CN112115555A - Method for monitoring instantaneous oil consumption of automobile in intelligent networking environment - Google Patents

Method for monitoring instantaneous oil consumption of automobile in intelligent networking environment Download PDF

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CN112115555A
CN112115555A CN202011013109.0A CN202011013109A CN112115555A CN 112115555 A CN112115555 A CN 112115555A CN 202011013109 A CN202011013109 A CN 202011013109A CN 112115555 A CN112115555 A CN 112115555A
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CN112115555B (en
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李克强
刘巧斌
王建强
李升波
高博麟
许庆
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Tsinghua University
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Abstract

The invention discloses a method for monitoring instantaneous oil consumption of an automobile in an intelligent networking environment, which comprises the following steps: presetting a normalized variable trigonometric function model, and predicting the fuel consumption rate:
Figure DDA0002698156230000011
where v denotes the correspondence of the vehicle speed with the fuel consumption rate to be predicted, a denotes the correspondence of the vehicle acceleration with the fuel consumption rate to be predicted, and β1、β2、β3And beta4For a known parameter, vminRepresenting the minimum value of the speed, v, in the set of measured data samples for all the typical road conditionsmaxRepresenting the maximum value of the speed, a, in the set of measured data samples under all the typical road conditionsminRepresents the minimum value of the acceleration in the measured data sample set under all the comprehensive typical road conditions, amaxAnd the maximum value of the speed in the measured data sample set under all the typical road conditions is integrated. The method can predict the transient oil consumption under the Internet of vehicles by using only limited 4 parameters.

Description

Method for monitoring instantaneous oil consumption of automobile in intelligent networking environment
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method for monitoring instantaneous oil consumption of an automobile in an intelligent networking environment.
Background
The development of intelligent networked automobile technology is continuously changing the appearance of the traditional traffic system, and simultaneously, the performances of the automobile in the aspects of energy conservation, safety, environmental protection, comfort and the like are expected to be comprehensively improved. In order to explore an optimal control method for economic driving of an automobile in an intelligent networking environment, a simple, accurate and reliable oil consumption model needs to be provided as input so as to develop the next research on energy-saving control and optimization methods. Therefore, the automobile fuel consumption modeling technology is one of the difficulties and the key points of the automobile intelligent energy-saving optimization control technology.
Engine speed omega obtained by calibration of existing engine bench testeAnd torque TtqPolynomial regression fuel consumption model be＝f(we,Ttq) The fuel consumption law of the automobile engine under the steady-state working condition is reflected, the fuel consumption of the automobile under the transient state cannot be accurately described, and the polynomial regression involves more parameters and has high difficulty in parameter identification. In addition, for each engine with different models, specific oil consumption model parameters can be obtained only by carrying out calibration independently, the test cost is high, and the calculation cost for calculating the oil consumption of the macroscopic traffic flow by adopting the method is extremely high.
In the prior art, the unit mass of the automobile power under different running conditions of the automobile, namely the specific power under different working conditions, is calculated through theoretical derivation or empirical formula fitting, and the specific power and the fuel consumption rate of the automobile under different working conditions are subjected to data fitting, so that a mathematical model of the fuel consumption is obtained. However, the theoretical expression and the empirically fitted relation of the change of the specific power of the automobile with the driving condition are difficult to determine, and in addition, the specific power and the oil consumption do not have a one-to-one correspondence relationship, a plurality of oil consumptions under the same specific power need to be subjected to statistical distribution modeling, and the average value of the oil consumptions is taken as a training sample of the model. The fuel consumption model calculated by the specific power can reflect the fuel consumption characteristics of the transient working condition of the automobile to a certain extent, but the model complexity is still high, and the specific power depends on factors such as automobile models and road conditions, so that the fuel consumption model established by the method cannot meet the model and the optimization control requirements of the energy-saving technology in the intelligent network connection environment.
Disclosure of Invention
The object of the present invention is to provide a method for monitoring the instantaneous fuel consumption of a vehicle using a normalized variable trigonometric function, which overcomes or at least alleviates at least one of the above-mentioned drawbacks of the prior art.
In order to achieve the purpose, the invention provides a method for monitoring the instantaneous oil consumption of an automobile in an intelligent networking environment, which is characterized by comprising the following steps:
presetting a normalized variable trigonometric function model, and predicting the fuel consumption rate:
Figure BDA0002698156210000021
where v denotes the correspondence of the vehicle speed with the fuel consumption rate to be predicted, a denotes the correspondence of the vehicle acceleration with the fuel consumption rate to be predicted, and β 1, β2、β3And beta4For a known parameter, vminRepresenting the minimum value of the speed, v, in the set of measured data samples for all the typical road conditionsmaxRepresenting the maximum value of the speed, a, in the set of measured data samples under all the typical road conditionsminRepresents the minimum value of the acceleration in the measured data sample set under all the comprehensive typical road conditions, amaxRepresenting all the comprehensive typical road conditionsThe maximum value of velocity in the next set of measured data samples.
Further, the method for obtaining the normalized variable trigonometric function model comprises the following steps:
step 1, the speed v and the acceleration a of a vehicle can be acquired through a vehicle networking platform, and the actually measured real-time fuel consumption rate b is combinedeConstruction of instantaneous Fuel consumption rate b as provided by equation (1)eMathematical relationship with velocity v and acceleration a:
be＝β1·a·v·sin(arc tan(β2·a+β3·v)+β4·a·v) (1)
step 2, preprocessing the actually measured data sample set under the typical road working conditions, and filtering noise data such as abnormal values in the data;
step 3, in order to facilitate the subsequent parameter identification, the instantaneous fuel consumption rate beRespectively carrying out normalization processing on the speed v and the acceleration, and mapping the actual measurement data sample set under the comprehensive typical road working conditions to [0, 1%]In the interval of (2), the unification of all variables on the magnitude is realized;
step 4, establishing a normalized variable trigonometric function model shown in the vertical type (4), and estimating beta1、β2、β3And beta4The numerical value of (A):
be0＝β1·a0·v0·sin(arc tan(β2·a0+β3·v0)+β4·a0·v0) (4)
in the formula (4), be0Oil consumption, v, normalized by step 30Oil consumption normalized by step 3, a0Normalizing the processed acceleration through step 3;
step 5, beta obtained according to step 41、β2、β3And beta4The independent variable and the dependent variable of the normalized variable trigonometric function model shown in the formula (4) are subjected to inverse normalization to obtain the normalized variable trigonometric function model.
Further, the data preprocessing method adopted in step 2 is as follows: and (3) calculating the mean value and the standard deviation of the data, subtracting the numerical value in the data from the mean value, rejecting the data of which the absolute value exceeds 3 times of the standard deviation, and considering the rest data as normal data.
Further, the step 3 performs normalization processing by using a data normalization method shown in formula (2):
Figure BDA0002698156210000031
in the formula (2), x0For normalized variables, x is the original value of the preprocessed variable output from step 2, xminIs the minimum value, x, of the preprocessed variable output by step 2 among all sample valuesmaxIs the maximum value of the preprocessed variable output by step 2 among all sample values.
Further, said step 4 estimates β1、β2、β3And beta4The method comprises the following steps:
firstly, carrying out primary identification on parameters by adopting an intelligent algorithm; secondly, taking the intelligent algorithm parameter identification result as an initial value, and introducing a nonlinear least square method to carry out secondary identification on the parameters; in the parameter identification process, the adopted objective function is the sum of squares of errors between the predicted value and the measured value of the model.
Further, a normalization variable trigonometric function model is preset in the vehicle-mounted controller or the cloud control platform.
Due to the adoption of the technical scheme, the invention has the following advantages:
the oil consumption model modeling method provided by the invention can be applied to oil consumption prediction under the internet of vehicles, does not depend on specific vehicle types and engines, does not need to acquire engine rotating speed and engine torque data, can be used for estimating transient oil consumption, and can provide input of an energy consumption model for energy-saving control and optimization under the intelligent internet traffic environment. In addition, the proposed fuel consumption model applies a trigonometric function combination model, high-precision fitting of highly nonlinear fuel consumption data can be realized, and a fitting effect equivalent to a 6-parameter rotating speed-torque polynomial regression model can be realized only by using limited 4 parameters. Finally, the parameter identification method combining the intelligent algorithm and the nonlinear least square method is adopted, so that the problem of low parameter identification efficiency of the intelligent algorithm is effectively avoided, the problem of difficulty in selecting the initial value for parameter identification of the nonlinear least square method is solved, and the efficiency and the precision of parameter identification are considered.
Drawings
Fig. 1 is a flowchart of a method for monitoring instantaneous oil consumption of an automobile by using a normalized variable trigonometric function according to an embodiment of the present invention.
FIG. 2 is a plot of measured vehicle speed.
FIG. 3 shows the fuel consumption prediction effect of the present invention.
FIG. 4 is a graph of the predicted effect of a speed-torque polynomial fit.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
The invention takes modeling of a group of measured oil consumption data of a whole vehicle as an example, as shown in figure 1, and illustrates a method for monitoring the instantaneous oil consumption of a vehicle by a normalized variable trigonometric function, which comprises the following steps:
presetting a normalized variable trigonometric function model, and predicting the fuel consumption rate:
Figure BDA0002698156210000041
where v denotes the correspondence of the vehicle speed with the fuel consumption rate to be predicted, a denotes the correspondence of the vehicle acceleration with the fuel consumption rate to be predicted, and β1、β2、β3And beta4For a known parameter, vminRepresenting the minimum value of the speed, v, in the set of measured data samples for all the typical road conditionsmaxRepresenting the maximum value of the speed, a, in the set of measured data samples under all the typical road conditionsminIndicating the minimum acceleration in the measured data sample set under all the typical road conditionsValue of amaxAnd the maximum value of the speed in the measured data sample set under all the typical road conditions is integrated.
In the above embodiment, according to a user requirement, the normalization variable trigonometric function model may be preset in the vehicle-mounted controller, or the normalization variable trigonometric function model may be preset in the cloud control platform. The setting can also set a normalized variable trigonometric function model at other intelligent terminals.
In one embodiment, the method for obtaining the normalized variable trigonometric function model comprises the following steps:
step 1, as shown in fig. 2, dynamic kinematic parameters such as speed v and acceleration a of a vehicle can be obtained through a vehicle networking platform, and the measured real-time fuel consumption rate b is combinedeConstruction of instantaneous Fuel consumption rate b as provided by equation (1)eMathematical relationship with velocity v and acceleration a:
be＝f(θ，β)＝β1·a·v·sin(arc tan(β2·a+β3·v)+β4·a·v) (1)
in the formula (1), θ is a measured dynamic kinematic parameter of the automobile, including a speed v and an acceleration a.
The embodiment of the invention directly applies the data of the Internet of vehicles to realize the acquisition of the dynamic kinematic parameters of the vehicle, thereby providing input for the establishment of the transient fuel consumption model of the vehicle.
And 2, preprocessing the actually measured data sample set under the typical road working conditions, filtering noise data such as abnormal values in the data, and providing favorable conditions for improving the reliability of the model.
The data preprocessing method adopted in this embodiment is a 3 σ method, that is, the mean value and the standard deviation of the data are calculated, the difference is made between the numerical value in the data and the mean value, the data whose absolute value exceeds 3 times the standard deviation is removed, and the remaining data is regarded as normal data. Of course, the actual measurement data sample set under each typical road condition can be preprocessed by adopting the existing smoothing filtering method and the like.
The data preprocessing is carried out on the obtained automobile dynamic kinematic parameters and the fuel consumption rate data through the steps, abnormal point data are removed, and a data set with the abnormal points removed is used as a sample set for subsequent modeling and model parameter identification.
Step 3, in order to facilitate the subsequent parameter identification, the instantaneous fuel consumption rate beRespectively carrying out normalization processing on the speed v and the acceleration, and mapping the actual measurement data sample set under the comprehensive typical road working conditions to [0, 1%]In the interval of (2), the unification of the magnitude of each variable is realized.
In this embodiment, the data normalization method shown in formula (2) is adopted to perform normalization processing:
Figure BDA0002698156210000051
in the formula (2), x0For normalized variables, x is the original value of the preprocessed variable output from step 2, xminIs the minimum value, x, of the preprocessed variable output by step 2 among all sample valuesmaxIs the maximum value of the preprocessed variable output by step 2 among all sample values.
As shown in formula (2), the expression of the inverse normalization process is shown in formula (3):
x＝(xmax-xmin)x0+xmin (3)
the velocity v, the acceleration a and the instantaneous fuel consumption b are expressed by the equation (2)eRespectively converted into corresponding normalized velocities v0Normalized acceleration a0And normalized instantaneous specific fuel consumption be0。
It should be noted that the present embodiment may also adopt a nonlinear data normalization method in the prior art.
In the embodiment, the dynamic kinematic parameter theta and the real-time fuel consumption rate b of the automobile are actually measuredeOn the basis of the dynamic kinematics parameter theta, the variables and the corresponding fuel consumption rates are normalized to obtain the normalized dynamic kinematics parameter theta0And normalized fuel consumption be0To eliminate the magnitude difference between the different variables of the model provided by equation (1), the complexity of the model can be reduced to adoptAnd the high-precision oil consumption prediction effect is realized by using fewer parameters, and further modeling and parameter identification are facilitated.
Step 4, establishing a normalized variable trigonometric function model shown in the vertical type (4), and estimating beta1、β2、β3And beta4The numerical value of (A):
be0＝β1·a0·v0·sin(arc tan(β2·a0+β3·v0)+β4·a0·v0) (4)
to p in the formula (4)1、β2、β3And beta4And carrying out accurate estimation, wherein the parameter is preliminarily identified by adopting an intelligent algorithm. The intelligent algorithm may be, for example, an existing genetic algorithm, a particle swarm algorithm, a gravity search algorithm, a simulated annealing algorithm, a harmony search algorithm, and the like.
On the basis of a primary parameter identification result obtained by an intelligent algorithm, the intelligent algorithm parameter identification result is used as an initial value, and then a nonlinear least square method is introduced to carry out secondary identification on the parameters, so that the problem that the initial value of the nonlinear least square method parameter identification is difficult to select can be effectively solved, and the problem of low intelligent algorithm parameter identification efficiency can be avoided.
In the parameter identification process, the adopted objective function is the sum of squares of errors between the model predicted value and the measured value, and can also be a correlation coefficient, a mean square error, a dispersion coefficient, a gray correlation degree, a logarithmic mean square error and the like.
The combined parameter identification method for the non-linear fuel consumption model is adopted, and the contradiction between the parameter identification cost and the parameter identification accuracy is effectively balanced aiming at the problems of low parameter identification efficiency and low precision of the non-linear fuel consumption model.
In another embodiment, β in formula (4)1、β2、β3And beta4The accurate estimation can be carried out by adopting a Newton iteration method and other methods to carry out parameter identification once and then adopting an improved nonlinear least square method, such as a moving average nonlinear least square methodAnd (5) secondary parameter identification.
The fuel consumption model provided by the embodiment of the invention provides a trigonometric function prediction model of a normalized variable, vehicle dynamic kinematic parameters (such as speed v and acceleration) obtained by an internet of vehicles are directly applied to fit the fuel consumption, the accuracy of a fuel consumption model parameter identification result is improved by a combined parameter identification method combining an intelligent algorithm and a least square method, and the parameter identification efficiency is ensured.
The embodiment of the invention solves the contradiction between the model parameter identification precision and efficiency by a combined parameter identification method combining an intelligent algorithm and a nonlinear least square method, and firstly adopts the intelligent algorithm to carry out primary identification on the model parameters. The result of parameter identification of the intelligent algorithm is used as an initial value of parameter identification, and the nonlinear least square method is adopted to carry out secondary identification on the model parameters, so that the defects of low parameter identification efficiency of the intelligent algorithm and difficulty in determining the initial value of parameter identification of the nonlinear least square method are effectively overcome, and the accuracy of parameter identification is ensured while the parameter identification efficiency is improved.
Step 5, beta obtained according to step 41、β2、β3And beta4The independent variable and the dependent variable of the normalized variable trigonometric function model shown in the formula (4) are subjected to inverse normalization to obtain a final oil consumption model b shown in the formulae：
Figure BDA0002698156210000071
Fig. 3 and 4 show the comparison between the predicted fuel consumption and the measured fuel consumption obtained by applying the proposed fuel consumption model and the conventional speed-torque polynomial model, respectively. As is clear from a comparison between fig. 3 and fig. 4, the determination coefficient between the prediction result and the actual measurement result obtained by the two methods is about 0.92, and therefore the accuracy is equivalent.
The oil consumption model provided by the embodiment of the invention has only 4 parameters, while the traditional 2-degree polynomial rotation speed-torque model has 6 parameters, and the parameter identification method is applied to identify the parameters, so that the contradiction between the accuracy and the efficiency of the nonlinear oil consumption model parameter identification is effectively solved.
Moreover, the results of modeling and parameter identification of the measured data sample set under the typical road working conditions are integrated, so that the automobile oil consumption model established by the modeling method and the parameter identification method provided by the invention only adopts 4 parameters, and the precision equivalent to the 6-parameter torque-rotating speed polynomial regression model is realized.
The invention aims to solve the problem of prediction of automobile traffic flow oil consumption in an intelligent network connection environment. In order to fully utilize information such as vehicle speed, acceleration and the like acquired by the Internet of vehicles technology, the invention provides a method for directly establishing a fuel consumption rate model by adopting vehicle transient kinematic parameters and dynamically predicting the fuel consumption of an automobile in real time, thereby laying a model foundation for an energy-saving optimization control technology under an intelligent Internet traffic system and reducing the data acquisition cost of fuel prediction.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. A method for monitoring instantaneous oil consumption of an automobile in an intelligent networking environment is characterized by comprising the following steps:
presetting a normalized variable trigonometric function model, and predicting the fuel consumption rate:
Figure FDA0002698156200000011
where v denotes the correspondence of the vehicle speed with the fuel consumption rate to be predicted, a denotes the correspondence of the vehicle acceleration with the fuel consumption rate to be predicted, and β1、β2、β3And beta4For a known parameter, vminRepresenting the minimum value of the speed, v, in the set of measured data samples for each typical road condition under all typical road conditionsmaxRepresenting the maximum speed, a, of the set of measured data samples under all of the typical road conditionsminRepresenting the minimum value of the acceleration in the measured data sample set under all the typical road conditionsmaxAnd the maximum value of the speed in the measured data sample set under all the comprehensive typical road conditions is shown.
2. The method for monitoring the instantaneous oil consumption of the automobile in the intelligent networking environment according to claim 1, wherein the method for acquiring the normalized variable trigonometric function model comprises the following steps:
step 1, the speed v and the acceleration a of a vehicle can be acquired through a vehicle networking platform, and the actually measured real-time fuel consumption rate b is combinedeConstruction of instantaneous Fuel consumption rate b as provided by equation (1)eMathematical relationship with velocity v and acceleration a:
be＝β1·a·v·sin(arc tan(β2·a+β3·v)+β4·a·v) (1)
step 2, preprocessing the actually measured data sample set under the typical road working conditions, and filtering noise data such as abnormal values in the data;
step 3, in order to facilitate the subsequent parameter identification, the instantaneous fuel consumption rate beRespectively carrying out normalization processing on the speed v and the acceleration, and mapping the actual measurement data sample set under the comprehensive typical road working conditions to [0, 1%]In the interval of (2), the unification of all variables on the magnitude is realized;
step 4, establishing a normalized variable trigonometric function model shown in the vertical type (4), and estimating beta1、β2、β3And beta4The numerical value of (A):
be0＝β1·a0·v0·sin(arc tan(β2·a0+β3·v0)+β4·a0·v0) (4)
in the formula (4), be0Oil consumption, v, normalized by step 30Oil consumption normalized by step 3, a0Normalizing the processed acceleration through step 3;
step 5, beta obtained according to step 41、β2、β3And beta4The independent variable and the dependent variable of the normalized variable trigonometric function model shown in the formula (4) are subjected to inverse normalization to obtain the normalized variable trigonometric function model.
3. The method for monitoring the instantaneous oil consumption of the automobile in the intelligent networking environment according to claim 2, wherein the data preprocessing method adopted in the step 2 comprises the following steps: and (3) calculating the mean value and the standard deviation of the data, subtracting the numerical value in the data from the mean value, rejecting the data of which the absolute value exceeds 3 times of the standard deviation, and considering the rest data as normal data.
4. The method for monitoring the instantaneous oil consumption of the automobile in the intelligent networking environment according to claim 2, wherein the step 3 is normalized by a data normalization method shown in formula (2):
Figure FDA0002698156200000021
in the formula (2), x0For normalized variables, x is the original value of the preprocessed variable output from step 2, xminIs the minimum value, x, of the preprocessed variable output by step 2 among all sample valuesmaxIs the maximum value of the preprocessed variable output by step 2 among all sample values.
5. The method for monitoring the instantaneous oil consumption of the automobile in the intelligent networking environment as claimed in claim 2, wherein the step 4 estimates the beta1、β2、β3And beta4The method comprises the following steps:
firstly, carrying out primary identification on parameters by adopting an intelligent algorithm;
secondly, taking the intelligent algorithm parameter identification result as an initial value, and introducing a nonlinear least square method to carry out secondary identification on the parameters;
in the parameter identification process, the adopted objective function is the sum of squares of errors between the predicted value and the measured value of the model.
6. The method for monitoring the instantaneous oil consumption of the automobile under the intelligent networking environment of any one of claims 1 to 5, wherein a normalized variable trigonometric function model is preset in an on-board controller or a cloud control platform.
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