CN113744430A - Method for predicting required power of automobile running based on road change - Google Patents
Method for predicting required power of automobile running 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 required power of automobile running based on road change, which comprises the following steps: initializing; the road gradient parameter detection module detects gradient parameters, and the gradient parameters are corrected by 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 parameters and the running parameters of each basic working condition in the running working condition database as comparison quantities, records the running parameters of the actual working conditions changing along with time when the closeness is smaller than a threshold value, and supplements the corrected running parameters and the corresponding actual running working conditions to the running working condition database; the driving parameter prediction module selects the driving parameter of the basic working condition with the maximum closeness as the predicted value of the driving parameter of the actual working condition; the road rolling resistance parameter module selects a corresponding rolling resistance coefficient from a rolling resistance coefficient library; and the required power calculation module calculates the predicted value of the required power of the automobile. The accuracy of the demand power prediction is improved.
Description
Technical Field
The invention belongs to the technical field of required power for automobile running, and particularly relates to a method for predicting required power for automobile running based on road change.
Background
At present, new energy automobiles are developed rapidly, and the prediction of the automobile running demand power of hybrid electric automobiles is always an important development problem. The output power of the engine and the output power of the motor are determined by adopting a proper energy distribution control strategy through predicting the required running power of the vehicle and the running condition of the vehicle, so that the highest fuel economy or other optimization targets are realized. The prediction accuracy degree of the required power is very important, most of the existing methods for predicting the required power of the automobile assume that the automobile only runs on a road surface with a constant rolling resistance coefficient and a variable longitudinal gradient, so that the online updating of the vehicle running parameters and the rolling resistance coefficient cannot be realized, and the prediction result is influenced.
Dynamic tilt sensors, which are sensors specifically designed for use in dynamic environments, are typically used in road grade parameter detection modules. An accelerometer and a gyroscope are arranged in the device, the gyroscope can be used for measuring the angular velocity of a moving object, but the gyroscope cannot measure the static inclination angle of the object. So an acceleration sensor is added for static measurement. And in addition, optimization measures such as correction compensation and the like can accurately measure the acceleration and angle change conditions of the moving object.
Disclosure of Invention
The invention provides a method for predicting required power of automobile running based on road change. The main focus is to update the gradient change of the road and the selection of the rolling resistance coefficient on line in the driving process and predict the required power of the automobile driving. Most of the existing methods for predicting the required power of the automobile assume that the automobile only runs on a road surface with a constant rolling resistance coefficient and a variable longitudinal gradient, so that the online updating of the running parameters and the rolling resistance coefficient of the automobile cannot be realized. The invention provides a method for predicting the required power for automobile running by predicting the future running parameters of an automobile, which replaces the gradient with only longitudinal change by a composite gradient synthesized by a longitudinal gradient and a transverse gradient, and particularly adopts a measurement correction module, a heuristic algorithm and a fuzzy control method to correct part of measured running parameters and select corresponding rolling resistance coefficients.
The invention updates the gradient, the rolling resistance coefficient and the 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 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 required power for driving the vehicle 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 running demand power prediction method based on road change, which is a road change parameter characterized by a gradient parameter and a rolling resistance coefficient, and realizes the prediction of the automobile running demand power by an automobile running demand power prediction system consisting of a road gradient parameter detection module, an automobile 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 database, a demand power calculation module and a control unit;
the prediction method comprises the following specific steps:
s101: initializing, wherein the gradient parameter and the running parameter are set to be 0, the 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 the gradient parameters are corrected by the first measured value correction module;
s103: the vehicle running characteristic detection module detects the running parameters of the vehicle, and the second measured value correction module corrects the running parameters by adopting a weighted average algorithm and updates the running parameters stored in the control unit;
s104: the control unit takes the corrected driving parameters and the driving parameters of each basic working condition in the driving condition database as comparison quantities, adopts an Euclidean proximity method, records the driving parameters of the actual working condition changing along with time when the proximity is smaller than a threshold value, and supplements the corrected driving parameters and the corresponding actual driving condition to the driving condition database;
s105: the driving parameter prediction module selects a basic working condition with the maximum closeness from a driving working condition database as a driving working condition by adopting an Euclidean closeness method according to the corrected driving parameter, and takes the driving parameter in the driving working condition as a prediction value of the driving 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 parameters;
s107: and the required power calculation module calculates the predicted value of the required power of the automobile by using an automobile power balance equation according to the corrected gradient parameter, the predicted value of the driving 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 the road surface in the running process of the vehicle.
Further, the driving parameters detected by the vehicle driving characteristic detection module include: the running distance of the automobile, the average wheel rotating speed, the maximum wheel rotating speed, the standard deviation of the wheel rotating speed, the maximum acceleration, the maximum deceleration, the average acceleration, the standard deviation of the acceleration, the average tire pressure of the wheel, the maximum wheel slip rate, the average wheel slip rate, the change rate of the opening degree of an accelerator pedal, the change rate of the opening degree of a brake pedal, the proportion of the time with the speed of less than 15km/h, the proportion of the time with the speed of more than 15km/h, the proportion of the time with the speed of less than 40km/h, the proportion of the time with the speed of more than 40km/h and the acceleration of more than 10m/s in delta t time2The time proportion and the acceleration of the2Less than 10m/s2The time proportion and the acceleration of (1) are higher than 0 and lower than 5m/s2Is higher than 10m/s2Is higher than 5m/s2Less than 10m/s2The time proportion and deceleration of (1) is higher than 0 and lower than 5m/s2The time of (a).
Further, the plurality of basic working conditions are as follows: according to different driving parameters corresponding to different time, a clustering method is used for subdividing four major driving conditions of congested urban areas, suburban areas and high speed to obtain a subclass of driving conditions representing driving parameters changing along with time, and a plurality of basic working conditions of a driving condition database are formed.
Furthermore, the automobile with tires of different specifications, sizes, tire pressures and patterns runs on different road surfaces, and all rolling resistance coefficients in the running process are obtained by adopting a test method to form a rolling resistance coefficient library.
The invention discloses a method for predicting the required power of automobile running based on road change, which aims at the problem that the required power of automobile running cannot be accurately predicted due to frequent change of road conditions in the running process of the automobile, and mainly has the following advantages:
(1) the invention takes the installation angle of the equipment into consideration and carries out installation correction, thereby improving the accuracy of the method.
(2) The invention takes the influence of the change of the included angle between the automobile and the road surface under some working conditions into consideration and corrects the change.
(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 realized on the existing vehicle equipment, and the price of other required equipment is lower.
Drawings
Fig. 1 is a schematic composition diagram of a system for predicting required power for driving an automobile based on road changes according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for predicting required power for driving 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 lateral direction of the automobile and the road surface.
FIG. 5 is a schematic diagram of measuring the mount angle of a dynamic tilt sensor.
Detailed Description
The present invention will be further described with reference to the following examples and drawings, but the present invention is not limited thereto.
The method for predicting the required power for automobile running based on the road change can solve the problem of inaccurate prediction of the required power for automobile running caused by the road change and improve the accuracy of the prediction of the required power.
The following will describe in further detail a method for predicting required power for driving an automobile based on road changes according to the present invention with reference to the accompanying drawings and embodiments.
In an embodiment, the invention provides a method for predicting required power for automobile running based on road change, which is characterized by road change parameters based on gradient parameters and rolling resistance coefficients, and the required power for automobile running is predicted by an automobile required power prediction system, as shown in fig. 1, the automobile required power prediction system comprises a road gradient parameter detection module, a vehicle running characteristic detection module, a first measurement value correction module, a second measurement finger correction module, a running parameter prediction module, a running condition database, a road rolling resistance parameter module, a rolling resistance coefficient database, a required 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 running parameters;
the prediction method comprises the following specific steps:
s101: initializing, wherein the gradient parameter and the running parameter are set to be 0, the running condition database comprises a plurality of basic conditions, and each basic 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 corrected gradient parameters are obtained through correction of the first measured value correction module, and the gradient parameters stored in the control unit are updated;
in one 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 during the running process of the automobile. And 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 the 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 one 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 characteristic detection module. When the driving parameter part needs to be corrected, the corrected driving parameter comprises the driving parameter part which is partially corrected and the driving parameter part which is not corrected and is detected by the vehicle driving characteristic detection module.
S104: the control unit takes the corrected driving parameters and the driving parameters of each basic working condition in the driving condition database as comparison quantities, adopts an Euclidean proximity method, records the driving parameters of the actual working condition changing along with time when the proximity is smaller than a threshold value, and supplements the corrected driving parameters and the corresponding actual driving condition to the driving condition database;
s105: the driving parameter prediction module selects a basic working condition with the maximum closeness from a driving working condition database as a driving working condition by adopting an Euclidean closeness method according to the corrected driving parameter, and linearly predicts the driving parameter within a delta t time under the working condition as a predicted value of the driving parameter of an actual working condition; Δ t represents time.
When the corrected driving parameters and the corresponding actual driving conditions are supplemented to the driving condition database in step S104, the basic operating condition with the maximum closeness selected in step S105 is the actual driving condition supplemented to the driving condition database in step S104, and at this time, the predicted value of the driving parameters is obtained by using a linear estimation method (that is, the linear prediction of the driving parameters in the next Δ t time is performed according to the measured and corrected driving parameters).
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 parameters;
s107: and the required power calculation module calculates the predicted value of the required power of the automobile by using an automobile power balance equation according to the corrected gradient parameter, the predicted value of the driving parameter and the rolling resistance coefficient.
Steps S102 and S103 may be performed simultaneously or sequentially, and steps S105 and S106 may be performed simultaneously or sequentially.
In one embodiment, the invention provides a method for predicting required power for driving an automobile based on road change, which provides a basis for a control strategy of a hybrid electric vehicle. The installation angle correction when the device is installed and the correction to the measurement angle under special working conditions are considered in the process. The flow chart is shown in fig. 2. The method comprises the following specific steps:
s201, a running condition database is established, different running parameters of four types of running conditions, namely a jammed urban area, an urban area, a suburban area and a high speed are collected, a clustering method is used for subdividing the four types of running conditions into three subclasses respectively, and 12 types of running conditions representing the running parameters are obtained, wherein the 12 types of running conditions comprise a first-level jammed urban area, a second-level jammed urban area, a third-level jammed urban area, a first-level urban area, a second-level urban area, a third-level urban area, a first-level suburban area, a third-level suburban area, a first-level high speed, a second-level high speed and a third-level high speed (the smaller the numerical value is, the higher the jamming degree is), basic working conditions of the running condition database are formed, and each basic working condition comprises a plurality of running parameters which change along with time.
S202, establishing a rolling resistance coefficient library, driving on different road surfaces through automobiles with different specifications, sizes, tire pressures and pattern tires, and obtaining all rolling resistance coefficients in the driving process by adopting a test method to form the rolling resistance coefficient library.
S203, initialization, where the gradient parameter and the travel parameter in the control unit are both set to 0, and step S204 and step S209 are executed.
S204, the road gradient parameter detection module acquires gradient parameters in the driving process of the automobile, wherein the gradient parameters comprise a vehicle pitch angle, a roll angle and an included angle between the vehicle and the road surface.
The pitch angle refers to the included angle between the longitudinal axis of the vehicle and the horizontal plane, the roll angle refers to the included angle between the transverse axis of the vehicle and the horizontal plane, and the longitudinal pitch angle theta can be measured when the gradient changes in the running process of the automobile through a dynamic tilt angle sensor internally provided with a triaxial accelerometer and a gyroscope1And transverse roll angle theta2。
Because the vehicle can incline towards the rear of the driving direction and the outer side of the centrifugal motion when the automobile accelerates and moves at high speed and centrifugally (such as a road cross slope), so that the result of the slope measurement is seriously influenced, the infrared distance meter is used for solving the change of the included angle between the vehicle and the road surface by utilizing the geometric relation and the real slope change.
The included angle data between the vehicle and the road surface is acquired through the infrared distance measuring instrument: 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 the running process, as shown in fig. 3 and 4, the change of the included angle between the vehicle and the road surface can be measured by the following formula:
γ1＝arctan-1[(Lf-Lr)/L1]
γ2＝arctan-1[(LL-LR)/L2]
γ1is the longitudinal angle between the vehicle and the road surface, gamma2Is the transverse angle between the vehicle and the road surface, LfIs the distance, L, measured by a range finder mounted on the front of the vehiclerIs the distance, L, measured by a distance meter mounted at the rear of the vehicleLIs the distance, L, measured by a distance meter mounted on the left part of the vehicleRIs the distance, L, measured by a distance meter mounted on the right part of the vehicle1Is the distance between the front and rear devices, L2Is the distance between the left and right devices.
S205, the first measured value correction module judges whether the mounting angle is measured, if so, the step S206 is executed, and if not, the step S207 is executed;
the mounting angle may not need to be measured if known, and therefore the first measured value correction module first determines whether to measure the mounting angle. When the first measured value correction module measures the mounting angle, the gradient parameter is corrected according to the measured mounting angle, and when the first measured value correction module does not measure the mounting angle, the known mounting angle is directly adopted to correct the gradient parameter.
S206, the first measured value correction module measures the installation angle of the dynamic tilt sensor.
The dynamic tilt sensor assembly may have an installation angle when installed on a vehicle. The measured pitch angle and roll angle are therefore corrected for the installation angle. No measurement may be required where the mounting angle is known. If the specific installation angle is not known, the vehicle can be statically placed on a horizontal road surface, the instrument has an installation angle, so that the vehicle has acceleration in a static state, and a static longitudinal acceleration a is measured by using a dynamic inclination angle sensor with a built-in three-axis accelerometer and a built-in gyroscope01And static lateral acceleration a02As 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 tilt angle sensor, and g is the gravity acceleration.
The mounting angle of the dynamic tilt sensor device itself is measured by:
α1＝arcsin-1(a01/g)；
α2＝arcsin-1(a02/g)；
wherein alpha is1Is the longitudinal mounting angle, alpha, of the dynamic tilt sensor2Is the transverse mounting angle of the dynamic tilt sensor, a01And a02The static longitudinal acceleration and the static transverse acceleration of the automobile on a horizontal road surface are respectively, and g is the gravity acceleration.
And S207, correcting the gradient parameter by the first measured value correcting module.
Specifically, step S207 includes:
and S2071, the first measured value correction module calculates the longitudinal gradient and the transverse gradient of the road according to the mounting angle to serve as corrected gradient parameters.
The road gradient during travel is determined by the following equation:
i1＝θ1-γ1-α1；
i2＝θ2-γ2-α2；
wherein i1Is the longitudinal gradient of the road; i.e. i2Is the road transverse gradient; gamma ray1And gamma2Respectively a longitudinal included angle and a transverse included angle between the vehicle and the road surface; theta1And theta2Measured pitch and roll angles;
and S2072, the first measured value correction module calculates the synthetic gradient according to the longitudinal gradient and the transverse gradient of the road to serve as the corrected gradient parameter.
In the conventional power prediction, most of the assumption is that the automobile runs on a road with only longitudinal gradient change, and the fact that the transverse gradient change exists in the actual road is usually ignored. The present invention takes into account the change in lateral slope, so the prediction of power required to overcome the slope resistance can instead be calculated using the composite of longitudinal and lateral slope, i.e., the composite slope. Measured longitudinal slope i of the road1And road lateral gradient i2Can be regarded as two vectors in a vector space, and the composite gradient i in the driving process of the automobile can be obtained according to the principle of vector composition3Will combine slope i3Stored in the control unit.
S208, the control unit acquires the corrected gradient parameter.
The control unit obtains the corrected grade parameter from the first measured value correction module.
S209, the vehicle running characteristic detection module detects the running parameters of the automobile.
Measuring vehicles by various sensors on vehiclesDriving parameters including: distance traveled by the automobile within delta t time, average wheel speed, maximum wheel speed, wheel speed standard deviation, maximum acceleration, maximum deceleration, average acceleration, acceleration standard deviation, wheel average tire pressure, wheel maximum slip rate, wheel average slip rate, accelerator pedal opening change rate, brake pedal opening change rate, proportion of time that the speed is less than 15km/h, speed that is greater than 15km/h, proportion of time that the speed is less than 40km/h, proportion of time that the speed is greater than 40km/h, acceleration that is greater than 10m/s2The time proportion and the acceleration of the2Less than 10m/s2The time proportion and the acceleration of (1) are higher than 0 and lower than 5m/s2Is higher than 10m/s2Is higher than 5m/s2Less than 10m/s2The time proportion and deceleration of (1) is higher than 0 and lower than 5m/s2The time of (a).
S210, the second measured value correction module corrects the longitudinal acceleration and the road adhesion coefficient of the vehicle according to the running parameters, updates the longitudinal acceleration (average acceleration within delta t time) of the vehicle in the running parameters, and executes step S211 and step S214.
The vehicle speed is first obtained. The running speed of the automobile is difficult to obtain directly, so the running speed can be obtained indirectly through a wheel speed sensor arranged on a wheel. The instantaneous rotational speed of the wheel, i.e., the average wheel rotational speed ω over time Δ t, can be converted as an instantaneous vehicle speed into a vehicle driving speed Ua using the formula Ua ═ ω r, where r is the wheel radius.
The vehicle longitudinal acceleration can be calculated from the vehicle speed and corrected from the measured value. An array is established in a control unit according to a plurality of collected vehicle speeds in a certain time period, then mean value filtering is carried out on the data of the first half and the data of the second half in the array respectively, namely the mean values of all the data of the first half and the data of the second half in the array are obtained respectively, then the two mean values are used as processed filtering values, then the two obtained filtering values are subtracted, and the time is divided to calculate the longitudinal acceleration of the vehicle. And then correcting the obtained longitudinal acceleration of the vehicle, and adopting a weighted average algorithm. For measuringLongitudinal acceleration a of vehicle1(i.e., the average acceleration of the vehicle over the time Δ t) by a weight factor x1Giving the automobile driving speed UaBack calculated longitudinal acceleration a of the vehicle2(i.e. taking the difference between two filtered values of vehicle speed, dividing by the time interval between two filtered values of vehicle speed) by a weighting factor y1The weight coefficient satisfies x1<y1，x1+y1When 1, the correction value output is x1*a1+y1*a2。
And obtaining the road adhesion coefficient and correcting according to the calculated value. The road condition can be judged according to the peak value adhesion coefficient of the road surface. Nowadays, more and more vehicles are equipped with a drive antiskid control system to prevent the tires from slipping when the driving force of the vehicle is too large to exceed the maximum traction force provided by the road surface when the vehicle is started. By controlling the optimum slip ratio of the tire, the driving force coefficient is kept near the peak road adhesion coefficient. And the road surface peak value adhesion coefficient information at the moment can be obtained by the reverse calculation of the maximum slip rate of the wheels and fed back to the control system.
Road surface peak value adhesion coefficient phi obtained by reverse calculation according to maximum wheel slip rate1And then, the equivalent gradient value is reversely calculated by the corrected longitudinal acceleration of the vehicle. The adhesion coefficient phi of the road surface at the moment can be inversely calculated according to the calculated equivalent gradient2. Then, the obtained road surface adhesion coefficient is corrected, and a weighted average algorithm is also adopted, namely, the road surface peak adhesion coefficient phi fed back by the antiskid driving control system is measured1A weight coefficient x2Coefficient of adhesion phi of road surface inversely calculated for equivalent gradient2A weight coefficient y2The weight coefficient satisfies x2<y2，x2+y2When 1, the correction value output is x2*Φ1+y2*Φ2。
S211, the control unit judges whether the closeness is smaller than a threshold value.
Setting a threshold value sigmamin. The control unit takes the updated driving parameters and the driving parameters in the driving condition database as comparison quantities and adopts Euclidean labelsThe closeness method determines whether the closeness of the actual driving condition is smaller than a set threshold, if so, executes step S212, and if not, executes step S213.
The invention adopts Euclidean closeness with convenient operation and strong practicability to predict the driving working condition, and takes the standard working condition most similar to the current working condition as the type of the driving working condition in a period of time in the future. Let SiRepresenting the ith working condition for a sample in a driving condition database, wherein the driving condition database comprises the most basic 12 small basic working conditions and the actual driving condition supplemented with the driving condition database, M is a sample to be identified (namely the actual driving condition of the vehicle), and after the original data is standardized by using a Min-Max standardization function, S is carried outiEuclidean closeness σ (S) to MiAnd M) is represented as:
in which n is the operating mode SiNumber of driving parameters in, j represents operating mode SiThe jth driving parameter in (c). S calculated according to Euclidean proximity formulaiCloseness to M, determining σ1(S，M)＝min{σ(S1，M)，…，σ(Si，M)，…，σ(SN，M)}<σminWhether or not it is established, σ1And (S, M) represents the driving condition with the minimum closeness between M in the driving condition database, the value of i is 1, 2, …, and N is the number of the working conditions in the driving condition database.
S212, the control unit supplements the current running condition into a running condition database.
If present within a certain Δ t time σ1(S，M)＝min{σ(S1，M)，…，σ(Si，M)，…，σ(SN，M)}<σminIf the actual driving condition M is recorded, the corrected driving parameter and the corresponding actual driving condition are added to the driving condition database, wherein when the actual driving condition M is added to the driving condition database, the number of the driving conditions is increasedThe number of conditions in the database is N + 1.
S213, selecting the running working condition with the maximum closeness by the running parameter prediction module;
and updating the driving condition database after judging whether the current actual driving condition can be supplemented to the driving condition database. The driving parameter prediction module selects the driving working condition in the garage with the maximum closeness to the current driving working condition from the driving working condition database by adopting an Euclidean closeness method according to the driving parameters, and when the actual driving working condition M is supplemented into the driving working condition database, the sigma is added into the driving working condition database2(S，M)＝max{σ(S1，M)，…，σ(Si，M)，…，σ(SN+1，M)}，σ2(S, M) represents the driving condition with the maximum closeness between the driving condition database and M, namely the actual driving condition M and the actual driving condition M supplemented to the driving condition database have the maximum closeness, and at the moment, a linear prediction method is adopted to obtain a predicted value of the driving parameter (namely linear prediction of the driving parameter in the next delta t time is carried out according to the driving parameter after measurement and correction), wherein the linear prediction method is a prediction method which analyzes the linear relation between the dependent variable and the independent variable by using a plurality of independent variables influencing a plurality of dependent variables. Common statistical indicators include: average, increase and decrease, and average increase and decrease. In the invention, the driving parameters such as the vehicle speed, the corrected acceleration, the accelerator pedal opening change rate, the brake pedal opening change rate and the like are linearly estimated by measuring. When the actual driving condition M is not supplemented into the driving condition database, σ2(S，M)＝max{σ(S1，M)，…，σ(Si，M)，…，σ(SN，M)}，σ2(S, M) represents the driving condition with the maximum closeness degree between M in the driving condition database, and the sigma is2And (S, M) taking the linear prediction of the running parameter in the next delta t time corresponding to the (S, M) as the predicted value of the actual working condition running parameter.
And S214, selecting a corresponding rolling resistance coefficient from a rolling resistance coefficient library by a 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 corresponding rolling resistance coefficient from the rolling resistance coefficient library.
The rolling resistance coefficient value is related to the speed of the automobile, the running road condition, the tire pressure, the structure and the material of the tire, and the like. 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 the driving process on line. And a heuristic algorithm is adopted, namely when the vehicle speed is lower than a certain speed, the rolling resistance is unchanged. When the speed of the automobile is less than 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 runs at high speed, the rolling resistance coefficient f can be selected according to the rolling resistance coefficient library. By adopting a heuristic algorithm, the calculation time can be greatly shortened, and the online rapid application can be realized.
And selecting the tire rolling resistance coefficient f under the corresponding working condition from a rolling resistance coefficient library stored in the control unit by adopting a fuzzy control method according to the tire type, the corrected road adhesion coefficient and the tire pressure k measured by the tire pressure sensor.
S215, the required power calculating module calculates the predicted value of the required power of the automobile.
According to the principle of power balance, the driving power required by the vehicle should be the sum of the powers required to overcome all the resistances. Assuming constant air resistance, the power P required to overcome rolling resistancefThe power P required for overcoming the gradient resistance is estimated by updating the rolling resistance coefficient fiBy calculating the resultant gradient i3The power P required to achieve the estimation and overcome the acceleration resistancejThe estimation is realized through the corrected longitudinal acceleration value a of the automobile.
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 driving parameter and the rolling resistance coefficient:
Pf＝(3600*ηt)-1*m*g*f*cosθ1*Ua；
Pi＝(3600*ηt)-1*m*g*i3*Ua；
Pw＝(76140*ηt)-1*CD*A*Ua 3；
Pj＝(3600*ηt)-1*m*δ*a；
ΣP＝Pf+Pi+Pw+Pj
wherein etatIs the total transmission ratio of the automobile transmission system; f is the selected rolling resistance coefficient; m is the mass of the automobile; g is the acceleration of gravity; theta1Is the measured pitch angle; i.e. i3Is the composite grade; u shapeaIs the vehicle speed; cDIs the air resistance coefficient; a is the frontal area of the automobile; delta is the automobile rotating mass conversion coefficient; a is a predicted value of the longitudinal acceleration of the automobile; pfIs the power required to overcome rolling resistance, PiIs the power required to overcome the slope resistance, PwIs the power required to overcome the air resistance, PjIs the power required to overcome the acceleration resistance.
The method for predicting the required power for driving the automobile based on the road change is described in detail, and the implementation description is only used for helping to understand the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A method for predicting the required power of automobile running based on road change is characterized in that the method is based on road change parameters represented by gradient parameters and rolling resistance coefficients, and the method is used for predicting the required power of automobile running by an automobile running required power prediction system which comprises a road gradient parameter detection module, an automobile 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 database, a required power calculation module and a control unit;
the prediction method comprises the following specific steps:
s101: initializing, wherein the gradient parameter and the running parameter are set to be 0, the running condition database comprises a plurality of basic conditions, and each basic 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 the gradient parameters are corrected by the first measured value correction module;
s103: the vehicle running characteristic detection module detects the running parameters of the vehicle, and the second measured value correction module corrects the running parameters by adopting a weighted average algorithm and updates the running parameters stored in the control unit;
s104: the control unit takes the corrected driving parameters and the driving parameters of each basic working condition in the driving condition database as comparison quantities, adopts an Euclidean proximity method, and supplements the corrected driving parameters and the corresponding actual driving conditions to the driving condition database when the proximity is smaller than a threshold value;
s105: the driving parameter prediction module selects a basic working condition with the maximum closeness from a driving working condition database as a driving working condition by adopting an Euclidean closeness method according to the corrected driving parameter, and linearly predicts the driving parameter within a delta t time under the driving working condition as a predicted value of the driving parameter of the actual driving 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 parameters;
s107: and the required power calculation module calculates the predicted value of the required power of the automobile by using an automobile power balance equation according to the corrected gradient parameter, the predicted value of the driving parameter and the rolling resistance coefficient.
2. The method as claimed in claim 1, wherein the gradient parameters detected by the road gradient parameter detection module include a longitudinal pitch angle, a transverse roll angle, and a longitudinal angle and a transverse angle between the vehicle and the road surface during the driving of the vehicle.
3. The method for predicting the required driving power of the automobile based on the road change as claimed in claim 1, wherein the driving parameters of the vehicle driving characteristic detection module comprise: the running distance of the automobile, the average wheel rotating speed, the maximum wheel rotating speed, the vehicle speed wheel standard difference, the maximum acceleration, the maximum deceleration, the average acceleration, the acceleration standard difference, the wheel average tire pressure, the wheel maximum slip rate, the wheel average slip rate, the accelerator pedal opening change rate, the brake pedal opening change rate, the proportion of the time with the speed lower than 15km/h, the proportion of the time with the speed higher than 15km/h, the proportion of the time with the speed lower than 40km/h, the proportion of the time with the speed higher than 40km/h and the proportion of the acceleration higher than 10m/s in delta t time2The time proportion and the acceleration of the2Less than 10m/s2The time proportion and the acceleration of (1) are higher than 0 and lower than 5m/s2Is higher than 10m/s2Is higher than 5m/s2Less than 10m/s2The time proportion and deceleration of (1) is higher than 0 and lower than 5m/s2The time of (a).
4. The method for predicting the required power of the running of the automobile based on the road change as claimed in claim 1, wherein the plurality of basic working conditions are as follows: according to different driving parameters corresponding to different time, a clustering method is used for subdividing four major driving conditions of congested urban areas, suburban areas and high speed to obtain a subclass of driving conditions representing driving parameters changing along with time, and a plurality of basic working conditions of a driving condition database are formed.
5. The method for predicting the required driving power of the automobile based on the road change as claimed in claim 1, wherein the automobile with tires of different specifications, sizes, tire pressures and patterns is driven on different roads, and all rolling resistance coefficients in the driving process are obtained by a test method to form a rolling resistance coefficient library.
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