the road gradient is not taken into consideration, and the road gradient has an important influence on the vehicle's driving performance, economy, and emission characteristics. On the other hand, vehicle energy consumption and emissions increase significantly with increasing gradient. On the other hand, the gradient of the road significantly increases the power demand of the vehicle, possibly causing the vehicle to shift into a lower gear in order to ensure sufficient hill-climbing ability.

Although gradients are important, they are rarely included in current modes. The main causes can be summarized into the following two.

First, it is difficult to accurately collect slopes, and the demands on in-vehicle terminals are strict, and there are usually two methods for acquiring slopes in the industry. The first is to install a GNSS base station and obtain altitude information using GPSRTK mode to obtain slope information. This method has problems in that it is difficult to provide base station information (large amount of work and high cost) and that hardware and software resources for acquisition and data processing are large. The second method is to calculate the slope based on the altitude according to the geographical information. However, since the accuracy of the altitude signal is low, the error in the calculated gradient value is large. Second, the development of gradient mode is difficult and requires consideration of gradient, speed and time simultaneously, and there is a complex coupling relationship between the three.

At present, the development methods of gradient mode mainly include short-range analysis method and time-series prediction method. Many slope modes are mainly based on the division of short segments, but are only suitable for vehicle types with long driving segments and simple driving modes, such as tractors, which belong to the direct interception method, and are suitable for slope, speed and Time-complex join problems remain unsolved. Time series analysis methods mainly make predictions using algorithms such as Markov and fuzzy logic, and gradient information is included in the finally constructed mode. Because no data is collected from actual vehicle operation, there are problems such as obvious distortions and discontinuities in steps.

In short, it is of important academic significance and engineering application value to study the gradient data collection method and establish a mode that combines gradient, speed and time according to practice.

In view of the above, it is an object of the present invention to provide a development device for a mode that combines slope and speed, in order to solve the deficiencies of the conventional device.

In order to achieve the above object, the technical solution of the present invention is realized as follows.

The combined slope and speed mode development equipment includes a power supply module, a clock module, a microcontroller, a storage module, a GPS/SINS combined navigation module, a CAN controller, a CAN transceiver module, a GPRS/4G module and a human-computer interaction module; The clock module is fully connected to a power supply module, a microcontroller, and a GPRS/4G module, respectively, and the clock module, microcontroller, and CAN controller are integrated into one control module, and the control module is connected to a GPS/4G module. The CAN controller is fully duplex connected to one end of the CAN transceiver module, and the other end of the CAN transceiver module is fully duplex connected to the SINS composite navigation module, human computer interaction module, and storage module, respectively. Double connected.

Compared with the prior art, the combined slope and speed mode development device according to the present invention has the following advantages:

The combination gradient and speed mode development device according to the present invention is simple in structure, rational in design, and can collect data from the actual running of the vehicle. The mode is developed using a mode construction method that combines gradient and speed, and has server platform and interactive data transmission, which is easy to operate and popularize.

In addition, another object of the present invention is that the traditional gradient acquisition method has drawbacks such as low acquisition accuracy, high cost, and many limited conditions, and the traditional mode development method has the disadvantages of time, speed, etc. , Development of a mode that combines slope and speed in order to overcome the deficiency of not being able to effectively resolve the complex coupling relationship of slope and provide support for better detection of vehicle energy consumption and emissions. The purpose is to provide a method.

In order to achieve the above object, the technical solution of the present invention is realized as follows.

How to develop a mode that combines slope and speed is
S1: collecting vehicle running data using a mode development device that combines slope and speed;
S2, dividing the short segment;
S3, calculating gradients and screening motion segments;
S4, filtering the gradient, selecting a low-pass filter to reduce the influence of the interfering signal on the gradient value, and calibrating the frequency and order of filtering based on the distance-gradient power spectral density and the filtering effect;
S5: determining weighting factors for urban, suburban, and highway, and calculating threshold values;
S6, building urban, suburban, and high-speed mode libraries and analyzing motion features and slope features;
S7, constructing a mode combining speed and gradient;

Furthermore, the collection of vehicle running data in step S1 is as follows:
S11, determining the cities and lines to be collected;
S12, collecting data with a development device in a mode that combines slope and speed;

Furthermore, the division of the short segment in step S2 is as follows:
S21, dividing the traveling segment of the vehicle into a motion segment and an idle segment;
S22, determining whether the motion segment time is less than 10 seconds, and if so, deleting this motion segment; otherwise, switching to the next motion segment.

Furthermore, the calculation of the gradient and the cleaning of the motion segments in step S3 are performed as follows:
S31, calculating a slope value θ using a slope calculation formula;
S32, calculating a segment volatility value F using a volatility calculation formula;
S33, setting a speed variation threshold, deleting motion segments with speed variation greater than the threshold, and screening normal driving segments.

Furthermore, the determination of the weighting coefficients for urban, suburban, and expressway and the calculation of the threshold values in step S5 are as follows:
S51, obtaining the vehicle mileage of the different roads by multiplying the average flow rate of the different roads by the corresponding road length;
S52, calculate the ratio of the total mileage of road vehicles in the three classes of urban, suburban, and expressway to the vehicle mileage of the entire road network, and calculate the mileage ratio of road vehicles in each class as the weight factor for each speed section. the step of
S53, including the step of statistically obtaining the cumulative distribution of speed-traveling distance for three classes of roads: urban, suburban, and highway, and setting the 90% speed quantile of urban and suburban roads as the threshold of the speed section; 3. The method for developing a combined gradient and speed mode according to claim 2.

Furthermore, the construction of urban, suburban, and high-speed mode libraries and analysis of motion features and slope features in step S6 are as follows:
S61, dividing the driving segments into urban, suburban and high-speed short segment libraries according to different maximum speeds;
S62, calculating motion characteristics of urban, suburban and high speed mode libraries;
S63, calculating joint distributions of road gradient-gradient change rate of urban, suburban, and express mode libraries, respectively;

Furthermore, the construction of the mode combining speed and gradient in step S7 is as follows:
S71, setting the total mode time based on the daily average running time of the vehicle;
S72, obtaining different speed interval times by multiplying the total time by weight factors of different road surfaces;
S73, constructing a velocity mode by a short range analysis method;
S74, screening gradient mode segments based on the motion segment times of different speed intervals in the speed mode, and determining an optimal combination of segments as the gradient mode of the motion segment of each speed interval using a minimum error sum of squares method; ,
S75, setting an idle segment between two motion segments including gradients, and obtaining the gradient of the idle segment by linear interpolation;
S76, setting the gradient mode curve of the idle segment by the method of linear interpolation and combining all motion segments and idle segments to obtain the gradient mode curve;
S77, combining the slope mode and the speed mode to obtain a combined slope and speed mode.

Compared with the prior art, the combined slope and velocity mode development method described in the present invention has the following advantages.

The method for developing a mode that combines slope and speed described in the present invention can solve the problem of difficulty in collecting conventional road slopes, and can calculate road slopes more accurately. The present invention provides a method for collecting road gradients using a combined GPS/SINS system, which is suitable for collecting road gradients and is based on the development of gradient mode. The construction of a mode combining slope and speed based on the data segment of the vehicle actually traveling and the joint distribution of the road slope--slope change rate can better reflect the change in road slope. In summary, the method for developing the combined slope and speed mode according to the present invention can provide technical support for government standardization in the field of energy consumption and emissions, development and test design of enterprise vehicle types, and is of great importance. It has great social importance and economic value.

The drawings forming a part of the invention are for a further understanding of the invention, and the illustrative embodiments of the invention and their description are for the interpretation of the invention and do not overshadow the invention. It is not limited.

2 is a flowchart for constructing a combination mode of a development device and a development method of a mode combining slope and speed according to an embodiment of the present invention. 1 is a terminal configuration diagram of a development device and method for a mode combining slope and speed according to an embodiment of the present invention; FIG. FIG. 2 is a schematic diagram of a normal operation segment of the development device and method for a mode combining slope and speed according to an embodiment of the present invention. FIG. 2 is a schematic diagram of the simultaneous distribution of slope and slope change rate of the development apparatus and development method of a mode combining slope and speed according to an embodiment of the present invention. FIG. 2 is a schematic diagram of a speed and slope mode of a development apparatus and a development method of a mode combining slope and speed according to an embodiment of the present invention.

Embodiments of the invention and features in the embodiments can be combined with each other insofar as there is no conflict.
In the description of the present invention, "center", "vertical direction", "horizontal direction", "top", "bottom", "front", "rear", "left", "right", "vertical", "horizontal" ”, “top”, “bottom”, “inside”, “outside”, etc., refer to directions or relationships that are shown in the drawings and may facilitate or explain the present invention. For simplicity only, it is not intended to indicate or imply that the devices or components referred to have a particular orientation or must be constructed or operated in a particular orientation. Furthermore, terms such as "first", "second", etc. are for descriptive purposes only and indicate or imply relative importance or implicitly indicate the number of technical features being indicated. It's not a thing. Thereby, a feature defined as "first,""second," etc. may explicitly include or implicitly include one or more such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

In the description of the present invention, it should be explained that the terms "attachment", "coupling", and "connection" should be broadly understood unless there is a clear provision or limitation to the contrary. For example, it may be a fixed connection, a detachable connection, or an integral connection. The connection may be mechanical or electrical. It may be a direct connection, an indirect connection through an intermediate, or a communication between two parts. Those skilled in the art can understand the specific meanings of the above terms in the present invention depending on the specific situation.

Hereinafter, the present invention will be described in detail in combination with examples with reference to the drawings.
Interpretation of nouns:
The error sum of squares is also called residual sum of squares, intergroup sum of squares, etc. After fitting a suitable model according to the n observations, the unfitted portion is called the residual, and the sum of all n residual squares is called the error sum of squares. The minimum error sum of squares method is to minimize the error sum of squares by finding the optimal combination.
Chi-square test: The chi-square test is a very widely used hypothesis testing method. Applications of categorical data to statistical inference include chi-square tests for comparing two proportions or proportions of two components, chi-square tests for comparing proportions of multiple proportions or proportions of multiple components, and categorical This includes data correlation analysis.
Linear interpolation: Linear interpolation is an interpolation method in which the interpolation function is a first-order polynomial and the interpolation error at the interpolation node is zero. Compared with other interpolation methods such as parabolic interpolation, linear interpolation has the characteristics of simplicity and convenience. The geometric meaning of linear interpolation is to approximately represent the primitive function by a straight line passing through points A and B in the schematic diagram. Linear interpolation can approximately replace the primitive function, and can also obtain values that are not in the table in the process of searching the table by calculation.

As shown in Figures 1 to 5, the development equipment for the combined gradient and speed mode includes a power module, a clock module, a microcontroller, a storage module, a GPS/SINS combined navigation module, a CAN controller, a CAN transceiver module, a GPRS/ The clock module includes a 4G module and a human-computer interaction module, and the clock module is fully duplex connected to a power supply module, a microcontroller, and a GPRS/4G module, respectively, and the clock module, microcontroller, and CAN controller are integrated into one control module. The control module is integrated into a GPS/SINS combined navigation module, a human-computer interaction module, and a storage module, respectively, in full-duplex connection, and the CAN controller is connected in full-duplex to one end of the CAN transceiver module, The other end is full-duplex connected to the vehicle CAN/OBD interface. In this embodiment, the power supply module, clock module, microcontroller, storage module, GPS/SINS combined navigation module, CAN controller, CAN transceiver module, GPRS/4G module and human-computer interaction module are all conventional technologies. The device for developing a mode that combines slope and speed according to the present invention can collect data from the actual running of a vehicle, develops a mode using a method of constructing a mode that combines slope and speed, and develops a mode using a server platform. and interactive data transmission. This device is applied to a vehicle equipped with OBD/CAN. Its main functions are as follows.

Collect CAN/OBD parameters including VCU vehicle speed, engine rotation speed, brake pedal ratio, etc.
Collect GPS parameters including vehicle speed, horizontal speed, vertical speed, brake pedal ratio, etc.
Transmission reliability is high using 4G communication and server platform data interaction.
Use the remote control to enter parameters that are not reported on the CAN channel by the vehicle manufacturer, such as air conditioning, passenger, etc. information.
Smart sleep to avoid battery voltage drop and reduce invalid data.

The combined slope and speed mode development device according to the present invention includes a power supply module, a microcontroller, a storage module, a GPS/SINS combined navigation module, a CAN analysis module, a GPRS/4G module, and a human-computer interaction module. The structure of the module is shown in FIG.
The power supply module primarily provides voltage regulation functionality for the device. The external power supply must be a direct current power supply with a voltage range of 9-36V DC. It may be powered directly by a vehicle cigarette lighter power socket, or it may be powered by another external power source.
The microcontroller module and storage module primarily provide the media and processor for the device. The medium can store computer instructions that are executed by the at least one processor to cause the at least one processor to perform the gradient mode construction method. The method includes parts such as collecting vehicle running data, screening module for segmentation, screening normal driving segments, calculating slope, calculating speed interval weights, and constructing a mode combining slope and speed.

The combined GPS/SINS navigation module and CAN analysis module provides analysis functionality of GPS/CAN parameters for the device. A CAN analysis module is connected to the vehicle CAN interface to analyze the messages uploaded by the vehicle and obtain the vehicle status parameters. The GPS/SINS combined navigation module obtains GPS geographic information of the vehicle, such as latitude and longitude, vertical speed, horizontal speed, and GPS speed, through satellite positioning.

The GPRS/4G module provides data remote transmission functionality for the device. The device uses 4G communication and server platform data interaction, and has the advantages of reliable transmission and high speed.

The human-computer interaction module provides human-computer interaction functionality for the device, including display, remote control, and the like. This module provides a visual interface for the device to display current vehicle status parameters in real time and allows parameters not reported on the CAN channel by the vehicle manufacturer (e.g. air conditioning) using the remote control. and passenger information) to collect more vehicle condition parameters.

How to develop a mode that combines slope and speed is
S1: collecting vehicle running data using a mode development device that combines slope and speed;
S2, dividing the short segment;
S3, calculating gradients and screening motion segments;
S4, filtering the gradient, selecting a low-pass filter to reduce the influence of the interfering signal on the gradient value, and calibrating the frequency and order of filtering based on the distance-gradient power spectral density and the filtering effect;
S5: determining weighting factors for urban, suburban, and highway, and calculating threshold values;
S6, building urban, suburban, and high-speed mode libraries and analyzing motion features and slope features;
S7, constructing a mode combining speed and gradient;

The present invention solves the problem of difficulty in collecting road gradients in the past, and can calculate road gradients more accurately, is suitable for collecting road gradient data over a wide range, and lays the foundation for the development of gradient modes. In addition, a method for collecting road gradients using a GPS/SINS composite system is provided. The construction of a mode combining slope and speed based on the data segment of the vehicle actually traveling and the joint distribution of the road slope--gradient change rate can better reflect the change in road slope. In summary, the method and apparatus for developing a combined slope and speed mode according to the present invention can provide technical support for government standardization in the field of energy consumption and emissions, development and test design of corporate vehicle types. , have significant social importance and economic value.

Collection of vehicle running data in step S1 is as follows:
S11, determining the cities and lines to be collected based on the needs of mode construction;
S12, collecting data with a development device in a mode that combines slope and speed;

In this example, step 1 of the method for developing a mode that combines slope and speed: Based on the needs of mode construction, determine the cities and railways to be collected, use the autonomous driving or planned route driving method, and use the GPS Data including vehicle speed, horizontal speed, vertical speed, engine rotation speed, etc. will be collected at a sampling frequency of 4Hz or higher and transmitted in real time to the mode data management platform via the GPRS network.

The short segment division in step S2 is as follows:
S21, dividing the travel segment of the vehicle into a motion segment and an idle segment;
S22, determining whether the motion segment time is less than 10 seconds, and if so, deleting this motion segment; otherwise, switching to the next motion segment.

In this example, Step 2 of the method for developing the mode combining slope and speed: Based on the GPS vehicle speed and engine rotation speed, the vehicle travel segment is divided into a motion segment and an idle segment, and the motion segment with a time of less than 10 seconds is used. Delete segments.

Calculation of gradient and screening of motion segments in step S3 includes:
S31, calculating a slope value θ using a slope calculation formula;
S32, calculating a segment volatility value F using a volatility calculation formula;
S33, setting a speed variation threshold, deleting motion segments with speed variation greater than the threshold, and screening normal driving segments.

In this embodiment, step 3 of the method for developing a mode that combines slope and speed: Calculate the road slope value θ corresponding to each time of the motion segment using the calculation formula shown below.

However, vz is the vertical speed and vx is the horizontal speed, and when the horizontal speed is very small and the vehicle suddenly accelerates and decelerates, the error in the slope value is large. Current processing method: Set the gradient to 0 in a mode where the horizontal speed is less than 1 km/h. A speed variation threshold is set, motion segments with speed variation greater than the threshold are deleted, and normal driving segments are screened. The formula for calculating the degree of variability is as follows.

However, F is the segment variation degree. vmax,i is the i-th local maximum value between 20% and 80% of the segment time, and j is the number of local maximum values. vmin,i is the i-th minimum value between 20% and 80% of the segment time, and k is the number of minimum values. vmean is the average value between 20% and 80% of the segment time.

In this embodiment, step 4 of the method for developing a combined slope and velocity mode: select a low-pass filter to reduce the influence of the interference signal on the slope value. Calibrate the filtering frequency and order based on the distance-gradient power spectral density and the filtering effect.

In step S5, the determination of weighting coefficients for cities, suburbs, and expressways and calculation of threshold values are as follows:
S51, obtaining the vehicle mileage of the different roads by multiplying the average flow rate of the different roads by the corresponding road length;
S52, calculate the ratio of the total mileage of road vehicles in the three classes of urban, suburban, and expressway to the vehicle mileage of the entire road network, and calculate the mileage ratio of road vehicles in each class as the weight factor for each speed section. the step of
S53, including the step of statistically obtaining the cumulative distribution of speed-traveling distance for three classes of roads: urban, suburban, and highway, and setting the 90% speed quantile of urban and suburban roads as the threshold of the speed section; 3. The method for developing a combined gradient and speed mode according to claim 2.
In this example, step 5 of the method for developing a mode that combines slope and speed: The structural ratio of the driving road surface (urban, suburban, highway) of the vehicle type is statisticized, and the average traffic flow rate of the different road surfaces is calculated based on the structural ratio of the different road surfaces. is multiplied to obtain the traffic volume ratio of different road surfaces, that is, the weight factor of different road surfaces. Step 6: Divide the urban, suburban, and high-speed short segment libraries based on the maximum speed threshold of the segment, and the maximum speed threshold is determined by the 90% quantile of the road speed distribution corresponding to the city and suburban.

The construction of urban, suburban, and high-speed mode libraries and analysis of motion characteristics and slope characteristics in step S6 are as follows:
S61, dividing the driving segments into urban, suburban and high-speed short segment libraries according to different maximum speeds;
S62, calculating motion characteristics of urban, suburban and high speed mode libraries;
S63, calculating joint distributions of road gradient-gradient change rate of urban, suburban, and express mode libraries, respectively;

The construction of the mode combining speed and gradient in step S7 is as follows:
S71, setting the total mode time based on the daily average running time of the vehicle;
S72, obtaining different speed interval times by multiplying the total time by weight factors of different road surfaces;
S73, constructing a velocity mode by a short range analysis method;
S74, screening gradient mode segments based on the motion segment times of different speed intervals in the speed mode, and determining an optimal combination of segments as the gradient mode of the motion segment of each speed interval using a minimum error sum of squares method; ,
S75, setting an idle segment between two motion segments including gradients, and obtaining the gradient of the idle segment by linear interpolation;
S76, setting the gradient mode curve of the idle segment by the method of linear interpolation and combining all motion segments and idle segments to obtain the gradient mode curve;
S77, combining the slope mode and the speed mode to obtain a combined slope and speed mode.

In this embodiment, step 7 of the method for developing a mode combining slope and speed: setting the mode total time based on the daily average driving time of the vehicle (the total time is less than 2400 seconds), and setting the total time on different road surfaces. Multiply the weighting factor of to get the time of different speed intervals.
Step 8: Build velocity mode using short range analysis method. Screen gradient mode segments based on the time of motion segments of different velocity intervals in velocity mode. Calculate the error sum of squares of the slope-slope change rate distribution of multiple slope segment combinations in different speed intervals and the slope-slope change rate distribution of all motion segments in the corresponding speed interval, and calculate the error sum of squares from each speed interval. Select a combination and set it to gradient mode for this speed section.

Step 9: Set an idle segment between two motion segments containing gradients, and obtain the gradient of the idle segment by linear interpolation between the end point gradient value of the previous motion segment and the start point gradient value of the next motion segment. Set the slope value of the first idle segment to the start slope value of the first motion segment, set the slope value of the last idle segment to the end slope value of the last motion segment, and combine all motion and idle segments. Obtain the gradient mode curve.

Step 10: Combine the slope mode and speed mode to obtain a combined slope and speed mode.

Example 1
Hereinafter, the method and apparatus of the invention will be described in more detail with reference to the drawings. Figure 1 shows the entire flow of the development method for a mode that combines slope and speed, and the specific steps are as follows.

Actual driving data collection Based on the needs of mode construction, determine the city and line to be collected, use autonomous driving or planned route driving method, collect data, and manage mode data via GPRS network. Submit to the platform in real time. Among them, the data acquisition module must include a hardware controller, a GPS/SINS acquisition module, a CAN analysis module, a 4G module, etc., and the terminal configuration is shown in FIG. 2. The collection parameters must include GPS vehicle speed, horizontal speed, vertical speed, engine rotation speed, etc., and the collection frequency is 4Hz or higher.

Dividing short segments: Dividing short segments: Divide the running segment into multiple short segments, one of which is a motion segment, based on the basis for dividing short segments from the start of one idle to the start of the next idle. and adjacent idle segments. In it, the motion of the vehicle from one stop to the next start is defined as the idle segment, the motion of the vehicle from one start to the next stop is defined as the motion segment, and then the short segment is defined as the motion segment and the idle segment. Divide into. Segments with a duration of less than 10 seconds are not suitable for playback on the hub and their ratio is low, so they are deleted.

Gradient Calculation and Motion Segment Screening The measurement of the road slope angle on which the vehicle is currently traveling is realized by calculating the ratio of the vertical speed and horizontal speed of the current sampling point to obtain the tangent value of the road surface slope. The calculation formula is as follows.

However, θ is the slope angle of the road the vehicle is currently traveling on, the unit is °, vz is the vertical speed of the vehicle at this time, the unit is km/h, and vx is the horizontal speed of the vehicle at this time, the unit is is km/h.

When the vehicle is running at low speed, the vehicle speed is low, so the ratio fluctuates within a large range, causing an error. In addition, rapid acceleration and deceleration of the vehicle body causes the vehicle body to tilt, causing a large error in the measured road slope. Therefore, it is necessary to remove the sport driving segment. The specific processing method is as follows.
If the horizontal speed is less than 1 km/h, set the slope to 0.

A volatility threshold is set and the motion segments whose volatility is greater than the threshold are deleted, and the normal driving segment is shown in FIG. The calculation process for volatility is as follows.
(1) Select the data between 20% and 80% of the time in the motion segment, and calculate the average value, maximum value, and minimum value.
(2) The formula for calculating the degree of variability is as follows.

However, F is the segment variation degree. vmax,i is the i-th local maximum value between 20% and 80% of the segment time, and j is the number of local maximum values. vmin,i is the i-th minimum value between 20% and 80% of the segment time, and k is the number of minimum values. vmean is the average value between 20% and 80% of the segment time.

Gradient Filtering GPS is affected by interfering signals and has problems such as glitches and steps. This patent uses a Butterworth filter to filter data. Compared to other filters, the Butterworth filter has the advantage of balanced characteristics in three respects: linear phase, attenuation slope, and load characteristics. In the process of using a Butterworth filter, two parameters need to be calibrated: order and cutoff frequency.

Since the various interference signals received by the GPS are relatively high frequency signals, a low pass filter is selected to reduce noise. The velocity-time-gradient data obtained by data collection is converted into distance-gradient data, and power spectrum density analysis is performed on the distance-gradient data. After being interfered with by various signals, the frequency range of the calculated road gradient increases obviously. To avoid the cutoff frequency of the filter being too low, the frequency corresponding to the 95% quantile of the total power density is selected as the initial cutoff frequency.

The order of filtering is selected by comparing filtering effects across multiple experiments. According to the filtering frequency selection method described above, filtering is performed using filters of second order or higher, and the filtering order is selected using the absolute error as an index. In this patent, the filtering order is chosen as 4.
Determination of urban, suburban and highway weighting factors and calculation of thresholds

When developing gradients, weighting factors need to be calculated. The present invention uses the proportion of road vehicle travel distance of each class on the road surface as a weighting factor, calculates the road vehicle travel distance of three classes of urban, suburban, and expressway, and finally calculates the weight factor of each speed section. get. The formula for calculating road travel distance is as follows.

However, VKTk is the sum of k-class road vehicle travel distances, nk is the number of k-class roads, Qk,i is the daily average flow rate of the i-th roadway on k-class roads, and Lk, i is the length of the i-th roadway of the k class road.

The formula for calculating the weight factor for each speed section is as follows.

However, ωi is the weight of the speed section, and VKTi is the sum of the road vehicle travel distances of the i class.

The cumulative value of vehicle travel distance in each speed section (speed interval is 1 km/h) on roads of three classes: urban, suburban, and expressways is calculated, and the speed-travel on roads of three classes of urban, suburban, and expressways is calculated. Obtain the cumulative distribution of distances. The speed zone threshold is the 90% speed quantile of urban and suburban roads.
Construction of urban, suburban, and high-speed mode libraries and analysis of motion features and gradient features The screened driving segments are divided into urban, suburban, and high-speed short segment libraries according to the differences in maximum speed. Operating time, acceleration time, deceleration time, constant speed time, idle time, operating distance, maximum speed, average speed, operating speed, speed standard deviation, maximum acceleration, average acceleration of acceleration segment for urban, suburban and high speed mode libraries. , calculate the motion features including the minimum acceleration, the average acceleration of the deceleration segment, and the acceleration standard deviation. The joint distributions of road slope-gradient change rate for urban, suburban and express mode libraries are calculated respectively, and the joint distributions are shown in Figure 4.

Construction of speed-gradient combination mode Set the mode total time based on the daily average driving time of the vehicle (the total time is less than 2400 seconds), and calculate the time of different speed sections by multiplying the total time by the weight factor of different road surfaces. get. According to statistics and analysis, the weight factors of urban, suburban and high-speed light commercial vehicles are 31.17%, 39.66% and 29.17%, respectively. The duration of the gradient mode is set to 1800 s, and the total short distance time of each speed section is calculated based on the weighting coefficient. Time of each section: Ti = 1800*ωi, i = 1, 2, 3. City: 735 seconds, suburban: 615 seconds, highway: 450 seconds. The velocity mode is constructed using the short range analysis method set forth in standard GB/T38146.1. The velocity mode curve is shown in FIG.

Based on the time and the number of segments of the urban speed section in the speed mode curve, the number and time of the gradient segments of the urban speed section to be selected in the gradient mode are selected. According to the Cartesian product, freely combine the slope segments selected based on the segment time, and compare the errors with respect to the slope-gradient change rate distribution of the combined segments and the simultaneous distribution of the slope-gradient change rate of the urban speed section, Determine the optimal urban gradient mode based on the error comparison results. The suburban and high-speed gradient modes are similar.

As shown in Figure 5, an idle segment is set between two motion segments that include gradients, and the gradient of the idle segment is determined by linear interpolation between the end point gradient value of the previous motion segment and the start point gradient value of the next motion segment. obtain. Set the slope value of the first idle segment to the start slope value of the first motion segment, set the slope value of the last idle segment to the end slope value of the last motion segment, and combine all motion and idle segments. Obtain the gradient mode curve.
As shown in FIG. 5, the slope mode and speed mode are combined to obtain a combined slope and speed mode.

The above description is only a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and principles of the present invention shall fall within the protection scope of the present invention.

Claims (4)

including a power supply module, a clock module, a microcontroller, a storage module, a GPS/SINS combined navigation module, a CAN controller, a CAN transceiver module, a GPRS/4G module and a human-computer interaction module;
The clock module is fully connected to a power supply module, a microcontroller, and a GPRS/4G module, respectively, and the clock module, microcontroller, and CAN controller are integrated into one control module, and the control module is connected to a GPS/4G module. The CAN controller is fully duplex connected to one end of the CAN transceiver module, and the other end of the CAN transceiver module is fully duplex connected to the SINS composite navigation module, human computer interaction module, and storage module, respectively. double connected,
The microcontroller at least controls the collection of vehicle driving data, the screening module for segmentation, the screening of normal driving segments, the calculation of slope, the calculation of weights of speed intervals, and the construction of a combined slope and speed mode. , the GPS/SINS combined navigation module acquires at least GPS geographic information of the vehicle including latitude and longitude, vertical speed, horizontal speed, and GPS speed through satellite positioning, and the CAN controller at least acquires the vehicle's GPS geographic information including latitude and longitude, vertical speed, horizontal speed, and GPS speed through satellite positioning; Connecting to the CAN interface, analyzing the messages uploaded by the vehicle and obtaining the vehicle status parameters, the GPRS/4G module at least provides data remote transmission function through 4G communication and data interaction of the server platform, and The computer interaction module at least provides for real-time display of vehicle condition parameters and collection of vehicle body condition parameters via the display and remote control;
Step S1, at least the GPS/SINS combined navigation module and the CAN controller collect vehicle driving data;
Step S2, at least the microcontroller divides the short segment;
step S3, at least said microcontroller calculates gradients and screens motion segments;
Step S4, at least the microcontroller filters the gradient, selects a low-pass filter, reduces the influence of the interference signal on the gradient value, and selects the frequency and order of filtering based on the power spectral density of the distance-gradient and the filtering effect. calibrate,
step S5, at least the microcontroller determines urban, suburban and high speed weighting factors and calculates threshold values;
step S6, at least the microcontroller builds an urban, suburban, and fast mode library and analyzes motion features and slope features;
Step S7, at least the microcontroller establishes a combined speed and slope mode;
The weighting coefficient is calculated by calculating the ratio of the total mileage of road vehicles in the three classes of urban, suburban, and expressway to the vehicle mileage of the entire road network, and calculating the ratio of the total mileage of each class to the sum of the mileage of road vehicles in the three classes. It is the ratio of the distance traveled by road vehicles,
The construction of the mode combining speed and slope in step S7 differs from step S71 in setting the mode total time based on the average daily driving time of the vehicle and in step S72 multiplying the total time by weighting factors of different road surfaces. S73, constructing a speed mode by a short range analysis method; S74, screening gradient mode segments based on the motion segment times of different speed zones in the speed mode, and using a minimum error sum of squares method; S75: setting an idle segment between two motion segments including gradients, and determining the gradient of the idle segment by linear interpolation; S76, setting the gradient mode curve of the idle segment by the method of linear interpolation, and combining all motion segments and idle segments to obtain the gradient mode curve; S77, combining the gradient mode and the speed mode to obtain the gradient mode curve; and obtaining a mode combining speed and speed.
A development device for a mode that combines gradient and speed. A development method using the development device for a mode combining slope and speed according to claim 1,
Step S1: collecting vehicle driving data via at least the GPS/SINS combined navigation module and the CAN controller;
step S2, at least via the microcontroller, dividing a short segment;
step S3, at least via said microcontroller, calculating gradients and screening motion segments;
Step S4, at least through the microcontroller, filter the gradient, select a low-pass filter to reduce the influence of the interference signal on the gradient value, and filter the frequency based on the distance-gradient power spectral density and the filtering effect; and calibrating the order.
step S5, at least via the microcontroller, determining urban, suburban and high speed weighting factors and calculating threshold values;
Step S6: building an urban, suburban and fast mode library and analyzing motion features and gradient features via at least said microcontroller;
step S7, establishing a combined speed and slope mode via at least the microcontroller;
The weighting coefficient is calculated by calculating the ratio of the total mileage of road vehicles in the three classes of urban, suburban, and expressway to the vehicle mileage of the entire road network, and calculating the ratio of the total mileage of each class to the sum of the mileage of road vehicles in the three classes. It is the ratio of the distance traveled by road vehicles,
The construction of the mode combining speed and slope in step S7 differs from step S71 in setting the mode total time based on the average daily driving time of the vehicle and in step S72 multiplying the total time by weighting factors of different road surfaces. S73, constructing a speed mode by a short range analysis method; S74, screening gradient mode segments based on the motion segment times of different speed zones in the speed mode, and using a minimum error sum of squares method; S75: setting an idle segment between two motion segments including gradients, and determining the gradient of the idle segment by linear interpolation; S76, setting the gradient mode curve of the idle segment by the method of linear interpolation, and combining all motion segments and idle segments to obtain the gradient mode curve; S77, combining the gradient mode and the speed mode to obtain the gradient mode curve; and obtaining a mode combining speed and speed.
A development method using a mode development device that combines gradient and speed. According to claim 2, the collection of vehicle travel data in step S1 includes the following steps: S11, determining the city and railway line to be collected; and S12, collecting the data by a development device in a mode combining slope and speed. How to develop modes that combine slope and speed as described. The short segment division in step S2 includes the following steps: step S21, dividing the running segment of the vehicle into a motion segment and an idle segment, and step S22, determining whether the motion segment time is less than 10 seconds, and if so, 3. A method for developing a combined slope and speed mode as claimed in claim 2, including the step of deleting this motion segment and otherwise switching to the next motion segment.

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