CN111198032A - Real-time estimation method for automobile mass - Google Patents
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Abstract
The invention discloses a real-time estimation method for automobile quality, which comprises 4 steps: data acquisition, automobile dynamics modeling, real-time estimation and quality evaluation. Determining a calculation scheme of the automobile mass according to the type of the driving data, determining the relation between force, acceleration and mass so as to establish a mathematical model of dynamics, then applying a KF (Kalman Filter) or RLS (recursive least squares) algorithm to carry out real-time estimation, and finally determining the automobile mass through a stationarity index or an error covariance index. The method can be suitable for the data input of the vehicle weight calculation model with different data quality, can meet the use requirements under various data conditions, can directly acquire all algorithm data from the vehicle-mounted terminal, can realize the use requirements of a management layer or a vehicle research and development layer on the vehicle quality through zero cost, and has the capability of commercialization.
Description
Technical Field
The invention relates to the technical field of data analysis and prediction algorithms, in particular to a real-time estimation method for automobile quality.
Background
At the same time, current laws and regulations and transport vehicle fleet configurations place real-time output demands on vehicle quality. In the rapid development of automobile technology, electronic control strategy design, performance real-time analysis and the like become core technologies of automobile research and development, wherein automobile quality is one of important reference indexes influencing the design of the core technology, and especially for heavy trucks with sensitive quality, if the index can be obtained, the method has important significance for the construction and optimization of the core technology. Therefore, the real-time calculation of the vehicle weight is an urgent technical problem to be solved, no matter in the management angle or the vehicle performance improvement angle.
The existing methods for acquiring the vehicle weight index can be divided into a direct measurement method and an indirect calculation method. The direct measurement method is to measure the vehicle weight through a sensor, but the method has the problems of high cost, complex calibration, periodic calibration in the subsequent use process and the like, and cannot be popularized in a large scale. The indirect calculation method estimates the vehicle weight through the relation between the acceleration and the force according to the longitudinal dynamics of the vehicle, has the advantages of zero cost, real-time performance and the like, can meet the calculation requirements of various scenes, and has larger application space and prospect.
The automobile longitudinal dynamics comprises driving force, braking force, rolling resistance, air resistance, gradient resistance, acceleration resistance and the like, the collected automobile data type determines the structure of a dynamic Model, and algorithms comprise Kalman Filter (KF), secure Least Square (RLS), KF, Model Prediction Control (MPC) and the like.
The existing indirect calculation method mainly has the following limitations in vehicle weight estimation: 1) the accuracy of the vehicle weight calculation is low: the simplified calculation model enables the vehicle weight prediction to fluctuate greatly and not to be high in accuracy; 2) the calculation conditions are harsh: when the data cannot meet the calculation conditions, the existing researched algorithm can cause the situation that the vehicle weight calculation cannot be carried out or the effect cannot reach the expectation. These limitations limit the application of indirect calculation methods to a great extent, and greatly restrict the wide application of indirect calculation methods.
SUMMARY OF THE PATENT FOR INVENTION
The inventor designs and develops a real-time estimation method for the automobile quality through long-term exploration and practice, and well solves the problems in the background technology.
The technical scheme of the invention is as follows:
a real-time estimation method for automobile quality is characterized by comprising 4 steps: data acquisition, modeling of vehicle dynamics, real-time estimation, quality evaluation, wherein,
the data acquisition comprises the steps of acquiring actual driving data, automobile power configuration parameters and automobile driving parameters, wherein the actual driving data comprises time, speed, rotating speed, torque or automobile body acceleration and braking force, the automobile power configuration parameters comprise a transmission speed ratio, a main reducer speed ratio, a tire radius and transmission system transmission efficiency, and the automobile driving parameters comprise a road rolling resistance coefficient, an air resistance coefficient, a windward area and a gradient;
the automobile dynamics modeling step comprises the following steps: determining the calculation scheme of the automobile mass according to the type of the driving data, determining the relationship between force, acceleration and mass, then determining the specific parameters and form of the dynamic model according to the mutual relationship between the force, the acceleration and the mass so as to establish the mathematical model of the dynamics,
when the relation between braking force and acceleration is adopted, the corresponding model is
When the relation between braking force and speed is adopted, the corresponding model is
When the relation between the driving force and the acceleration is adopted, if the driving parameters of the automobile are included, the corresponding model is
If the vehicle driving parameters are not included, the corresponding model is
When the relation between the driving force and the speed is adopted, if the driving parameters of the automobile are included, the corresponding model is
If the vehicle driving parameters are not included, the corresponding model is
Wherein a is acceleration, v is velocity, m is mass in kg, TsIs a time interval, TbFor braking force, TeAs engine torque, igTo the transmission ratio, i0At a final speed reduction ratio of ηtFor driveline efficiency, r is the tire radius, FfTo rolling resistance, FwAs air resistance, FjIs the slope resistance;
the real-time estimation step comprises the following steps: substituting the automobile dynamics model into a prediction algorithm, and performing real-time prediction on the automobile quality by an iterative computation method, namely substituting the automobile dynamics model into the prediction algorithm and performing real-time prediction on the automobile quality by the iterative computation method;
the quality evaluation step comprises: and performing effective vehicle weight evaluation according to the vehicle weight covariance and the vehicle weight predicted value in a period of time, and taking the evaluation value of the effective vehicle weight as the final predicted value of the vehicle weight.
Preferably, the real-time estimation step adopts an Extended Kalman Filter (EKF) algorithm, and the state prediction process comprises
The status update process includes
Wherein XkIs a predicted value of a state quantity, ZkIs an observed value, H is an observation matrix, WkIs process noise, VkTo observe noise, PkIs a covariance matrix of state quantities, fkA Jacobian matrix for partial derivation of each state quantity for a state equation, Q a process noise covariance matrix, R an observation noise covariance matrix, KkIs a Kalman gain.
Preferably, the real-time estimation algorithm employs an RLS algorithm:
L(k)＝p(k)θ(k)＝p(k-1)·θ(k)·(1+θT(k)·p(k-1)·θ(k))-1
p(k)＝(1-L(k)θT(k))·P(k-1)
wherein y is a model expression, theta is a state quantity, Var is an error,
is the state quantity, L is the gain, and p is the covariance matrix.
Preferably, the quality evaluation algorithm is calculated based on a lower stationarity index,
Si＝std(mi+mi+1+…+mi+M)，
n is the total number of samples for mass calculation, and M is the sample amount for dynamic evaluation of mass.
Preferably, the quality assessment algorithm is calculated from an error covariance index,
Pi＝min(Pi，Pi+1，...，Pi+M)，
wherein, i is 1, 2, N-M, N is the total number of samples for mass calculation, and M is the sample amount for dynamic mass evaluation.
The method has the advantages that on the premise of certain basic data guarantee (1939 co-improvement), the method can adapt to the data input of the vehicle weight calculation model with different data quality, expand the condition range of vehicle weight calculation, provide a corresponding vehicle weight calculation scheme for each type of condition, meet the use requirements under various data conditions, directly obtain all algorithm data from a vehicle-mounted terminal, realize the use requirements of a management layer or a vehicle research and development layer on the vehicle quality through zero cost, and have the capability of commercialization.
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FIG. 1 is a general flow diagram of the present invention.
Detailed Description
The present invention and its embodiments will now be further described with reference to the accompanying drawings.
The invention discloses a real-time estimation method of automobile mass, which comprises 4 steps: data acquisition, automobile dynamics modeling, real-time estimation and quality evaluation. The data acquisition comprises three parts: actual driving data, vehicle power configuration parameters and vehicle driving parameters. The actual driving data includes: time, vehicle speed, rotational speed, torque or vehicle body acceleration, braking force, etc. Wherein, the data sampling frequency is above 1 Hz. Wherein, the time is absolute time, the vehicle speed unit is kilometer/hour (km/h), the rotating speed unit is revolution per second (r/min), the torque unit is torsion meter (n.m), the acceleration of the vehicle body is collected by an accelerometer arranged on the vehicle, and the Brake can be obtained by EBS (electronic Brake systems). The vehicle power configuration parameters are the main parameters of the vehicle dynamic components, including transmission speed ratio, main reducer speed ratio, tire radius and drive train transmission efficiency. The automobile driving parameters comprise road rolling resistance coefficient, air resistance coefficient, windward area, gradient and the like.
The scheme design of the automobile dynamics modeling confirms an automobile mass calculation scheme according to the type of driving data, and specifies the calculation direction, wherein the specific scheme comprises four schemes:
a. the automobile mass is estimated by adopting the relation between the braking force and the acceleration, the driving data are the speed, the rotating speed, the torque, the braking force and the acceleration of the automobile body, and the corresponding model is
b. The relation between braking force and speed is used to estimate the mass of car, the driving data includes speed, rotation speed, torque and braking force, and the corresponding model is
c. The relation between the driving force and the acceleration is adopted to estimate the automobile mass, the driving data includes the speed, the rotating speed, the torque and the automobile body acceleration, if the driving data includes the automobile driving parameters, the corresponding model is
If there is no driving parameter, the corresponding model is
d. The relation between the driving force and the speed is adopted to estimate the automobile mass, the driving data includes the speed, the rotating speed and the torque, if the driving data includes the automobile driving parameters, the corresponding model is
If there is no driving parameter, it corresponds toThe model is
Wherein, the unit is m/s 2; is the speed; is mass in kg; is the time interval in units of s; is the braking force, with the unit of N; is the engine torque; is the transmission speed ratio; is the main speed reduction ratio; for driveline transmission efficiency; is the tire radius; is rolling resistance, related to the rolling resistance coefficient, in units of N; is air resistance, is related to air resistance coefficient and frontal area, and has the unit of N; slope resistance, in relation to slope, is given in units of N.
The real-time estimation model of the automobile quality can comprise a plurality of algorithms, such as KF, RLS and the like, and the specific algorithms are as follows:
(1) the basic method of KF is as follows:
a. state prediction
b. Status update
Wherein, XkIs a predicted value of a state quantity, ZkIs an observed value, H is an observation matrix, WkIs process noise, VkTo observe noise, PkIs a covariance matrix of state quantities, fkA Jacobian matrix of the partial derivatives for the state quantities is solved for the state equations,q is the process noise covariance matrix, R is the observation noise covariance matrix, KkIs a Kalman gain.
(2) The basic method of RLS is as follows:
L(k)＝p(k)θ(k)＝p(k-1)·θ(k)·(1+θT(k)·p(k-1)·θ(k))-1
p(k)＝(1-L(k)θT(k))·P(k-1)
wherein y is a model expression, theta is a state quantity, Var is an error,
is the state quantity, L is the gain, and p is the covariance matrix.
Determining the final vehicle weight by adopting a quality evaluation algorithm, evaluating the predicted quality according to methods such as stability, error covariance and the like, and taking the screened quality as the output quality of the vehicle; for the dry stability index:
Si＝std(mi+mi+1+…+mi+M)；
for error covariance, variance indicators:
Pi＝min(Pi，Pi+1，...，Pi+M)
i＝1，2，...，N-M，
wherein, N is the total number of samples calculated by the quality, and M is the sample amount of dynamic quality evaluation.
Based on the steps, the method and the device for estimating the automobile quality obtain the automobile quality real-time estimation scheme and algorithm, and can effectively estimate the automobile quality.
One embodiment of the present invention is as follows:
first, vehicle state data and vehicle configuration data are collected from a test vehicle. The collected driving data includes vehicle speed v, gear and torque TeThe data sampling frequency is 1 Hz; the automobile power configuration parameter is a speed changer speed ratio ignSpeed ratio i of main reducer0Tire radius r, driveline transmission efficiency ηt(ii) a The driving parameters of the vehicle are not available.
ig＝ign(n＝1，2，...，N)。
According to the collected data type, a scheme of adopting the relation between the driving force and the speed is determined, the mass m of the automobile is estimated, and therefore the scheme is used for modeling,
the vehicle weight real-time estimation model can have various algorithms such as KF, RLS and the like, and the KF is taken as an example to build a real-time estimation model of the vehicle mass. The vehicle speed and the mass are used as the state quantity X. The time derivative of the vehicle speed is the acceleration; the mass is considered constant, and the derivative is 0, so the differential equation expression can be:
thus, the equation of state of KF is
And predicting the automobile quality through a state prediction and state update method of the KF.
And finally, adopting a quality evaluation algorithm of a dynamic time window, and if the standard deviation of the predicted vehicle weight data in the time window is smaller than a threshold value and the vehicle weight variance of the error covariance matrix reaches a certain condition, judging the average vehicle weight of the time window as the effective vehicle weight. And selecting the average value of the effective vehicle weights as the output predicted vehicle weight m. Meanwhile, the accuracy of the prediction quality is improved by adopting multiple predictions:
m＝(m1+…+mM)/M。
it should be noted that the above is only the embodiment or descriptions of the present disclosure, and the protection scope of the present disclosure is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present disclosure, and all the changes or substitutions should be covered by the protection scope of the present disclosure. The protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. A real-time estimation method for automobile quality is characterized by comprising 4 steps: data acquisition, modeling of vehicle dynamics, real-time estimation, quality evaluation, wherein,
the data acquisition step comprises: acquiring actual driving data, automobile power configuration parameters and automobile driving parameters, wherein the actual driving data comprises time, speed, rotating speed, torque or automobile body acceleration and braking force, the automobile power configuration parameters comprise a speed changer speed ratio, a main speed reducer speed ratio, a tire radius and transmission system transmission efficiency, and the automobile driving parameters comprise a road rolling resistance coefficient, an air resistance coefficient, a windward area and a gradient;
the automobile dynamics modeling step comprises the following steps: confirming a calculation scheme of the automobile mass according to the type of driving data, determining the mutual relation of force, acceleration and mass, then determining the specific parameters and form of a dynamic model according to the mutual relation of the force, the acceleration and the mass so as to establish a mathematical model of the dynamics,
when the relation between braking force and acceleration is adopted, the corresponding model is
When the relation between braking force and speed is adopted, the corresponding model is
When the relation between the driving force and the acceleration is adopted, if the driving parameters of the automobile are included, the corresponding model is
If the vehicle driving parameters are not included, the corresponding model is
When the relation between the driving force and the speed is adopted, if the driving parameters of the automobile are included, the corresponding model is
If the vehicle driving parameters are not included, the corresponding model is
Where a is acceleration, k is time, v is velocity, m is mass in kg, TsIs a time interval, T is a time continuous value, TbFor braking force, TeAs engine torque, igTo the transmission ratio, i0At a final speed reduction ratio of ηtFor driveline efficiency, r is the tire radius, FfTo rolling resistance, FwAs air resistance, FjIs the slope resistance;
the real-time estimation step comprises the following steps: substituting the automobile dynamics model into a prediction algorithm, and predicting the automobile quality in real time by an iterative computation method;
the quality evaluation step comprises: and performing effective vehicle weight evaluation according to the vehicle weight covariance and the vehicle weight predicted value in a period of time, and taking the evaluation value of the effective vehicle weight as the final predicted value of the vehicle weight.
2. The method according to claim 1, wherein the real-time estimation step adopts EKF algorithm, and the state prediction process comprises
The status update process includes
Wherein XkIs a predicted value of the state quantity, k is time, ZkIs an observed value, H is an observation matrix, WkIs process noise, VkTo observe noise, PkIs a covariance matrix of state quantities, fkA Jacobian matrix for partial derivation of each state quantity for a state equation, Q a process noise covariance matrix, R an observation noise covariance matrix, KkIs a Kalman gain.
3. The real-time estimation method of automobile mass according to claim 1, characterized in that the real-time estimation step employs an RLS algorithm:
L(k)＝p(k)θ(k)＝p(k-1)·θ(k)·(1+θT(k)·p(k-1)·θ(k))-1
p(k)＝(1-L(k)θT(k))·P(k-1)
wherein y is a model expression, n is the number of data points, i is the number of data points meeting the requirements, k is the time, theta is a coefficient matrix of the state quantity, Var is an error,
is a parameter matrix of the state quantity, L is the gain, and p is a covariance matrix.
4. The real-time estimation method of automobile quality according to claim 1, characterized in that the quality evaluation step is calculated based on a stationarity index,
Si＝std(mi+mi+1+…+mi+M)，
wherein, i is the number of data points meeting the requirements, i is 1, 2.
5. The real-time automobile quality estimation method according to claim 1 or claim 4, wherein the quality evaluation step is calculated based on an error covariance index,
Pi＝min(Pi，Pi+1，...，Pi+M)，
wherein, i is the number of data points meeting the requirements, i is 1, 2.
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