CN114821854A - Method for evaluating influence of working condition switching on vehicle oil consumption - Google Patents
Method for evaluating influence of working condition switching on vehicle oil consumption Download PDFInfo
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Abstract
The invention provides a method for evaluating influence of working condition switching on vehicle oil consumption, which comprises the following steps of: selecting vehicle characteristic parameters to determine the weight of each parameter; determining a typical vehicle according to the parameter weight; carrying out hub oil consumption test and actual road oil consumption test on new and old calibration typical vehicles; calculating an actual oil consumption value; calculating an authentication oil consumption difference, a hub calibration oil consumption difference, an actual and hub oil consumption difference and an actual oil consumption difference according to the hub and actual oil consumption values; and calculating the difference of the oil consumption of each type of vehicle. The method for evaluating the influence of working condition switching on the vehicle oil consumption is based on a least square support vector machine and a clustering algorithm to provide a method for determining a typical vehicle; the method comprises the steps of carrying out hub rotation and actual road oil consumption testing on new and old calibrated vehicles, providing a working condition block method, calculating an actual road oil consumption value by utilizing a temperature correction factor and a working condition block weight, determining the actual oil consumption of the automobile under an actual complex road environment, and finally providing a method for calculating the oil consumption difference of the new and old calibrated vehicles before and after working condition switching.
Description
Technical Field
The invention belongs to the field of transportation, and particularly relates to a method for evaluating influence of working condition switching on vehicle oil consumption.
Background
The working condition is a common basic technology in the automobile industry, is the basis of vehicle development and calibration, and is the basis for formulating the oil consumption and emission test limit value of the vehicle. The working conditions can change along with the continuous change of factors such as the vehicle holding capacity, the road structure, the traffic condition and the like. And each country updates the working condition curve of the country at intervals so as to represent the vehicle running condition of the current actual road. As an important component of vehicle oil consumption and emission regulations, the switching of the working condition curves can bring about two influences, namely the change of type authentication oil consumption caused by the change of the working condition and the change of the enterprise calibration strategy, and the difference of actual oil consumption caused by the change of the enterprise calibration strategy.
Disclosure of Invention
In view of the above, the present invention provides a method for evaluating an influence of a working condition switch on a vehicle fuel consumption, so as to solve a problem of a lack of the working condition switch influence evaluation method. The method is convenient for effective government supervision, evaluation of actual performance of the enterprise vehicle types and improvement of the acceptance of the public on the actual fuel consumption of the vehicle.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a method for evaluating influence of working condition switching on vehicle oil consumption comprises the following steps:
s1, selecting and dimensionless vehicle characteristic parameters;
s2, performing oil consumption prediction by using a least square support vector machine based on the dimensionless vehicle characteristic parameters, and determining the weight of each parameter;
s3, determining a typical vehicle based on the weight of each parameter and cluster analysis;
s4, calibrating the vehicle according to the new working condition, and carrying out the hub oil consumption test of the new and old calibrated vehicles to obtain 4 hub oil consumption values of the new and old working conditions of the new and old calibrated vehicles;
s5, building a fleet and carrying out actual road oil consumption test;
s6, determining the actual road working condition distribution;
s7, determining a temperature correction factor;
s8, calculating the actual road working condition distribution oil consumption;
s9, calculating the actual road oil consumption to obtain 2 actual oil consumption values of the new and old calibration vehicles;
s10, respectively calculating a typical vehicle authentication oil consumption difference value, a typical vehicle hub calibration oil consumption difference value, a typical vehicle actual and hub calibration oil consumption difference value and a typical vehicle actual oil consumption difference value according to 4 hub oil consumption values of the new and old working conditions of the new and old calibration vehicles and 2 actual oil consumption values of the new and old calibration vehicles;
and S11, respectively calculating the hub calibrated fuel consumption difference value, the actual fuel consumption difference value and the actual fuel consumption difference value of each type of vehicle according to the hub calibrated fuel consumption difference value of the typical vehicle, the actual fuel consumption difference value of the typical vehicle and the hub calibrated fuel consumption difference value of the typical vehicle and the actual fuel consumption difference value of the typical vehicle.
Further, the fuel consumption prediction and the determination of the weight of each parameter in step S2 include the following steps:
A1. selecting characteristic parameters as a training set, taking hundred kilometers of oil consumption as a target set, and performing prediction training on the oil consumption by using a least square support vector machine;
A2. one characteristic parameter is removed in each training, and finally, the prediction precision reduction rate is determined according to RMSE error evaluation;
A3. and calculating the weight of each characteristic parameter according to the prediction precision reduction rate, wherein the higher the weight value is, the higher the importance of the parameter to the final fuel consumption prediction is represented.
Further, the actual road condition distribution in step S6 includes the steps of:
b1, dividing the data actually collected by the new and old calibrated typical vehicle models into short segments respectively, and deleting the segments of the air conditioner;
b2, dividing the speed into three speed intervals of low, medium and high according to the maximum speed, and calculating the power demand distribution condition of each speed interval;
and B3, dividing the power requirements of different speed intervals into five gears, and obtaining the distribution of the working condition blocks of the speed-power matrix.
Further, the actual road fuel consumption in step S9 is a calculation value of the actual road condition distribution fuel consumption after the temperature correction factor is calculated and weighted by mileage.
Further, the difference value of the fuel consumption for the typical vehicle authentication in step S10 is the difference value between the fuel consumption value of the hub after the new calibration vehicle type condition is switched and the fuel consumption value of the hub before the old calibration vehicle type condition is switched.
Further, in step S10, the difference value between the calibrated fuel consumption of the typical vehicle hub in the step S is the difference value between the fuel consumption of the hub after the new calibrated vehicle type condition is switched and the fuel consumption of the hub after the old calibrated vehicle type condition is switched.
Further, the difference between the actual fuel consumption of the typical vehicle and the hub calibrated fuel consumption in step S10 is the difference between the actual fuel consumption of the new calibrated vehicle and the fuel consumption of the hub under the new calibration condition.
Further, the difference value between the actual fuel consumption of the typical vehicle in step S10 is the difference value between the actual fuel consumption of the new calibrated vehicle and the actual fuel consumption of the old calibrated vehicle.
Further, the difference value of the calibrated fuel consumption of each type of vehicle hub in the step S11 is the average driving mileage of the vehicle remaining quantity per year of the typical vehicle hub calibrated fuel consumption difference;
in step S11, the difference between the actual fuel consumption of each vehicle and the fuel consumption of the hub is the difference between the actual fuel consumption of the typical vehicle and the fuel consumption of the hub, and the average driving mileage per year;
the actual fuel consumption difference value of each vehicle type in step S11 is the actual fuel consumption difference of a typical vehicle, the vehicle remaining amount, and the average driving mileage.
Compared with the prior art, the method for evaluating the influence of the working condition switching on the vehicle oil consumption has the following advantages:
(1) the invention discloses a method for evaluating influence of working condition switching on vehicle oil consumption, and provides a method for determining a typical vehicle by utilizing a least square support vector machine model and cluster analysis based on vehicle whole vehicle characteristic parameters. The method comprises the steps of carrying out hub rotation and actual road oil consumption testing on new and old calibrated vehicles, providing a working condition block method according to speed-power distribution systematicness, calculating an actual road oil consumption value by utilizing a temperature correction factor and a working condition block weight, effectively determining the actual oil consumption of the automobile under the actual complex road environment, and finally providing a method for calculating the oil consumption difference of the new and old calibrated vehicles before and after working condition switching.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the overall structure according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the profile coefficient distribution according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an actual driving speed-power distribution according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a short stroke segment according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of the temperature change of Tianjin in 2020 according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
The noun explains:
dimensionless: non-dimensionalization (nondimensionalization or dimensionless) refers to the removal of some or all units of an equation relating to physical quantities by a suitable variable substitution for the purpose of simplifying experiments or calculations, and is an important processing idea in scientific research.
Least square method: the least square method is a mathematical tool widely applied in the fields of various disciplines of data processing such as error estimation, uncertainty, system identification and prediction, forecast and the like.
As shown in fig. 1 to 5, a method for evaluating influence of operating condition switching on fuel consumption of a vehicle includes the following steps:
s1, selecting and dimensionless vehicle characteristic parameters;
s2, performing oil consumption prediction by using a least square support vector machine based on the dimensionless vehicle characteristic parameters, and determining the weight of each parameter;
s3, determining a typical vehicle based on the weight of each parameter and cluster analysis;
s4, calibrating the vehicle according to the new working condition, and carrying out the hub oil consumption test of the new and old calibrated vehicles to obtain 4 hub oil consumption values of the new and old working conditions of the new and old calibrated vehicles;
s5, building a fleet and carrying out actual road oil consumption test;
s6, determining the actual road working condition distribution;
s7, determining a temperature correction factor;
s8, calculating the actual road working condition distribution oil consumption;
s9, calculating the actual road oil consumption to obtain 2 actual oil consumption values of the new and old calibration vehicles;
s10, respectively calculating a difference value of the oil consumption of the typical vehicle authentication, a difference value of the oil consumption of the typical vehicle hub calibration, a difference value of the oil consumption of the typical vehicle actual calibration and the oil consumption of the hub calibration and a difference value of the oil consumption of the typical vehicle actual calibration and the oil consumption of the hub calibration according to the 4 items of hub oil consumption values of the new and old calibration vehicles and the 2 items of actual oil consumption values of the new and old calibration vehicles;
and S11, respectively calculating the hub calibrated fuel consumption difference value, the actual fuel consumption difference value and the actual fuel consumption difference value of each type of vehicle according to the hub calibrated fuel consumption difference value of the typical vehicle, the actual fuel consumption difference value of the typical vehicle and the hub calibrated fuel consumption difference value of the typical vehicle and the actual fuel consumption difference value of the typical vehicle.
The invention provides a method for determining a typical vehicle by utilizing a least square support vector machine model and cluster analysis based on vehicle characteristic parameters. The method comprises the steps of carrying out hub rotation and actual road oil consumption testing on new and old calibrated vehicles, providing a working condition block method according to speed-power distribution systematicness, calculating an actual road oil consumption value by utilizing a temperature correction factor and a working condition block weight, effectively determining the actual oil consumption of the automobile under the actual complex road environment, and finally providing a method for calculating the oil consumption difference of the new and old calibrated vehicles before and after working condition switching.
The fuel consumption prediction and the determination of the weight of each parameter in step S2 include the following steps:
A1. selecting characteristic parameters as a training set, taking hundred kilometer oil consumption as a target set, and performing prediction training on the oil consumption by using a least square support vector machine;
A2. one characteristic parameter is removed in each training, and finally, the prediction precision reduction rate is determined according to RMSE error evaluation;
A3. and calculating the weight of each characteristic parameter according to the prediction precision reduction rate, wherein the higher the weight value is, the higher the importance of the parameter to the final fuel consumption prediction is represented.
The actual road condition distribution in step S6 includes the steps of:
b1, dividing the data actually collected by the new and old calibrated typical vehicle models into short segments respectively, and deleting the segments of the air conditioner;
b2, dividing the speed into three speed intervals of low, medium and high according to the maximum speed, and calculating the power demand distribution condition of each speed interval;
and B3, dividing the power requirements of different speed intervals into five gears, and obtaining the distribution of the working condition blocks of the speed-power matrix.
The actual road oil consumption in step S9 is a calculated value weighted by the actual road condition distribution oil consumption and mileage calculated by the temperature correction factor.
In step S10, the difference value of the fuel consumption for typical vehicle authentication is the difference value between the fuel consumption for the hub after the new calibration vehicle type condition is switched and the fuel consumption for the hub before the old calibration vehicle type condition is switched.
In step S10, the difference value of the calibrated fuel consumption of the typical vehicle hub is the difference value between the fuel consumption of the hub after the new calibrated vehicle type condition is switched and the fuel consumption of the hub after the old calibrated vehicle type condition is switched.
The difference between the actual fuel consumption of the typical vehicle and the hub calibrated fuel consumption in step S10 is the difference between the actual fuel consumption of the new calibrated vehicle and the fuel consumption of the hub under the new calibration new operating condition.
The difference value of the actual fuel consumption of the typical vehicle in the step S10 is the difference value between the actual fuel consumption of the new calibrated vehicle and the actual fuel consumption of the old calibrated vehicle.
The difference value of the calibrated fuel consumption of each type of vehicle hub in the step S11 is the average driving mileage of the vehicle retained volume and the year of the typical vehicle hub calibrated fuel consumption difference;
in step S11, the difference between the actual fuel consumption of each vehicle and the fuel consumption of the hub is the difference between the actual fuel consumption of the typical vehicle and the fuel consumption of the hub, and the average driving mileage per year;
the actual fuel consumption difference value of each vehicle type in step S11 is the actual fuel consumption difference of a typical vehicle, the vehicle remaining amount, and the average driving mileage.
It should be noted that the working condition in the invention is an old working condition before switching and a new working condition after switching, the old calibration refers to the calibration of the vehicle by using the old working condition, and the new calibration refers to the calibration of the vehicle by using the new working condition.
In this embodiment, the method for evaluating the influence of the working condition switching on the fuel consumption of the vehicle includes the following steps:
s1, selecting and dimensionless vehicle characteristic parameters;
in this embodiment, the engine maximum power, the maximum power corresponding rotation speed, the maximum torque corresponding rotation speed, the transmission gear number, the primary gear ratio and other characteristic parameters are selected for non-dimensionalization.
S2, oil consumption prediction is carried out by using a least square support vector machine, and the weight of each parameter is determined;
taking the non-dimensionalized characteristic parameters as input and oil consumption as output, predicting the oil consumption by using a least square support vector machine, and determining the weight of each parameter according to the reduction rate of prediction precision;
s3, selecting a typical vehicle;
performing vehicle clustering analysis based on the determined parameter weights, and selecting a vehicle type represented by a Euclidean distance closest point from a clustering center as a typical vehicle type;
s4, vehicle calibration is carried out according to the new working condition, and a hub oil consumption test is carried out;
carrying out vehicle recalibration on a typical vehicle type according to the working condition after switching, and carrying out oil consumption tests on four groups of rotating hubs before and after switching the working condition on a new calibrated typical vehicle and an old calibrated typical vehicle; the working condition before the light vehicle is switched refers to WLTC working condition, and the working condition after the light vehicle is switched refers to CLTC-P working condition.
S5, building a fleet and carrying out road oil consumption test;
respectively constructing a motorcade for new and old calibrated typical vehicle types, carrying out actual road oil consumption test, and recording data such as oil consumption, an air conditioner switch, ambient temperature and the like;
s6, determining the actual road working condition distribution;
dividing actually acquired data of new and old calibrated typical vehicle types into short segments, deleting segments of an air conditioner, dividing the segments into three speed intervals of low, medium and high according to the maximum speed, calculating the power demand distribution condition of each speed interval, dividing the power demands of different speed intervals into five grades, and obtaining the distribution of speed-power matrix working condition blocks;
s7, determining a temperature correction factor;
because the temperature condition of actual road acquisition is not controllable, a corresponding temperature correction factor is calculated according to the actual acquisition temperature of each segment in the working condition block and the corresponding oil consumption;
s8, calculating the actual road working condition distribution oil consumption;
calculating the oil consumption of each short stroke segment according to the acquired data, and calculating the average oil consumption of each working condition block in hundred kilometers according to the working condition block to which the segment belongs and the temperature correction factor;
s9, calculating the actual road oil consumption;
weighting the average oil consumption of each working condition block by using the mileage corresponding to the distribution of the actual working condition blocks, and finally obtaining the actual oil consumption of the new and old calibration typical vehicle types;
s10, calculating the 4-item fuel consumption difference of the typical vehicle
The typical vehicle authentication oil consumption difference is the new calibration new working condition vehicle hub oil consumption value-the old calibration old working condition vehicle hub oil consumption value;
the typical vehicle hub calibration oil consumption difference is the new calibration new working condition vehicle hub oil consumption value-the old calibration new working condition vehicle hub oil consumption value;
the difference value between the actual oil consumption of the typical vehicle and the oil consumption of the rotating hub is equal to the actual oil consumption of the newly-calibrated vehicle, namely the oil consumption of the rotating hub under the new working condition;
the difference value of the actual oil consumption of the typical vehicle is the actual oil consumption of the new calibration vehicle-the actual oil consumption of the old calibration vehicle;
s11, calculating fuel consumption difference values of various vehicle types
The calibrated fuel consumption difference of the rotating hubs of the typical vehicle is obtained by keeping the average driving mileage of the vehicle per year;
the difference between the actual vehicle and the hub of the typical vehicle and the average driving mileage of the vehicle retained quantity per year are obtained to obtain the difference between the actual fuel consumption of each type of vehicle and the fuel consumption of the hub;
and the actual fuel consumption difference of each type of vehicle is obtained by the average driving mileage of the vehicle. The evaluation method provides a method for determining a typical vehicle by using a least square support vector machine model and cluster analysis based on vehicle whole vehicle characteristic parameters. The method comprises the steps of carrying out hub rotation and actual road oil consumption testing on new and old calibrated vehicles, providing a working condition block method according to speed-power distribution systematicness, calculating an actual road oil consumption value by utilizing a temperature correction factor and a working condition block weight, effectively determining the actual oil consumption of the automobile under the actual complex road environment, and finally providing a method for calculating the oil consumption difference of the new and old calibrated vehicles before and after the working condition is switched.
Example 1:
the method of the present invention is further described in detail below with reference to the accompanying drawings. Fig. 1 is a route chart of the patent technology, and the specific steps are as follows:
selecting characteristic parameters and carrying out dimensionless:
selecting characteristic parameters of 200 types of light vehicles: 1) vehicle servicing quality; 2) maximum total mass of the vehicle; 3) The highest factory speed; 4) the wheel base; 5) a maximum number of passengers; 6) maximum power of the engine; 7) the maximum power corresponds to the rotating speed; 8) a maximum torque; 9) the maximum torque corresponds to the rotating speed; 10) a transmission gear number; 11) A primary transmission ratio; 12) for example, table 1 shows part of characteristic parameter data, and each characteristic parameter unit is different, so that dimensionless processing is required, where formula (1) is a characteristic parameter normalization formula, and table 2 is a result after normalization.
In the formula: x i Is normalized data of the ith characteristic parameter, x is raw data of the ith characteristic parameter, x i,max Is the maximum value of the ith characteristic parameter, x i,min Is the minimum value of the ith characteristic parameter.
TABLE 1 raw data of characteristic parameters
TABLE 2 normalization of characteristic parameters
Using a least square support vector machine to predict oil consumption, and determining the weight of each parameter:
taking the first 11 characteristic parameters as a training set, taking the oil consumption per hundred kilometers as a target set, using a least square support vector machine to train and predict the oil consumption, simultaneously respectively removing one characteristic parameter from each subsequent training, predicting for 11 times, and finally determining a prediction precision reduction rate T according to RMSE error evaluation (as shown in formula (2)) i The weight of each characteristic parameter is calculated (formula (3)), wherein the larger the weight value is, the higher the importance of the parameter to the final fuel consumption prediction is, as shown in table 3.
Where i is 1,2,3 … 11, in the order of the parameters removed;
m is 200, i is 1,2,3 … 200 for selecting the number of vehicles;
Y ij to reject the predicted fuel consumption value, X, of the ith parameter of the jth vehicle j "predicted value of oil consumption without parameters being eliminated.
Weight ratio u of each parameter i The calculation formula is as follows:
TABLE 3 determination of weights of parameters
| RMSE | Rate of decrease T i /％ | Parameter weight u i /％ | |
| Parameters not rejected | 0.0153 | - | - |
| Rejection parameter x 1 | 0.0189 | 23.53 | 6.09 |
| Rejection parameter x 2 | 0.0233 | 52.29 | 13.53 |
| … | … | … | |
| Rejection parameter x 11 | 0.0197 | 28.75 | 7.44 |
Determining a typical vehicle:
selecting a typical vehicle by utilizing k-means cluster analysis, wherein the method comprises the following four steps:
(1) determining the number of the clustering centers k, dividing the sample into k different clustering clusters, and determining the value of the clustering centers according to the contour coefficient (as shown in figure 2), wherein the value is shown in formula (5):
s i ＝(b i -a i )/max(b i ,a i ) (5)
wherein: a is i Is the average distance a from the sample point to other sample points in the same cluster i ，a i The smaller the sample, the more this sample should be clustered to the cluster; b i Average distance b from the sample point to other cluster sample points i ，s i Is the contour coefficient.
s i The closer to 1, the description of a i Far greater than bi, the more reasonable the classification is; s i Closer to-1, indicating that the classification is not reasonable;
calculating an average contour coefficient, and determining the number of clustering centers k; when k is 3, s i When k is 4, s is 0.289 i ＝0.301。
The number of clusters is selected to be k-4.
(2) Selecting a clustering center based on the feature weight u i And determining sample points contained in each cluster according to the Euclidean distance, wherein the sample points are shown in formula (6):
in the formula: d j Is the Euclidean distance, X, of the sample to the center of the cluster ik "is the value of the ith characteristic parameter corresponding to the kth cluster center.
(3) Enabling each sample point to return to a clustering center closest to the sample point in the Euclidean distance to serve as a new clustering cluster, and solving the mean value of the sample points of each clustering cluster to serve as a new clustering center;
(4) and (4) repeating the steps (2) and (3) until the clustering center is not changed, and selecting the sample point closest to the clustering center as a typical vehicle.
According to the new working condition, vehicle calibration is carried out, hub oil consumption test is carried out, a motorcade is built, and an actual road test is carried out:
and calibrating the vehicle emission oil consumption of the selected typical vehicle according to the switched working condition curve to obtain a new calibrated vehicle, and respectively performing normal-temperature hub emission oil consumption tests of the old working condition and the new working condition on the new and old calibrated vehicles. Four groups of fuel consumption results of the typical vehicle hub are obtained: the method comprises the steps of calibrating an oil consumption value of a hub under an old working condition of an old vehicle, calibrating an oil consumption value of a hub under a new working condition of an old vehicle, calibrating an oil consumption value of a hub under an old working condition of a new vehicle, and calibrating an oil consumption value of a hub under a new working condition of a new vehicle. And simultaneously, 10 vehicles are respectively selected for the new and old calibration typical vehicles to form a motorcade, wherein the motorcade comprises private vehicles and commercial vehicles, an autonomous driving method is adopted to test the actual road oil consumption for one year, the acquisition frequency is 4Hz, and the relevant data of the oil consumption such as the real-time rotating speed of an engine, the torque, the opening degree of a throttle valve, the opening and closing of an accelerator pedal and an air conditioner are recorded.
Determining the actual road condition distribution:
dividing the actual data (as shown in fig. 3) of the vehicle according to a short stroke segment, wherein the short stroke segment is composed of an idle speed segment and a motion segment (as shown in fig. 4).
And deleting the air conditioning fragment in the short-stroke fragment, and dividing the short-stroke fragment into three speed intervals of low speed, medium speed and high speed according to the maximum speed of the fragment, wherein the division criterion is that the maximum speed does not exceed 60km/h, 80km/h and 120km/h respectively, because the upper limit of the speed of the urban main road is 60km/h, the speed of the express way is 80km/h and the speed of the expressway is 120 km/h. Calculating the power demand distribution condition of each speed interval, dividing the power demands of different speed intervals into five gears, specifically dividing the power demands according to the maximum power of the vehicle and the actually acquired power data, taking the vehicle shown in fig. 3 as a representative, sequencing the low-speed intervals according to the power magnitude of the vehicles one by one second, and averagely dividing the low-speed intervals into 5 gears according to the sequencing, so that the power of the low-speed intervals is divided into (20, 0], (0,14.6], (14.6,35.7], (35.7,70.4] and (70.4, 98) 5 intervals, and similarly, the medium-speed and high-speed intervals are similarly processed, and finally the speed-power 3 x 5 matrix working condition block distribution is obtained;
determining a temperature correction factor:
the acquired data is annual random driving data, the final oil consumption calculation is greatly influenced due to different driving temperature environments of the vehicle, so that the influence of the calculated oil consumption on the temperature is required to be corrected, a 2020-year temperature change curve in Tianjin city is shown in FIG. 5, and the oil consumption values of a typical vehicle at different temperatures are shown in Table 4.
TABLE 4 oil consumption at various ambient temperatures
| Temperature/. degree.C | Average oil consumption per L100 km of fragment -1 | Temperature correction factor |
| (-10，-5] | 10.51 | 0.755 |
| (-5，0] | 9.44 | 0.892 |
| (0，5] | 8.41 | 0.941 |
| (5，10] | 8.22 | 0.966 |
| (10，15] | 8.09 | 0.981 |
| (15，20] | 7.99 | - |
| (20，25] | 7.95 | - |
| (25，30] | 7.96 | - |
| (30，35] | 7.94 | - |
| (35，40] | 7.91 | - |
It can be known from the table that the oil consumption increases with the decrease of the temperature, and the oil consumption change difference is smaller when the temperature is above 15 ℃, and the current oil consumption regulation standard is referred, so the oil consumption at normal temperature (20, 25] ° c is taken as the temperature correction basis to correct the oil consumption with the ambient temperature below 15 ℃, and the calculation formula of the temperature correction factor is as follows:
r＝F/F l (7)
f is (20, 25)]Oil consumption at DEG C, F l For oil consumption at different temperatures.
Calculating the oil consumption of the actual road working condition block:
calculating the average power and average speed of each segment, counting each segment into the corresponding working condition block, correcting the oil consumption of the segment according to the temperature correction factor corresponding to each segment, and finally calculating the oil consumption F of each working condition block ij As in formula (8):
wherein, F ijn And D is the number of the segments corresponding to the blocks with different working conditions.
Calculating the actual road oil consumption:
and according to the driving mileage corresponding to each working condition block, performing weighted calculation on the working condition blocks to finally obtain the actual oil consumption values of the old calibration vehicle and the new calibration vehicle.
In the formula: and w is the mileage ratio of each working condition block.
Calculating the difference of fuel consumption of 4 items of a typical vehicle:
the typical vehicle authentication oil consumption difference is the new calibration new working condition vehicle hub oil consumption value-the old calibration old working condition vehicle hub oil consumption value;
the typical vehicle hub calibration oil consumption difference is the new calibration new working condition vehicle hub oil consumption value-the old calibration new working condition vehicle hub oil consumption value;
the difference value between the actual oil consumption of the typical vehicle and the oil consumption of the rotating hub is equal to the actual oil consumption of the newly-calibrated vehicle, namely the oil consumption of the rotating hub under the new working condition;
the difference value of the actual oil consumption of the typical vehicle is the actual oil consumption of the new calibration vehicle-the actual oil consumption of the old calibration vehicle;
calculating the difference of the overall oil consumption of various vehicles:
1. and (4) the fuel consumption difference of each type of vehicle calibration rotating hub is equal to the fuel consumption difference of the calibration rotating hub, and the vehicle remaining quantity is equal to the annual driving mileage.
2. The difference between the actual fuel consumption and the hub is the actual fuel consumption value of the newly-calibrated vehicle-the new working condition fuel consumption value of the newly-calibrated vehicle; and (3) the actual oil consumption difference of each type of vehicle from the hub is equal to the actual oil consumption difference from the hub, namely the average driving mileage of the vehicle.
3. The actual oil consumption difference is the actual oil consumption value of the new calibration vehicle-the actual oil consumption value of the old calibration vehicle; and the actual fuel consumption difference of each type of vehicle is the actual fuel consumption difference, namely the average driving mileage of the vehicle.
In conclusion, the working condition evaluation influence method provided by the invention can effectively solve the problems caused by working condition switching, and is beneficial to effective government supervision, evaluation of actual performance of enterprise vehicle types and improvement of the acceptance of people on actual fuel consumption of vehicles.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A method for evaluating influence of working condition switching on vehicle oil consumption is characterized by comprising the following steps: the method comprises the following steps:
s1, selecting and dimensionless vehicle characteristic parameters;
s2, performing oil consumption prediction by using a least square support vector machine based on the dimensionless vehicle characteristic parameters, and determining the weight of each parameter;
s3, determining a typical vehicle based on the weight of each parameter and cluster analysis;
s4, calibrating the vehicle according to the new working condition, and carrying out the hub oil consumption test of the new and old calibrated vehicles to obtain 4 hub oil consumption values of the new and old working conditions of the new and old calibrated vehicles;
s5, building a fleet and carrying out actual road oil consumption test;
s6, determining the actual road working condition distribution;
s7, determining a temperature correction factor;
s8, calculating the actual road working condition distribution oil consumption;
s9, calculating the actual road oil consumption to obtain 2 actual oil consumption values of the new and old calibration vehicles;
s10, respectively calculating a typical vehicle authentication oil consumption difference value, a typical vehicle hub calibration oil consumption difference value, a typical vehicle actual and hub calibration oil consumption difference value and a typical vehicle actual oil consumption difference value according to 4 hub oil consumption values of the new and old working conditions of the new and old calibration vehicles and 2 actual oil consumption values of the new and old calibration vehicles;
and S11, respectively calculating the hub calibrated fuel consumption difference value, the actual fuel consumption difference value and the actual fuel consumption difference value of each type of vehicle according to the hub calibrated fuel consumption difference value of the typical vehicle, the actual fuel consumption difference value of the typical vehicle and the hub calibrated fuel consumption difference value of the typical vehicle and the actual fuel consumption difference value of the typical vehicle.
2. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: the fuel consumption prediction and the determination of the weight of each parameter in step S2 include the following steps:
A1. selecting characteristic parameters as a training set, taking hundred kilometers of oil consumption as a target set, and performing prediction training on the oil consumption by using a least square support vector machine;
A2. one characteristic parameter is removed in each training, and finally, the prediction precision reduction rate is determined according to RMSE error evaluation;
A3. and calculating the weight of each characteristic parameter according to the prediction precision reduction rate, wherein the higher the weight value is, the higher the importance of the parameter to the final fuel consumption prediction is represented.
3. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: the actual road condition distribution in step S6 includes the steps of:
b1, dividing the data actually collected by the new and old calibrated typical vehicle models into short segments respectively, and deleting the segments of the air conditioner;
b2, dividing the speed into three speed intervals of low, medium and high according to the maximum speed, and calculating the power demand distribution condition of each speed interval;
and B3, dividing the power requirements of different speed intervals into five gears, and obtaining the distribution of the working condition blocks of the speed-power matrix.
4. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: the actual road oil consumption in step S9 is a calculation value of the actual road condition distribution oil consumption calculated by the temperature correction factor and weighted by mileage.
5. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: in step S10, the difference value of the fuel consumption for typical vehicle authentication is the difference value between the fuel consumption for the hub after the new calibration vehicle type condition is switched and the fuel consumption for the hub before the old calibration vehicle type condition is switched.
6. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: in step S10, the difference value of the calibrated fuel consumption of the typical vehicle hub is the difference value between the fuel consumption of the hub after the new calibrated vehicle type condition is switched and the fuel consumption of the hub after the old calibrated vehicle type condition is switched.
7. The method for evaluating influence of working condition switching on fuel consumption of the vehicle according to claim 1, wherein the method comprises the following steps: the difference between the actual fuel consumption of the typical vehicle and the hub calibrated fuel consumption in step S10 is the difference between the actual fuel consumption of the new calibrated vehicle and the fuel consumption of the hub under the new calibration new operating condition.
8. The method for evaluating the influence of the working condition switching on the oil consumption of the vehicle as claimed in claim 1, wherein: the difference value of the actual fuel consumption of the typical vehicle in the step S10 is the difference value between the actual fuel consumption of the new calibrated vehicle and the actual fuel consumption of the old calibrated vehicle.
9. The method for evaluating the influence of the working condition switching on the oil consumption of the vehicle as claimed in claim 1, wherein: the difference value of the calibrated fuel consumption of each type of vehicle hub in the step S11 is the average driving mileage of the vehicle retained volume and the year of the typical vehicle hub calibrated fuel consumption difference;
in step S11, the difference between the actual fuel consumption of each vehicle and the fuel consumption of the hub is the difference between the actual fuel consumption of the typical vehicle and the fuel consumption of the hub, and the average driving mileage per year;
the actual fuel consumption difference value of each vehicle type in step S11 is the actual fuel consumption difference of a typical vehicle, the vehicle remaining amount, and the average driving mileage.
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