CN114312808B - Method for estimating weight, gradient and speed of intelligent driving vehicle - Google Patents
Method for estimating weight, gradient and speed of intelligent driving vehicle Download PDFInfo
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Abstract
The invention discloses a method for estimating the weight, gradient and speed of an intelligent driving vehicle, which belongs to the field of intelligent driving automobiles and comprises the following steps: acquiring longitudinal acceleration information a of a vehicle along a vehicle forward direction from an in-vehicle IMU (inertial sensor) x The method comprises the steps of carrying out a first treatment on the surface of the The slave vehicle CAN bus is based on the sampling frequencyAcquiring longitudinal speed component information v of a current vehicle along a vehicle forward direction x Real-time torque information T of motor rq . According to the parameters acquired by the CAN bus, the vehicle calibration data adopts a combination method of least square method and extended Kalman filtering, so that the mass, the gradient and the vehicle speed information of the vehicle CAN be estimated, and the vehicle calibration method has great significance in the field of vehicle control.
Description
Technical Field
The invention relates to the technical field of intelligent driving automobiles, in particular to a method for estimating the weight, gradient and speed of an intelligent driving automobile.
Background
Automobile quality plays a significant role in the field of automobile control. Particularly, in a vehicle in which the load of a large electric bus or a large electric operation is often changed, the quality of the vehicle has a decisive influence on the control performance of the vehicle. However, the current estimation of the mass of the vehicle is not mature, and the traditional weighing mode and the theoretical longitudinal dynamics are insufficient, so that the dynamic property and the economical efficiency of the vehicle are severely restricted.
In the current automatic driving field, gradient information measured by an inertial sensor is affected by various disturbance and installation problems, errors exist in the measured gradient information, data are inaccurate, and gradient estimation and automobile quality estimation are needed to be carried out by combining a related estimation method, so that the high-precision position control capability of a vehicle in the automatic driving field is solved and improved.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent driving vehicle weight, gradient and speed estimation method.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for estimating weight, grade and speed of an intelligently driven vehicle, comprising the steps of:
the method comprises the following steps:
acquiring longitudinal acceleration information a of a vehicle along a vehicle forward direction from an in-vehicle IMU (inertial sensor) x ；
The slave vehicle CAN bus is based on the sampling frequencyAcquiring longitudinal speed component information v of a current vehicle along a vehicle forward direction x Real-time torque information T of motor rq ；
Obtaining relevant vehicle parameters according to the vehicle calibration data;
assuming that the vehicle is on a slope or flat ground with a slope angle β, the longitudinal dynamics formula of the vehicle can be obtained:where M is the vehicle mass (total mass), g is the gravitational acceleration, F d For driving the vehicle, F r For rolling resistance of vehicle, F a Air resistance in the running process of the vehicle;
judging the starting and ending conditions of the estimation method according to the conditions of the vehicle speed and the acceleration;
the weight estimation of the vehicle can be carried out according to a longitudinal dynamics formula, the resistance and the weight estimation value of the vehicle are estimated by adopting a recursive least square method, and a related estimation value can be obtained;
the slope of the vehicle and the speed of the vehicle are estimated, a longitudinal dynamics formula can be rewritten by a discretization state space equation, and then an extended Kalman filter is adopted to obtain a related estimated value;
and (3) carrying out averaging treatment on the estimated values of the weight and the gradient, namely dividing the sum of the estimated values by the estimated times to obtain the estimated values of the estimated weight and the gradient of the vehicle, and finally taking the average value obtained when the estimation is finished.
Further, the vehicle calibration data is obtained according to vehicle indexes and related vehicle parameter experiments, wherein the parameters comprise: ratio i of motor output to wheel w Mechanical efficiency eta of the drive train T Free radius r of tyre f Ratio w of rolling radius to free radius of tyre r Coefficient of rolling resistance f of vehicle r The windward area A, the air density rho and the air resistance coefficient C of the vehicle D Vehicle no-load estimated weight m o 。
Further, in the longitudinal dynamics formula of the vehicle,can be obtained by the longitudinal acceleration a of the vehicle-mounted IMU x Instead, i.e.)>
Further, in the longitudinal dynamics formula of the vehicle, F d For vehicle driving force, the formula can be usedTo calculate, wherein F d The unit is cow (N).
Further, in the longitudinal dynamics formula of the vehicle, F r For rolling resistance of the vehicle, the rolling resistance can be represented by formula F r ＝f r Mgcos beta, where F r The unit is cow (N).
Further, in the longitudinal dynamics formula of the vehicle, F a For air resistance, can be represented by the formulaTo calculate, wherein F a The unit is cow (N).
Further, the method comprises the steps of,the starting and ending conditions of the estimation method are: when the vehicle starts, the longitudinal acceleration a of the vehicle x For the first time greater than a certain threshold a s Time (threshold a) s Normally take 0m/s 2 ) The trigger is true and then remains true for whatever acceleration value is, while satisfying that the vehicle speed is greater than a certain threshold v s Time (threshold v) s Normally take 1m/s 2 ) Then the estimation method is started, and the starting time is T s The method comprises the steps of carrying out a first treatment on the surface of the When the estimation method is started, the speed of the vehicle is monitored, and when the speed of the vehicle is greater than a certain threshold value v e Time (threshold v) e Normally take 4m/s 2 ) The estimation method is ended with the ending time T e The estimated time period of the vehicle is T S ～T e 。
Further, the recursive least square method is as follows:
let F R ＝F r +F a Wherein F is R Is the resistance of the vehicle, F d ＝ma x +F R ；
Order theAnd->True value of mass and resistance, +.>And->For the estimated value of the mass and the resistance, the estimated expression of the resistance and the mass is +.>
Further, the extended kalman filter is: based on the longitudinal dynamics formula, the method concretely comprises the following steps:
the extended Kalman filtering comprises two calculation processes, namely time updating and measurement updating, wherein a time updating equation is used for calculating a priori state estimated value and a priori error covariance forward;
the measurement update equation is used to combine the prior state estimate and the measured variable to generate a posterior estimate of the state and update the posterior error covariance of the estimated state, and is specifically as follows:
the time update equation is:
wherein:the optimal estimated value of the state variable at the last moment; p (P) k-1 The covariance of the error at the previous moment; />A priori estimates of state variables; />Is a priori error covariance; q is the identity matrix, < >>J f Jacobian matrix obtained by partial derivative of state variable;
the Jacobian matrix is:
the measurement update equation is:
wherein: k (K) k Is Kalman gain;a posterior estimate for the state variable; p (P) k Is the posterior error covariance; i is an identity matrix; h is a measurement matrix; r is the measurement noise covariance, and takes the value of 100.
Further comprising a vehicle controller for performing the method according to any of the preceding claims 1-9.
Compared with the prior art, the invention has the beneficial effects that:
according to the parameters acquired by the CAN bus, the vehicle calibration data adopts a combination method of least square method and extended Kalman filtering, so that the mass, the gradient and the vehicle speed information of the vehicle CAN be estimated, and the vehicle calibration method has great significance in the field of vehicle control.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a flow chart of a method for estimating weight, gradient and speed of an intelligent driving vehicle according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Referring to fig. 1, a method of estimating weight, gradient and speed of an intelligent driving vehicle, the method comprising the steps of:
acquiring longitudinal acceleration information a of a vehicle along a vehicle forward direction from an in-vehicle IMU (inertial sensor) x ；
The slave vehicle CAN bus is based on the sampling frequencyAcquiring longitudinal speed component information v of a current vehicle along a vehicle forward direction x Real-time torque information T of motor rq ；
Obtaining relevant vehicle parameters according to the vehicle calibration data;
assuming that the vehicle is on a slope or flat ground with a slope angle β, the longitudinal dynamics formula of the vehicle can be obtained:where M is the vehicle mass (total mass), g is the gravitational acceleration, F d For driving the vehicle, F r For rolling resistance of vehicle, F a Is the air resistance during the running of the vehicle.
Since errors may exist in the acquisition and calculation of the instantaneous acceleration of the vehicle starting and the estimation of the method is not always performed, the starting and ending conditions of the estimation method can be judged according to the conditions of the vehicle speed and the acceleration; the time period is called a stable starting time of the vehicle, and the accuracy is high when the estimation of the vehicle is performed in the stable starting time.
The weight estimation of the vehicle can be carried out according to a longitudinal dynamics formula, the resistance and the weight estimation value of the vehicle are estimated by adopting a recursive least square method, and a related estimation value can be obtained;
the slope of the vehicle and the speed of the vehicle are estimated, a longitudinal dynamics formula can be rewritten by a discretization state space equation, and then an extended Kalman filter is adopted to obtain a related estimated value;
and (3) carrying out averaging treatment on the estimated values of the weight and the gradient, namely dividing the sum of the estimated values by the estimated times to obtain the estimated values of the estimated weight and the gradient of the vehicle, and finally taking the average value obtained when the estimation is finished.
The estimation of the weight and the gradient of the vehicle is often carried out from the beginning to the end, and the estimated value has fluctuation and continuity, so that the estimated value of the weight and the gradient is required to be averaged finally, namely, the estimated value sum is divided by the estimated number of times. And finally taking an average value obtained at the end of the estimation.
In a specific embodiment of the present application, the vehicle calibration data is obtained according to a vehicle index and related vehicle parameter experiment, where the parameters include: ratio i of motor output to wheel w Mechanical efficiency eta of the drive train T Free radius r of tyre f Ratio w of rolling radius to free radius of tyre r Coefficient of rolling resistance f of vehicle r The windward area A, the air density rho and the air resistance coefficient C of the vehicle D Vehicle no-load estimated weight m o 。
In a specific embodiment of the present application, in the longitudinal dynamics formula of the vehicle,can be obtained by the longitudinal acceleration a of the vehicle-mounted IMU x Instead, i.e.)>
In a specific embodiment of the present application, in the longitudinal dynamics formula of the vehicle, F d For vehicle driving force, the formula can be usedTo calculate, wherein F d The unit is cow (N).
In a specific embodiment of the present application, in the longitudinal dynamics formula of the vehicle, F r For rolling resistance of the vehicle, the rolling resistance can be represented by formula F r ＝f r Mgcos beta, where F r The unit is cow (N).
In a specific embodiment of the present application, in the longitudinal dynamics formula of the vehicle, F a For air resistance, can be represented by the formulaTo calculate, wherein F a The unit is cow (N).
In a specific embodiment of the present application, the starting and ending conditions of the estimation method are: when the vehicle isAt start-up, longitudinal acceleration a of the vehicle x For the first time greater than a certain threshold a s Time (threshold a) s Normally take 0m/s 2 ) The trigger is true and then remains true for whatever acceleration value is, while satisfying that the vehicle speed is greater than a certain threshold v s Time (threshold v) s Normally take 1m/s 2 ) Then the estimation method is started, and the starting time is T s The method comprises the steps of carrying out a first treatment on the surface of the When the estimation method is started, the speed of the vehicle is monitored, and when the speed of the vehicle is greater than a certain threshold value v e Time (threshold v) e Normally take 4m/s 2 ) The estimation method is ended with the ending time T e The estimated time period of the vehicle is T s ～T e 。
In a specific embodiment of the present application, the recursive least squares method is:
let F R ＝F r +F a Wherein F is R Is the resistance of the vehicle, F d ＝Ma x +F R ；
Order theAnd->True value of mass and resistance, +.>And->For the estimated value of the mass and the resistance, the estimated expression of the resistance and the mass is +.>
In the estimated time period, let m, n be the number of sampling points, then the estimated value is obtained according to the least square methodAnd->The following should be satisfied:
the recursive least squares method of sampling estimates resistance and mass, the recursive form of the estimation being:
then for resistance estimation there are:
then for quality estimation there is:
in a specific embodiment of the present application, the extended kalman filter is: based on the longitudinal dynamics formula, the method concretely comprises the following steps:
considering the road route design specifications, the road gradient is generally small, a system of related differential equations is obtained assuming cos β≡1, sin β≡tanβ=i, where M (t) is derived from claim 8:
the EKF algorithm is based on a discrete state equation, and a forward Euler method is adopted to carry out discretization processing, so that a discretization state difference equation f is obtained:
the state equation of the system is:
the system measurement equation is:
the EKF (extended kalman filter) includes two computational processes, time update and measurement update.
The time update equation calculates the priori state estimation value and the priori error covariance forward; the measurement update equation combines the prior state estimate and the measured variable to produce a posterior estimate of the state and updates the posterior error covariance of the estimated state. The algorithm recursively proceeds, and only the estimated value of the state variable at the last moment and the measured value of the current state variable are needed to obtain the estimated value of the current state variable.
The time update equation is:
wherein:the optimal estimated value of the state variable at the last moment; p (P) k-1 The covariance of the error at the previous moment; />A priori estimates of state variables; />Is a priori error covariance; q is the identity matrix of the unit cell,/>J f jacobian matrix obtained by partial derivative of state variable;
the Jacobian matrix is:
the measurement update equation is:
wherein: k (K) k Is Kalman gain;a posterior estimate for the state variable; p (P) k Is the posterior error covariance; i is an identity matrix; h is a measurement matrix; r is the measurement noise covariance, and takes the value of 100.
Implementations should include a vehicle controller configured to perform the process described above.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (2)
1. A method for estimating the weight, grade and speed of an intelligently driven vehicle, comprising the steps of:
acquiring longitudinal acceleration information a of a vehicle along a vehicle advancing direction according to an on-board IMU x ；
The slave vehicle CAN bus is based on the sampling frequencyAcquiring longitudinal speed component information v of a current vehicle along a vehicle forward direction x Real-time torque information T of motor rq ；
Obtaining relevant vehicle parameters according to the vehicle calibration data;
assuming that the vehicle is on a slope or flat ground with a slope angle β, the longitudinal dynamics formula of the vehicle can be obtained:wherein M is the mass of the vehicle, g is the gravitational acceleration, F d For driving the vehicle, F r For rolling resistance of vehicle, F a Air resistance in the running process of the vehicle;
judging the starting and ending conditions of the estimation method according to the conditions of the vehicle speed and the acceleration;
estimating the weight of the vehicle according to a longitudinal dynamics formula, estimating the resistance and the weight of the vehicle by adopting a recursive least square method, and obtaining a related estimated value;
estimating the gradient and the speed of the vehicle, rewriting a discretization state space equation on a longitudinal dynamics formula, and then adopting extended Kalman filtering to obtain a related estimated value;
averaging the estimated values of the weight and the gradient, namely dividing the sum of the estimated values by the estimated times to obtain the estimated values of the estimated weight and the gradient of the vehicle, and finally taking an average value obtained when the estimation is finished;
the vehicle calibration data is obtained according to vehicle indexes and related vehicle parameter experiments, wherein the parameters comprise: ratio i of motor output to wheel w Mechanical efficiency eta of the drive train T Free radius r of tyre f Ratio w of rolling radius to free radius of tyre r Coefficient of rolling resistance f of vehicle r The windward area A, the air density rho and the air resistance coefficient C of the vehicle D Vehicle no-load estimated weight m o ；
In the longitudinal dynamics formula of the vehicle,can be obtained by the longitudinal acceleration a of the vehicle-mounted IMU x Instead, i.e.)>
In the longitudinal dynamics formula of the vehicle, F d For vehicle driving force, the formula can be usedTo calculate, wherein F d The unit is cow;
in the longitudinal dynamics formula of the vehicle, F r For rolling resistance of the vehicle, the rolling resistance can be represented by formula F r ＝f r Mgcos beta, where F r The unit is cow;
in the longitudinal dynamics formula of the vehicle, F a For air resistance, can be represented by the formulaTo calculate, wherein F a The unit is cow;
the starting and ending conditions of the estimation method are: when the vehicle starts, the longitudinal acceleration a of the vehicle x For the first time greater than a certain threshold a s The trigger is true and then remains true for any value of acceleration while satisfying the speed of the vehicle above a certain threshold v s When the time is, the estimation method is started, and the starting time is T s The method comprises the steps of carrying out a first treatment on the surface of the When the estimation method is started, the speed of the vehicle is monitored, and when the speed of the vehicle is greater than a certain threshold value v e When the estimation method is finished, the finishing time is T e The estimated time period of the vehicle is T s ～T e ；
The recursive least square method is specifically as follows:
let F R ＝F r +F a Wherein F is R Is the resistance of the vehicle, F d ＝Ma x +F R ；
Order theAnd->True value of mass and resistance, +.>And->For the estimated value of the mass and the resistance, the estimated expression of the resistance and the mass is +.>
The extended kalman filter is: based on the longitudinal dynamics formula, the method concretely comprises the following steps:
the extended Kalman filtering comprises two calculation processes, namely time updating and measurement updating, wherein a time updating equation is used for calculating a priori state estimated value and a priori error covariance forward;
the measurement update equation is used to combine the prior state estimate and the measured variable to generate a posterior estimate of the state and update the posterior error covariance of the estimated state, and is specifically as follows:
the time update equation is:
wherein:the optimal estimated value of the state variable at the last moment; p (P) k-1 The covariance of the error at the previous moment; />A priori estimates of state variables; />Is a priori error covariance; q is the identity matrix, < >>J f Jacobian matrix obtained by partial derivative of state variable;
the Jacobian matrix is:
the measurement update equation is:
wherein: k (K) k Is Kalman gain;a posterior estimate for the state variable; p (P) k Is the posterior error covariance; i is an identity matrix; h is a measurement matrix; r is the measurement noise covariance, and takes the value of 100.
2. The method of estimating weight, grade and speed of an intelligently driven vehicle according to claim 1, comprising a vehicle controller for performing the method as set forth in the preceding claim 1.
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