CN113744430B - Automobile driving demand power prediction method based on road change - Google Patents
Automobile driving demand power prediction method based on road change Download PDFInfo
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L3/00—Measuring torque, work, mechanical power, or mechanical efficiency, in general
- G01L3/24—Devices for determining the value of power, e.g. by measuring and simultaneously multiplying the values of torque and revolutions per unit of time, by multiplying the values of tractive or propulsive force and velocity
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0841—Registering performance data
Abstract
The invention provides a method for predicting automobile driving demand power based on road change, which comprises the following steps: initializing; the road gradient parameter detection module detects gradient parameters and corrects the gradient parameters through the first measured value correction module; the vehicle running characteristic detection module detects running parameters, and the second measured value correction module corrects the running parameters; the control unit takes the corrected running parameter and the running parameter of each basic working condition in the running working condition database as comparison quantity, records the running parameter of the actual working condition which changes with time when the closeness is smaller than a threshold value, and supplements the corrected running parameter and the corresponding actual running working condition to the running working condition database; the running parameter prediction module selects the running parameter of the basic working condition with the maximum closeness as the predicted value of the running parameter of the actual working condition; the road rolling resistance parameter module selects a corresponding rolling resistance coefficient from the rolling resistance coefficient library; the required power calculation module calculates a predicted value of the required power of the automobile. And the accuracy of the demand power prediction is improved.
Description
Technical Field
The invention belongs to the technical field of automobile driving demand power, and particularly relates to an automobile driving demand power prediction method based on road change.
Background
At present, the development of new energy automobiles is rapid, wherein the prediction of the automobile running demand power of a hybrid electric automobile is always an important problem of development. The output power of the engine and the motor is determined by predicting the running demand power of the vehicle and the running working condition of the vehicle and adopting a proper energy distribution control strategy, so that the highest fuel economy or other optimization targets are realized. The accuracy of the prediction of the required power is very important, and most of the existing methods for predicting the required power of the automobile assume that the automobile only runs on the road surface with the constant rolling resistance coefficient and the longitudinal gradient change, so that the on-line update of the running parameters and the rolling resistance coefficient of the automobile cannot be realized, and the prediction result is affected.
Dynamic tilt sensors, which are sensors specifically designed for use in dynamic environments, are typically used in the road grade parameter detection module. The accelerometer and the gyroscope are arranged in the device, the gyroscope can be used for measuring the angular velocity of a moving object, but the gyroscope cannot be used for measuring the static dip angle of the object. An acceleration sensor is added to make a static measurement. And in addition, by optimization measures such as correction and compensation, the acceleration and angle change condition of the moving object can be accurately measured.
Disclosure of Invention
The invention provides an automobile driving demand power prediction method based on road change. The main emphasis is on the selection of the gradient change and the rolling resistance coefficient of the online updated road in the driving process and the prediction of the driving demand power of the automobile. Most of the existing methods for predicting the required power of the automobile assume that the automobile only runs on the road surface with the constant rolling resistance coefficient and the longitudinal gradient change, so that the on-line updating of the running parameters and the rolling resistance coefficient of the automobile cannot be realized. The invention provides a method for predicting the running demand power of an automobile by adopting a measurement correction module, a heuristic algorithm and a fuzzy control method to correct partial measurement running parameters and selecting corresponding rolling resistance coefficients and predicting future running parameters of the automobile.
The invention updates the gradient, rolling resistance coefficient and future driving parameters when the road changes by using a geometric relation, a database and a fuzzy control method on the basis of a road gradient parameter detection module, a vehicle driving characteristic detection module, a measured value correction module, a driving parameter prediction module, a driving working condition database, a road rolling resistance parameter module, a rolling resistance coefficient database, a required power calculation module, a control unit and other modules, and predicts the driving required power of an automobile by using a power balance equation.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
the invention provides an automobile driving demand power prediction method based on road change, which is characterized by road change parameters based on gradient parameters and rolling resistance coefficients, and comprises an automobile driving demand power prediction system consisting of a road gradient parameter detection module, an automobile driving characteristic detection module, a first measured value correction module, a second measured value correction module, a driving parameter prediction module, a driving condition database, a road rolling resistance parameter module, a rolling resistance coefficient library, a demand power calculation module and a control unit, wherein the automobile driving demand power prediction system is used for realizing the prediction of automobile driving demand power;
the specific steps of the prediction method are as follows:
s101: initializing, wherein the gradient parameter and the running parameter are set to 0, a running condition database comprises a plurality of basic conditions, and each basic condition comprises a plurality of running parameters;
s102: the road gradient parameter detection module detects gradient parameters in the running process of the automobile, and updates the gradient parameters stored in the control unit after being corrected by the first measured value correction module;
s103: the vehicle running characteristic detection module detects running parameters of the vehicle, the second measured value correction module corrects the running parameters by adopting a weighted average algorithm, and the running parameters stored in the control unit are updated;
s104: the control unit takes the corrected running parameters and the running parameters of each basic working condition in the running condition database as comparison quantities, adopts a Euclidean proximity method, records the running parameters of the actual working conditions which change along with time when the proximity is smaller than a threshold value, and supplements the corrected running parameters and the corresponding actual running conditions into the running condition database;
s105: the running parameter prediction module selects a basic working condition with the maximum closeness from a running working condition database as a running working condition according to the corrected running parameter by adopting a Euclidean closeness method, and takes the running parameter in the running working condition as a predicted value of the running parameter of an actual working condition;
s106: the road rolling resistance parameter module selects a corresponding rolling resistance coefficient from a rolling resistance coefficient library by adopting a heuristic algorithm and a fuzzy control method according to the corrected driving parameter;
s107: the demand power calculation module calculates the predicted value of the demand power of the automobile by utilizing an automobile power balance equation according to the corrected gradient parameter, the predicted value of the running parameter and the rolling resistance coefficient.
Further, the gradient parameters detected by the road gradient parameter detection module comprise a longitudinal pitch angle, a transverse roll angle, a longitudinal included angle and a transverse included angle between the vehicle and a road surface in the running process of the vehicle.
Further, the running parameters detected by the vehicle running characteristic detection module include: the distance travelled by the vehicle in delta t time, average wheel speed, maximum wheel speed, standard deviation of wheel speed, maximum acceleration, maximum deceleration, average acceleration, standard deviation of acceleration, average tire pressure of the wheel, maximum slip rate of the wheel, average slip rate of the wheel, rate of change of accelerator opening, rate of change of brake opening, proportion of time at a speed of less than 15km/h, proportion of time at a speed of more than 15km/h, proportion of time at a speed of less than 40km/h, proportion of time at a speed of more than 40km/h, acceleration of more than 10m/s 2 The proportion of time and the acceleration is higher than 5m/s 2 Below 10m/s 2 The time proportion of (2) is higher than 0 and lower than 5m/s 2 The proportion of time and the deceleration is higher than 10m/s 2 The proportion of time and the deceleration is higher than 5m/s 2 Below 10m/s 2 The ratio of the time to the deceleration is higher than 0 and lower than 5m/s 2 Is a proportion of the time of (2).
Further, the plurality of basic conditions refer to: according to different running parameters corresponding to different time, a clustering method is used for subdividing four major running conditions of a congested urban area, a suburban area and a high speed to obtain minor running conditions representing running parameters changing along with time, and a plurality of basic conditions of a running condition database are formed.
Further, by using automobiles equipped with tires of different specifications, sizes, tire pressures and patterns, the automobiles run on different road surfaces, and all rolling resistance coefficients in the running process are obtained by adopting a test method, so that a rolling resistance coefficient library is formed.
Aiming at the problem that the road condition frequently changes in the running process of the automobile and the running demand power of the automobile cannot be accurately predicted, the invention discloses a road change-based automobile running demand power prediction method, which mainly has the following advantages:
(1) The invention considers the installation angle of the equipment and carries out installation correction, thereby improving the accuracy of the method.
(2) The invention considers the influence of the change of the included angle between the automobile and the road surface under some working conditions and corrects the included angle.
(3) The invention adopts a method that the measured value and the calculated value use weighted average to calculate the output value, thereby improving the numerical precision.
(4) The invention selects the rolling resistance coefficient by using a heuristic algorithm and a fuzzy control method, and can realize faster online response.
(5) The method can be implemented on the existing vehicle equipment, and the price of other needed equipment is low.
Drawings
Fig. 1 is a schematic diagram of an automobile driving demand power prediction system based on road change according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for predicting driving demand power of an automobile based on road change according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of the measurement of the angle between the longitudinal direction of the automobile and the road surface.
Fig. 4 is a schematic diagram of the measurement of the angle between the transverse direction of the automobile and the road surface.
Fig. 5 is a schematic view of measuring the mounting angle of a dynamic tilt sensor.
Detailed Description
The present invention will be further described with reference to examples and drawings, but the present invention is not limited thereto.
The method for predicting the automobile running demand power based on the road change can solve the problem of inaccurate automobile running demand power prediction caused by the road change, and improves the accuracy of demand power prediction.
The following describes in further detail a method for predicting power demand for driving an automobile based on road change according to the present invention with reference to the drawings and embodiments.
In an embodiment, the invention provides a method for predicting the running demand power of an automobile based on road change, which is based on road change parameters represented by gradient parameters and rolling resistance coefficients, wherein the running demand power prediction system of the automobile is used for predicting the running demand power of the automobile, and as shown in fig. 1, the running demand power prediction system of the automobile comprises a road gradient parameter detection module, a vehicle running characteristic detection module, a first measured value correction module, a second measured value correction module, a running parameter prediction module, a running working condition database, a road rolling resistance parameter module, a rolling resistance coefficient library, a demand power calculation module and a control unit; the control unit comprises a first data storage unit and a second data storage unit, wherein the first data storage unit is used for storing gradient parameters, and the second data storage unit is used for storing driving parameters;
the specific steps of the prediction method are as follows:
s101: initializing, wherein the gradient parameter and the running parameter are set to 0, a plurality of basic working conditions are included in a running working condition database, and each basic working condition comprises a plurality of running parameters which change along with time;
s102: the road gradient parameter detection module acquires gradient parameters in the running process of the automobile, the gradient parameters are corrected by the first measured value correction module, the corrected gradient parameters are obtained, and the gradient parameters stored in the control unit are updated;
in an embodiment, when the gradient parameter does not need to be corrected, the corrected gradient parameter is the gradient parameter obtained by the road gradient parameter detection module in the running process of the automobile. When the gradient parameter needs to be corrected, calculating a new gradient parameter according to the original gradient parameter to serve as the corrected gradient parameter.
S103: the vehicle running characteristic detection module detects running parameters of the vehicle, the second measured value correction module corrects the running parameters by adopting a weighted average algorithm to obtain corrected running parameters, and the running parameters stored in the control unit are updated;
in an embodiment, when the driving parameter does not need to be corrected, the corrected driving parameter is the driving parameter of the vehicle detected by the vehicle driving feature detection module. When the driving parameter part needs to be corrected, the corrected driving parameter comprises the driving parameter which is partially corrected and the driving parameter which is detected by the driving characteristic detection module of the vehicle and is not corrected.
S104: the control unit takes the corrected running parameters and the running parameters of each basic working condition in the running condition database as comparison quantities, adopts a Euclidean proximity method, records the running parameters of the actual working conditions which change along with time when the proximity is smaller than a threshold value, and supplements the corrected running parameters and the corresponding actual running conditions into the running condition database;
s105: the running parameter prediction module selects a basic working condition with the maximum closeness from a running working condition database as a running working condition according to the corrected running parameter by adopting a Euclidean closeness method, and takes the linear prediction of the running parameter in the next delta t time of the working condition as a predicted value of the running parameter of an actual working condition; Δt represents time.
When the corrected running parameter and the corresponding actual running condition are added to the running condition database in step S104, the basic condition with the maximum proximity selected in step S105 is the actual running condition added to the running condition database in step S104, and the predicted value of the running parameter is obtained by adopting a linear prediction method (i.e. the linear prediction of the running parameter in the next Δt time is performed according to the measured and corrected running parameter).
S106: the road rolling resistance parameter module selects a corresponding rolling resistance coefficient from a rolling resistance coefficient library by adopting a heuristic algorithm and a fuzzy control method according to the corrected driving parameter;
s107: the demand power calculation module calculates the predicted value of the demand power of the automobile by utilizing an automobile power balance equation according to the corrected gradient parameter, the predicted value of the running parameter and the rolling resistance coefficient.
The steps S102 and S103 may be performed simultaneously or sequentially, and the steps S105 and S106 may be performed simultaneously or sequentially.
In an embodiment, the invention provides a method for predicting the power required by driving of an automobile based on road change, which provides a basis for a control strategy of a hybrid electric vehicle. In the process, the correction of the installation angle during the installation of the device and the correction of the measurement angle under special working conditions are considered. The flow chart is shown in fig. 2. The method comprises the following specific steps:
s201, establishing a running condition database, collecting different running parameters of four main running conditions of a congested city area, a suburb and a high speed, using a clustering method to divide the four main running conditions into three subclasses respectively, obtaining 12 subclasses of running conditions representing the running parameters, wherein the 12 subclasses of running conditions comprise a first-stage congested city area, a second-stage congested city area, a third-stage congested city area, a first-stage city area, a second-stage city area, a third-stage suburb, a second-stage suburb, a third-stage suburb, a first-stage high speed, a second-stage high speed and a third-stage high speed (the smaller the numerical value is, the higher the congestion degree is), and forming basic conditions of the running condition database, and each basic condition comprises a plurality of running parameters which change along with time.
S202, establishing a rolling resistance coefficient library, driving on different roads by automobiles with tires of different specifications, sizes, tire pressures and patterns, and obtaining all rolling resistance coefficients in the driving process by adopting a test method to form the rolling resistance coefficient library.
S203, initializing, and setting the gradient parameter and the running parameter in the control unit to 0, and executing step S204 and step S209.
S204, a road gradient parameter detection module acquires gradient parameters in the running process of the automobile, wherein the gradient parameters comprise a pitch angle, a roll angle and an included angle between the automobile and a road surface.
The pitch angle refers to an included angle between a longitudinal axis of the vehicle and a horizontal plane, the roll angle refers to an included angle between a transverse axis of the vehicle and the horizontal plane, and the longitudinal pitch angle theta during gradient change in the running process of the automobile can be measured through a dynamic inclination angle sensor with a built-in triaxial accelerometer and gyroscope 1 And a transverse roll angle theta 2 。
When the automobile moves forward in acceleration and centrifugally moves at high speed (such as a road cross slope), the automobile can incline to the rear side in the running direction and the outside of the centrifugal movement, so that the gradient measurement result is seriously influenced, an infrared range finder is used, the change of the included angle between the automobile and the road surface in the case of the situation is obtained by utilizing the geometric relationship, and the real gradient change is obtained.
Acquiring included angle data between a vehicle and a road surface through an infrared range finder: including the longitudinal angle between the vehicle and the road surface and the transverse angle between the vehicle and the road surface. When the longitudinal gradient and the transverse gradient of the vehicle change during running, as shown in fig. 3 and 4, the change of the included angle between the vehicle and the road surface can be measured by:
γ 1 ＝arctan -1 [(L f -L r )/L 1 ]
γ 2 ＝arctan -1 [(L L -L R )/L 2 ]
γ 1 is the longitudinal included angle between the vehicle and the road surface, gamma 2 Is the transverse included angle between the vehicle and the road surface, L f Is the distance measured by a distance meter arranged at the front part of the vehicle, L r Is the distance measured by a distance meter installed at the rear part of the vehicle, L L Is the distance measured by a distance meter arranged at the left part of the vehicle, L R Is the distance measured by a distance meter arranged at the right part of the vehicle, L 1 Is the distance between the front and the back equipment, L 2 Is the distance between the left and right devices.
S205, the first measured value correction module judges whether to measure the installation angle, if so, the step S206 is executed, and if not, the step S207 is executed;
the mounting angle may not need to be measured under known conditions, and therefore the first measurement value correction module first determines whether to measure the mounting angle. When the first measured value correction module measures the installation angle, the slope parameter is corrected according to the measured installation angle, and when the first measured value correction module does not measure the installation angle, the slope parameter is corrected by directly adopting the known installation angle.
S206, the first measured value correction module measures the installation angle of the dynamic inclination sensor.
The dynamic tilt sensor assembly may have an installation angle when installed on a vehicle. The measured pitch angle and roll angle are thus taken into account for the correction of the mounting angle. The measurement may not be necessary if the mounting angle is known. The vehicle can be left to stand without knowing the specific mounting angleOn a horizontal road surface, because the instrument has an installation angle, acceleration exists in a static state, and a dynamic inclination angle sensor with a built-in triaxial accelerometer and gyroscope is used for measuring static longitudinal acceleration a 01 And static lateral acceleration a 02 As shown in fig. 5, the relationship between the static acceleration a and the mounting angle is as follows:
α＝arcsin -1 (a/g)；
wherein a is the static acceleration of the automobile on a horizontal road surface, alpha is the installation angle of the dynamic inclination angle sensor, and g is the gravity acceleration.
The mounting angle of the dynamic inclination sensor device itself is measured by:
α 1 ＝arcsin -1 (a 01 /g)；
α 2 ＝arcsin -1 (a 02 /g)；
wherein alpha is 1 Is the longitudinal installation angle alpha of the dynamic inclination sensor 2 Is the transverse installation angle of the dynamic inclination angle sensor, a 01 And a 02 Respectively, the static longitudinal acceleration and the static transverse acceleration of the automobile on a horizontal road surface, and g is the gravity acceleration.
S207, the first measured value correction module corrects the gradient parameter.
Specifically, step S207 includes:
s2071, a first measured value correction module calculates the longitudinal gradient and the transverse gradient of the road according to the installation angle as corrected gradient parameters.
The road gradient during running is found from the following formula:
i 1 ＝θ 1 -γ 1 -α 1 ；
i 2 ＝θ 2 -γ 2 -α 2 ；
wherein i is 1 Is the road longitudinal grade; i.e 2 Is the road transverse gradient; gamma ray 1 And gamma 2 The longitudinal included angle and the transverse included angle between the vehicle and the road surface are respectively; θ 1 And theta 2 For the pitch and roll angles measured;
s2072, the first measured value correction module calculates a composite gradient according to the longitudinal gradient and the transverse gradient of the road to serve as a corrected gradient parameter.
Most of the conventional power prediction assumes that the vehicle is traveling on a road with only longitudinal gradient changes, and tends to ignore that the actual road has lateral gradient changes. The present invention takes into account the change in the lateral gradient, so that the power prediction required to overcome the gradient resistance can be calculated using the combined value of the longitudinal gradient and the lateral gradient, i.e., the combined gradient. Measured longitudinal gradient i of road 1 And road lateral gradient i 2 Can be regarded as two vectors in a vector space, and the synthetic gradient i in the running process of the automobile can be obtained according to the principle of vector synthesis 3 Will synthesize slope i 3 Stored in the control unit.
S208, the control unit acquires the corrected gradient parameters.
The control unit acquires the corrected gradient parameter from the first measured value correction module.
S209, a vehicle driving characteristic detection module detects driving parameters of the automobile.
The driving parameters of the automobile are measured through various sensors on the automobile, and the method comprises the following steps: the vehicle distance, average wheel speed, maximum wheel speed, standard deviation of wheel speed, maximum acceleration, maximum deceleration, average acceleration, standard deviation of acceleration, average tire pressure, maximum slip rate of wheel, average slip rate of wheel, rate of change of accelerator opening, rate of change of brake opening, proportion of time at a speed of less than 15km/h, proportion of time at a speed of more than 15km/h, proportion of time at a speed of less than 40km/h, proportion of time at a speed of more than 40km/h, acceleration of more than 10m/s in Δt time 2 The proportion of time and the acceleration is higher than 5m/s 2 Below 10m/s 2 The time proportion of (2) is higher than 0 and lower than 5m/s 2 The proportion of time and the deceleration is higher than 10m/s 2 The proportion of time and the deceleration is higher than 5m/s 2 Below 10m/s 2 The ratio of the time to the deceleration is higher than 0 and lower than 5m/s 2 Is a proportion of the time of (2).
S210, the second measurement value correction module corrects the vehicle longitudinal acceleration and the road surface adhesion coefficient according to the running parameter, and updates the vehicle longitudinal acceleration (average acceleration in Δt time) in the running parameter, and performs steps S211 and S214.
The vehicle speed is first obtained. The running speed of the automobile is difficult to directly obtain, so that the running speed of the automobile can be indirectly obtained through a wheel speed sensor arranged on a wheel. The instantaneous rotational speed of the wheels, i.e. the average wheel rotational speed ω over Δt time, can be converted as an instantaneous vehicle speed into a vehicle running speed Ua using the formula ua=ω×r, where r is the wheel radius.
The vehicle longitudinal acceleration may be calculated from the vehicle speed and corrected based on the measured value. And establishing an array in the control unit according to the acquired multiple vehicle speeds in a certain time period, respectively carrying out mean value filtering on the data of the first half and the second half in the array, namely respectively obtaining the mean value of all the data in the first half and the second half of the array, taking the two mean values as processed filter values, subtracting the obtained two filter values, and dividing the two filter values by the time to calculate the longitudinal acceleration of the vehicle. And correcting the obtained longitudinal acceleration of the vehicle, and adopting a weighted average algorithm. Giving a measured longitudinal acceleration a of the vehicle 1 (i.e. average acceleration of the vehicle over Δt) a weight factor x 1 For the running speed U of the automobile a Inverse calculated vehicle longitudinal acceleration a 2 (i.e. taking the difference between the two vehicle speed filtered values divided by the time interval between the two vehicle speed filtered values) a weight coefficient y 1 The weight coefficient satisfies x 1 <y 1 ，x 1 +y 1 =1, the output correction value is x 1 *a 1 +y 1 *a 2 。
And obtaining the road adhesion coefficient and correcting according to the calculated value. The road condition judgment can be carried out according to the road surface peak attachment coefficient. Nowadays, more and more vehicles are equipped with a driving anti-slip control system, which prevents the driving force from being excessively large beyond the maximum adhesion force provided by the road surface when the vehicle is started, and the tires from slipping. The driving force coefficient is kept near the peak road surface adhesion coefficient by controlling the optimum slip ratio of the tire. The road surface peak attachment coefficient information at the moment can be obtained by back calculation of the maximum slip rate of the wheels and is fed back to the control system.
The peak attachment coefficient phi of the road surface obtained by back calculation according to the maximum slip rate of the wheel 1 And then reversely calculating an equivalent gradient value by using the corrected longitudinal acceleration of the vehicle. The adhesion coefficient phi of the road surface at the moment can be reversely calculated according to the calculated equivalent gradient 2 . Then the obtained road surface adhesion coefficient is corrected, and a weighted average algorithm is adopted, namely the road surface peak adhesion coefficient phi fed back by the anti-skid driving control system is measured 1 Weight coefficient x 2 Giving the adhesion coefficient phi of the road surface reversely calculated by the equivalent gradient 2 Weight coefficient y 2 The weight coefficient satisfies x 2 <y 2 ，x 2 +y 2 =1, the output correction value is x 2 *Φ 1 +y 2 *Φ 2 。
S211, the control unit judges whether the closeness is smaller than a threshold value.
Setting a threshold sigma min . The control unit uses the updated running parameters and the running parameters in the running condition database as comparison amounts, and adopts a Euclidean proximity method to judge whether the proximity of the actual running condition is smaller than a set threshold value, if so, the step S212 is executed, and if not, the step S213 is executed.
The invention predicts the running working condition by adopting Euclidean proximity with convenient operation and strong practicability, and takes the standard working condition which is the most similar to the current working condition as the class of the running working condition in a future period of time. Set S i For the sample in the driving condition database, the ith working condition is represented, the driving condition database comprises 12 most basic subclasses of basic working conditions and the actual driving working conditions of the supplementary driving condition database, M is the sample to be identified (namely the actual driving working conditions of the vehicle), and after the original data is standardized by using a Min-Max standardization function, S i Euclidean proximity σ to M (S i M) is expressed as:
wherein n is the working condition S i The number of driving parameters in j represents the condition S i The j-th travel parameter in the range. S calculated according to Euclidean proximity formula i Proximity to M, judging sigma 1 (S，M)＝min{σ(S 1 ，M)，…，σ(S i ，M)，…，σ(S N ，M)}<σ min Whether or not to establish sigma 1 (S, M) represents the running condition with the minimum closeness between the running condition database and M, i has the value of 1,2, …, N, N is the number of the working conditions in the running condition database.
S212, the control unit supplements the current running working condition to the running working condition database.
If present within a certain delta t time 1 (S，M)＝min{σ(S 1 ，M)，…，σ(S i ，M)，…，σ(S N ，M)}<σ min The actual running condition M is considered to be recorded, and the corrected running parameter and the corresponding actual running condition are added to the running condition database, wherein when the actual running condition M is added to the running condition database, the number of the conditions in the running condition database is n+1.
S213, a running parameter prediction module selects a running condition with the maximum closeness;
and after judging whether the current actual running condition can be supplemented into the running condition database, updating the running condition database. The running parameter prediction module selects an in-warehouse running condition with the maximum closeness with the current running condition from a running condition database by adopting a Euclidean closeness method according to the running parameters, and sigma is adopted when the actual running condition M is supplemented into the running condition database 2 (S，M)＝max{σ(S 1 ，M)，…，σ(S i ，M)，…，σ(S N+1 ，M)}，σ 2 (S, M) represents the driving condition with the maximum closeness between the driving condition database and M, namely the maximum closeness between the actual driving condition M and the actual driving condition M added into the driving condition database, and a linear prediction method is adopted to obtain a predicted value of the driving parameter (namelyLinear prediction of the running parameter in the next Δt time is performed based on the measured and corrected running parameter), and the linear prediction method is a prediction method in which a plurality of independent variables affecting a plurality of dependent variables are used for analysis, and a linear relationship between the dependent variables and the independent variables is analyzed. Common statistical indicators include: average number, increment, and average increment. In the invention, the vehicle speed, the corrected acceleration, the accelerator pedal opening change rate, the brake pedal opening change rate and other running parameters are measured to estimate linearly. When the actual running condition M is not supplemented to the running condition database 2 (S，M)＝max{σ(S 1 ，M)，…，σ(S i ，M)，…，σ(S N ，M)}，σ 2 (S, M) represents the driving condition with the maximum closeness between M and the driving condition database, and sigma 2 And (S, M) corresponding to the linear prediction of the running parameter in the next delta t time is taken as the predicted value of the running parameter of the actual working condition.
S214, selecting a corresponding rolling resistance coefficient from the rolling resistance coefficient library by the road rolling resistance parameter module according to the driving parameters by adopting a heuristic algorithm and a fuzzy control method.
The invention adopts heuristic algorithm and fuzzy control method to select the corresponding rolling resistance coefficient from the rolling resistance coefficient library.
The rolling resistance coefficient value and the automobile speed, the road condition, the tire pressure, the tire structure, the material and the like are all related. The invention focuses on the influence of different road conditions and vehicle loads on the rolling resistance coefficient, and adopts the fuzzy control principle to update the rolling resistance coefficient in running on line. And a heuristic algorithm is adopted, namely rolling resistance is unchanged when the vehicle speed is lower than a certain speed. When the speed of a general automobile is below 100km/h, the rolling resistance coefficient of the automobile is considered to be unchanged, and the rolling resistance coefficient under the corresponding road surface can be directly selected according to the road surface adhesion coefficient. When the automobile is running at a high speed, the rolling resistance coefficient f may be selected according to the rolling resistance coefficient bank. By adopting a heuristic algorithm, the calculation time can be greatly shortened, and the online rapid application can be realized.
According to the tire type, the corrected road adhesion coefficient and the tire pressure k measured by the tire pressure sensor adopt a fuzzy control method to select the tire rolling resistance coefficient f under the corresponding working condition from the rolling resistance coefficient library stored in the control unit.
S215, the required power calculation module calculates a predicted value of the required power of the automobile.
According to the principle of power balance, the driving power required by the automobile should be the sum of the powers required to overcome all the resistances. Assuming constant air resistance, the power P required to overcome rolling resistance f Estimation is achieved by updating the rolling resistance coefficient f, the power P required to overcome the gradient resistance i By calculating the resultant gradient i 3 Realizing estimation, overcoming the power P required by acceleration resistance j The estimation is carried out by means of the corrected longitudinal acceleration value a of the vehicle.
The required power calculation module calculates the predicted value of the required power of the automobile by utilizing an automobile power balance equation according to the corrected gradient parameter, the predicted value of the running parameter and the rolling resistance coefficient:
P f ＝(3600*η t ) -1 *m*g*f*cosθ 1 *U a ；
P i ＝(3600*η t ) -1 *m*g*i 3 *U a ；
P w ＝(76140*η t ) -1 *C D *A*U a 3 ；
P j ＝(3600*η t ) -1 *m*δ*a；
ΣP＝P f +P i +P w +P j
wherein eta t Is the total transmission ratio of the automobile transmission system; f is the selected rolling resistance coefficient; m is the mass of the car; g is the gravitational acceleration; θ 1 Is the pitch angle measured; i.e 3 Is a synthetic grade; u (U) a Is the running speed of the automobile; c (C) D Is the air resistance coefficient; a is the windward area of the automobile; delta is the conversion coefficient of the rotating mass of the automobile; a is a predicted value of the longitudinal acceleration of the automobile; p (P) f Is the power required to overcome the rolling resistance, P i Is the power required to overcome the resistance of the grade,P w is the power required for overcoming air resistance, P j Is the power required to overcome the acceleration resistance.
The method for predicting the driving demand power of the automobile based on road change provided by the invention is described in detail, and the implementation description is only used for helping to understand the method and the core idea of the invention; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. The method is characterized in that the method is based on road change parameters represented by gradient parameters and rolling resistance coefficients, and comprises a road gradient parameter detection module, a vehicle running characteristic detection module, a first measured value correction module, a second measured value correction module, a running parameter prediction module, a running condition database, a road rolling resistance parameter module, a rolling resistance coefficient library, a required power calculation module and a control unit to form an automobile running required power prediction system, wherein the automobile running required power prediction system is used for predicting automobile running required power;
the specific steps of the prediction method are as follows:
s101: initializing, wherein the gradient parameter and the running parameter are set to 0, a plurality of basic working conditions are included in a running working condition database, and each basic working condition comprises a plurality of running parameters which change along with time;
s102: the road gradient parameter detection module detects gradient parameters in the running process of the automobile, and updates the gradient parameters stored in the control unit after being corrected by the first measured value correction module; the first measured value correction module calculates a composite gradient according to the longitudinal gradient of the road and the transverse gradient of the road to be used as a corrected gradient parameter;
s103: the vehicle running characteristic detection module detects running parameters of the vehicle, and the second measured value correction module corrects the longitudinal acceleration of the vehicle and the road surface peak attachment coefficient according to the running parameters and updates the running parameters stored in the control unitVehicle longitudinal acceleration; the vehicle longitudinal acceleration is defined as
Average acceleration over time; when the driving parameter part needs to be corrected, the corrected driving parameter comprises the driving parameter which is partially corrected and the driving parameter which is detected by the driving characteristic detection module of the vehicle and is not corrected;
s104: the control unit takes the corrected running parameter and the running parameter of each basic working condition in the running working condition database as comparison quantity, adopts a Euclidean proximity method, and supplements the corrected running parameter and the corresponding actual running working condition into the running working condition database when the proximity is smaller than a threshold value;
s105: the running parameter prediction module selects a basic working condition with the maximum closeness from a running working condition database as a running working condition by adopting a Euclidean closeness method according to the corrected running parameter, and the next running working condition is selected
The linear prediction of the running parameters in time is used as the predicted value of the running parameters of the actual running conditions;
s106: the road rolling resistance parameter module judges the road condition according to the running parameter and the road condition through the road peak attachment coefficient, adopts a heuristic algorithm and a fuzzy control method, selects a corresponding rolling resistance coefficient from a rolling resistance coefficient library when the vehicle speed is higher than a preset speed, and selects the rolling resistance coefficient under the corresponding road condition according to the road peak attachment coefficient when the vehicle speed is not higher than the preset speed;
s107: the required power calculation module calculates the predicted value of the required power of the automobile by utilizing an automobile power balance equation according to the corrected gradient parameter, the predicted value of the running parameter and the rolling resistance coefficient; the automobile power balance equation is as follows:
wherein the method comprises the steps of
Is the total transmission ratio of the automobile transmission system; f is the selected rolling resistance coefficient; m is the mass of the car; g is the gravitational acceleration; />
Is the pitch angle measured; />
Is a synthetic grade; />
Is the running speed of the automobile; />
Is the air resistance coefficient; a is the windward area of the automobile; delta is the conversion coefficient of the rotating mass of the automobile; a is a predicted value of the longitudinal acceleration of the automobile; p (P) f Is the power required to overcome the rolling resistance, P i Is the power required to overcome the gradient resistance, P w Is the power required for overcoming air resistance, P j Is the power required to overcome the acceleration resistance;
wherein the saidThe driving parameters detected by the vehicle driving characteristic detection module include:
the maximum slip rate of the wheels in time, and the correction step of the peak road adhesion coefficient comprises the following steps:
obtaining the peak attachment coefficient phi of the road surface according to the maximum slip rate back calculation of the wheel 1 The corrected longitudinal acceleration of the vehicle is used for back calculation to obtain an equivalent gradient value, and the road surface peak attachment coefficient phi at the moment is obtained through back calculation of the equivalent gradient value 2 ；
Given peak road adhesion coefficient phi 1 Weight coefficient x 2 Given the peak road surface attachment coefficient phi inversely calculated from the equivalent gradient value 2 Weight coefficient y 2 And x is 2 <y 2 ，x 2 +y 2 When =1, the correction value of the road surface peak attachment coefficient is
。
2. The method for predicting the power required by the vehicle running based on the road change according to claim 1, wherein the gradient parameters detected by the road gradient parameter detection module comprise a longitudinal pitch angle, a transverse roll angle, a longitudinal angle and a transverse angle between the vehicle and the road surface in the running process of the vehicle.
3. The method for predicting power demand for driving a vehicle according to claim 1, wherein the driving parameters detected by the driving characteristic detection module further comprise:
the running distance of the automobile, the average wheel rotating speed, the maximum wheel rotating speed, the standard deviation of the wheel rotation speed, the maximum acceleration, the maximum deceleration, the average acceleration, the standard deviation of the acceleration, the average tire pressure of the wheels, the average slip rate of the wheels, the change rate of the opening degree of an accelerator pedal,The opening rate of the brake pedal, the proportion of time with the speed lower than 15km/h, the proportion of time with the speed higher than 15km/h, the proportion of time with the speed lower than 40km/h, the proportion of time with the speed higher than 40km/h, and the acceleration higher than 10m/s 2 The proportion of time and the acceleration is higher than 5m/s 2 Below 10m/s 2 The time proportion of (2) is higher than 0 and lower than 5m/s 2 The proportion of time and the deceleration is higher than 10m/s 2 The proportion of time and the deceleration is higher than 5m/s 2 Below 10m/s 2 The ratio of the time to the deceleration is higher than 0 and lower than 5m/s 2 Is a proportion of the time of (2).
4. The method for predicting the power required for driving an automobile based on road change according to claim 1, wherein the plurality of basic conditions are: according to different running parameters corresponding to different time, a clustering method is used for subdividing four major running conditions of a congested urban area, a suburban area and a high speed to obtain minor running conditions representing running parameters changing along with time, and a plurality of basic conditions of a running condition database are formed.
5. The method for predicting the running demand power of an automobile based on road change according to claim 1, wherein the automobile is driven on different road surfaces by being provided with tires of different specifications, sizes, tire pressures and patterns, and all rolling resistance coefficients during the running are obtained by adopting a test method to form a rolling resistance coefficient library.
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