CN114954494B - Heavy commercial vehicle load rapid estimation method - Google Patents

Heavy commercial vehicle load rapid estimation method Download PDF

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CN114954494B
CN114954494B CN202210674496.5A CN202210674496A CN114954494B CN 114954494 B CN114954494 B CN 114954494B CN 202210674496 A CN202210674496 A CN 202210674496A CN 114954494 B CN114954494 B CN 114954494B
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vehicle
speed
data
load
segment
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CN114954494A (en
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刘海潮
张波
王裕
毛祥党
杨汉
潘齐洪
邱继旭
李军
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Guangxi Yuchai Machinery Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/10—Buses
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/12—Trucks; Load vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/40—Altitude
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Control Of Transmission Device (AREA)

Abstract

The invention discloses a rapid estimation method for heavy commercial vehicle load, which relates to the technical field of vehicle load detection and solves the technical problem of high cost of the existing vehicle load detection method, and the method comprises the steps of leading out vehicle running data of different road sections from a vehicle-mounted terminal background system according to a fixed data template; determining a relation between a running resistance coefficient and vehicle mass, fitting a vehicle running resistance curve according to the sliding data of vehicle running data, and calculating the running resistance coefficient; slicing and screening vehicle running data to obtain uniform speed gentle slope segments; establishing a longitudinal dynamics equation of the vehicle according to the model of the vehicle; obtaining an estimation model containing a running resistance coefficient, and calculating the vehicle load of the constant-speed gentle slope segment by using the estimation model; and averaging the vehicle loads estimated by different road segments to obtain an estimated load. According to the invention, the load of the vehicle can be effectively estimated without adding an additional measuring sensor, and a powerful support is provided for matching and optimizing the design of the vehicle for enterprises.

Description

Heavy commercial vehicle load rapid estimation method
Technical Field
The invention relates to the technical field of vehicle load detection, in particular to a method for rapidly estimating the load of a heavy commercial vehicle.
Background
The load condition of the heavy commercial vehicle is an important parameter to be monitored by the related traffic management department, and is also important data in the road spectrum analysis process of the market segment in the vehicle performance matching development of the whole vehicle enterprise.
In order to accurately predict the load condition of a vehicle, a great deal of researches are carried out by a great deal of students at present, and the methods are mainly divided into two types: a sensor-based estimation method is characterized in that the quality identification is simplified by adding a corresponding sensor on a vehicle, but the production cost of the vehicle is increased, so that the actual engineering requirements are difficult to meet; the other is mass estimation based on a longitudinal dynamics model of the vehicle, the method obtains the estimated mass by acquiring information such as driving torque, acceleration, vehicle speed and the like from a CAN bus and performing data processing through algorithms such as a recursive least square method, an extended Kalman filter and the like, but the method has high requirements on the data mass and is difficult to achieve for common vehicles.
The Chinese patent document with publication number of CN 112819031A discloses a vehicle-mounted weight prediction method and system, electronic equipment and medium, and creatively provides a vehicle load prediction method based on relevant vector machine model training. The method comprises the following implementation steps: (a) vehicle travel data acquisition and parameter calculation: acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different loading conditions; calculating to obtain the current torque and the current transmission ratio of the transmission; (b) fragment cleavage and uniform fragment screening; (c) training a correlation vector machine model; (d) predicting load: and for the vehicle with unknown load, inputting the average transmission ratio, the average speed and the average torque of the screened segments into the related vector machine model to obtain the load prediction result of each segment, and further obtaining the load prediction result of the vehicle. The method is simple and easy to implement, is not limited by site conditions, and needs to acquire relevant operation characteristic parameters of the vehicle under the known loading condition to perform model training. However, in numerous vehicles running in a practical broad consumer market, it is difficult or costly to directly obtain characteristic data for the relevant vehicle under known loading conditions.
Accordingly, there is a need for a method that can estimate the mass of a vehicle based on the existing configuration of the vehicle without adding additional corresponding measurement sensors.
Disclosure of Invention
The invention aims to provide a rapid load estimating method for a heavy commercial vehicle, which aims to solve the technical problems of the prior art, does not need to additionally increase a corresponding measuring sensor and has low cost.
The technical scheme of the invention is as follows: a method for rapid estimation of the load of a heavy commercial vehicle comprising:
s1, deriving vehicle driving data of different road sections from a vehicle-mounted terminal background system according to a fixed data template, wherein the driving data at least comprises a vehicle speed v, an altitude, an engine torque percentage and an engine speed;
s2, determining a relation between a running resistance coefficient and vehicle mass, fitting a vehicle running resistance curve according to the sliding data of vehicle running data, and calculating the running resistance coefficient;
if the vehicle is subjected to the sliding resistance test, performing a second-order polynomial fitting by adopting actual sliding data of the vehicle under different masses to obtain a relation between a running resistance coefficient and the mass of the vehicle:
otherwise, fitting the vehicle which does not acquire the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dumper, a passenger car and a city passenger car, the relation between the running resistance coefficient and the vehicle mass is as follows:
when the vehicle is a tractor, the relation between the running resistance coefficient and the vehicle mass is as follows:
s3, checkingValidity of vehicle running data, dividing vehicle running state, extracting high-speed segment, and controlling vehicle acceleration a, road gradient i and engine torque T eq Total reduction ratio i g i o Vehicle driving force F t Calculating and checking parameters; slicing and screening vehicle running data according to the vehicle speed and the gradient to obtain uniform speed gentle slope segments;
s4, building a longitudinal dynamics equation of the vehicle according to the vehicle type of the vehicle:
wherein i is g 、i o For the gearbox speed ratio and the final reduction ratio, η is the mechanical efficiency of the drive train; c (C) D S is wind resistance coefficient and windward area, v is vehicle speed, alpha is gradient angle, f is wheel rolling resistance coefficient, and r is tire rolling radius; m is the mass of the vehicle; delta is the conversion coefficient of the rotating mass of the automobile, and comprises the rotational inertia of a flywheel and the rotational inertia of a wheel;
according to the road design specifications such as JTG B01, CJJ 37, and the like, the maximum gradient of urban roads, general highways and expressways is limited to be within 5 DEG, and then the equation (4) is converted into an estimation model containing the running resistance coefficient assuming that cosα=1, sin α≡α≡i:
calculating the vehicle load of the constant-speed gentle slope segment by using an estimation model;
s5, estimating the vehicle load m of different road segments i Make a determination if all m i All conform toWherein sigma is the set error, the method meets the requirement and outputs the estimated load +.> Is m i Average value of (2); otherwise, returning to the step S1 to reselect the vehicle running data of different road sections for calculation.
As a further improvement, in step S3, the vehicle travel data analysis, calculation, screening, and short segment extraction are performed including the steps of:
s31, checking the effectiveness of the vehicle speed v and the altitude, firstly detecting and removing burrs of the vehicle speed v and the altitude, and secondly smoothing data;
s32, dividing the running state of the vehicle, removing idle speed and low-speed time slices, and extracting continuous high-speed movement slices;
s33, obtaining the rolling radius r of the vehicle tyre, the mechanical efficiency eta of the transmission system and the maximum torque T of the engine max ；
The acceleration a is obtained by a speed versus time difference, and the acceleration at the kth time is expressed as:
a(k)＝diff(v(k))/Δt
a(1)＝0
v (k) is the vehicle speed at the moment k, and Δt is the time interval of sampling by the vehicle-mounted terminal system;
total reduction ratio i g i o The method is characterized by comprising the following steps of:
wherein n is the engine speed;
the road gradient i is obtained by the ratio of the difference in altitude to the difference in vehicle travel distance over Δt, and the road gradient at the kth time is expressed as:
i(k)≈diff(Alt(k))/s_dis(k)×100％
i(1)＝0
wherein Alt (k) is the elevation at time k, s_dis (k) is the vehicle travel distance in time Δt at time k;
engine torque T eq By engine maximum torque T max Percentage of torque T _per The calculated engine torque at the kth time is expressed as:
T eq (k)＝T max ×T _per (k)/100；
s34, small-segment cutting and uniform-speed gentle slope segment screening;
firstly, slicing vehicle running data by adopting a moving window method, and setting a certain window overlapping rate; calculating the variation coefficient of the vehicle speed signal of each small segment, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform-speed segments;
secondly, slicing the uniform velocity segments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of gradient signals of each uniform velocity segment, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform velocity gentle slope segments.
Further, in step S31, burr detection and elimination are performed by using the Fillouliers () function of the MAD method, and after elimination of the discrete points, compensation is performed by using the linear difference of the adjacent points.
Further, in step S31, the data smoothing is performed by using a Savitzky-Golay method, and the calling function format is y=smooth_sg5_3 (x_ori, n), where smooth_sg5_3 is a compiled m file, x_ori is original data, and n is the smoothing number.
Further, in step S32, a motion segment with a vehicle speed of 30km/h or more is extracted as a continuous high-speed motion segment.
Further, in step S34, the window of the moving window method is 6 to 16.
Further, the window of the moving window method is 10.
Further, in step S34, the vehicle speed variation coefficient of the segment j is calculated as follows:
cv(j)＝std(v j )/mean(v j )
wherein std (v) j ) Mean (v j ) For the speed of segment jAverage value.
Further, in step S34, the gradient coefficient of variation of the segment m is calculated as follows:
cv(m)＝std(i m )/abs(mean(i m ))
wherein std (i) m ) Mean (i) is the standard deviation of the slope of segment m m ) Is the average value of the gradient of the segment m.
Further, in step S4, root searching is performed on the higher-order polynomials by using the roots () function, the vehicle loads of the uniform gentle slope segments are ordered from small to large, and the average value of the vehicle loads between the median or 20% to 80% of the bit lines is taken as the prediction result of the vehicle loads.
Advantageous effects
Compared with the prior art, the invention has the advantages that:
the method provided by the invention has low cost and high efficiency, and based on the existing configuration of most vehicles in the market, the load of the vehicles actually running in the market can be effectively estimated without adding additional measuring sensors, and a powerful support is provided for matching and optimizing the design of the vehicles for enterprises.
Drawings
FIG. 1 is a basic flow chart of a heavy commercial vehicle load estimation method of the present invention;
FIG. 2 is a background export data template of the vehicle-mounted terminal system of the present invention;
FIG. 3 is a tractor code recommended drag coefficient fit of the present invention;
FIG. 4 is a graph showing recommended driving resistance coefficients for different vehicle models provided by the present invention;
FIG. 5 is a flow chart of analysis, calculation and screening of vehicle travel characteristic data according to the present invention;
FIG. 6 is a graph of vehicle speed signal spike detection and correction in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of overlapping vehicle operation data screening windows according to the present invention;
FIG. 8 is a vehicle mass estimation distribution histogram of an embodiment of the present invention;
fig. 9 is a normal distribution diagram of vehicle mass estimation according to an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to specific embodiments in the drawings.
Referring to fig. 1-9, a method for rapidly estimating the load of a heavy commercial vehicle, the flowchart of which is shown in fig. 1, comprises the following steps:
s1, vehicle driving data of different road sections are derived from a vehicle-mounted terminal background system according to a fixed data template, and as shown in FIG 2, the number n of the road sections is more than or equal to 5, and the driving data at least comprises a vehicle speed v, an altitude, an engine torque percentage and an engine speed;
s2, determining a relation between a running resistance coefficient and vehicle mass, and fitting a vehicle running resistance curve according to the sliding data of vehicle running data to calculate the running resistance coefficient, wherein the running resistance coefficient comprises the following coefficient A, coefficient B and coefficient C; as shown in the figure 3 of the drawings,
if the vehicle is subjected to the sliding resistance test, performing a second-order polynomial fitting by adopting actual sliding data of the vehicle under different masses to obtain a relation between a running resistance coefficient and the mass of the vehicle:
otherwise, fitting the vehicle which does not acquire the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dumper, a passenger car and a city passenger car, the relation between the running resistance coefficient and the vehicle mass is as follows:
when the vehicle is a tractor, the relation between the running resistance coefficient and the vehicle mass is as follows:
several calculated running resistance coefficients of commercial vehicle models are shown in fig. 4. The calculation of the running resistance coefficient is a further technology, and the calculation process of the coefficient A, the coefficient B and the coefficient C can be referred to in the literature: liu Haichao, li Weicong, jiang Shijun, he, determination of coefficient of sliding resistance of electric car and application [ J ]. Passenger car technology and research.2018 (06). 43-45;
s3, checking the validity of vehicle running data, dividing the running state of the vehicle, extracting high-speed fragments, and determining the acceleration a of the vehicle, the road gradient i and the engine torque T eq Total reduction ratio i g i o Vehicle driving force F t Calculating and checking parameters; slicing and screening vehicle running data according to the vehicle speed and the gradient to obtain uniform speed gentle slope segments;
s4, building a longitudinal dynamics equation of the vehicle according to the model of the vehicle:
wherein i is g 、i o For the gearbox speed ratio and the final reduction ratio, η is the mechanical efficiency of the drive train; c (C) D S is wind resistance coefficient and windward area, v is vehicle speed, alpha is gradient angle, f is wheel rolling resistance coefficient, and r is tire rolling radius; m is the mass of the vehicle; delta is the conversion coefficient of the rotating mass of the automobile, and comprises the rotational inertia of a flywheel and the rotational inertia of a wheel;
according to the road design specifications such as JTG B01, CJJ 37, and the like, the maximum gradient of urban roads, general highways and expressways is limited to be within 5 DEG, and then the equation (4) is converted into an estimation model containing the running resistance coefficient assuming that cosα=1, sin α≡α≡i, i is the road gradient:
wherein f=a+bv;
calculating the vehicle load of the constant-speed gentle slope segment by using an estimation model;
s5, estimating the vehicle load m of different road segments i Make a determination if all m i All conform toWherein sigma is the set error, the method meets the requirement and outputs the estimated load +.> Is m i Average value of (2); otherwise, returning to the step S1 to reselect the vehicle running data of different road sections for calculation. According to the Rhin criterion, if +.>Then consider m i Is a normal point for judging the load m i Whether an outlier (singular point) exists, and if so, need to be culled.
As shown in fig. 5, in step S3, the vehicle travel data analysis, calculation, screening, and short segment extraction are performed including the steps of:
s31, checking the effectiveness of the vehicle speed v and the altitude, firstly detecting and removing burrs of the vehicle speed v and the altitude, and secondly smoothing data; the Fillouliers () function of the MAD method is adopted to detect and reject burrs, the window length of the Fillouliers () function is 10, and the discrete points are subjected to compensation by adopting the linear difference value of the adjacent points after being rejected, as shown in figure 6; the data smoothing adopts a Savitzky-Golay method, a calling function format is y=smooth_Sg5_3 (x_ori, n), wherein the smooth_Sg5_3 is a compiled m file, the x_ori is original data, and the n is smoothing times;
s32, dividing the running state of the vehicle, removing idle speed and low-speed time slices, and extracting continuous high-speed movement slices; preferably, a motion segment with the speed of more than or equal to 30km/h is extracted as a continuous high-speed motion segment;
s33, obtaining the rolling radius r of the vehicle tyre, the mechanical efficiency eta of the transmission system and the maximum torque T of the engine max ；
The acceleration a is obtained by a speed versus time difference, and the acceleration at the kth time is expressed as:
a(k)＝diff(v(k))/Δt
a(1)＝0
v (k) is the vehicle speed at the moment k, and Δt is the time interval of sampling by the vehicle-mounted terminal system;
total reduction ratio i g i o The method is characterized by comprising the following steps of:
wherein n is the engine speed, r/min;
the road gradient i is obtained by the ratio of the difference in altitude to the difference in vehicle travel distance over Δt, and the road gradient at the kth time is expressed as:
i(k)≈diff(Alt(k))/s_dis(k)×100％
i(1)＝0
wherein Alt (k) is the elevation at time k, s_dis (k) is the vehicle travel distance in time Δt at time k;
engine torque T eq By engine maximum torque T max Percentage of torque T _per The calculated engine torque at the kth time is expressed as:
T eq (k)＝T max ×T _per (k)/100；
acceleration upper and lower limit values are respectively + -2 m/s2; the upper limit value and the lower limit value of the gradient are respectively +/-9%, the gradient is generally converted into percentage for calculation, and the actual description of the gradient of the road is also percentage description, such as 10% gradient;
s34, small-segment cutting and uniform-speed gentle slope segment screening;
firstly, slicing vehicle running data by adopting a moving window method, setting a certain window overlapping rate, and in the embodiment, setting a window overlapping rate of 50%, as shown in fig. 7; calculating the variation coefficient of the vehicle speed signal of each small segment, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform-speed segments;
secondly, slicing the uniform velocity segments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of gradient signals of each uniform velocity segment, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform velocity gentle slope segments. The window of the moving window method is 6 to 16, preferably 10.
Further, in step S34, the vehicle speed variation coefficient of the segment j is calculated as follows:
cv(j)＝std(v j )/mean(v j )
wherein std (v) j ) Mean (v j ) The average value of the vehicle speed of segment j.
In step S34, the gradient coefficient of variation of the segment m is calculated as follows:
cv(m)＝std(i m )/abs(mean(i m ))
wherein std (i) m ) Mean (i) is the standard deviation of the slope of segment m m ) Is the average value of the gradient of the segment m.
In step S4, root searching is performed on the higher-order polynomials by adopting a roots () function, the vehicle loads of the uniform-speed gentle slope segments are ordered from small to large, and the average value of the vehicle loads between the median or 20% to 80% of the bit lines is taken as a prediction result of the vehicle loads.
Corresponding calculation programs can be compiled according to the method, a parameterized graphical user interface can be developed, and data batch processing and load estimation can be performed.
Practical application
In the invention, a tractor is taken as an example, and considering that most of automobile enterprises optimize rolling resistance and wind resistance of vehicles better at present and actual running resistance, the coefficient of the running resistance is calculated and multiplied by the coefficient of proportionality lambda, and the coefficient of proportionality is empirically taken as 0.85-1, preferably 0.95. Substituting the running resistance coefficient of the tractor into an estimation model (5) to carry out numerical solution to obtain:
that is to say,
f(m)＝H 3 m 3 +H 2 m 2 +H 1 m+H 0
H 3 ＝λc 3 v 2
H 2 ＝λc 2 v 2
H 1 ＝λc 1 v 2 +λb 1 v+λa 1 +gi+δa
and (3) root searching is carried out on the higher-order polynomials by adopting a roots () function, load estimation results of uniform-speed gentle slope segments are ordered from small to large, and the average value or the median of the load results between 20% and 80% of bit lines are taken as a prediction result of the vehicle load, wherein the vehicle load distribution situation is shown in fig. 7 and 8.
Vehicle load m estimated by different road segments of tractor i ＝[43.61,45.44,44.67,46.50, 46.39]The final output estimated vehicle load is 45.322 tons, and the actual vehicle load is 49 tons, with an estimated error of about 7.5%.
The method is simple, convenient and feasible, low in cost and high in efficiency, and the load condition of the vehicle can be accurately and rapidly estimated only by acquiring the vehicle running data from the background of the vehicle-mounted terminal, so that the method has a certain effect in the process of matching and developing the vehicle performance.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these do not affect the effect of the implementation of the present invention and the utility of the patent.

Claims (6)

1. A method for rapidly estimating the load of a heavy commercial vehicle, comprising:
s1, deriving vehicle driving data of different road sections from a vehicle-mounted terminal background system according to a fixed data template, wherein the driving data at least comprises vehicle speedAltitude, engine torque percentage, engine speed;
s2, determining a relation between a running resistance coefficient and vehicle mass, fitting a vehicle running resistance curve according to the sliding data of vehicle running data, and calculating the running resistance coefficient;
if the vehicle is subjected to the sliding resistance test, performing a second-order polynomial fitting by adopting actual sliding data of the vehicle under different masses to obtain a relation between a running resistance coefficient and the mass of the vehicle:
（1）；
otherwise, fitting the vehicle which does not acquire the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dumper, a passenger car and a city passenger car, the relation between the running resistance coefficient and the vehicle mass is as follows:
（2）；
when the vehicle is a tractor, the relation between the running resistance coefficient and the vehicle mass is as follows:
（3）；
s3, checking the effectiveness of vehicle running data, dividing the running state of the vehicle, extracting high-speed fragments and accelerating the vehicleRoad grade->Engine torque->Total reduction ratio->Vehicle driving force +.>Calculating and checking parameters; slicing and screening vehicle running data according to the vehicle speed and the gradient to obtain uniform speed gentle slope segments;
s4, building a longitudinal dynamics equation of the vehicle according to the vehicle type of the vehicle:
（4）
wherein,、/>for the transmission ratio and final drive ratio, +.>Is the mechanical efficiency of the drive train; />、/>Is wind resistance coefficient and windward area, +.>For the speed of the vehicle>Is a slope angle>For the wheel rolling resistance coefficient +.>Is the rolling radius of the tire; />Is the vehicle mass; />The conversion coefficient of the rotating mass of the automobile comprises the rotational inertia of a flywheel and the rotational inertia of a wheel;
assume that=1，/>Converting the formula (4) into an estimation model containing the running resistance coefficient:
（5）
wherein,；
calculating the vehicle load of the constant-speed gentle slope segment by using an estimation model;
s5, estimating the load of the vehicle on different road segmentsMake a determination if all->All meet->WhereinFor setting the error, the estimated load +.>，/>Is->Average value of (2); otherwise, returning to the step S1 to reselect the vehicle running data of different road sections for calculation;
in step S3, the vehicle running data analysis, calculation, screening, and short segment extraction are performed, including the steps of:
s31, regarding the vehicle speedThe effectiveness check is carried out on the altitude, firstly, the vehicle speed is +.>Detecting and removing burrs at the altitude, and smoothing data;
s32, dividing the running state of the vehicle, removing idle speed and low-speed time slices, and extracting continuous high-speed movement slices;
s33, obtaining the rolling radius of the vehicle tyreMechanical efficiency of the drive train->Maximum torque of engine->；
Acceleration ofObtained by speed versus time difference, < ->The acceleration at time is expressed as:
wherein,is->Vehicle speed at time,/->Sampling time intervals for the vehicle-mounted terminal system;
total speed reduction ratioThe method is characterized by comprising the following steps of:
wherein n is the engine speed;
road gradeBy->The ratio of the altitude difference to the vehicle distance difference in time is obtained +.>The road gradient at the moment is expressed as:
wherein,is->Elevation of moment->Is->Time->Distance travelled by the vehicle in time;
engine torqueBy engine torque capacity->Percent of torque->Calculated, th->The engine torque at time is expressed as:
；
s34, small-segment cutting and uniform-speed gentle slope segment screening;
firstly, slicing vehicle running data by adopting a moving window method, and setting a certain window overlapping rate; calculating the variation coefficient of the vehicle speed signal of each small segment, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform-speed segments;
secondly, slicing the uniform speed fragments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of gradient signals of each uniform speed fragment, and taking 30% fragments with smaller variation coefficient to form a new time sequence as uniform speed gentle slope fragments;
in step S31, burr detection and elimination are performed by adopting a Fillouliers () function of the MAD method, and after discrete points are eliminated, linear differences of adjacent points are adopted for compensation;
in step S31, the data smoothing is performed by using a Savitzky-Golay method, the calling function format is y=smooth_sg5_3 (x_ori, n), where smooth_sg5_3 is a compiled m file, x_ori is original data, and n is the smoothing times;
in step S32, a motion segment with a vehicle speed not less than 30km/h is extracted as a continuous high-speed motion segment.
2. The rapid estimation method of the loading capacity of a heavy commercial vehicle according to claim 1, wherein in step S34, the window of the moving window method is 6 to 16.
3. The method for rapid estimation of the loading of a heavy commercial vehicle according to claim 2, wherein the window of the moving window method is 10.
4. The rapid estimation method of the load of a heavy commercial vehicle according to claim 1, wherein in step S34, the segments areThe vehicle speed variation coefficient of (2) is calculated by the following steps:
wherein,is a segment->Standard deviation of vehicle speed>Is a segment->Average value of vehicle speed.
5. The rapid estimation method of the load of a heavy commercial vehicle according to claim 1, wherein in step S34, the segments areThe gradient coefficient of variation is calculated by:
wherein,is a segment->Standard deviation of slope>Is a segment->Average value of gradient.
6. The rapid estimation method of heavy commercial vehicle load according to claim 1, wherein in step S4, root searching is performed on the higher-order polynomials by using roots () function, the vehicle loads of the uniform-speed gentle slope segments are ordered from small to large, and the average value of the vehicle load between the median or 20% to 80% of the bit lines is taken as the prediction result of the vehicle load.
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