CN114954494A - Heavy commercial vehicle load rapid estimation method - Google Patents
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Abstract
The invention discloses a heavy commercial vehicle load rapid estimation method, relates to the technical field of vehicle load detection, and solves the technical problem of high cost of the existing vehicle load detection method; determining a relational expression of the driving resistance coefficient and the vehicle mass, and fitting a vehicle driving resistance curve according to the sliding data of the vehicle driving data to calculate the driving resistance coefficient; slicing and screening vehicle driving data to obtain a constant-speed gentle slope segment; establishing a longitudinal vehicle dynamics equation according to the vehicle type of the vehicle; obtaining an estimation model containing a driving resistance coefficient, and calculating the vehicle load of the uniform-speed gentle slope segment by using the estimation model; and averaging the estimated vehicle loads of different road sections to obtain the estimated load. According to the invention, the vehicle load can be effectively estimated without adding an additional measuring sensor, and powerful support is provided for the matching optimization design of vehicles of enterprises.
Description
Technical Field
The invention relates to the technical field of vehicle load detection, in particular to a method for quickly estimating the load of a heavy commercial vehicle.
Background
The load condition of the heavy commercial vehicle is an important parameter which needs to be monitored by a related traffic control department, and is important data in the road spectrum analysis process of the whole vehicle enterprise, particularly the road spectrum analysis process of the market segmentation.
In order to accurately predict the load condition of a vehicle, a great number of researchers make many researches, which are mainly divided into two types of methods: one is a sensor-based estimation method, which simplifies the quality identification by additionally installing corresponding sensors on a vehicle, but increases the production cost of the vehicle at the same time, and is difficult to meet the actual requirements of engineering; the other is quality estimation based on a vehicle longitudinal dynamics model, the method obtains information such as driving torque, acceleration and vehicle speed from a CAN bus, and performs data processing through algorithms such as a recursive least square method and an extended Kalman filter to obtain the estimated quality, but the method has high requirements on the data quality and is often difficult to achieve for common vehicles.
Chinese patent publication No. CN 112819031 a discloses a vehicle weight prediction method and system, an electronic device, and a medium, and creatively provides a vehicle weight prediction method based on a correlation vector machine model training. The method comprises the following implementation steps: (a) vehicle driving 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 load conditions; calculating to obtain the current torque and the current transmission ratio of the transmission; (b) cutting segments and screening constant-speed segments; (c) training a correlation vector machine model; (d) load prediction: and for the vehicle with unknown load, inputting the average transmission gear ratio, the average speed and the average torque of the screened segments into the correlation vector machine model to obtain the load prediction result of each segment, and further obtaining the prediction result of the vehicle load. 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 load condition to train the model. However, in the countless vehicles that run in the actual consumer market, it is difficult or costly to directly obtain characteristic data for known load conditions of the relevant vehicle.
Therefore, a method for estimating the mass of a vehicle based on the existing configuration of the vehicle without additionally adding corresponding measuring sensors is needed.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art, and aims to provide a method for quickly estimating the load of a heavy commercial vehicle, which does not need to additionally increase a corresponding measuring sensor and has low cost.
The technical scheme of the invention is as follows: a heavy commercial vehicle load rapid estimation method, comprising:
s1, vehicle driving data of different road sections are derived from a vehicle-mounted terminal background system according to a fixed data template, wherein the driving data at least comprise a vehicle speed v, an altitude, an engine torque percentage and an engine rotating speed;
s2, determining a relational expression of the driving resistance coefficient and the vehicle mass, and fitting a vehicle driving resistance curve according to the sliding data of the vehicle driving data to calculate the driving resistance coefficient;
if the vehicle is subjected to the sliding resistance test, performing quadratic polynomial fitting by adopting actual sliding data of the vehicle under different qualities to obtain a relational expression of the driving resistance coefficient and the vehicle quality:
otherwise, fitting the vehicle which does not obtain the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dump truck, a passenger car and a city passenger car, the relational expression of the running resistance coefficient and the vehicle mass is as follows:
when the vehicle is a tractor, the relation between the driving resistance coefficient and the vehicle mass is as follows:
s3, checking the validity of the vehicle running data, dividing the vehicle running state, extracting high-speed segments, and carrying out vehicle acceleration a, road gradient i and engine torque T eq And a total reduction ratio i g i o And vehicle driving force F t Calculating and checking parameters; slicing and screening vehicle driving data according to the vehicle speed and the gradient to obtain a constant-speed gentle slope segment;
s4, establishing a vehicle longitudinal dynamic equation according to the vehicle type of the vehicle:
wherein i g 、i o Is the speed ratio and the main speed reduction ratio of the gearbox, and eta is the mechanical efficiency of the transmission system; c D S is a wind resistance coefficient and a windward area, v is a vehicle speed, alpha is a gradient angle, f is a wheel rolling resistance coefficient, and r is a tire rolling radius; m is the vehicle mass; delta is the conversion coefficient of the rotating mass of the automobile, including the moment of inertia of a flywheel and the moment of inertia of wheels;
according to road design specifications such as JTG B01 road engineering technical standard and CJJ 37 urban road engineering design specification, the maximum slopes of urban roads, general roads, and expressway roads are all limited within 5 °, and if cos α ≈ 1 and sin α ≈ tan α ≈ i, equation (4) is converted into an estimation model including a driving resistance coefficient:
calculating the vehicle load of the uniform-speed gentle slope segment by using an estimation model;
s5, estimating the vehicle load m of different road sections i Making a judgment if all m i All conform to
Wherein, if the sigma is a set error, the load meets the requirement and the estimated load is output
Is m i Average value of (d); otherwise, the process returns to step S1 to reselect the vehicle travel data of a different link for calculation.
As a further improvement, in step S3, the performing of the vehicle travel data analysis, calculation, screening and short segment extraction includes the steps of:
s31, checking the validity of the vehicle speed v and the altitude, firstly detecting and eliminating burrs of the vehicle speed v and the altitude, and secondly smoothing data;
s32, dividing the running state of the vehicle, removing time segments of idling and low speed, and extracting continuous high-speed motion segments;
s33, obtaining the rolling radius r of the vehicle tire, the mechanical efficiency eta of a transmission system and the maximum torque T of an engine max ；
The acceleration a is obtained by a velocity-versus-time difference, and the acceleration at the k-th instant is expressed as:
a(k)＝diff(v(k))/Δt
a(1)＝0
wherein v (k) is the vehicle speed at the moment k, and Δ t is the time interval of sampling by the vehicle-mounted terminal system;
total reduction ratio i g i o Obtained by the following calculation:
wherein n is the engine speed;
the road gradient i is obtained by the ratio of the difference in elevation to the difference in distance traveled by the vehicle over Δ t, and the road gradient at the k-th time is represented as:
i(k)≈diff(Alt(k))/s_dis(k)×100％
i(1)＝0
wherein alt (k) is the altitude at the moment k, and s _ dis (k) is the running distance of the vehicle within the time delta t at the moment k;
engine torque T eq By engine torque capacity T max And torque percentage T _per Calculated, the engine torque at time k is expressed as:
T eq (k)＝T max ×T _per (k)/100；
s34, small segment cutting and uniform-speed gentle slope segment screening are carried out;
firstly, slicing vehicle driving data by adopting a moving window method, and setting a certain window overlapping rate; calculating the variation coefficient of each small segment vehicle speed signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform velocity segments;
secondly, slicing the uniform speed segments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of each uniform speed segment gradient signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform speed gentle slope segments.
Further, in step S31, a filullierers () function of the MAD method is used to perform burr detection and elimination, and the linear difference of the adjacent points is used to compensate after the discrete points are eliminated.
Further, in step S31, the Savitzky-Golay method is adopted for data smoothing, and the format of the calling function 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.
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 6-16.
Further, the window of the moving window method is 10.
Further, in step S34, the vehicle speed variation coefficient of segment j is calculated in the following manner:
cv(j)＝std(v j )/mean(v j )
wherein std (v) j ) Mean (v) as the standard deviation of the vehicle speed for segment j j ) The average of the vehicle speed for segment j.
Further, in step S34, the gradient coefficient of variation of the segment m is calculated by:
cv(m)＝std(i m )/abs(mean(i m ))
wherein std (i) m ) Mean (i) as standard deviation of m slope of the segment m ) The average of the m slopes of the segment.
Further, in step S4, a root of the high-order polynomial is found by using a roots () function, the vehicle loads of the uniform-speed and gentle-slope segments are sorted from small to large, and the average value of the vehicle loads between the median or the 20% to 80% of the branch 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, can effectively estimate the load of the vehicle actually running in the market without adding an additional measuring sensor based on the existing configuration of most vehicles in the market, and provides powerful support for the matching optimization design of the vehicles of 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 terminal system of the present invention;
FIG. 3 illustrates a tractor rule recommended drag coefficient fit of the present invention;
FIG. 4 is a graph of the recommended driving resistance coefficients for different vehicle models provided by the present invention;
FIG. 5 is a flow chart of the present invention for analyzing, calculating and screening vehicle driving characteristic data;
FIG. 6 is a graph illustrating vehicle speed signal spike detection and correction in accordance with an embodiment of the present invention;
FIG. 7 is an overlay of the vehicle operation data screening windows according to the present invention;
FIG. 8 is a histogram of a vehicle mass estimation distribution according to 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 shown in the drawings.
Referring to fig. 1-9, a method for fast estimating a load of a heavy commercial vehicle, a flowchart of which is shown in fig. 1, includes the following steps:
s1, deriving vehicle driving data of different road sections from a vehicle-mounted terminal background system according to a fixed data template, wherein the number n of the road sections is more than or equal to 5 sections as shown in figure 2, and the driving data at least comprises a vehicle speed v, an altitude, an engine torque percentage and an engine rotating speed;
s2, determining a relational expression of the driving resistance coefficient and the vehicle mass, fitting a vehicle driving resistance curve according to the sliding data of the vehicle driving data, and calculating the driving resistance coefficient, wherein the driving resistance coefficient comprises the following coefficient A, coefficient B and coefficient C; as shown in figure 3 of the drawings,
if the vehicle is subjected to the sliding resistance test, performing quadratic polynomial fitting by adopting actual sliding data of the vehicle under different qualities to obtain a relational expression of a driving resistance coefficient and the vehicle quality:
otherwise, fitting the vehicle which does not obtain the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dump truck, a passenger car and a city passenger car, the relational expression of the running resistance coefficient and the vehicle mass is as follows:
when the vehicle is a tractor, the relation between the driving resistance coefficient and the vehicle mass is as follows:
fig. 4 shows the running resistance coefficients of several commercial vehicle types obtained by calculation. The calculation of the running resistance coefficient is a further technology, and the calculation processes of the coefficient A, the coefficient B and the coefficient C can be referred to documents: liu hai tide, li wei qiang, jiang Shi jun, he bin, ning han.
S3, checking the validity of the vehicle running data, dividing the vehicle running state, extracting high-speed segments, and carrying out vehicle acceleration a, road gradient i and engine torque T eq Total reduction ratio i g i o And vehicle driving force F t Calculating and checking parameters; slicing and screening vehicle driving data according to the vehicle speed and the gradient to obtain a constant-speed gentle slope segment;
s4, establishing a longitudinal vehicle dynamics equation according to the vehicle type of the vehicle:
wherein i g 、i o To becomeA gearbox speed ratio and a main reduction ratio, wherein eta is the mechanical efficiency of the transmission system; c D S is a wind resistance coefficient and a windward area, v is a vehicle speed, alpha is a slope angle, f is a wheel rolling resistance coefficient, and r is a tire rolling radius; m is the vehicle mass; delta is the conversion coefficient of the rotating mass of the automobile, including the moment of inertia of a flywheel and the moment of inertia of wheels;
according to road design specifications such as JTG B01 road engineering technical standard and CJJ 37 urban road engineering design specification, the maximum gradients of urban roads, general roads and expressway roads are all limited within 5 °, so assuming that cos α is 1, sin α is approximately equal to tan α i, and i is the road gradient, equation (4) is converted into an estimation model containing the driving resistance coefficient:
wherein f is a + Bv;
calculating the vehicle load of the uniform-speed gentle slope segment by using an estimation model;
s5, estimating the vehicle load m of different road sections i Making a judgment if all m i All conform to
Wherein, if the sigma is a set error, the load meets the requirement and the estimated load is output
Is m i Average value of (d); otherwise, the process returns to step S1 to reselect the vehicle travel data of a different link for calculation. According to the criterion of Rhein if
Then consider m i Is a normal point and is used for judging the load m i Whether outliers (singular points) exist, and if so, elimination is required.
As shown in fig. 5, the analyzing, calculating, screening and short segment extracting of the vehicle traveling data at step S3 includes the steps of:
s31, checking the validity of the vehicle speed v and the altitude, firstly detecting and eliminating burrs of the vehicle speed v and the altitude, and secondly smoothing data; performing burr detection and elimination by using a Filluluters () function of an MAD method, wherein the window length of the Filluluters () function is 10, and compensating by using a linear difference value of adjacent points after discrete points are eliminated, as shown in FIG. 6; the data smoothing adopts a Savitzky-Golay method, the format of a calling function is y-smooth _ SG5_3(x _ ori, n), wherein smooth _ SG5_3 is a compiled m file, x _ ori is original data, and n is smoothing times;
s32, dividing the running state of the vehicle, removing time segments of idling and low speed, and extracting continuous high-speed motion segments; preferably, a motion segment with the vehicle 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 tire, the mechanical efficiency eta of a transmission system and the maximum torque T of an engine max ；
The acceleration a is obtained by a velocity-versus-time difference, and the acceleration at the k-th instant is expressed as:
a(k)＝diff(v(k))/Δt
a(1)＝0
wherein v (k) is the vehicle speed at the moment k, and Δ t is the time interval of sampling by the vehicle-mounted terminal system;
overall reduction ratio i g i o Obtained by the following calculation:
wherein n is the engine speed in r/min;
the road gradient i is obtained by the ratio of the difference in elevation to the difference in distance traveled by the vehicle over Δ t, and the road gradient at the k-th time is represented as:
i(k)≈diff(Alt(k))/s_dis(k)×100％
i(1)＝0
wherein alt (k) is the altitude at the moment k, and s _ dis (k) is the vehicle running distance within the time delta t at the moment k;
engine torque T eq By engine torque capacity T max And torque percentage T _per Calculated, the engine torque at time k is expressed as:
T eq (k)＝T max ×T _per (k)/100；
the upper limit value and the lower limit value of the acceleration are respectively +/-2 m/s 2; the upper and lower limit values of the gradient are respectively +/-9%, the gradient is generally converted into percentage for calculation, and the actual description of the road gradient is also described in percentage, such as 10% gradient;
s34, small segment cutting and uniform-speed gentle slope segment screening are carried out;
firstly, slicing the vehicle driving data by adopting a moving window method, and setting a certain window overlapping rate, in the embodiment, setting a 50% window overlapping rate, as shown in fig. 7; calculating the variation coefficient of each small segment vehicle speed signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform velocity segments;
secondly, slicing the uniform speed segments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of each uniform speed segment gradient signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform speed gentle slope segments. The window of the moving window method is 6-16, and preferably, the window of the moving window method is 10.
Further, in step S34, the vehicle speed variation coefficient of segment j is calculated in the following manner:
cv(j)＝std(v j )/mean(v j )
wherein std (v) j ) Mean (v) as the standard deviation of the vehicle speed for segment j j ) The average of the vehicle speed for segment j.
In step S34, the gradient coefficient of variation of the segment m is calculated by:
cv(m)＝std(i m )/abs(mean(i m ))
wherein std (i) m ) Mean is the standard deviation of the m slope of the segmenti m ) The average of the m slopes of the segment.
In step S4, a roots of the high-order polynomial is found by using a roots () function, the vehicle loads of the uniform-speed gentle slope segments are sorted from small to large, and the average value of the vehicle loads between the median or the 20% to 80% of the branch line is taken as the prediction result of the vehicle loads.
Corresponding calculation programs can be compiled according to the method, and a parameterized graphical user interface is developed to perform data batch processing and load estimation.
Practical application
The tractor is taken as an example, and considering that most automobile enterprises at present optimize rolling resistance and wind resistance of vehicles well and actual running resistance, a proportional coefficient lambda can be multiplied on the basis of obtaining a running resistance coefficient by calculation, and the proportional coefficient is 0.85-1, preferably 0.95 according to experience. Substituting the driving resistance coefficient of the tractor into the estimation model (5) to carry out numerical solution to obtain:
that is to say that the first and second electrodes,
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
root searching is carried out on the high-order polynomial by adopting a roots () function, the load estimation results of the uniform-speed gentle slope segments are sorted from small to large, the average value or the median of the load results between 20% and 80% of branch lines is taken as the prediction result of the vehicle load, and the vehicle load distribution condition is shown in fig. 7 and 8.
Estimated vehicle load m of different sections of the tractor i ＝[43.61,45.44,44.67,46.50, 46.39]And the final output is estimated to be 45.322 tons and the actual load of the vehicle is 49 tons after the test is met, and the estimation error is about 7.5 percent.
Therefore, the method is simple, convenient and easy to implement, low in cost and high in efficiency, the vehicle load condition can be estimated accurately and quickly only by acquiring the vehicle running data from the background of the vehicle-mounted terminal, and the method has a certain effect in the vehicle performance matching and developing process.
The above is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that several variations and modifications can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the utility of the patent.
Claims (10)
1. A method for fast estimation of heavy commercial vehicle load, comprising:
s1, vehicle driving data of different road sections are derived from a vehicle-mounted terminal background system according to a fixed data template, wherein the driving data at least comprise a vehicle speed v, an altitude, an engine torque percentage and an engine rotating speed;
s2, determining a relational expression of the driving resistance coefficient and the vehicle mass, and fitting a vehicle driving resistance curve according to the sliding data of the vehicle driving data to calculate the driving resistance coefficient;
if the vehicle is subjected to the sliding resistance test, performing quadratic polynomial fitting by adopting actual sliding data of the vehicle under different qualities to obtain a relational expression of the driving resistance coefficient and the vehicle quality:
otherwise, fitting the vehicle which does not obtain the actual sliding resistance temporarily by adopting a rule recommended resistance coefficient;
when the vehicle is any one of a truck, a dump truck, a passenger car and a city passenger car, the relational expression of the running resistance coefficient and the vehicle mass is as follows:
when the vehicle is a tractor, the relation between the driving resistance coefficient and the vehicle mass is as follows:
s3, checking the validity of the vehicle running data, dividing the vehicle running state, extracting high-speed segments, and carrying out vehicle acceleration a, road gradient i and engine torque T eq Total reduction ratio i g i o And vehicle driving force F t Calculating and checking parameters; slicing and screening vehicle driving data according to the vehicle speed and the gradient to obtain a constant-speed gentle slope segment;
s4, establishing a vehicle longitudinal dynamics equation according to the vehicle type of the vehicle:
wherein i g 、i o Is the speed ratio and the main speed reduction ratio of the gearbox, and eta is the mechanical efficiency of the transmission system; c D S is a wind resistance coefficient and a windward area, v is a vehicle speed, alpha is a gradient angle, f is a wheel rolling resistance coefficient, and r is a tire rolling radius; m is the vehicle mass; delta is the conversion coefficient of the rotating mass of the automobile, including the moment of inertia of a flywheel and the moment of inertia of wheels;
according to road design specifications such as JTG B01 highway engineering technical standard and CJJ 37 highway engineering design specification, the maximum slopes of urban roads, general roads and expressway roads are all limited within 5 °, so assuming that cos α is 1 and sin α is approximately equal to tan α, equation (4) is converted into an estimation model including a driving resistance coefficient:
wherein f is a + Bv;
calculating the vehicle load of the uniform-speed gentle slope segment by using an estimation model;
s5, estimating the vehicle load m of different road sections i Making a judgment if all m i All conform to
Wherein, if the sigma is a set error, the load meets the requirement and the estimated load is output
Is m i Average value of (d); otherwise, the process returns to step S1 to reselect the vehicle travel data of a different link for calculation.
2. The heavy commercial vehicle load rapid estimation method according to claim 1, wherein the vehicle driving data analysis, calculation, screening and short segment extraction at step S3 comprises the steps of:
s31, checking the validity of the vehicle speed v and the altitude, firstly detecting and eliminating burrs of the vehicle speed v and the altitude, and secondly smoothing data;
s32, dividing the running state of the vehicle, removing time segments of idling and low speed, and extracting continuous high-speed motion segments;
s33, obtaining the rolling radius r of the vehicle tire, the mechanical efficiency eta of a transmission system and the maximum torque T of an engine max ；
The acceleration a is obtained by a velocity-versus-time difference, and the acceleration at the k-th instant is expressed as:
a(k)＝diff(v(k))/Δt
a(1)＝0
wherein v (k) is the vehicle speed at the moment k, and Δ t is the time interval of sampling by the vehicle-mounted terminal system;
total reduction ratio i g i o Obtained by the following calculation:
wherein n is the engine speed;
the road gradient i is obtained by the ratio of the difference in elevation to the difference in distance traveled by the vehicle over Δ t, and the road gradient at the k-th time is represented as:
i(k)≈diff(Alt(k))/s_dis(k)×100％
i(1)＝0
wherein alt (k) is the altitude at the moment k, and s _ dis (k) is the vehicle running distance within the time delta t at the moment k;
engine torque T eq By engine torque capacity T max And torque percentage T _per Calculated, the engine torque at time k is expressed as:
T eq (k)＝T max ×T _per (k)/100；
s34, small segment cutting and uniform-speed gentle slope segment screening are carried out;
firstly, slicing vehicle driving data by adopting a moving window method, and setting a certain window overlapping rate; calculating the variation coefficient of each small segment vehicle speed signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform velocity segments;
secondly, slicing the uniform speed segments by adopting a moving window method, setting a certain window overlapping rate, calculating the variation coefficient of each uniform speed segment gradient signal, and taking 30% segments with smaller variation coefficient to form a new time sequence as uniform speed gentle slope segments.
3. The method as claimed in claim 2, wherein in step S31, the burrs are detected and removed by using filulliers () function of MAD method, and the linear difference of the nearby points is used for compensation after the discrete points are removed.
4. The heavy commercial vehicle load rapid estimation method according to claim 2, wherein in step S31, the data smoothing is performed by Savitzky-Golay method, and the format of the calling function is y-smooth _ SG5_3(x _ ori, n), wherein smooth _ SG5_3 is compiled m-file, x _ ori is raw data, and n is smoothing times.
5. The heavy commercial vehicle load rapid estimation method according to claim 2, wherein the motion segment with the vehicle speed of more than or equal to 30km/h is extracted as the continuous high-speed motion segment in step S32.
6. The heavy commercial vehicle load rapid estimation method according to claim 2, wherein in step S34, the window of the moving window method is 6-16.
7. The method of claim 6, wherein the window of the moving window method is 10.
8. The method as claimed in claim 2, wherein in step S34, the vehicle speed variation coefficient of segment j is calculated as:
cv(j)＝std(v j )/mean(v j )
wherein std (v) j ) Mean (v) as the standard deviation of the vehicle speed for segment j j ) The average of the vehicle speed for segment j.
9. The method for rapidly estimating the load of a heavy commercial vehicle as claimed in claim 2, wherein in step S34, the gradient variation coefficient of segment m is calculated by:
cv(m)＝std(i m )/abs(mean(i m ))
wherein std (i) m ) Mean (i) as standard deviation of m slope of the segment m ) The average of the m slopes of the segment.
10. The method as claimed in claim 1, wherein in step S4, a root-finding is performed on the high-order polynomial by using roots () function, the vehicle loads of the uniform-speed gentle-slope segments are sorted from small to large, and the average value of the vehicle loads between the median or 20% to 80% of the sublevel is taken as the prediction result of the vehicle loads.
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