CN112429010A - Method for estimating vehicle mass and road gradient - Google Patents
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
- B60W40/13—Load or weight
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/076—Slope angle of the road
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60—VEHICLES IN GENERAL
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- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
- B60W2050/0052—Filtering, filters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
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Abstract
The invention relates to a method for estimating the mass and the road gradient of a whole vehicle, which comprises the following steps: when the vehicle speed is less than the preset vehicle speed: adopting a vehicle full load value of a preset proportion as a vehicle mass estimated value, and calculating the road gradient according to a measured value of a vehicle longitudinal acceleration sensor and a calculated value of vehicle acceleration; when the vehicle speed is greater than or equal to the preset vehicle speed: and establishing an extended Kalman state transition and observation equation by taking the deformation of the vehicle speed, the vehicle mass and the road gradient as state quantities and the vehicle speed and the longitudinal acceleration as observed quantities, and estimating the vehicle mass and the road gradient according to an extended Kalman filtering formula. The method for estimating the vehicle mass and the road gradient does not need to set gradient intervals when estimating the vehicle mass and the road gradient, has low requirement on working conditions, and is suitable for heavy vehicles and various working conditions.
Description
Technical Field
The invention relates to the field of vehicle control, in particular to a method for estimating the mass of a whole vehicle and the gradient of a road.
Background
At present, the mass of the whole vehicle and the gradient of a road are important parameters in a vehicle dynamic model, and the dynamic property and the economical efficiency of the vehicle can be effectively improved by accurately estimating the mass and the gradient.
In the related technology, a vehicle mass estimation method is that a matrix composed of vehicle mass and road surface gradient is constructed firstly, then a plurality of theoretical values of vehicle longitudinal acceleration are calculated according to a vehicle dynamic model, the vehicle mass and the road surface gradient matrix, finally the actually measured acceleration value is compared with a calculated value, and the estimated value of the vehicle mass and the road gradient is determined according to the comparison result, but the estimation method needs to construct the whole vehicle mass and the road gradient matrix in advance, for a heavy vehicle with large tonnage or a mountain road with large gradient fluctuation, proper whole vehicle mass and gradient interval can be difficult to set, if the interval is small, the calculated amount is overlarge, if the interval is large, the accuracy of the estimation result can be greatly reduced, and the method is not suitable for the heavy vehicle and partial working conditions; the method comprises the steps of collecting a first acceleration when the vehicle is in a neutral gear, collecting a second acceleration when the vehicle is in a gear, constructing a whole vehicle dynamic equation by taking the vector sum of the first acceleration and the second acceleration as an actual acceleration, and iteratively estimating the mass of the whole vehicle by using a least square method, wherein the acceleration of the vehicle in the neutral gear and the acceleration of the vehicle in the gear are required to be collected when the mass of the vehicle is estimated by the method; when the occasions of neutral gear are less after the vehicle runs, the method has higher requirements on working conditions.
Disclosure of Invention
The embodiment of the invention provides a method for estimating the mass of a whole vehicle and the gradient of a road, which aims to solve the problems that in the related technology, a matrix of the mass of the whole vehicle and the gradient of the road is constructed in advance, and for heavy vehicles with large tonnage or mountain roads with large gradient fluctuation, proper mass of the whole vehicle and gradient intervals are difficult to set and are not suitable for the heavy vehicles and partial working conditions; and the method for collecting the heating of the neutral acceleration of the vehicle also has higher requirement on the working condition.
In a first aspect, a method for estimating a vehicle mass and a road gradient is provided, which includes the following steps: when the vehicle speed is less than the preset vehicle speed: adopting a vehicle full load value of a preset proportion as a vehicle mass estimated value, and calculating the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value; when the vehicle speed is greater than or equal to the preset vehicle speed: establishing an extended Kalman filtering state transition equation according to the vehicle speed, the vehicle mass and the road gradient change rule; establishing an extended Kalman filtering observation equation by taking the vehicle speed and the longitudinal acceleration of the vehicle as observed quantities; and constructing an extended Kalman filtering formula according to the state transition equation and the observation equation to estimate the vehicle mass and the road gradient.
In some embodiments, when the vehicle speed is less than a preset vehicle speed: the method adopts a vehicle full load value with a preset proportion as a vehicle mass estimated value, and calculates the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value, and comprises the following steps:
two vehicle speeds v of a vehicle in delta t time are collected1And v2And calculating the acceleration a of the vehicleVeh：
Collecting the measured value a of the vehicle longitudinal acceleration sensorLong(ii) a According to the vehicle acceleration aVehAnd the vehicle longitudinal acceleration measurement aLongCalculating a road gradient θ:
wherein g is the acceleration of gravity.
In some embodiments, when the vehicle speed is less than a preset vehicle speed: the method adopts a vehicle full load value with a preset proportion as a vehicle mass estimated value, and calculates the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value, and further comprises the following steps: when the vehicle shifts and turns, the calculated road gradient is subjected to filter processing.
In some embodiments, the filtering the calculated road gradient includes: and calibrating according to the differential value of the lateral acceleration and the longitudinal acceleration of the vehicle to obtain the filtering rate.
In some embodiments, the establishing a state transition equation based on vehicle speed, vehicle mass, and road grade change comprises:
and (3) inputting v, 1/m and sin (theta + beta) serving as state quantities into an extended Kalman filter to construct a state transition equation:
where v is the vehicle speed, m is the vehicle mass, θ is the road grade, β ═ tan-1Mu, mu is the rolling resistance coefficient, TsWhich represents a step of time in size,
as vehicle acceleration, x1,kVehicle speed v, x at time k2,kIs 1/m, x of time k3,kSin (theta + beta) at time k,
is a priori estimate of the time at k +1,
a priori estimate of the vehicle speed v at time k +1,
is an a priori estimate of 1/m at time k +1,
is an a priori estimate of sin (θ + β) at time k + 1.
In some embodiments, the establishing a state transition equation according to the vehicle speed, the vehicle mass and the road gradient change law further includes the following steps:
establishing a vehicle dynamic equation:
wherein v is the speed of the vehicle,
for vehicle acceleration, TwheelFor vehicle wheel end drive torque, rwheelIs the wheel radius, CdIs the wind resistance coefficient, AfIs the frontal area, ρairThe air density is shown, mu is a rolling resistance coefficient, Δ m is a mass added value caused by rotation of components, m is vehicle mass, theta is road gradient, and g is gravity acceleration; definition of beta-tan-1μ and neglecting the additional mass Δ m caused by the rotation of the component, the vehicle dynamics equation is rewritten as:
will be provided with
Substituting the state transition equation:
the state transition equation can be expressed as follows:
in some embodiments, establishing an observation equation using the vehicle speed and the vehicle longitudinal acceleration as observed quantities includes:
according to road gradient
And equations of vehicle dynamics
Obtaining the longitudinal acceleration a of the vehicleLongAnd the longitudinal acceleration a of the vehicleLongAnd taking the vehicle speed as an observed quantity, and establishing an observation equation as follows:
wherein
calculated for vehicle acceleration, v vehicle speed, TwheelFor vehicle wheel end drive torque, rwheelIs the wheel radius, CdIs the wind resistance coefficient, AfIs the frontal area, ρairFor air density, m is vehicle mass, θ is road grade, g is acceleration of gravity, β is tan-1Mu, mu is rolling resistance coefficient; z is a radical ofk+1Is the observation matrix at time k +1, z1,k+1Vehicle speed at time k +1, z2,k+1Longitudinal acceleration of the vehicle at time k +1, x1,k+1Vehicle speed at time k +1, x2,k+11/m, x at the time k +13,k+1Sin (θ + β) at time k + 1.
In some embodiments, said constructing an extended kalman filter formula from said state transition equation and said observation equation to estimate vehicle mass and road grade comprises:
determining a state transition matrix A and a noise matrix W according to the state transition equation; determining an observation matrix H according to the observation equation; defining a process excitation noise covariance matrix
Wherein delta1、δ2、δ3For the elements of the matrix Q, a noise covariance matrix is measured
R1、R2Is an element of the matrix R; substituting the state transition matrix A, the noise matrix W, the observation matrix H and the noise covariance matrices Q and R into an extended Kalman filter formula:
In some embodiments, said constructing an extended kalman filter formula from said state transition equation and said observation equation to estimate vehicle mass and road grade further comprises the steps of: and calculating the change rates of the vehicle mass and the road gradient, and determining the estimated value to be converged and outputting the vehicle mass m and the road gradient theta when the change rates of the vehicle mass and the road gradient meet a preset threshold value.
In some embodiments, the using the preset-ratio vehicle full load value as the vehicle mass estimation value includes: the vehicle full load mass is M, and when the vehicle speed is less than the preset vehicle speed, the vehicle mass M is 1/2M.
The technical scheme provided by the invention has the beneficial effects that:
the embodiment of the invention provides a method for estimating the mass of a whole vehicle and the gradient of a road, which can avoid that the estimated mass deviation of the vehicle is too large when the vehicle is fully loaded or unloaded because the full-load value of the vehicle in a preset proportion is used as the estimated mass value when the vehicle speed is less than the preset vehicle speed; the longitudinal acceleration and the acceleration data of the vehicle can be continuously collected, and the road gradient can be calculated in real time; when the vehicle speed is greater than or equal to the preset vehicle speed, the vehicle is in a stable operation stage, the vehicle mass and the road gradient are estimated by adopting an extended Kalman filtering formula established by an established extended Kalman filtering state transfer equation and an extended Kalman filtering observation equation, no requirement is required on neutral gear or on-gear working conditions, the limitation on the vehicle operation working conditions is small, the scheme is verified by a real vehicle, and the vehicle mass and the road gradient can be accurately estimated and removed for heavy vehicles and complex mountain roads.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a vehicle mass and road grade estimation method provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a road surface gradient provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The embodiment of the invention provides a method for estimating the mass of a whole vehicle and the gradient of a road, which can solve the problem that in the related technology, a matrix of the mass of the whole vehicle and the gradient of the road is constructed in advance, and for heavy vehicles with large tonnage or mountain roads with large gradient fluctuation, proper mass of the whole vehicle and gradient intervals are difficult to set and are not suitable for the heavy vehicles and partial working conditions; and the method for collecting the heating of the neutral acceleration of the vehicle also has higher requirement on the working condition.
Referring to fig. 1 and 2, a method for estimating a vehicle mass and a road gradient according to an embodiment of the present invention includes the following steps:
step S101: when the vehicle speed is less than the preset vehicle speed: and adopting a vehicle full load value of a preset proportion as a vehicle mass estimated value, and calculating the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value.
In this embodiment, in step S101, the method for determining the critical vehicle speed is 25km/h, that is, when the vehicle speed v is less than 25km/h, the vehicle is in a starting stage, and the method for determining the full load value of the vehicle at the preset ratio as the estimated vehicle mass value includes: the vehicle runs on a slope, the full load mass of the vehicle (namely the total weight of the full load vehicle including the dead weight of goods, people and the vehicle) is M, when the vehicle speed v is less than 25km/h, the vehicle mass M is 1/2M, and the full vehicle half-load value is adopted as the estimated mass of the vehicle, so that the situation that the estimated mass of the vehicle is too large when the vehicle is full or no-load is avoided, and the time of subsequent cycle calculation is shortened.
In some embodiments, in step S101, the calculating the road gradient according to the vehicle longitudinal acceleration measurement value and the vehicle acceleration calculation value includes the following steps:
two vehicle speeds v of a vehicle in delta t time are collected1And v2And calculating the acceleration a of the vehicleVeh：
Acquisition of vehicle longitudinal acceleration measurements a using sensorsLong。
Calculating a value a from the longitudinal acceleration of the vehicleVehAnd a vehicle longitudinal acceleration measurement aLongCalculating a road gradient θ:
wherein g is the acceleration of gravity.
In some optional embodiments, in step S101, when the vehicle speed is less than the preset vehicle speed: the method adopts a vehicle full load value with a preset proportion as a vehicle mass estimated value, and calculates the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value, and further comprises the following steps: when the vehicle is shifting gears and turning, the calculated road gradient θ is subjected to filter processing.
Further, the filtering the calculated road gradient includes: and calibrating according to differential values of the lateral acceleration and the longitudinal acceleration of the vehicle to obtain a filtering rate, wherein the larger the differential value is, the smaller the filtering rate is, when the change rate of the road gradient is less than or equal to the filtering rate, the normal output road gradient is the final estimated value of the road gradient at the starting stage, and when the change rate of the road gradient is greater than the filtering rate, the road gradient is estimated according to the road gradient at the previous moment and the filtering rate.
Step S102: when the vehicle speed is greater than or equal to the preset vehicle speed: establishing an extended Kalman filtering state transition equation according to the vehicle speed, the vehicle mass and the road gradient change rule; establishing an extended Kalman filtering observation equation by taking the vehicle speed and the longitudinal acceleration of the vehicle as observed quantities; and constructing an extended Kalman filtering formula according to the state transition equation and the observation equation to estimate the vehicle mass and the road gradient.
In some embodiments, in step S102, when the vehicle speed is greater than or equal to the preset vehicle speed, that is, the vehicle speed v is greater than or equal to 25km/h, the vehicle is in a stable operation phase; the method for establishing the state transition equation according to the vehicle speed, the vehicle mass and the road gradient change rule comprises the following steps:
the vehicle mass and road gradient change rule is as follows: in a short time, the vehicle mass and road gradient change is small and can be ignored; and (3) inputting v, 1/m and sin (theta + beta) serving as state quantities into an extended Kalman filter to construct a state transition equation:
where v is the vehicle speed, m is the vehicle mass, θ is the road grade, β ═ tan-1Mu, mu is the rolling resistance coefficient, TsWhich represents a step of time in size,
as vehicle acceleration, x1,kVehicle speed v, x at time k2,kIs 1/m, x of time k3,kSin (theta + beta) at time k,
is a priori estimate of the time at k +1,
a priori estimate of the vehicle speed v at time k +1,
is an a priori estimate of 1/m at time k +1,
is an a priori estimate of sin (θ + β) at time k + 1.
Further, in step S102, the establishing a state transition equation according to the vehicle speed, the vehicle mass and the road gradient change law further includes the following steps:
analysis of the force applied to a vehicle traveling on a slope as shown in FIG. 2, the force applied is mainly the wheel-end driving force FTAir resistance FairRamp resistance FgAnd rolling resistance Fμ。
Wheel end driving force FTIs a positive force for driving the vehicle to move forwards, and the magnitude of the positive force is determined by the radius r of the wheelwheelAnd torque T applied to the wheelsengineDetermining:
in practical applications, the wheel end drive torque is generally calculated from the engine torque: t iswheel＝Tengine*it*ηt*id*ηwheelWherein T isengineFor engine output torque, which can be obtained by measurement, itIs the transmission ratio of the gearbox, etatFor transmission efficiency of gearboxes, idIs a main reduction ratio, ηwheelThe tire transmission efficiency is improved.
Air resistance is defined by air density ρairCoefficient of aerodynamics CdVehicle speed v, and vehicle frontal area AfDetermining:
ramp resistance FgIs a component of gravity along the direction of the ramp, the resistance of the ramp is a negative force when climbing the ramp, and the resistance F of the ramp is a negative force when descending the rampgCan be considered as a positive force in one direction with the driving force: fgMg sin θ, where θ represents the road slope, the uphill slope is positive, the downhill slope is negative, and g is the acceleration of gravity.
Rolling resistance FμResulting from the friction and deformation of the tire, determined mainly by the rolling resistance coefficient μ, the road gradient θ, the vehicle mass m, and the gravitational acceleration: fμ＝μmg cosθ。
From the above, the vehicle dynamics equation is established:
wherein v is the speed of the vehicle,
for vehicle acceleration, TwheelFor vehicle wheel end drive torque, rwheelIs the wheel radius, CdIs the wind resistance coefficient, AfIs the frontal area, ρairFor air density, μ is the rolling resistance coefficient, Δ m is the added value of mass due to component rotation, m is vehicle mass, θ is road grade, g is acceleration of gravity, and m and θ are unknowns.
Definition of beta-tan-1μ and neglecting the additional mass Δ m caused by the rotation of the component, the vehicle dynamics equation is rewritten as:
will be provided with
Substituting the state transition equation:
the state transition equation can be expressed as follows:
in some embodiments, in step S102, establishing an observation equation by using the vehicle speed and the vehicle longitudinal acceleration as observed quantities includes:
according to road gradient
And equations of vehicle dynamics
The longitudinal acceleration a of the vehicle can be obtainedLongAnd the longitudinal acceleration a of the vehicleLongAnd taking the vehicle speed v as an observed quantity, and establishing an observation equation as follows:
wherein,
calculated for vehicle acceleration, v vehicle speed, TwheelFor vehicle wheel end drive torque, rwheelIs the wheel radius, CdIs the wind resistance coefficient, AfIs the frontal area, ρairFor air density, m is vehicle mass, θ is road grade, g is acceleration of gravity, β is tan-1Mu, mu is rolling resistance coefficient; z is a radical ofk+1Is the observed quantity at time k +1, z1,k+1Vehicle speed at time k +1, z2,k+1Longitudinal acceleration of the vehicle at time k +1, x1,k+1Vehicle speed at time k +1, x2,k+11/m, x at the time k +13,k+1Sin (θ + β) at time k + 1.
Further, in step S102, the step of constructing an extended kalman filter formula according to the state transition equation and the observation equation to estimate the vehicle mass and the road gradient includes the following specific steps:
by solving the partial derivatives of the state transition equations, the state transition matrix a and the noise matrix W can be determined as follows:
wherein A isk+1Is the state transition matrix at time k +1,
which is the final estimate of the vehicle speed v at time k,
is the final estimate of 1/m at time k.
And (3) solving the partial derivative of the observation equation to determine an observation matrix H:
wherein H isk+1Is the observation matrix at the time k + 1.
And defining a process excitation noise covariance matrix
Wherein delta1、δ2、δ3For the elements of the matrix Q, a noise covariance matrix is measured
R1、R2Are elements of the matrix R.
Substituting the state transition matrix A, the noise matrix W, the observation matrix H and the noise covariance matrices Q and R into an extended Kalman filter formula:
Further, in step S102, the method for estimating the vehicle mass and the road gradient by constructing an extended kalman filter formula according to the state transition equation and the observation equation further includes the following steps: and calculating the change rates of the vehicle mass m and the road gradient theta, and determining the estimated value to be converged and outputting the vehicle mass m and the road gradient theta when the change rates of the vehicle mass m and the road gradient theta meet preset threshold values.
The principle of the method for estimating the finished automobile mass and the road gradient provided by the embodiment of the invention is as follows:
when the vehicle speed is lower than the preset vehicle speed, the vehicle full load value of the preset proportion is used as the estimated vehicle mass value, so that the estimated vehicle mass deviation can be avoided to be too large when the vehicle is full or no-load; the vehicle longitudinal acceleration measured value and the vehicle acceleration calculated value data can be continuously collected, and the road gradient can be calculated in real time; when the vehicle speed is greater than or equal to the preset vehicle speed, the vehicle is in a stable operation stage, an expanded Kalman filtering formula is constructed by adopting the established state transfer equation and the observation equation to estimate the vehicle mass and the road gradient, no requirement is imposed on the neutral gear or the in-gear working condition, the limitation on the vehicle operation working condition is small, the scheme is verified by a real vehicle, and the vehicle mass and the road gradient can be accurately estimated and removed for heavy vehicles and complex mountain roads.
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are intended to be inclusive and mean, for example, that they may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
It is to be noted that, in the present invention, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for estimating the mass and the road gradient of a finished automobile is characterized by comprising the following steps:
when the vehicle speed is less than the preset vehicle speed:
adopting a vehicle full load value of a preset proportion as a vehicle mass estimated value, and calculating the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value;
when the vehicle speed is greater than or equal to the preset vehicle speed:
establishing an extended Kalman filtering state transition equation according to the vehicle speed, the vehicle mass and the road gradient change rule;
establishing an extended Kalman filtering observation equation by taking the vehicle speed and the longitudinal acceleration of the vehicle as observed quantities;
and constructing an extended Kalman filtering formula according to the state transition equation and the observation equation to estimate the vehicle mass and the road gradient.
2. The vehicle mass and road grade estimation method according to claim 1, wherein when the vehicle speed is less than a preset vehicle speed: the method adopts a vehicle full load value with a preset proportion as a vehicle mass estimated value, and calculates the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value, and comprises the following steps:
two vehicle speeds v of a vehicle in delta t time are collected1And v2And calculating the acceleration a of the vehicleVeh：
Collecting the measured value a of the vehicle longitudinal acceleration sensorLong；
According to the vehicle acceleration aVehAnd the vehicle longitudinal acceleration measurement aLongCalculating a road gradient θ:
wherein g is the acceleration of gravity.
3. The vehicle mass and road grade estimation method according to claim 1, wherein when the vehicle speed is less than a preset vehicle speed: the method adopts a vehicle full load value with a preset proportion as a vehicle mass estimated value, and calculates the road gradient according to a vehicle longitudinal acceleration measured value and a vehicle acceleration calculated value, and further comprises the following steps:
when the vehicle shifts and turns, the calculated road gradient is subjected to filter processing.
4. The vehicle mass and road grade estimation method of claim 3, wherein said filtering the calculated road grade comprises:
and calibrating according to the differential value of the lateral acceleration and the longitudinal acceleration of the vehicle to obtain the filtering rate.
5. The vehicle mass and road slope estimation method of claim 1, wherein establishing a state-transfer equation based on vehicle speed, vehicle mass, and road slope change laws comprises:
and (3) inputting v, 1/m and sin (theta + beta) serving as state quantities into an extended Kalman filter to construct a state transition equation:
wherein v is a vehicleSpeed, m is vehicle mass, θ is road grade, β ═ tan-1Mu, mu is the rolling resistance coefficient, TsWhich represents a step of time in size,
as vehicle acceleration, x1,kVehicle speed v, x at time k2,kIs 1/m, x of time k3,kSin (theta + beta) at time k,
is a priori estimate of the time at k +1,
a priori estimate of the vehicle speed v at time k +1,
is an a priori estimate of 1/m at time k +1,
is an a priori estimate of sin (θ + β) at time k + 1.
6. The vehicle mass and road grade estimation method of claim 5, wherein the establishing of the state transition equation based on vehicle speed, vehicle mass and road grade change law further comprises the steps of:
establishing a vehicle dynamic equation:
wherein v is the speed of the vehicle,
for vehicle acceleration, TwheelFor vehicle wheel end drive torque, rwheelIs the wheel radius, CdIs the wind resistance coefficient, AfIs the frontal area, ρairIn terms of air density,. mu.m is the rolling resistance coefficient, and. DELTA.m is the mass due to rotation of the memberThe added value is added, m is the vehicle mass, theta is the road gradient, and g is the gravity acceleration;
definition of beta-tan-1μ and neglecting the additional mass Δ m caused by the rotation of the component, the vehicle dynamics equation is rewritten as:
will be provided with
Substituting the state transition equation:
the state transition equation can be expressed as follows:
7. the vehicle mass and road gradient estimation method according to claim 1, wherein establishing an observation equation using the vehicle speed and the vehicle longitudinal acceleration as observed quantities comprises:
according to road gradient
And equations of vehicle dynamics
Obtaining the longitudinal acceleration a of the vehicleLongAnd the longitudinal acceleration a of the vehicleLongAnd taking the vehicle speed as an observed quantity, and establishing an observation equation as follows:
8. The vehicle mass and road slope estimation method of claim 1, wherein the constructing an extended kalman filter formula from the state transition equation and the observation equation to estimate vehicle mass and road slope comprises:
determining a state transition matrix A and a noise matrix W according to the state transition equation;
determining an observation matrix H according to the observation equation;
defining a process excitation noise covariance matrix
Wherein delta1、δ2、δ3For the elements of the matrix Q, a noise covariance matrix is measured
R1、R2Is an element of the matrix R;
substituting the state transition matrix A, the noise matrix W, the observation matrix H and the noise covariance matrices Q and R into an extended Kalman filter formula:
9. The vehicle mass and road slope estimation method of claim 1, wherein the vehicle mass and road slope are estimated by constructing an extended kalman filter formula according to the state transition equation and the observation equation, further comprising the steps of:
and calculating the change rates of the vehicle mass and the road gradient, and determining the estimated value to be converged and outputting the vehicle mass m and the road gradient theta when the change rates of the vehicle mass and the road gradient meet a preset threshold value.
10. The vehicle mass and road grade estimation method according to claim 1, wherein the using of the preset proportion vehicle full load value as the vehicle mass estimation value comprises:
the vehicle full load mass is M, and when the vehicle speed is less than the preset vehicle speed, the vehicle mass M is 1/2M.
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