JP2023078055A - Device and method for developing automobile traveling mode including road grade - Google Patents

Abstract

To provide a device and method for developing an automobile traveling mode including a road grade.SOLUTION: A development device comprises: a collection data processing module; a motion segment library partitioning and weighting factor determination module; a gradient mode and velocity mode feature parameter extraction module; a mode building module; and an electronic apparatus. The development device extracts characteristic parameters of a gradient mode and a velocity mode by actually collecting data such as a vehicle speed and a gradient in a vehicle traveling process. The development device constructs an automobile traveling mode including the gradient mode by screening a representative motion segment that simultaneously satisfies characteristic parameters of the gradient mode and the velocity mode by methods for extreme value test, mean value test and minimum deviation sum of squares test. The development device can accurately evaluate actually traveling fuel economy and emission levels for mountain and urban vehicles, by the drum test of a test room using this mode as a test mode.SELECTED DRAWING: Figure 4

Description

TECHNICAL FIELD The present invention belongs to the field of transportation, and more particularly, to an apparatus and method for developing vehicle driving modes including road gradients.

At present, all driving modes used in fuel consumption and emission tests of Chinese automobiles are speed-time modes. Since road grade information is not included, it is not possible to reflect the impact of grade changes on overall vehicle fuel economy and emissions. A change in grade will inevitably cause a change in overall vehicle traction, which in turn leads to a change in engine mode point and ultimately to a change in overall vehicle fuel economy and emissions. Therefore, by using simple speed-time vehicle driving modes, it is not possible to accurately evaluate the actual driving fuel consumption and emission characteristics of vehicles in mountainous areas and urban areas, and vehicle driving including gradient modes cannot be accurately evaluated. I need to develop a mod.

In view of these, the present invention solves the problem that the conventional automobile driving mode cannot accurately evaluate the actual driving fuel consumption and emission characteristics of vehicles in mountainous areas and urban areas. It is an object of the present invention to provide a vehicle driving mode development device including

To achieve the above objects, the technical solutions of the present invention are implemented as follows.

A vehicle driving mode development device including road gradient includes a collection data processing module, a motion segment library division and weight factor determination module, a gradient mode and speed mode feature parameter extraction module, a mode construction module and an electronic device, wherein the collection The data processing module, the motion segment library division and weighting factor determination module, the gradient mode and velocity mode feature parameter extraction module, and the mode construction module are signal-connected in order, and the collected data processing module, the motion segment library division and weighting module are sequentially connected. The coefficient determination module, gradient mode and velocity mode feature parameter extraction module, and mode construction module are all connected to the electronic equipment,
The electronic device includes a processor and memory communicatively coupled to the processor for storing instructions for execution by the processor.

Compared with the prior art, the vehicle driving mode including road gradient development device according to the present invention has the following advantages.

The vehicle driving mode development device including road gradient according to the present invention is simple in structure and reasonable in design. Each module separately divides, cleans and supplements the vehicle running segment, calculates the weighting factor of different speed sections, extracts the characteristic parameters of slope mode and speed mode, screens the typical motion segments of different speed sections, By finally constructing a vehicle driving mode that includes road gradients, it effectively solves the problem that the driving mode cannot accurately evaluate the actual driving fuel economy and emission characteristics of vehicles in mountainous areas and cities. solve.

Another object of the present invention is to actually collect data such as vehicle speed and gradient that change synchronously with time in the vehicle driving process, so as to construct a vehicle driving mode that includes a gradient mode. It is to provide a driving mode development method. Using this mode as a test mode, laboratory drum tests can accurately assess real-world fuel economy and emissions levels for mountain and urban vehicles.

To achieve the above objects, the technical solutions of the present invention are implemented as follows.

The development method of the car driving mode including the road gradient is
S1, dividing, cleaning and replenishing the vehicle operation segment by the collected data processing module;
S2, determining slow, medium, and high speed motion segment library partitioning and weighting factors by a motion segment library partitioning and weighting factor determination module;
S3, extracting the characteristic parameters of the gradient mode and the velocity mode by means of the characteristic parameter extraction module of the gradient mode and the velocity mode;
S4, building a vehicle driving mode, including road grade, by a mode building module.

Furthermore, the division, cleaning and replenishment of vehicle operation segments described in step S1 are
A1, based on the vehicle speed and engine rotation speed of the collected data, determining the idling and motion states of the vehicle according to the determining principle;
A2, dividing the idle and motion segments according to the decision principle;
A3, cleaning and supplementing the motion segment according to the motion segment data missing rate, maximum acceleration/deceleration and maximum vehicle speed requirements;
A4, calculating the total idle and motion segment times respectively to obtain the idle to motion time ratio of the vehicle.

The judging principle described in step A1 is:
A11, determining whether vehicle speed <1 km/h and engine speed >0 rpm, if yes, determine that the vehicle is in an idle state, otherwise proceed to the next step;
A12, determining whether the vehicle speed≧1 km/h and the engine speed>0 rpm, if so, determine that the vehicle is in motion; if not, switch to the next vehicle operation segment; ,including.

Further, the cleaning and replenishment of motion segments in step A3:
A31, determine whether the maximum acceleration amax of the motion segment is greater than 6m/s2 or whether the minimum deceleration amin of the motion segment is less than -6m/s2; directly deleting, else proceeding to the next step;
A32, determine whether the maximum speed of the motion segment is less than 5km/h or greater than 120km/h, if yes, delete this motion segment directly; step to step, and
A33, determining whether the missing rate is greater than or equal to 5%, if yes, directly deleting this motion segment, otherwise proceeding to the next step;
A34, filling in missing data including vehicle speed and slope using a cubic B-spline interpolation method.

Furthermore, the slow, medium, and fast motion segment library division and weighting factor determination in step S2 are:
B1, partitioning the slow, medium, and fast motion segment libraries;
B2, determining low, medium, and high speed interval weighting factors.

The slow, medium, and fast motion segment library division in step B1 is
B11, determine whether the average speed of the motion segment is greater than 0 km/h and is less than or equal to 30 km/h, if yes, it is a low speed motion segment library; a step of proceeding to the step of
B12, determine whether the average speed of the motion segment is greater than 30km/h and less than or equal to 40km/h, if yes, it is a medium speed motion segment library; otherwise, a step to proceed to the next step;
B13, determining whether the average speed of the motion segment is greater than 40km/h, if yes, high speed motion segment library; otherwise, switching to the next motion segment.

Furthermore, the characteristic parameter extraction of the gradient mode and the velocity mode in step S3 is
C1, extracting feature parameters of gradient modes;
C2, extracting characteristic parameters of velocity modes.

Furthermore, the construction of the vehicle driving mode including the road gradient in step S4 is
D1, screening typical motion segments;
D2, determining the number and duration of motion segments that make up the slow, medium, and fast modes;
D3, building motion segment combinations and modes.

Further, the screening of typical motor segments in step D1 is
D11, performing extremum testing to test and screen the motion segment based on the extreme parameter information of the motion segment;
D12, performing a mean test to test and screen the motion segment based on the mean parameter information of the motion segment;
D13, screening slow, medium, and fast typical motion segments using the least deviation sum of squares principle, respectively.

Furthermore, the determination of the number and duration of motion segments that make up the slow, medium, and high speed modes in step D2 is
D21, calculating the average time of each motion segment in the slow, medium, and fast typical motion segment libraries, respectively, to determine the number of motion segments that make up the slow, medium, and fast modes;
D22, statistics of the duration of the motion segments of slow, medium and fast intervals;
D23, calculating the number of motion segments with corresponding durations, sorting the durations of the motion segments, and calculating the cumulative frequency distribution of the durations of the motion segments;
D24, based on the number of motion segments determined by different speed intervals, divide the cumulative distribution into several halves, calculate the duration corresponding to the 50% quantile of each halves, and and a step of time.

Furthermore, the combination of motion segments and construction of modes in step D3 is
D31, selecting motion segments from the slow, medium and fast motion segment libraries respectively and randomly combining them based on the number of motion segments to form several slow, medium and fast modes;
D32, screening low, medium, and high speed modes that simultaneously satisfy the joint slope-slope change rate distribution and the joint velocity-acceleration distribution;
D33, Statistics the driving rules of the vehicle, determine the average time of idle in the vehicle start phase as the idle mode at the beginning of the mode, idle during and at the end of the motion segment based on the number of intervals between motion segments; adding each segment;
D34, building a vehicle driving mode including road gradient.

Compared with the prior art, the vehicle driving mode development method including road gradient according to the present invention has the following advantages.

A method for developing a vehicle driving mode including road gradient according to the present invention, wherein the present invention actually collects data such as vehicle speed and gradient in the vehicle driving process to determine characteristic parameters of gradient mode and speed mode. Extract. Constructing a vehicle running mode including a slope mode by screening representative motion segments that simultaneously satisfy the characteristic parameters of the slope mode and the speed mode by methods of extreme value test, mean value test and minimum deviation sum of squares test, The slope and vehicle speed change synchronously with time. Using this mode as a test mode, laboratory drum tests can accurately assess real-world fuel economy and emissions levels for mountain and urban vehicles.

The drawings forming a part of the present invention are for further understanding of the present invention, and the exemplary embodiments of the present invention and their description are for interpreting the present invention. It is not limited.

FIG. 4 is a schematic diagram of gradient mode feature parameters of the vehicle driving mode development device and method including road gradient according to an embodiment of the present invention; FIG. 4 is a schematic diagram of speed mode characteristic parameters of the vehicle driving mode development device and method including road gradient according to an embodiment of the present invention; FIG. 4 is an exemplary schematic diagram for determining the duration of a low-speed interval motion segment of the vehicle driving mode development apparatus and method including road gradient according to an embodiment of the present invention; FIG. 3 is an exemplary schematic diagram of a vehicle driving mode including road gradient of the vehicle driving mode including road gradient development method and apparatus according to an embodiment of the present invention;

It should be noted that embodiments of the invention and features in embodiments can be combined with each other unless they conflict.

In describing the present invention, it is to be understood that "middle", "longitudinal", "horizontal", "upper", "lower", "front", "back", "left", "right", Directions or relationships indicated by terms such as "vertical", "horizontal", "top", "bottom", "inside", "outside" are those shown in the drawings and are used to describe the present invention. Merely for the sake of ease or simplification of description, it is not intended to indicate or imply that the devices or elements referred to have a particular orientation, or that they must be configured or operated in a particular orientation. Accordingly, it should not be taken as limiting the invention. Also, the terms "first", "second", etc. are only for the purpose of describing the purpose and indicate or imply relative importance or implicitly indicate the number of technical features indicated. should not be taken for granted. Thereby, a feature that is qualified as "first," "second," etc., may expressly or implicitly include one or more of such features. In the description of the present invention, unless otherwise stated, "plurality" means two or more.

In describing the present invention, the terms "attachment", "coupling", and "connection" should be broadly understood unless otherwise clearly defined or limited. For example, it may be a fixed connection, a detachable connection, or an integral connection. It may be a mechanical connection or an electrical connection. It may be a direct connection, an indirect connection through an intermediate medium, or a communication inside two components. Those skilled in the art can understand the specific meanings of the above terms in the present invention according to the specific situation.

Hereinafter, the present invention will be described in detail in combination with embodiments with reference to the drawings.

Noun Interpretation:

Cubic Spline Interpolation: CubicSplineInterpolation (abbreviated as Spline Interpolation) is a smooth curve through a series of data points, mathematically obtaining a set of curve functions by solving the 3-moment equation It's a process.

In actual calculations, boundary conditions need to be introduced to complete the calculations. Although general computational methods books do not explain the definition of no-node boundaries, all numerical computation software such as Matlab uses no-node boundary conditions as default boundary conditions.

Sum of Squares of Deviations: Sum of Squares of Deviations is the sum of the squared differences between each term and the mean term. Definition: Let x be a random variable and η=x−Ex, then η is called the deviation of x. It reflects the degree of deviation of x from the mathematical expectation E x .

As shown in FIGS. 1 to 4, the vehicle driving mode development device including road gradient includes a collection data processing module, a motion segment library division and weight factor determination module, a gradient mode and speed mode feature parameter extraction module, a mode a construction module and an electronic device, wherein the collected data processing module, the motion segment library division and weighting factor determination module, the gradient mode and velocity mode feature parameter extraction module, and the mode construction module are sequentially signal-connected; The processing module, the motion segment library partitioning and weighting factor determination module, the slope mode and velocity mode feature parameter extraction module, and the mode construction module are all connected to the electronic device,

The electronic device includes a processor and memory communicatively coupled to the processor for storing instructions for execution by the processor.

In this embodiment, the vehicle driving mode development device including road gradient includes a collection data processing module for segmenting, cleaning and supplementing vehicle driving segments, and a different speed interval motion segment library based on the average speed of the motion segments. and a motion segment library partitioning and weighting factor determination module for calculating the weighting factors of different speed intervals, and for extracting the feature parameters of the gradient mode and velocity mode of the low, medium and high speed segment libraries Gradient mode and speed mode feature parameter extraction module and to screen typical motion segments of different speed sections and match the duration of idle and motion modes to finally build car driving modes including road gradients and a mode-building module of

An electronic device is provided that includes at least one processor and a memory communicatively coupled to the at least one processor. However, instructions executable by the at least one processor are stored in the memory, and the instructions are executed by the at least one processor to cause the at least one processor to perform the method. A processor in this electronic device has at least the same advantages as the above method since the above method is executed.

A medium is provided having computer instructions stored thereon for performing the above method by a computer. The computer instructions on this medium have at least the same advantages as the method described above because the method is executed by a computer.

The development method of the car driving mode including the road gradient is
S1, dividing, cleaning and replenishing the vehicle operation segment by the collected data processing module;
S2, determining slow, medium, and high speed motion segment library partitioning and weighting factors by a motion segment library partitioning and weighting factor determination module;
S3, extracting the characteristic parameters of the gradient mode and the velocity mode by means of the characteristic parameter extraction module of the gradient mode and the velocity mode;
S4, building a vehicle driving mode, including road grade, by a mode building module.

The present invention extracts characteristic parameters of slope mode and speed mode by actually collecting data such as vehicle speed and slope in the vehicle running process. Constructing a vehicle running mode including a slope mode by screening representative motion segments that simultaneously satisfy the characteristic parameters of the slope mode and the speed mode by methods of extreme value test, mean value test and minimum deviation sum of squares test, The slope and vehicle speed change synchronously with time. Using this mode as a test mode, laboratory drum tests can accurately assess real-world fuel economy and emissions levels for mountain and urban vehicles.

The division, cleaning and replenishment of the vehicle operation segment described in step S1 are:
A1, based on the vehicle speed and engine rotation speed of the collected data, determining the idling and motion states of the vehicle according to the determining principle;
A2, dividing the idle and motion segments according to the decision principle;
A3, cleaning and supplementing the motion segment according to the motion segment data missing rate, maximum acceleration/deceleration and maximum vehicle speed requirements;
A4, calculating the total time of the idle segment and the motion segment, respectively, to obtain the idle to motion time ratio of the vehicle.

The judging principle described in step A1 is:
A11, determining whether vehicle speed <1 km/h and engine speed >0 rpm, if yes, determine that the vehicle is in an idle state, otherwise proceed to the next step;
A12, determining whether the vehicle speed≧1 km/h and the engine speed>0 rpm, if so, determine that the vehicle is in motion; if not, switch to the next vehicle operation segment; ,including.

The cleaning and replenishment of the motion segments in step A3 are:
A31, determine whether the maximum acceleration amax of the motion segment is greater than 6m/s2 or whether the minimum deceleration amin of the motion segment is less than -6m/s2; directly deleting, else proceeding to the next step;
A32, determine whether the maximum speed of the motion segment is less than 5km/h or greater than 120km/h, if yes, delete this motion segment directly; step to step, and
A33, determining whether the missing rate is greater than or equal to 5%, if yes, directly deleting this motion segment, otherwise proceeding to the next step;
A34, filling in missing data including vehicle speed and slope using a cubic B-spline interpolation method.

In the present embodiment, segmentation, cleaning and replenishment of vehicle operation segments

First, based on the vehicle speed and engine rotation speed of the collected data, the idling and motion states of the vehicle are determined. If ≧1 km/h and engine speed>0 rpm, it is determined that the vehicle is in motion.

Divide the idle and motion segments according to the above decision principle. The duration of the idle segment is defined as ≤300s. The motion segment starts at a vehicle speed of 0 km/h and ends again at a vehicle speed of 0 km/h, and the duration of the motion segment is defined as ≧5 s and ≦3600 s. There should be a one-to-one set of idle segments and motion segments.

Cleaning and supplementing the motion segment according to the data missing rate, maximum acceleration/deceleration and maximum vehicle speed requirements of the motion segment, if the maximum acceleration amax>6m/s2 or minimum deceleration amin<-6m/s2 of the motion segment , directly delete this motion segment, if the maximum velocity of the motion segment <5 km/h or >120 km/h, then directly delete this motion segment, if the missing rate of the motion segment ≧5%, then this motion segment is deleted directly, and if the missing rate is less than 5%, the cubic B-spline interpolation method is used to fill in the missing data, including vehicle speed and slope. At the same time that motion segments are deleted, the corresponding idle segments should be deleted at the same time.

Finally, the total time of the idle segment and the motion segment are respectively calculated to obtain the vehicle's idle and motion time ratio.

The slow, medium, and high motion segment library division and weighting factor determination in step S2 are:
B1, partitioning the slow, medium, and fast motion segment libraries;
B2, determining low, medium, and high speed interval weighting factors.

The slow, medium, and fast motion segment library division in step B1 is
B11, determine whether the average speed of the motion segment is greater than 0 km/h and is less than or equal to 30 km/h, if yes, it is a low speed motion segment library; a step of proceeding to the step of
B12, determine whether the average speed of the motion segment is greater than 30km/h and less than or equal to 40km/h, if yes, it is a medium speed motion segment library; otherwise, a step to proceed to the next step;
B13, determining whether the average speed of the motion segment is greater than 40km/h, if yes, high speed motion segment library; otherwise, switching to the next motion segment.

In this example, the slow, medium, and fast motion segment library division and weighting factor determination

(1) Slow, medium, and fast motion segment library division Based on the average velocity of the motion segment, divide the motion segment into slow, medium, and fast motion segment libraries. However, a motion segment whose average speed is greater than 0 km/h and less than or equal to 30 km/h is defined as a low-speed motion segment library. A motion segment with an average speed of greater than 30 km/h and less than or equal to 40 km/h is taken as a medium speed motion segment library. Motion segments with average speeds greater than 40 km/h are designated as high speed motion segment libraries.

(2) Determination of low-speed, medium-speed, and high-speed section weighting factors Statistics of the total time of all segments in the low-speed, medium-speed, and high-speed motion segment libraries, acquisition of low-speed, medium-speed, and high-speed weighting factors, and Matching the exercise time ratio, we finally get the duration of the idle, slow, medium and fast modes.

The extraction of characteristic parameters of the gradient mode and the velocity mode in step S3 is
C1, extracting feature parameters of gradient modes;
C2, extracting characteristic parameters of velocity modes.

In this embodiment, extraction of characteristic parameters of gradient mode and speed mode (1) Extraction of characteristic parameters of gradient mode 14 types of characteristic parameters such as average positive speed, average positive speed downhill, average negative speed uphill, average negative speed downhill, maximum positive speed uphill, maximum positive speed downhill, maximum negative speed uphill, maximum negative speed downhill, etc. Extract the gradient mode feature parameters of each motion segment in the motion segment library at each velocity containing the joint distribution of modalities.

(2) Extraction of characteristic parameters of speed mode Six types of characteristic parameters such as average vehicle speed, average positive acceleration, average negative acceleration, maximum vehicle speed, maximum positive acceleration, and maximum negative acceleration extract the feature parameters of the velocity modes of the motion segments in the motion segment library.

The construction of the vehicle driving mode including the road gradient in step S4 is
D1, screening typical motion segments;
D2, determining the number and duration of motion segments that make up the slow, medium, and fast modes;
D3, building motion segment combinations and modes.

Screening of a typical motor segment in step D1 consists of:
D11, performing extremum testing to test and screen the motion segment based on the extreme parameter information of the motion segment;
D12, performing a mean test to test and screen the motion segment based on the mean parameter information of the motion segment;
D13, screening slow, medium and fast typical motion segments by the joint slope-slope change rate and joint velocity-acceleration distributions using the least deviation sum of squares principle, respectively.

Determination of the number and duration of motion segments that constitute the low, medium, and high speed modes in step D2 is
D21, calculated the average time of each motion segment in the slow, medium, and fast typical motion segment libraries, respectively, and selected the slow, medium, and fast modes based on the duration of the slow, medium, and fast modes. determining the number of motion segments to constitute;
D22, statistics of the duration of the motion segments of slow, medium and fast intervals;
D23, calculating the number of motion segments in the corresponding duration, sorting the durations of the motion segments according to shortest to longest order, and calculating the cumulative frequency distribution of the durations of the motion segments;
D24, based on the number of motion segments determined by different speed intervals, divide the cumulative distribution into several halves, calculate the duration corresponding to the 50% quantile of each halves, and and a step of time.

The combination of motion segments and construction of modes in step D3 is
D31, selecting motion segments from the slow, medium and fast motion segment libraries respectively and randomly combining them based on the number of motion segments to form several slow, medium and fast modes;
D32, screening low, medium, and high speed modes that simultaneously satisfy the joint slope-slope change rate distribution and the joint velocity-acceleration distribution;
D33, Statistics the driving rules of the vehicle, determine the average time of idle in the vehicle start phase as the idle mode at the beginning of the mode, idle during and at the end of the motion segment based on the number of intervals between motion segments; adding each segment;
D34, building a vehicle driving mode including road gradient.

In this embodiment, construction of car driving mode including road gradient

(1) Screening of typical motion segments Screen typical motion segments from slow, medium and fast motion segment libraries in order. First, extremum test is performed to test and screen the motion segments based on nine parameters, such as uphill/downhill maximum gradient, uphill/downhill maximum positive/negative speed, maximum vehicle speed, and maximum acceleration/deceleration. Then, mean value test is performed to test and screen the motion segments based on 11 parameters, such as up-down slope time ratio, up-down slope average slope, up-down slope average positive/negative speed, average vehicle speed, average acceleration, average deceleration, etc. . Finally, based on the joint distribution of slope-slope change rate and the joint distribution of velocity-acceleration, the least deviation sum of squares principle is used to finally screen the low, medium and high speed typical motion segments.

(2) Determination of the number and duration of motion segments that make up the low, medium, and high speed modes Based on the duration of the fast and fast modes, determine the number of motion segments that make up the slow, medium and fast modes.

To determine the time of the motion segment, the duration of the motion segment in the low, medium and high speed sections was statistically calculated, the number of motion segments in the corresponding duration was calculated, and according to the order from shortest to longest The motion segment durations are sorted and a cumulative frequency distribution of the motion segment durations is calculated. Based on the number of motion segments determined by different speed intervals, the cumulative distribution is divided into several halves, and the duration corresponding to the 50% quantile of each halves is obtained by calculation, and the motion segment time and do.

(3) Combination of motion segments and construction of modes Based on the time of the motion segments, motion segments are selected from the low-speed, medium-speed, and high-speed motion segment libraries, randomly combined based on the number of motion segments, and finally to form several low, medium and high speed modes.

Based on the least deviation sum of squares principle, the low speed, medium speed, and high speed modes that most simultaneously satisfy the joint distribution of slope-slope change rate and the joint distribution of velocity-acceleration are screened. Statistics of the driving rules of the vehicle, determination of the average time of idle during the vehicle start phase as idle mode at the beginning of the mode, and idle segments during and at the end of the motion segment based on the number of intervals between motion segments. respectively, and finally construct a car driving mode including the road gradient.
Example 1

Hereinafter, the method of the invention will be described in more detail with reference to the drawings, and the specific steps are as follows.

Segmentation, cleaning and replenishment of vehicle operating segments

First, based on the vehicle speed and engine rotation speed of the collected data, the idling and motion states of the vehicle are determined. If ≧1 km/h and engine speed>0 rpm, it is determined that the vehicle is in motion.

Divide the idle and motion segments according to the above decision principle. The duration of the idle segment is defined as ≤300s. The motion segment starts at a vehicle speed of 0 km/h and ends again at a vehicle speed of 0 km/h, and the duration of the motion segment is defined as ≧5 s and ≦3600 s. There should be a one-to-one set of idle segments and motion segments.

Cleaning and supplementing the motion segment according to the data missing rate, maximum acceleration/deceleration and maximum vehicle speed requirements of the motion segment, if the maximum acceleration amax>6m/s2 or minimum deceleration amin<-6m/s2 of the motion segment , directly delete this motion segment, if the maximum velocity of the motion segment <5 km/h or >120 km/h, then directly delete this motion segment, if the missing rate of the motion segment ≧5%, then this motion segment is deleted directly, and if the missing rate is less than 5%, the cubic B-spline interpolation method is used to fill in the missing data, including vehicle speed and slope. At the same time that motion segments are deleted, the corresponding idle segments should be deleted at the same time.

Finally, calculate the total time of the idle segment and the motion segment respectively, and obtain the vehicle's idle to motion time ratio of 22.11% and 77.89% respectively.

Slow, medium, and fast motion segment library partitioning and weighting factor determination

Based on the average velocity of the motion segments, divide the motion segments into slow, medium and fast motion segment libraries. However, a motion segment whose average speed is greater than 0 km/h and less than or equal to 30 km/h is defined as a low-speed motion segment library. A motion segment with an average speed of greater than 30 km/h and less than or equal to 40 km/h is taken as a medium speed motion segment library. Motion segments with average speeds greater than 40 km/h are designated as high speed motion segment libraries.

Statistics the total time of all segments in the low-, medium-, and high-speed motion segment library, obtain the low-, medium-, and high-speed weighting coefficients, and match the idle-to-motion time ratio of the vehicle, and finally idle, Get the duration of slow, medium and fast modes. Based on the analysis of collected data, it is found that the idle, low speed, medium speed and high speed weighting factors are 22.11%, 24.28%, 30.89% and 22.72% respectively, and the low speed electric drive assembly We set the duration of the load mode to 1800 s and calculate based on the weighting factors to find that the idle, low speed, medium speed and high speed mode durations are 398 s, 437 s, 556 s and 409 s respectively.

Extraction of Feature Parameters for Gradient and Velocity Modes

If the slope is ≧0.1%, it is defined as uphill. A slope is defined as downhill if the slope is ≦−0.1%.

The uphill time ratio Pup is the ratio of the total uphill time in the motion segment to the total motion segment time. The downhill time ratio Pdown is the ratio of the total downhill time in the motion segment to the total motion segment time.

Then, a slope-slope change rate joint distribution is obtained based on the slope and the slope change rate.

The formula for calculating the slope change rate is as follows.

where Gi' is the instantaneous gradient change rate for the i-th second, and the gradient change rates for the zero second and the last second are defined as zero.

The formula for calculating acceleration is as follows.

where ai is the acceleration at the i-th second, and the acceleration at the zero second and the last second is defined as zero.

Then, we obtain the joint velocity-acceleration distribution based on the velocity and acceleration.

Finally, the characteristic parameter values of the slope mode and speed mode of each speed section of low speed, medium speed and high speed are obtained, and the characteristic parameter values of the low speed section are shown in Table 1.

Construction of vehicle driving modes including road gradient Screening of motion segments within each speed interval by extreme value test, mean value test and minimum deviation sum of squares test in low, medium and high speed motion segment libraries respectively, thereby Obtain typical motion segments of low, medium and high speed.

Minimum sum-of-squares test: Based on the joint gradient-slope change rate and velocity-acceleration joint distributions, finally screen the slow, medium, and fast typical motion segments using the least-deviation sum-of-squares principle. do.

For the final screened slow, medium, and fast typical motion segments, the average time of each motion segment was calculated, respectively, and the average time of the slow, medium, and fast motion segments was 62.5 s, respectively. 184.3s and 410.3s. Since the durations of slow, medium, and fast are 437 s, 556 s, and 409 s, respectively, the numbers of slow, medium, and fast motion segments are 6.99, 3.02, and 1.00, respectively, which after rounding are , 7, 3 and 1.

Subsequently, the durations of the motion segments of the slow, medium, and high speed intervals are statistically calculated, the number of motion segments in the corresponding duration is calculated, and the durations of the motion segments are ordered according to shortest to longest order. Instead, we compute the cumulative frequency distribution of the duration of the motion segments. Based on the number of motion segments determined by different speed intervals, the cumulative distribution is divided into several halves, and the duration corresponding to the 50% quantile of each halves is obtained by calculation, and the motion segment time and do. Taking the low-speed section as an example, the cumulative distribution of the segment times is shown in FIG.

Based on the time of the motion segment, select motion segments from the slow, medium, and fast motion segment libraries respectively, randomly combine them based on the number of motion segments, and finally select several slow, medium, and fast modes to form Further, based on the principle of minimum deviation sum of squares, the low speed, medium speed, and high speed modes that most simultaneously satisfy the joint gradient-gradient change rate distribution and the joint velocity-acceleration distribution are screened. Statistics of the driving rules of the vehicle, determination of the average time of idle during the vehicle start phase as idle mode at the beginning of the mode, and idle segments during and at the end of the motion segment based on the number of intervals between motion segments. add respectively. Statistically, the driving rules of the vehicle are determined to have an average idle time of 35 s in the vehicle starting phase, a total of 10 intervals of motion segments, and a total of 11 intervals considering mode endpoints. As shown in FIG. 4, if the determined idle time is 398 s, and after subtracting the idle time at the start point, it becomes 363 s, then the idle time at each interval and the end point is 33 s, and the idle time at the idle mode is 33 s. becomes 0, finally completing the construction of the car driving mode including the road gradient.

In short, the present invention extracts slope mode and speed mode characteristic parameters by actually collecting data such as vehicle speed and slope in the vehicle running process. Constructing a vehicle running mode including a slope mode by screening representative motion segments that simultaneously satisfy the characteristic parameters of the slope mode and the speed mode by methods of extreme value test, mean value test and minimum deviation sum of squares test, The slope and vehicle speed change synchronously with time. Using this mode as a test mode, laboratory drum tests can accurately assess real-world fuel economy and emissions levels for mountain and urban vehicles.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made without departing from the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

including a collected data processing module, a motion segment library division and weighting factor determination module, a slope mode and velocity mode feature parameter extraction module, a mode building module and an electronic device, wherein the collected data processing module, a motion segment library division and weighting factor determination module; The determination module, the gradient mode and velocity mode feature parameter extraction module, and the mode building module are sequentially signal-connected to the collected data processing module, the motion segment library division and weight factor determination module, and the gradient mode and velocity mode feature parameters. Both the extraction module and the mode building module are signal connected to the electronics,
An apparatus for developing vehicle driving modes including road gradients, wherein the electronic equipment includes a processor and a memory communicatively coupled to the processor for storing instructions to be executed by the processor. S1, dividing, cleaning and replenishing the vehicle operation segment by the collected data processing module;
S2, determining slow, medium, and high speed motion segment library partitioning and weighting factors by a motion segment library partitioning and weighting factor determination module;
S3, extracting the characteristic parameters of the gradient mode and the velocity mode by means of the characteristic parameter extraction module of the gradient mode and the velocity mode;
S4, building a vehicle driving mode including road gradient by a mode building module. The division, cleaning and replenishment of the vehicle operation segment described in step S1 are:
A1, based on the vehicle speed and engine rotation speed of the collected data, determining the idling and motion states of the vehicle according to the determining principle;
A2, dividing the idle and motion segments according to the decision principle;
A3, cleaning and supplementing the motion segment according to the motion segment data missing rate, maximum acceleration/deceleration and maximum vehicle speed requirements;
A4, calculating the total idle and motion segment times respectively to obtain the idle to motion time ratio of the vehicle;
The judging principle described in step A1 is:
A11, determining whether vehicle speed<1km/h and engine rotation speed>0rpm, if yes, determine that the vehicle is in an idle state, otherwise proceeding to the next step;
A12, determining whether the vehicle speed≧1 km/h and the engine speed>0 rpm, if yes, determine that the vehicle is in motion; if not, switch to the next vehicle operation segment; ,including,
3. A method for developing vehicle driving modes including road gradients according to claim 2. The cleaning and replenishment of the motion segments in step A3 are:
A31, determine whether the maximum acceleration amax of the motion segment is greater than 6m/s2 or whether the minimum deceleration amin of the motion segment is less than -6m/s2; directly deleting, else proceeding to the next step;
A32, determine whether the maximum speed of the motion segment is less than 5km/h or greater than 120km/h, if yes, delete this motion segment directly; step to step, and
A33, determining whether the missing rate is greater than or equal to 5%, if yes, directly deleting this motion segment, otherwise proceeding to the next step;
A34, filling in missing data including vehicle speed and grade using a cubic B-spline interpolation method. The slow, medium, and high motion segment library division and weighting factor determination in step S2 are:
B1, partitioning the slow, medium, and fast motion segment libraries;
B2, determining low, medium, and high speed interval weighting factors;
The slow, medium, and fast motion segment library division in step B1 is
B11, determine whether the average speed of the motion segment is greater than 0 km/h and is less than or equal to 30 km/h, if yes, it is a low speed motion segment library; a step of proceeding to the step of
B12, determine whether the average speed of the motion segment is greater than 30km/h and less than or equal to 40km/h, if yes, it is a medium speed motion segment library; otherwise, a step to proceed to the next step;
B13, determining whether the average speed of the motion segment is greater than 40km/h, if yes, high speed motion segment library; otherwise, switching to the next motion segment. 3. A method for developing a vehicle driving mode including a road gradient according to 2. The extraction of characteristic parameters of the gradient mode and the velocity mode in step S3 is
C1, extracting feature parameters of gradient modes;
C2, extracting characteristic parameters of speed modes. The construction of the vehicle driving mode including the road gradient in step S4 is
D1, screening typical motion segments;
D2, determining the number and duration of motion segments that make up the slow, medium, and fast modes;
D3, the step of constructing motion segment combinations and modes. Screening of a typical motor segment in step D1 consists of:
D11, performing extremum testing to test and screen the motion segment based on the extreme parameter information of the motion segment;
D12, performing a mean test to test and screen the motion segment based on the mean parameter information of the motion segment;
D13, using the least deviation sum of squares principle to screen low, medium and high speed typical motion segments respectively. Determination of the number and duration of motion segments that constitute the low, medium, and high speed modes in step D2 is
D21, calculating the average time of each motion segment in the slow, medium, and fast typical motion segment libraries, respectively, to determine the number of motion segments that make up the slow, medium, and fast modes;
D22, statistics of the duration of the motion segments of slow, medium and fast intervals;
D23, calculating the number of motion segments with corresponding durations, sorting the durations of the motion segments, and calculating the cumulative frequency distribution of the durations of the motion segments;
D24, based on the number of motion segments determined by different speed intervals, divide the cumulative distribution into several halves, calculate the duration corresponding to the 50% quantile of each halves, and 8. The method of developing a vehicle drive mode including road grade as recited in claim 7, comprising the step of timing. The combination of motion segments and construction of modes in step D3 is
D31, selecting motion segments from the slow, medium and fast motion segment libraries respectively and randomly combining them based on the number of motion segments to form several slow, medium and fast modes;
D32, screening low, medium, and high speed modes that simultaneously satisfy the joint slope-slope change rate distribution and the joint velocity-acceleration distribution;
D33, Statistics the driving rules of the vehicle, determine the average time of idle in the vehicle start phase as the idle mode at the beginning of the mode, idle during and at the end of the motion segment based on the number of intervals between motion segments; adding each segment;
D34, the step of building a vehicle driving mode including road grades.

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