CN107229801B - On-line identification method for rolling resistance coefficient of tire - Google Patents

On-line identification method for rolling resistance coefficient of tire Download PDF

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CN107229801B
CN107229801B CN201710436235.9A CN201710436235A CN107229801B CN 107229801 B CN107229801 B CN 107229801B CN 201710436235 A CN201710436235 A CN 201710436235A CN 107229801 B CN107229801 B CN 107229801B
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vehicle
rolling resistance
resistance coefficient
whole vehicle
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CN107229801A (en
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林楠
宗长富
施树明
张曼
牟宇
徐超
李文茹
于晓军
陈光辉
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Jilin University
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Abstract

The invention relates to an online identification method for a tire rolling resistance coefficient. Firstly, an identification model is established by utilizing a running equation, and then online identification of the rolling resistance coefficient is realized by combining an online clustering identification algorithm. The establishment of the identification model integrates the original running equation and the differential running equation, eliminates the influence of the mass of the whole vehicle and overcomes the defect that the rolling resistance coefficient needs to be calculated depending on the mass of the whole vehicle in the prior art. The traditional technology obtains the rolling resistance coefficient of the tire through a sliding test, is limited by a single test environment, and cannot adapt to the complex working condition of vehicle running. The established online identification algorithm can acquire important parameters of the whole vehicle online and is suitable for different vehicle running states and road environments.

Description

On-line identification method for rolling resistance coefficient of tire
Technical Field
The invention relates to an on-line identification method for a tire rolling resistance coefficient in an automobile automatic control technology, in particular to an on-line identification method for a tire rolling resistance coefficient by utilizing longitudinal acceleration sensor information and vehicle running information without whole vehicle quality parameters.
Background
The tire rolling resistance coefficient is an important parameter for controlling the economical efficiency and dynamic property of vehicle operation, and the tire rolling resistance has great influence on the longitudinal stress of the whole vehicle. With the development of automatic control technology, many vehicle control parameters can be identified on line. However, the rolling resistance coefficient is an abstract coefficient which simply expresses the rolling resistance as a proportional relation with the mass of the whole vehicle, and the physical model calculation of dynamics or expression of a generation mechanism of the dynamics is not easy to establish.
Under the condition of a whole vehicle, a common tire rolling resistance coefficient measuring method is a sliding test, and on the premise of cutting off power output, work is calculated according to running resistance and air resistance. The field test has many environmental restrictions, such as temperature, road conditions and the like, and the rolling resistance coefficient measured aiming at a specific environment cannot express the real rolling resistance under a changeable working condition. The method for establishing the tire rolling resistance coefficient on-line identification facing the whole vehicle control is to get rid of the severe environmental limitation of a test field, collect some available driving data in the driving process to identify the rolling resistance coefficient in real time, so that the automatic control of the whole vehicle can realize the self-adaptation of important parameters.
The traditional vehicle fixed parameter identification usually adopts a least square algorithm, however, the least square algorithm generates a larger error when the error of real sampled data is larger or is not Gaussian white noise.
Disclosure of Invention
The invention aims to provide an online identification method of rolling resistance coefficients, which aims to adapt to the fact that the rolling resistance coefficients can change along with the change of vehicle use conditions and achieve the self-adaption of important parameters of a whole vehicle, so as to achieve the online acquisition of the rolling resistance coefficients, adapt to different working condition environments and improve the performance of a control system of the dynamic property and the economical property of the whole vehicle.
The invention discloses an online clustering identification method for rolling resistance coefficients, which is an online clustering identification model for the rolling resistance coefficients established based on vehicle running state information and vehicle-mounted longitudinal acceleration information, and comprises the following steps:
s1. model initialization
Loading fixed parameters required by the model, wherein the fixed parameters comprise vehicle parameters and algorithm parameters;
the vehicle-finishing parameters comprise vehicle transmission efficiency η, tire rolling radius r and vehicle running acceleration avFlywheel moment of inertia IfWheel moment of inertia IwAir resistance coefficient CDThe windward area A of the whole vehicle, the air density rho and the gravity acceleration g;
the algorithm parameters include: number of clusters m, m initial cluster centers
Figure BDA0001318672110000021
S2, collecting vehicle running state information
The CAN bus information that needs to be synchronously acquired at each sampling moment includes: speed v of whole vehicle, engine speed n, clutch pedal signal, brake pedal signal and acceleration a provided by longitudinal acceleration sensorsen；
S3, judging whether the data is neutral gear sliding data or not, and if so, continuing to execute the subsequent steps; if not, returning to the step S2 to collect the vehicle running state information at the next moment;
s4, calculating the whole vehicle mass expression
Firstly, calculating the longitudinal running sliding resistance of the whole vehicle, namely Fwjw＝-Fw-Fjw，
In the formula:
Figure BDA0001318672110000022
is the air resistance;
Figure BDA0001318672110000023
is the wheel inertia force; a isvIs the vehicle travel acceleration, is the derivative of the vehicle speed;
and then calculating the expression of the mass of the whole vehicle by using a difference method: first, a difference amount Δ F of the sliding resistance is calculatedwjwDifferential quantity of acceleration sensor DeltaasenDifference Δ v from the square value of vehicle speed2Then, establishing a whole vehicle mass expression according to the differential running equation as follows: m ═ Δ Fwjw/Δasen；
S5, calculating a rolling resistance coefficient preliminary result
Substituting the whole vehicle mass expression into a running equation without driving force, and estimating the initial result of the rolling resistance coefficient at the sampling moment (i) by using a least square algorithm with a forgetting factor
Figure BDA0001318672110000024
The input quantity of least square is X ═ Δ Fwjwg, output amount is Y ═ FwjwΔasen-asenΔFwjw；
S6, utilizing the online K mean value clustering to the primary identification result to update the clustering center
Calculating the distance from the initial result of the rolling resistance coefficient to various cluster centers
Figure BDA0001318672110000025
Classifying the points to be clustered into a class with the shortest distance; updating cluster centers of input quantities
Figure BDA0001318672110000031
Wherein Fm(i) And Fm(i-1) are the cluster centers of the mth class at the current sampling instant and the previous sampling instant, respectively.
S7, calculating the ratio of various types of data, and judging whether the data at the current sampling moment is the type with the largest data volume ratio; if yes, executing the subsequent steps, and if not, returning to the step S2 to collect the driving state information again;
s8, further identifying the rolling resistance coefficient by using a least square algorithm;
and S9, calculating various data volumes, judging whether a termination condition is met or not, and judging that the algorithm is terminated when the class with the largest data volume meets the set number.
The invention establishes an online clustering identification model of the rolling resistance coefficient based on vehicle running information and longitudinal acceleration sensor information. The method of fusing the differential longitudinal dynamics formula and the original longitudinal dynamics formula is used, the influence of the whole vehicle mass on identification is eliminated, and the rolling resistance coefficient online estimation algorithm is established. The established model has the advantage of being suitable for complex working conditions, and the influence of bad data on the identification result can be effectively eliminated by the online K-means clustering algorithm. The invention designs the rolling resistance coefficient online identification method combining the least square algorithm and the online K-means clustering, and can effectively solve the problem that the non-Gaussian noise distribution has adverse effect on the identification result.
The online identification method for the rolling resistance coefficient can obtain the stable and reliable rolling resistance coefficient under the complex working condition environments of different finished automobile masses, road environment changes and the like, and is beneficial to improving the performance of a finished automobile dynamic property and economic control system.
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FIG. 1 is a schematic flow chart of a rolling resistance coefficient identification method according to the present invention;
Detailed Description
The following examples are given to further illustrate the present invention, but are not intended to limit the invention.
Example 1
Referring to fig. 1, an online identification method for a rolling resistance coefficient is a rolling resistance coefficient identification model established based on driving information and vehicle-mounted longitudinal acceleration information, and includes the following steps:
step S1: and initializing the model. And fixed parameters required by the loading model comprise vehicle parameters and algorithm parameters.
The vehicle-finishing parameters comprise vehicle transmission efficiency η, tire rolling radius r and vehicle running acceleration avFlywheel moment of inertia IfWheel moment of inertia IwAir resistance coefficient CDThe windward area A of the whole vehicle, the air density rho and the gravity acceleration g. The algorithm parameters include: number of clusters m, m initial cluster centers
Figure BDA0001318672110000041
Step S2: and collecting the vehicle running state information.
The CAN bus information that needs to be synchronously acquired at each sampling moment includes: the speed v of the whole vehicle,Engine speed n, clutch pedal signal, brake pedal signal and acceleration a provided by longitudinal acceleration sensorsen。
Step S3: and judging whether the data is neutral sliding data or not. If so, the subsequent steps are continued, and if not, the flow returns to S2 to collect the vehicle running state information at the next time.
Step S4: and calculating the mass expression of the whole vehicle.
The glide resistance is first calculated. The longitudinal stress balance equation of the whole vehicle is applied to the derivation of the sliding resistance expression. The longitudinal stress balance equation of the whole vehicle is as follows:
Ft＝Ff+Fw+Fi+Fj(1)
wherein the content of the first and second substances,
Figure BDA0001318672110000042
is the air resistance;
Figure BDA0001318672110000043
is the driving force of the automobile;
Figure BDA0001318672110000044
is the wheel acceleration resistance;
Figure BDA0001318672110000045
is flywheel acceleration resistance;
Figure BDA0001318672110000046
is the product of the transmission ratio of the transmission and the transmission ratio of the main speed reducer; a isvIs the vehicle travel acceleration, and is the derivative of the vehicle speed. The acceleration resistance is then rewritten as:
Fj＝Fja+Fjw+Fjf(2)
wherein, FjaFor translational acceleration resistance (F) of the whole vehicleja＝mav)；FjwFor resistance to acceleration of wheel rotation
Figure BDA0001318672110000047
FjfFor accelerating the rotation of flywheelsForce of
Figure BDA0001318672110000048
Substituting the expression of formula (2) into (1) arranging:
Ft＝Ff+Fw+Fi+Fja+Fjw+Fjf(3)
according to the longitudinal stress balance equation of the whole vehicle, no driving force and inertia force generated by an engine flywheel exist in the neutral gear sliding process, and finally a sliding resistance expression can be obtained: fwjw＝-Fw-Fjw。
On the other hand, the measurement values of the acceleration sensor are defined as: a issen＝gi+avWherein a issenIs the acceleration value (unit m/s) collected by the acceleration sensor2). Obtaining a rolling resistance coefficient identification model containing acceleration sensor information according to an acceleration sensor definition formula:
Fwjw＝m(gf+asen) (4)
and then calculating the whole vehicle mass expression by using the differential running equation. First, a difference amount Δ F of the sliding resistance is calculatedresDifferential quantity of acceleration sensor DeltaasenDifference Δ v from the square value of vehicle speed2Then, establishing a whole vehicle mass expression according to the differential running equation as follows: m ═ Δ Fwjw/Δasen
Step S5: and calculating a rolling resistance coefficient preliminary result.
Substituting the whole vehicle mass expression into a running equation without driving force, and estimating the initial result of the rolling resistance coefficient at the sampling moment (i) by using a least square algorithm with a forgetting factor
Figure BDA0001318672110000051
The input quantity of least square is X ═ Δ Fwjwg, output amount is Y ═ FwjwΔasen-asenΔFwjw
Step S6: and (5) utilizing the online K-means clustering to the primary identification result, and updating the clustering center.
Calculating the distance from the initial result of the rolling resistance coefficient to various cluster centers
Figure BDA0001318672110000052
And classifying the points to be clustered into the class with the shortest distance. Updating cluster centers of input quantities
Figure BDA0001318672110000053
Wherein Fm(i) And Fm(i-1) are the cluster centers of the mth class at the current sampling instant and the previous sampling instant, respectively.
Step S7: and calculating the ratio of various types of data, and judging whether the data at the current sampling moment is the type with the largest data volume ratio. If so, the subsequent steps are executed, and if not, the flow returns to the step of S2 to collect the driving state information again.
Step S8: the rolling resistance coefficient is further identified. And further identifying the rolling resistance coefficient by using a least square algorithm.
Step S9: and calculating various data volumes and judging whether the termination condition is met. The discrimination algorithm terminates when the class with the largest amount of data satisfies a certain number. The number of data recommended in this embodiment is 2000.

Claims (1)

1. An online identification method for a tire rolling resistance coefficient is an online clustering identification model for the rolling resistance coefficient established based on vehicle running state information and vehicle-mounted longitudinal acceleration information, and is characterized by comprising the following steps:
s1. model initialization
Loading fixed parameters required by the model, wherein the fixed parameters comprise vehicle parameters and algorithm parameters;
the vehicle-finishing parameters comprise vehicle transmission efficiency η, tire rolling radius r and vehicle running acceleration avFlywheel moment of inertia IfWheel moment of inertia IwAir resistance coefficient CDThe windward area A of the whole vehicle, the air density rho and the gravity acceleration g;
the algorithm parameters include: number of clusters m, m initial cluster centers
Figure FDA0002110944260000011
S2, collecting vehicle running state information
The CAN bus information that needs to be synchronously acquired at each sampling moment includes: speed v of whole vehicle, engine speed n, clutch pedal signal, brake pedal signal and acceleration a provided by longitudinal acceleration sensorsen；
S3, judging whether the data is neutral gear sliding data or not, and if so, continuing to execute the subsequent steps; if not, returning to the step S2 to collect the vehicle running state information at the next moment;
s4, calculating the whole vehicle mass expression
Firstly, calculating the longitudinal running sliding resistance of the whole vehicle, namely Fwjw＝-Fw-Fjw，
In the formula:
Figure FDA0002110944260000012
is the air resistance;
Figure FDA0002110944260000013
is the wheel inertia force; a isvIs the vehicle travel acceleration, is the derivative of the vehicle speed;
and then calculating the expression of the mass of the whole vehicle by using a difference method: first, a difference amount Δ F of the sliding resistance is calculatedwjwDifferential quantity of acceleration sensor DeltaasenDifference Δ v from the square value of vehicle speed2Then, establishing a whole vehicle mass expression according to the differential running equation as follows: m ═ Δ Fwjw/Δasen；
S5, calculating a rolling resistance coefficient preliminary result
Substituting the whole vehicle mass expression into a running equation without driving force, and estimating the initial result of the rolling resistance coefficient at the sampling moment i by using a least square algorithm with a forgetting factor
Figure FDA0002110944260000014
The input quantity of least square is X ═ Δ Fwjwg, output amount is Y ═ FwjwΔasen-asenΔFwjw；
S6, utilizing the online K mean value clustering to the primary identification result to update the clustering center
Calculating the distance from the initial result of the rolling resistance coefficient to various cluster centers
Figure FDA0002110944260000021
Classifying the points to be clustered into a class with the shortest distance; updating cluster centers of input quantities
Figure FDA0002110944260000022
Wherein Fm(i) And Fm(i-1) clustering centers of the mth class at the current sampling moment and the previous sampling moment respectively;
s7, calculating the ratio of various types of data, and judging whether the data at the current sampling moment is the type with the largest data volume ratio; if yes, executing the subsequent steps, and if not, returning to the step S2 to collect the driving state information again;
s8, further identifying the rolling resistance coefficient by using a least square algorithm;
and S9, calculating various data volumes, judging whether a termination condition is met or not, and judging that the algorithm is terminated when the class with the largest data volume meets the set number.
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WO2021227086A1 (en) * 2020-05-15 2021-11-18 华为技术有限公司 Method and apparatus for acquiring vehicle rolling resistance coefficient
CN113267345A (en) * 2021-04-23 2021-08-17 联合汽车电子有限公司 Method for predicting resistance of unknown road section in front of vehicle, storage medium, controller and system

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