CN109572707B - longitudinal vehicle speed estimation method for multi-wheel distributed electric drive system - Google Patents

longitudinal vehicle speed estimation method for multi-wheel distributed electric drive system Download PDF

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CN109572707B
CN109572707B CN201811416992.0A CN201811416992A CN109572707B CN 109572707 B CN109572707 B CN 109572707B CN 201811416992 A CN201811416992 A CN 201811416992A CN 109572707 B CN109572707 B CN 109572707B
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vehicle speed
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曾小华
王振伟
宋大凤
钱琦峰
张轩铭
姜效望
陈建新
李晓建
牛超凡
高福旺
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Jilin University
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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
    • B60W40/10—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 vehicle motion
    • B60W40/105—Speed

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Abstract

The invention provides an longitudinal speed estimation method of a multi-wheel distributed electric drive system, which aims to accurately estimate the speed of a vehicle to realize stability control.

Description

longitudinal vehicle speed estimation method for multi-wheel distributed electric drive system
Technical Field
The invention relates to automobile speed estimation methods, in particular to a longitudinal speed estimation method of a multi-wheel distributed electric drive system.
Background
Compared with the traditional single motor drive, the multi-wheel distributed electric drive system driving mode has the technical advantages of simplifying the chassis structure, improving the transmission efficiency, enhancing the control performance and the like, and the driving safety, the stability and the dynamic property of the system depend on a stable and efficient whole vehicle control system. The vehicle reference speed is a main parameter necessary for vehicle active safety control, and is a basis for realizing stability control of the multi-wheel distributed electric drive system, so that accurate estimation of the vehicle speed is a precondition for realizing good control of the vehicle.
Currently, the acquisition of the vehicle speed mainly includes a direct method and an indirect method. The direct method directly measures the driving speed of the vehicle by using the technologies such as an inertial navigation system, a GPS and the like, but has high cost and is greatly influenced by the environment. The indirect method mainly utilizes information of other sensors of the vehicle to establish a dynamic relation with the vehicle speed so as to estimate the vehicle speed. Such as Chinese patent publication No. CN106394561A, publication No. 2017-02-15; chinese patent publication No. CN101655504, publication No. 2010-02-24; chinese patent publication No. CN102009654A, publication No. 2011-04-13, etc., and estimating the vehicle speed by an intelligent control algorithm; these methods all require complicated tire models and vehicle models, involve more nonlinear operations, and have more limitations in practical applications.
Some patents, such as chinese patent publication No. CN104742888A, publication No. 2015-07-01, calculate wheel acceleration values according to wheel speeds of the wheels, and determine whether the vehicle is in a driving or braking condition; then, estimating the wheel reference vehicle speed by adopting a dynamic slope updating method, and determining the final reference vehicle speed by adopting various vehicle speed estimation algorithms under different working conditions; however, the invention does not consider the processing mode under the condition of wheel speed failure, so that the estimation of the vehicle speed is inaccurate. The invention solves the problems that the traditional vehicle speed estimation algorithm is limited in application due to the adoption of an acceleration sensor and the estimation is inaccurate due to the slip of a vehicle when ABS, ESP and TCS act, however, the invention is only researched by aiming at four-wheel vehicles and cannot be directly applied to a multi-wheel distributed driving system; in addition, the invention does not consider the problem of vehicle speed jump caused by switching the maximum wheel speed method and the minimum wheel speed method (or average wheel speed method).
Disclosure of Invention
In order to solve the technical problems, the invention provides longitudinal speed estimation methods of a multi-wheel distributed electric drive system based on a multi-wheel distributed drive test sample car, wherein the longitudinal speed estimation method of the multi-wheel distributed electric drive system integrates a direct method and an indirect method, the direct method combines the long-term high precision of a GPS (global position system) with the short-term high precision of an inertial navigation system, and a high-precision speed signal is obtained by using a multi-sensor data fusion technology, the indirect method is mainly applied to estimating a reference speed by fully using the existing vehicle data after the GPS signal fails, and the method specifically comprises the following steps:
and , judging whether the GPS signal is normal, wherein when the GPS works normally, the inertial navigation system feeds back a GPS normal operation code, the reference speed is a high-precision speed signal calculated by the GPS and the inertial navigation system by a direct method, when the vehicle encounters the conditions of high building shielding, electromagnetic interference and the like, the GPS signal fails, the inertial navigation system feeds back an abnormal GPS operation code, and at the moment, the reference speed is estimated by an indirect method.
Step two: in the direct method for estimating the vehicle speed, the invention adopts a mode of combining signals of a GPS and an inertial navigation system, and carries out corresponding data fusion on an upper computer to realize high-precision real-time acquisition of vehicle speed signals.
The GPS signal is used for providing a reference low-frequency speed signal and correcting a compensation error; the GPS receiver captures, tracks the satellite, receives, amplifies and demodulates the GPS signal to obtain data such as vehicle speed and the like; the inertial navigation system is used for providing a short-term high-precision reading signal; the inertial device is arranged on the carrier, the attitude matrix is calculated on a virtual computer digital platform in real time by utilizing the angular velocity measured by the gyroscope, the acceleration information output by the accelerometer is converted to a navigation coordinate system through the coordinate of the attitude matrix, and the information output of the navigation information is obtained through the calculation of the acceleration and the angular velocity.
The upper computer data fusion algorithm is a Kalman filtering method, GPS signals and an inertial navigation system are input into a Kalman filter for combined processing, and the optimal estimated vehicle speed is obtained.
Step three: in the indirect method for estimating the vehicle speed, firstly, a driving system of a multi-wheel distributed electric driving system is analyzed, then the rotating speed signals and the fault states of all motors are respectively read and converted into the corresponding vehicle speed;
the driving system of the multi-wheel distributed electric driving system comprises eight motors, each driving wheel adopts a driving mode of 'wheel edge motor + speed reducer', the whole vehicle comprises a front axis and a rear axis, axes are respectively provided with four motors, the left side of each axis comprises two motors, and the right side of each axis comprises two motors.
The vehicle is not provided with a wheel speed sensor and an ABS system under the current real vehicle condition, the invention reads the rotating speed signals of all motors from a CAN bus through an HCU, and according to a formula:
v＝wMG*π/30/i0*r*3.6 (1)
wherein v represents a vehicle speed in km/h; w is aMGThe motor rotating speed is represented, and the unit is r/min according to a motor CAN communication protocol; i.e. i0The speed ratio of the speed reducer is adopted; r represents the wheel radius, in m, and the rotational speed signal is converted into a corresponding vehicle speed signal.
Step four: vehicle speed estimation algorithm selection
And selecting a proper vehicle speed estimation method according to the running state of the vehicle and the fault state of the rotating speed signal of each motor. The indirect vehicle speed estimation algorithm provided by the invention comprises the following steps:
(1) minimum rotation speed method: and when the eight motor rotating speed signals are normal and the vehicle is in a driving state, estimating the vehicle speed by adopting a minimum rotating speed method. At present, the multi-wheel distributed electric drive system has three drive modes of front drive, rear drive and full drive. When the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the minimum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the minimum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, the minimum value of the eight motors is directly taken as a reference speed.
(2) Maximum rotation speed method: and when the eight motors have normal rotating speed signals and the driver steps on the brake pedal, the vehicle is in a braking state, and the vehicle speed is estimated by adopting a maximum rotating speed method. When the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the maximum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the maximum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, the maximum value of the eight motors is directly taken as a reference speed.
(3) When at least motor speed signals have faults, the average speed method is adopted to estimate the vehicle speed, and the method specifically comprises the following steps:
① when the system is in the forward drive mode, the HCU detects fault conditions for the four rear axis motors:
when at least rear axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal rear axis motor speed;
② when the system is in a rear drive mode, the HCU detects a fault condition for the four motors on the front axis:
when at least front axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal front axis motor speed;
③ when the system is in the full-drive mode, the calculated reference speed is the average value of the speed converted from the normal motor speed;
step five: vehicle speed jump filtering smoothing process
In order to realize the stable control of the vehicle speed and reduce the impact degree when the vehicle speed estimation algorithm is switched, smooth filtering processing is carried out on the jump of the vehicle speed when the vehicle speed estimation algorithm is switched. Real-time vehicle speed v estimated by vehicle speed estimation algorithmestiThe filtered vehicle speed v is obtained by low-pass filtersfinalThe calculation formula is as follows:
Figure GDA0002300242760000031
in the formula, Ts is a filter time constant.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method combines the direct method vehicle speed estimation and the indirect method vehicle speed estimation, can ensure that the system can obtain accurate vehicle speed when the GPS works normally in the aspect of , and can reasonably estimate the vehicle speed by fully utilizing vehicle information when a GPS signal fails in the aspect of , thereby reducing the dependence of the vehicle on the estimated vehicle speed of the GPS and the inertial navigation system and ensuring the good control of the vehicle.
(2) In the indirect method for estimating the vehicle speed, the fault state and the vehicle driving state of each motor rotating speed signal are fully considered, different vehicle speed estimation algorithms are selected according to different conditions, and the optimal vehicle speed estimation output is realized.
(3) When the vehicle speed estimation algorithm is switched, the invention carries out filtering smoothing treatment on the jump of the vehicle speed, and can improve the stability of the vehicle body and the riding comfort.
Drawings
The invention is further described with reference to the following drawings:
FIG. 1 is a general flowchart of a method for estimating longitudinal vehicle speed of a multi-wheel distributed drive proposed by the present invention;
FIG. 2 is a flow chart of direct method vehicle speed estimation proposed by the present invention;
FIG. 3 is a schematic diagram of the subject-multi-wheel distributed drive system configuration of the present invention;
FIG. 4 is a flow chart of a minimum rotation speed method in indirect vehicle speed estimation proposed by the present invention;
FIG. 5 is a flow chart of a maximum rotation speed method in indirect vehicle speed estimation proposed by the present invention;
FIG. 6 is a flow chart of the average rotational speed method in indirect vehicle speed estimation proposed by the present invention;
in the figure: 1-front axis left side motor 1, 2-front axis left side motor 2, 3-front axis right side motor 1, 4-front axis right side motor 2, 5-rear axis left side motor 1, 6-rear axis left side motor 2, 7-rear axis right side motor 1, 8-rear axis right side motor 2, 9-battery and battery management system BMS, 10-engine electric power unit.
The specific implementation mode is as follows:
the invention is described in more detail below with reference to the accompanying drawings:
the invention provides longitudinal vehicle speed estimation methods of a multi-wheel distributed electric drive system, which are characterized in that a direct method and an indirect method are fused, as shown in figure 1, wherein the direct method combines the long-term high precision of a GPS (global positioning system) with the short-term high precision of an inertial navigation system, and a multi-sensor data fusion technology is utilized to obtain a high-precision vehicle speed signal, the indirect method is mainly applied to estimating a reference vehicle speed by fully utilizing the existing vehicle data after the GPS signal fails, and the method specifically comprises the following steps:
and , judging whether the GPS signal is normal, wherein when the GPS works normally, the inertial navigation system feeds back a GPS normal operation code, the reference speed is a high-precision speed signal calculated by the GPS and the inertial navigation system by a direct method, when the vehicle encounters the conditions of high building shielding, electromagnetic interference and the like, the GPS signal fails, the inertial navigation system feeds back an abnormal GPS operation code, and at the moment, the reference speed is estimated by an indirect method.
Step two: in the direct method for estimating the vehicle speed, the invention adopts a mode of combining signals of a GPS and an inertial navigation system, and carries out corresponding data fusion on an upper computer to realize high-precision real-time acquisition of vehicle speed signals.
The flow of estimating the vehicle speed by the direct method is shown in fig. 2, and the GPS signal is used for providing a reference low-frequency speed signal and correcting a compensation error; the GPS receiver captures, tracks the satellite, receives, amplifies and demodulates the GPS signal to obtain data such as vehicle speed and the like; the inertial navigation system is used for providing a short-term high-precision reading signal; the inertial device is arranged on the carrier, the attitude matrix is calculated on a virtual computer digital platform in real time by utilizing the angular velocity measured by the gyroscope, the acceleration information output by the accelerometer is converted to a navigation coordinate system through the coordinate of the attitude matrix, and the information output of the navigation information is obtained through the calculation of the acceleration and the angular velocity.
The upper computer data fusion algorithm is a Kalman filtering method, GPS signals and an inertial navigation system are input into a Kalman filter for fusion processing, and the optimal estimated vehicle speed is obtained.
Step three: in the indirect method for estimating the vehicle speed, firstly, a driving system of a multi-wheel distributed electric driving system is analyzed, then the rotating speed signals and the fault states of all motors are respectively read and converted into the corresponding vehicle speed;
the configuration schematic diagram of the multi-wheel distributed electric drive system driving system is shown in fig. 3, the system is a serial hybrid power system, a battery and battery management system BMS9 engine motor group is an energy source of a whole vehicle, the system has eight driving motors 1-8, each driving wheel adopts a driving form of 'wheel edge motor + speed reducer', the whole vehicle has a front axis and a rear axis, axes are respectively provided with four motors, the left side of each axis comprises two motors, the right side of each axis comprises two motors, the motors 1-4 are in the sequence of the front axis from left to right, and the motors 5-8 are in the sequence of the rear axis from left to right.
The vehicle is not provided with a wheel speed sensor and an ABS system under the current real vehicle condition, the invention reads the rotating speed signals of all motors from a CAN bus through an HCU, and according to a formula:
v＝wMG*π/30/i0*r*3.6 (1)
wherein v represents a vehicle speed in km/h; w is aMGThe motor rotating speed is represented, and the unit is r/min according to a motor CAN communication protocol; i.e. i0The speed ratio of the speed reducer is adopted; r represents the radius, in m, and the rotation speed signal is converted into a corresponding vehicle speed signal.
Step four: vehicle speed estimation algorithm selection
And selecting a proper vehicle speed estimation method according to the running state of the vehicle and the fault state of the rotating speed signal of each motor. The indirect vehicle speed estimation algorithm provided by the invention comprises the following steps:
(1) minimum rotation speed method: when the eight motor speed signals are normal and the vehicle is in a driving state, the vehicle speed is estimated by adopting a minimum speed method, and the specific flow is shown in fig. 4. The multi-wheel distributed electric drive system has three drive modes of front drive, rear drive and full drive. When the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the minimum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the minimum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, the minimum value of the eight motors is directly taken as a reference speed.
(2) Maximum rotation speed method: when the eight motors have normal rotating speed signals and the driver steps on the brake pedal, the vehicle is in a braking state, the vehicle speed is estimated by adopting a maximum rotating speed method, and the specific flow is shown in fig. 5. When the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the maximum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the maximum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, the maximum value of the eight motors is directly taken as a reference speed.
(3) Average rotating speed method, when at least motor rotating speed signals have faults, the average rotating speed method is adopted to estimate the vehicle speed, the specific flow is shown in figure 6, and the specific flow specifically comprises the following steps:
① when the system is in the forward drive mode, the HCU detects fault conditions for the four rear axis motors:
when at least rear axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal rear axis motor speed;
② when the system is in a rear drive mode, the HCU detects a fault condition for the four motors on the front axis:
when at least front axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal front axis motor speed;
③ when the system is in the full-drive mode, the calculated reference speed is the average value of the speed converted from the normal motor speed;
step five: vehicle speed jump filtering smoothing process
For achieving speed of vehicleAnd the impact degree during the switching of the vehicle speed estimation algorithm is reduced, and smooth filtering processing is performed on the jump of the vehicle speed during the switching of the vehicle speed estimation algorithm. Real-time vehicle speed v estimated by vehicle speed estimation algorithmestiThe filtered vehicle speed v is obtained by low-pass filtersfinalThe calculation formula is as follows:
Figure GDA0002300242760000061
in the formula, Ts is a filter time constant.

Claims (1)

  1. A longitudinal vehicle speed estimation method for a multi-wheel distributed electric drive system, the method includes the following steps:
    step , judging whether the GPS signal is normal, wherein when the GPS works normally, the inertial navigation system feeds back a GPS normal operation code, the reference speed is a high-precision speed signal calculated by the GPS and the inertial navigation system by a direct method;
    step two: in the direct method for estimating the vehicle speed, a mode of combining signals of a GPS and an inertial navigation system is adopted, corresponding data fusion is carried out on an upper computer, and high-precision real-time acquisition of a vehicle speed signal is realized;
    the GPS receiver captures, tracks the satellite, receives, amplifies and demodulates the GPS signal to obtain data such as vehicle speed and the like; the inertial navigation system is characterized in that an inertial device is mounted on a carrier, an attitude matrix is calculated on a virtual computer digital platform in real time by utilizing the angular velocity measured by a gyroscope, acceleration information output by the accelerometer is converted to a navigation coordinate system through coordinates of the attitude matrix, and information output of the navigation information is obtained through calculation of the acceleration and the angular velocity; inputting the GPS signal and the inertial navigation system into a Kalman filter, and carrying out fusion processing to obtain the optimal estimated vehicle speed;
    step three: in the indirect method for estimating the vehicle speed, firstly, a driving system of a multi-wheel distributed electric driving system is analyzed, then the rotating speed signals and the fault states of all motors are respectively read and converted into the corresponding vehicle speed;
    the whole vehicle has two front and rear axes, axes are respectively provided with four motors, the left side of each axis comprises two motors, and the right side of each axis comprises two motors;
    reading the rotating speed signals of the motors from the CAN bus through the HCU, and according to a formula:
    v＝wMG*π/30/i0*r*3.6 (1)
    wherein v represents a vehicle speed in km/h; w is aMGThe motor rotating speed is represented, and the unit is r/min according to a motor CAN communication protocol; i.e. i0The speed ratio of the speed reducer is adopted; r represents the radius of the wheel, unit m, and the rotating speed signal is converted into a corresponding vehicle speed signal;
    step four: vehicle speed estimation algorithm selection
    Selecting a proper vehicle speed estimation method according to the running state of the vehicle and the fault state of each motor rotating speed signal; the adopted vehicle speed indirect estimation algorithm is as follows:
    (1) minimum rotation speed method: when the rotating speed signals of the eight motors are normal and the vehicle is in a driving state, estimating the vehicle speed by adopting a minimum rotating speed method; at present, the driving system of the multi-wheel distributed electric driving system has three driving modes of front driving, rear driving and full driving; when the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the minimum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the minimum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, directly taking the minimum value of the eight motors as a reference speed;
    (2) maximum rotation speed method: when the rotating speed signals of the eight motors are normal, and a driver steps on a brake pedal, and the vehicle is in a braking state, estimating the vehicle speed by adopting a maximum rotating speed method; when the system is in a front driving mode, the four motors on the front axis work, and the four motors on the rear axis do not work, so that the maximum value of the speeds of the four non-driving wheels on the rear axis is used as a reference speed; when the system is in a rear-drive mode, the four motors on the front axis do not work, and the four motors on the rear axis work, so that the maximum value of the speeds of the four non-driving wheels on the front axis is used as a reference speed; when the system is in a full-drive mode, directly taking the maximum value of the eight motors as a reference speed;
    (3) the average rotating speed method is used for estimating the vehicle speed by adopting the average rotating speed method when at least motor rotating speed signals have faults, and specifically comprises the following steps:
    ① when the system is in the forward drive mode, the HCU detects fault conditions for the four rear axis motors:
    when at least rear axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal rear axis motor speed;
    ② when the system is in a rear drive mode, the HCU detects a fault condition for the four motors on the front axis:
    when at least front axis four motors are normal, the calculated reference speed is the average value of the speed converted from the normal front axis motor speed;
    ③ when the system is in the full-drive mode, the calculated reference speed is the average value of the speed converted from the normal motor speed;
    step five: vehicle speed jump filtering smoothing process
    Real-time vehicle speed v estimated by vehicle speed estimation algorithmestiPassing through low-pass filters to obtain filtered vehicle speed vfinalThe calculation formula is as follows:
    Figure FDA0002300242750000021
    in the formula, Ts is a filter time constant.
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CN110329272B (en) * 2019-06-28 2020-11-20 潍柴动力股份有限公司 Vehicle speed adjusting method, device, equipment and computer readable storage medium
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CN102009654B (en) * 2010-11-12 2013-02-13 清华大学 Longitudinal speed evaluation method of full-wheel electrically-driven vehicle
CN103523022B (en) * 2013-10-30 2016-02-10 吉林大学 Hybrid vehicle vehicle speed estimation method
CN104742888B (en) * 2015-02-06 2017-03-29 中国第一汽车股份有限公司 Drive full a reference speed real-time detection method
CN106184225B (en) * 2016-07-08 2018-06-12 中国第一汽车股份有限公司 Longitudinal automobile speedestimate method of distributed type four-wheel-driven electrical vehicular power control
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