CN107168104B - Observer-based longitudinal speed control method for pure electric intelligent automobile - Google Patents

Observer-based longitudinal speed control method for pure electric intelligent automobile Download PDF

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CN107168104B
CN107168104B CN201710483937.2A CN201710483937A CN107168104B CN 107168104 B CN107168104 B CN 107168104B CN 201710483937 A CN201710483937 A CN 201710483937A CN 107168104 B CN107168104 B CN 107168104B
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torque
vehicle
speed
driving
resistance
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胡云峰
韩振宇
朱大吉
陈虹
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Jilin University
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Abstract

A method for controlling the longitudinal speed of a pure electric intelligent automobile based on an observer belongs to the technical field of automobile control. The invention aims to design a controller by utilizing a rolling time domain optimization control algorithm based on an observer, optimize the torque required by a driver through the controller, and then distribute driving and braking torque, thereby realizing the longitudinal speed control method of the observer-based pure electric intelligent automobile, which effectively controls the longitudinal speed. The invention realizes the combined simulation of Matlab/Simulink and AMESim, an interface module communicated with the Simulink is added in an AMESim interface, and after system compilation, model information in the AMESim is retained in the Simulink in the form of S-function, thereby realizing the combined simulation and communication of the Matlab/Simulink and the AMESim. The method mainly aims at the longitudinal speed control problem of the pure electric intelligent automobile, designs the observer aiming at important parameters of the system, and can well complete online optimization solution by a rolling time domain optimization control algorithm and meanwhile can explicitly process constraints.

Description

Observer-based longitudinal speed control method for pure electric intelligent automobile
Technical Field
The invention belongs to the technical field of automobile control.
Background
In order to reduce the occurrence of traffic accidents and reduce the influence of internal combustion engine automobiles on energy consumption and environmental pollution, along with the development of internet, information, electronics and intelligent technologies, the intelligent and electric technology of automobiles has become an effective way to solve the problems. In recent years, public automobile manufacturing enterprises such as the public, the bmw, the audi and the like, and famous internet enterprises such as the Baidu and the Google and the like continuously increase the investment of manpower and financial resources in the field of intelligent driving automobiles so as to seize the leading-edge technology of intelligent driving. The development of intelligent driving technology inevitably leads to a new and great revolution of the automobile industry. The longitudinal speed control is used as a bottom layer control algorithm of the pure electric intelligent automobile, and the control effect of the longitudinal speed control directly influences the safety, riding comfort and other performances of the intelligent automobile. For the pure electric intelligent automobile, the response speed of the motor is high, and the torque and the rotating speed of the motor are easy to obtain, so that a good basic condition is provided for longitudinal speed control of the pure electric intelligent automobile. For the control of the longitudinal speed of a centralized pure electric intelligent automobile, the following problems are mainly solved:
1. the control of the longitudinal speed of the intelligent automobile is realized by reasonably generating driving demand torque (including driving torque and braking torque) through designing a controller, so that the tracking control of the longitudinal speed is realized.
2. The longitudinal speed control system of the pure electric intelligent automobile has nonlinearity. At the same time, the output of the controller is to meet the hard constraints of the actuator motor and the brake, i.e. the drive and brake torque signals cannot exceed the actual maximum output torque of the motor and the maximum brake torque of the brake.
3. The electric automobile needs a power supply, a lithium battery pack is commonly used at present to supply power to the motor, and the supply voltage of the motor also influences the maximum output torque of the motor, so that the influence of the output voltage of the battery pack must be considered when the maximum output torque of the motor is considered, namely the actual maximum output torque of the motor is a variable constraint.
4. The system parameters are not measurable, and the vehicle mass is used as an important parameter influencing a system model, and the vehicle mass changes with the size and the weight of a passenger, but the vehicle mass is not measurable.
Disclosure of Invention
The invention aims to design a controller by utilizing a rolling time domain optimization control algorithm based on an observer, optimize the torque required by a driver through the controller, and then distribute driving and braking torque, thereby realizing the longitudinal speed control method of the observer-based pure electric intelligent automobile, which effectively controls the longitudinal speed.
The invention realizes the combined simulation of Matlab/Simulink and AMESim,
① setting the environment variables of PC computer to be related to each other;
②, adding an interface module for communicating with Simulink in an AMESim interface, and connecting variables needing communication between Matlab/Simulink and AMESim to the module;
③, after the system is compiled, the model information in AMESim is kept in Simulink in the form of S-function, thereby realizing the joint simulation and communication of the two.
The method comprises the following steps:
firstly, building a centralized electric automobile simulation model:
the electric automobile simulation model comprises an electric drive module, a transmission module, a tire module and vehicle longitudinal dynamics, and parameters of the whole automobile model are shown in a table I
Electric vehicle parameter meter
Figure 226375DEST_PATH_IMAGE001
Secondly, a rolling time domain optimization controller based on an observer:
2.1 controller-oriented design model building
2.1.1 vehicle longitudinal dynamics model
Without considering the lateral force, the longitudinal force of the vehicle on the slope is as follows according to Newton's second law:
Figure 825983DEST_PATH_IMAGE002
（1）
wherein:
Figure 433682DEST_PATH_IMAGE003
in order to improve the quality of the traveling crane,
Figure 984487DEST_PATH_IMAGE004
as a driving force,
Figure 968624DEST_PATH_IMAGE005
Is the running resistance;
Figure 422739DEST_PATH_IMAGE005
including air resistance
Figure 466918DEST_PATH_IMAGE006
Road surface frictional resistance
Figure 6484DEST_PATH_IMAGE007
Slope resistance
Figure 794311DEST_PATH_IMAGE008
And a mechanical braking force;
vehicle weight
Figure 837354DEST_PATH_IMAGE009
Quality of travelling crane
Figure 52434DEST_PATH_IMAGE003
The relationship is represented by the formula:
Figure 813717DEST_PATH_IMAGE010
（2）
wherein
Figure 405235DEST_PATH_IMAGE011
Is the inertia of one wheel of the vehicle,
Figure 66899DEST_PATH_IMAGE012
is the wheel radius;
the vehicle running on a slope is subjected to a slope resistance
Figure 452881DEST_PATH_IMAGE008
Comprises the following steps:
Figure 967038DEST_PATH_IMAGE013
（3）
wherein
Figure 831089DEST_PATH_IMAGE014
Is the acceleration of gravity;
air resistance to vehicles travelling on road surfaces
Figure 114303DEST_PATH_IMAGE006
Comprises the following steps:
Figure 671186DEST_PATH_IMAGE015
（4）
wherein
Figure 407061DEST_PATH_IMAGE016
In order to have a viscous density in air,
Figure 74803DEST_PATH_IMAGE017
in order to obtain the wind resistance coefficient,
Figure 212523DEST_PATH_IMAGE018
is the frontal area of the vehicle,
Figure 205887DEST_PATH_IMAGE019
which is the wind speed,
Figure 662014DEST_PATH_IMAGE020
is the vehicle speed;
neglecting the influence of the wind speed of the automobile, the air resistance is expressed as:
Figure 133446DEST_PATH_IMAGE021
（5）
frictional resistance
Figure 125673DEST_PATH_IMAGE007
Is the friction between the road and the tire, as determined by the following equation:
Figure 24359DEST_PATH_IMAGE022
（6）
wherein
Figure 734826DEST_PATH_IMAGE023
As the coefficient of friction of the road surface,
Figure 9950DEST_PATH_IMAGE024
is a viscous friction coefficient;
mechanical braking force
Figure 856683DEST_PATH_IMAGE025
，
Figure 191849DEST_PATH_IMAGE026
Is the braking torque;
the running resistance suffered by the vehicle is obtained as follows:
Figure 124033DEST_PATH_IMAGE027
（7）；
2.1.2 drive train modeling
2.1.2.1 Clutch
From the rigid assumption, the torque it transmits is:
Figure 937268DEST_PATH_IMAGE028
（8）
wherein
Figure 402623DEST_PATH_IMAGE029
In order to output the torque to the motor,
Figure 377532DEST_PATH_IMAGE030
in order to output the torque for the clutch,
Figure 62591DEST_PATH_IMAGE031
the rotating speed of the motor is output,
Figure 413938DEST_PATH_IMAGE032
outputting the rotating speed for the clutch;
2.1.2.2 speed changer
Transmission output torque
Figure 235264DEST_PATH_IMAGE033
The following formula is modeled:
Figure 646653DEST_PATH_IMAGE034
（9）
wherein
Figure 553429DEST_PATH_IMAGE035
In order to be a torsional damping coefficient,
Figure 708467DEST_PATH_IMAGE036
in order to output the rotational speed,
Figure 649878DEST_PATH_IMAGE037
in order to achieve the above-mentioned transmission ratio,
Figure 730705DEST_PATH_IMAGE038
in order to achieve the gear transmission ratio,
Figure 390356DEST_PATH_IMAGE039
is a main reduction ratio;
2.1.2.3 drive shaft
Figure 83506DEST_PATH_IMAGE040
（10）
Wherein
Figure 145003DEST_PATH_IMAGE041
For the output of the driving shaft,
Figure 898195DEST_PATH_IMAGE042
outputting a rotational speed for the drive shaft;
substituting formula 8 and formula 9 for formula 10 to obtain:
Figure 513984DEST_PATH_IMAGE043
（11）
by using
Figure 276404DEST_PATH_IMAGE044
Driving force, by
Figure 926828DEST_PATH_IMAGE045
Representing wheel radius, from the relationship between force and moment
Figure 850922DEST_PATH_IMAGE046
At the same time of vehicle speed
Figure 485165DEST_PATH_IMAGE047
Therefore, the combination formula 11 is obtained:
Figure 18652DEST_PATH_IMAGE048
（12）
wherein the radius of the wheel
Figure 789162DEST_PATH_IMAGE045
Is obtained from the following formula, wherein
Figure 884157DEST_PATH_IMAGE049
Is the radius of the wheel hub,
Figure 474539DEST_PATH_IMAGE050
in order to obtain the flat ratio of the tire,
Figure 578761DEST_PATH_IMAGE051
is the tire width;
Figure 203777DEST_PATH_IMAGE052
（13）；
2.2 joint observer:
2.2.1 recursive least squares quality identification
Combining equations 1 and 7, the following equation is obtained:
Figure 735253DEST_PATH_IMAGE053
Figure 812930DEST_PATH_IMAGE054
（14）
meanwhile, the combined formulas (2), (3), (4), (5) and (6) are arranged into a least square format, and the moment estimated value of the driving shaft
Figure 455264DEST_PATH_IMAGE055
Converted into driving force
Figure 934787DEST_PATH_IMAGE056
To obtain
Figure 870120DEST_PATH_IMAGE057
Figure 700672DEST_PATH_IMAGE058
（15）
Wherein
Figure 881118DEST_PATH_IMAGE059
Which is indicative of the longitudinal acceleration of the vehicle,
Figure 746306DEST_PATH_IMAGE060
equivalent rotating mass, value thereof
Figure 354005DEST_PATH_IMAGE061
Wherein
Figure 406274DEST_PATH_IMAGE062
Is the inertia of one wheel of the vehicle,
Figure 390411DEST_PATH_IMAGE063
is the wheel radius;
Figure 110105DEST_PATH_IMAGE064
the representation contains a drive-axis estimator system output,
Figure 888705DEST_PATH_IMAGE065
representing the available vector of data that is available,
Figure 428271DEST_PATH_IMAGE066
as the amount to be identified,
Figure 449054DEST_PATH_IMAGE067
is the process white noise of the system;
respectively defining the mass of the whole vehicle obtained by the system identification at K-1 and K moments as
Figure 23255DEST_PATH_IMAGE068
、
Figure 972757DEST_PATH_IMAGE069
And obtaining a quality identification model:
Figure 999619DEST_PATH_IMAGE070
（16）
in the formula (I), the compound is shown in the specification,
Figure 325558DEST_PATH_IMAGE071
the forgetting factor at the K-th moment;
forgetting factor
Figure 754265DEST_PATH_IMAGE072
The rule is as follows:
Figure 874668DEST_PATH_IMAGE073
（17）；
2.2.2 drive shaft moment observer
If the rotating speeds of two ends of the driving shaft can be measured, a driving shaft torque generation model is built:
Figure 388826DEST_PATH_IMAGE074
（18）
wherein
Figure 252877DEST_PATH_IMAGE075
The drive shaft torque is calculated for an open loop,
Figure 801670DEST_PATH_IMAGE076
、
Figure 591509DEST_PATH_IMAGE077
for the rotating speed of the two ends of the driving shaft,
Figure 327384DEST_PATH_IMAGE076
in order to output the rotating speed of the gearbox,
Figure 260705DEST_PATH_IMAGE077
as the rotational speed of the wheels,
Figure 398425DEST_PATH_IMAGE078
for the equivalent stiffness coefficient of the drive shaft,
Figure 126209DEST_PATH_IMAGE079
is the equivalent damping coefficient of the driving shaft;
obtaining an equivalent wheel dynamics model:
Figure 349380DEST_PATH_IMAGE080
（19）
wherein
Figure 86392DEST_PATH_IMAGE081
As an estimate of the rotational speed of the wheel,
Figure 78619DEST_PATH_IMAGE082
in order to drive the torque of the motor vehicle,
Figure 711726DEST_PATH_IMAGE083
in order to obtain the moment of resistance,
Figure 687772DEST_PATH_IMAGE084
to drive the moment of inertia at the ends of the axle shafts,
Figure 195851DEST_PATH_IMAGE085
（20）
driving torque passing resistance
Figure 308164DEST_PATH_IMAGE086
The mathematical model being solved, i.e.
Figure 377751DEST_PATH_IMAGE087
Combined type 7 di
Figure 309935DEST_PATH_IMAGE088
（21）
Wherein
Figure 388749DEST_PATH_IMAGE089
The slope-resisting moment is represented by,
Figure 89989DEST_PATH_IMAGE090
the air resistance torque is represented by the air resistance torque,
Figure 596057DEST_PATH_IMAGE091
the resistance torque to friction is represented by,
Figure 15537DEST_PATH_IMAGE092
in order to provide a mechanical braking torque,
Figure 632463DEST_PATH_IMAGE093
representing driving resistance moment; for driving shaft moment estimator
Figure 453788DEST_PATH_IMAGE094
Instead of the former
Figure 130757DEST_PATH_IMAGE093
；
Defining a deviation
Figure 559506DEST_PATH_IMAGE095
I.e. the estimated wheel speed minus the actual value, for deviations
Figure 714544DEST_PATH_IMAGE096
Derivation, combining equations 19 and 21 together:
Figure 655955DEST_PATH_IMAGE097
（22）
get control input
Figure 238246DEST_PATH_IMAGE098
In the form:
Figure 897898DEST_PATH_IMAGE099
（23）
wherein the content of the first and second substances,
Figure 591047DEST_PATH_IMAGE100
for virtual control input, a feedback linearization method is used, equation 22 linearized form
Figure 386965DEST_PATH_IMAGE101
（24）
The virtual control input is designed to be in the form of PI, namely:
Figure 140157DEST_PATH_IMAGE102
（25）
wherein
Figure 21526DEST_PATH_IMAGE103
，
Figure 783945DEST_PATH_IMAGE104
Is a coefficient of proportionality that is,
Figure 932905DEST_PATH_IMAGE105
is an integral coefficient;
combining equations 23 and 25, we get the controller:
Figure 122578DEST_PATH_IMAGE106
（26）
defining the Lyapunov function as formula (27):
Figure 225663DEST_PATH_IMAGE107
（27）
the two sides are derived:
Figure 260615DEST_PATH_IMAGE108
（28）
substituting formula (25) into the above formula and finishing to obtain:
Figure 296704DEST_PATH_IMAGE109
（29）
thus is provided with
Figure 391699DEST_PATH_IMAGE110
When it is, thus；
The drive shaft torque is therefore:
Figure 86303DEST_PATH_IMAGE112
（30）。
the invention discloses a longitudinal vehicle speed controller based on an observer, which comprises the following steps:
the conversion between the motor torque and the mechanical braking torque is carried out numerically by means of a transmission ratio, namely:
Figure 711319DEST_PATH_IMAGE113
（31）
i.e. the braking torque and the driving torque are uniformly expressed as
Figure 242794DEST_PATH_IMAGE114
，
The following relationships are obtained by the arrangement of the formulae (1), (7) and (12):
Figure 819007DEST_PATH_IMAGE115
（32）
wherein the longitudinal speed is selected as the quantity of state, i.e.
Figure 461341DEST_PATH_IMAGE116
Selecting driving demand as a control variable, i.e.
Figure 940864DEST_PATH_IMAGE117
The longitudinal speed is also selected as an output, i.e.
Figure 377661DEST_PATH_IMAGE118
(ii) a Discretizing the state space equation by Euler method
Figure 208214DEST_PATH_IMAGE119
Indicating the sampling step size, then
Figure 388660DEST_PATH_IMAGE120
At the moment, the discretization obtains a system discrete model as follows:
Figure 988268DEST_PATH_IMAGE121
（33）
Figure 595967DEST_PATH_IMAGE122
for the output coefficient matrix, a prediction time domain of the system is defined as
Figure 648237DEST_PATH_IMAGE123
Controlling the time domain to be
Figure 897953DEST_PATH_IMAGE124
Need to satisfy
Figure 585024DEST_PATH_IMAGE125
Then is at
Figure 629203DEST_PATH_IMAGE120
The predicted output sequence of time instants is represented as:
Figure 168769DEST_PATH_IMAGE126
（34）
simultaneously, optimizing control input sequence at time k
Figure 691017DEST_PATH_IMAGE127
Expressed as:
Figure 265218DEST_PATH_IMAGE128
（35）
at the sampling time
Figure 214719DEST_PATH_IMAGE120
The value of the state quantity is
Figure 507160DEST_PATH_IMAGE129
The prediction process for deriving the state quantity and the output quantity is as shown in equations (19) and (20):
Figure 567520DEST_PATH_IMAGE130
（36）
Figure 996227DEST_PATH_IMAGE131
（37）
at the same time, the reference input is the desired vehicle speed
Figure 382209DEST_PATH_IMAGE132
So, a reference input sequence is obtained:
Figure 394903DEST_PATH_IMAGE133
（38）
the following constraints need to be considered in the controller design:
Figure 258953DEST_PATH_IMAGE134
（39）
meanwhile, in order to ensure the tracking of longitudinal speed and improve the riding comfort, the controller selects a performance index target as follows:
Figure 542167DEST_PATH_IMAGE135
（40）
wherein
Figure 99050DEST_PATH_IMAGE136
；
Further describing the optimization problem as equation (41), i.e., the objective function
Figure 834925DEST_PATH_IMAGE137
The value is minimum:
Figure 768246DEST_PATH_IMAGE138
（41）
in the formula (41), the compound represented by the formula,
Figure 905966DEST_PATH_IMAGE139
reflects the deviation of the actual output vehicle speed from the desired vehicle speed,
Figure 633751DEST_PATH_IMAGE140
reflects the strength of the driving requirements,
Figure 856922DEST_PATH_IMAGE141
and
Figure 328355DEST_PATH_IMAGE142
weighting factors of the output signal sequence and the control signal sequence respectively; using NAG to solve the optimization problem of formula 41, optimizing the control input sequence of the system, and then using the first element in the sequence
Figure 819117DEST_PATH_IMAGE143
Acting on the system; at the next moment, the optimization solving process is repeated, namely the closed-loop optimization control of the longitudinal speed of the autonomous driving is carried out,
Figure 983382DEST_PATH_IMAGE144
（42）；
the torque distribution required for the optimized torque divides the driver torque demand by a drive torque greater than 0 and a brake torque less than or equal to 0, i.e. the torque distribution is divided into
Figure 693849DEST_PATH_IMAGE145
（43）
Wherein
Figure 437814DEST_PATH_IMAGE146
In order to optimize the driving torque demand,
Figure 815705DEST_PATH_IMAGE147
in order to expect the torque for the motor,
Figure 619713DEST_PATH_IMAGE148
for the braking torque, the braking torque is converted into a mechanical braking signal by the following formula
Figure 817477DEST_PATH_IMAGE149
：
Figure 630712DEST_PATH_IMAGE150
（44）。
The method mainly aims at the longitudinal speed control problem of the pure electric intelligent automobile, designs the observer aiming at important parameters of the system, and can well complete online optimization solution by a rolling time domain optimization control algorithm and meanwhile can explicitly process constraints. Specifically, a prediction equation of the driving torque demand is obtained by establishing a mechanism model of a longitudinal vehicle speed control system, then a cost function is constructed, constraint conditions are fully considered, and optimal driver torque demand is obtained through optimization and solution.
The invention has the beneficial effects that:
1. in the aspect of longitudinal vehicle speed control, most of traditional control algorithms are not based on models, the working conditions of vehicles running on an actual road are complicated and variable, and a set of controller parameters are difficult to find to meet all the working conditions. Meanwhile, the estimator is added into the design process of the controller, the influence of the parameter of the vehicle mass on the control of the longitudinal vehicle speed of the vehicle is restrained, the rolling time domain optimization control algorithm based on the observer is based on a mechanism model of the system during the design of the controller, and the observed vehicle mass and other vehicle running condition information are directly introduced into the mechanism model, so that the control of the longitudinal vehicle speed of the vehicle is more accurate.
2. The longitudinal vehicle speed control system designed in the invention is a nonlinear system, and the actuator hard constraints of a motor, a battery pack and a brake are considered, the traditional control algorithm cannot effectively process the constraints of the system, and the rolling time domain optimization control algorithm can effectively process the control problem with the constraints, and directly compiles the constraints into the S _ function in the simulink to solve on line during the solving.
Drawings
FIG. 1 is a block diagram of a rolling horizon-based optimized longitudinal vehicle speed control embodying the present invention;
FIG. 2 is a centralized AMESim vehicle model for an electric vehicle embodying the present invention;
FIG. 3 is a schematic diagram of longitudinal forces applied to a vehicle according to the present invention during a hill run;
FIG. 4 is a drive axle torque estimation scheme of the present invention;
FIG. 5 is a MAP of the maximum output torque MAP of the motor of the present invention;
FIG. 6 is a flow chart of the design of a longitudinal vehicle speed controller based on a rolling optimization algorithm according to the present invention;
FIG. 7 is a graph of expected vehicle speed in m/s for complex urban conditions, with time in abscissa and vehicle speed in s in ordinate, used in the controller effectiveness verification of the present invention;
FIG. 8 shows the simulation results of the present invention under the condition of a flat road, which are a mass identification contrast curve, a driving shaft torque estimation contrast curve and a vehicle speed tracking curve from top to bottom. Where the solid line is the actual vehicle mass and the dashed line is the identification mass in the mass identification contrast curve. The dotted line in the torque comparison curve is the estimated torque, and the solid line is the actual torque. In the comparison graph of the actual vehicle speed and the expected vehicle speed, a dotted line represents the actual vehicle speed, and a solid line represents the expected vehicle speed;
FIG. 9 shows the simulation results of the present invention under a constant heavy-gradient condition, which sequentially includes a mass identification contrast curve, a driving shaft torque estimation contrast curve, and a vehicle speed tracking curve from top to bottom. Where the solid line is the actual vehicle mass and the dashed line is the identification mass in the mass identification contrast curve. The dotted line in the torque comparison curve is the estimated torque, and the solid line is the actual torque. In the comparison graph of the actual vehicle speed and the expected vehicle speed, a dotted line represents the actual vehicle speed, and a solid line represents the expected vehicle speed;
FIG. 10 is a graph of road slope change during a grade change simulation in accordance with the present invention;
FIG. 11 is a simulation result of the present invention under a variable-gradient condition close to a real road surface, which is a mass identification contrast curve, a driving shaft torque estimation contrast curve and a vehicle speed tracking curve sequentially from top to bottom. Where the solid line is the actual vehicle mass and the dashed line is the identification mass in the mass identification contrast curve. The dotted line in the torque comparison curve is the estimated torque, and the solid line is the actual torque. In the comparison graph of the actual vehicle speed and the desired vehicle speed, the broken line indicates the actual vehicle speed, and the solid line indicates the desired vehicle speed.
Detailed Description
A control block diagram implemented by the electric automobile torque optimization method based on data drive prediction control is shown in figure 1, a vehicle speed optimization controller is built in Simulink, the input of the controller is the expected vehicle speed, the actual vehicle speed serves as a measurable signal, the quality and the gradient serve as measurable interference and are fed back to the controller in real time, Tmax is the maximum driving torque of the motor and is determined by the mechanical characteristics of the motor and the output voltage of a battery, the hard constraint condition of an actuator of the motor is reflected, and the influence of voltage reduction along with the increase of the discharge time of the battery on the performance of the whole automobile is reflected. The drive torque obtained by the vehicle control unit must be equal to or less than Tmax, which is therefore given to the controller as a constraint. The centralized pure electric vehicle model in fig. 2 is built in AMESim and is used for simulating the operation of a real vehicle. The controller optimizes driving demand torque, the driving demand torque is distributed into driving torque signals and braking torque signals through torque distribution, the driving torque signals and the braking torque signals are respectively sent to the motor and the braking module to control the running of the vehicle, and the actual speed of the vehicle is fed back to the controller as a feedback signal.
The control target of the invention is that the longitudinal vehicle speed controller compares the actual vehicle speed fed back in real time with the expected vehicle speed signal, optimizes to obtain the driving demand torque on the premise of meeting the constraint condition, then obtains the driving torque and the braking torque signal through torque distribution, and sends the driving torque and the braking torque signal to the motor and the braking module in the whole vehicle model to control the vehicle to run, and finally leads the actual vehicle speed to track the expected vehicle speed.
The invention provides a set of devices based on the operation principle and the operation process. Namely an offline electric vehicle torque optimization design test platform based on a PC. The construction and operation processes are as follows:
software selection
A controlled object of the control system and a simulation model of the controller are respectively built through software Matlab/Simulink and AMESim, the software versions are Matlab R2009a and AMESim R10, and solvers are respectively selected to be ode3 and Euler. The simulation step length is a fixed step length, and the step length is selected to be 0.01 s.
The invention aims to realize the combined simulation of Matlab/Simulink and AMESim.
① the environment variables of the PC must first be set as required to correlate the two.
②, adding an interface module for communicating with Simulink in an AMESim interface, and connecting variables needing to be communicated between Matlab/Simulink and AMESim to the module;
③, after the system is compiled finally, the model information in AMESim is kept in Simulink in the form of S-function, thereby realizing the joint simulation and communication of the two.
When the Simulink simulation model is run, the AMESim model is also calculated and solved at the same time. And data exchange is continuously carried out between the two in the simulation process. Recompilation is required if the model structure or parameter settings in the AMESim are modified. It is noted that the simulation steps for both must be identical.
The method comprises the following steps:
firstly, building a centralized electric automobile simulation model:
as shown in fig. 2, the whole electric vehicle simulation model includes several parts, such as an electric drive module, a transmission module, a tire module, and vehicle longitudinal dynamics, and the parameters of the whole vehicle model are shown in table one.
Electric vehicle parameter meter
Figure 597531DEST_PATH_IMAGE001
。
The electric drive system comprises a battery part and a motor part, wherein a battery pack of the pure electric vehicle is a lithium battery pack and is formed by connecting a plurality of single batteries in series and in parallel. The terminal voltage output by the battery pack is the sum of the output voltage of a single battery, and the output terminal voltage of the battery system is the voltage provided by the battery pack to the motor; the invention adopts a permanent magnet synchronous motor.
The transmission system comprises three parts, namely a transmission, a differential and a driving shaft. The power output by the motor is subjected to speed reduction and torque increase by the transmission through different gear radiuses to generate different speed ratios, the lateral dynamics of the vehicle is ignored, the output rotating speeds of the two sides of the differential are the same, namely the differential does not work, the output rotating speed of the differential is the input rotating speed of the driving shaft, the output rotating speed of the driving shaft is equal to the rotating speed of wheels, and the torque transmitted on the shaft is calculated through the rotating speed difference of the two ends of the driving shaft. The torque output by the motor is subjected to speed reduction and torque increase through different gear radiuses by the transmission, the main reduction ratio of the model is 2.2786, and the gear reduction ratio is 3.9431, namely the transmission ratio is 8.6847.
The vehicle longitudinal dynamics part, wherein the effects of driving force, braking force and driving resistance on the vehicle during driving are taken into consideration, wherein the driving resistance comprises air resistance, rolling resistance and friction resistance. In this module, parameters such as the overall mass of the vehicle, grade, wind speed, etc. can be set.
Secondly, a rolling time domain optimization controller based on an observer:
2.1 controller-oriented design model building
2.1.1 vehicle longitudinal dynamics model
In order to realize the research of longitudinal vehicle speed control and vehicle quality parameter estimation, a vehicle longitudinal dynamic model needs to be established. The longitudinal force applied to the vehicle running on the slope without considering the transverse force is shown in figure 3. In FIG. 3
Figure 103598DEST_PATH_IMAGE151
In order to be the gradient of the road,
Figure 21614DEST_PATH_IMAGE003
for the mass of the vehicle, the gravity is
Figure 638540DEST_PATH_IMAGE152
，
Figure 459865DEST_PATH_IMAGE004
As a driving force,
Figure 136834DEST_PATH_IMAGE006
The air resistance is the air resistance so that,
Figure 43610DEST_PATH_IMAGE007
is road surface friction resistance. According to Newton's second law:
Figure 933069DEST_PATH_IMAGE002
（1）
wherein:
Figure 140059DEST_PATH_IMAGE003
in order to improve the quality of the traveling crane,
Figure 722350DEST_PATH_IMAGE004
as a driving force,
Figure 382002DEST_PATH_IMAGE005
Is the running resistance;
Figure 75151DEST_PATH_IMAGE005
including air resistance
Figure 871069DEST_PATH_IMAGE006
Road surface frictional resistance
Figure 122797DEST_PATH_IMAGE007
Slope resistance
Figure 269744DEST_PATH_IMAGE008
And a mechanical braking force.
It should be noted that the weight of the vehicle
Figure 766585DEST_PATH_IMAGE009
Quality of travelling crane
Figure 417009DEST_PATH_IMAGE003
The driving mass is calculated by the inertia effect in the driving direction, and the relationship can be approximately expressed by the following formula:
Figure 341102DEST_PATH_IMAGE010
（2）
wherein
Figure 709767DEST_PATH_IMAGE011
Is the inertia of one wheel of the vehicle,
Figure 10298DEST_PATH_IMAGE012
is the wheel radius; driving/braking force
Figure 780808DEST_PATH_IMAGE004
In the following section, the following description will be made in detail with respect to the running resistance to which the vehicle is subjected during running
Figure 610224DEST_PATH_IMAGE005
An analytical presentation is performed.
The vehicle running on a slope is subjected to a slope resistance
Figure 731764DEST_PATH_IMAGE008
Comprises the following steps:
Figure 68942DEST_PATH_IMAGE013
（3）
wherein
Figure 428379DEST_PATH_IMAGE014
Is the acceleration of gravity;
Figure 959854DEST_PATH_IMAGE009
the vehicle weight. Considering the gradient in the AMESim model
Figure 37532DEST_PATH_IMAGE153
The road gradient is calculated according to percentage, the gradient is uniformly calculated according to percentage for the uniformity of gradient form in subsequent simulation experiment, and conversion is needed
Figure 945445DEST_PATH_IMAGE154
。
Air resistance to vehicles travelling on road surfaces
Figure 424968DEST_PATH_IMAGE006
Comprises the following steps:
Figure 127345DEST_PATH_IMAGE015
（4）
wherein
Figure 692318DEST_PATH_IMAGE016
In order to have a viscous density in air,
Figure 872764DEST_PATH_IMAGE017
in order to obtain the wind resistance coefficient,
Figure 472372DEST_PATH_IMAGE018
is the frontal area of the vehicle,
Figure 578606DEST_PATH_IMAGE019
which is the wind speed,
Figure 896455DEST_PATH_IMAGE020
is the vehicle speed; since the air resistance is small relative to the slope resistance and the frictional resistance, and then the wind speed of the automobile running in the city is small relative to the vehicle speed, the influence of the wind speed is ignored in the design of the controller, so that the air resistance is expressed as:
Figure 615013DEST_PATH_IMAGE021
（5）
frictional resistance
Figure 334707DEST_PATH_IMAGE007
Is the friction between the road and the tire, as determined by the following equation:
Figure 113307DEST_PATH_IMAGE022
（6）
wherein
Figure 918452DEST_PATH_IMAGE023
As the coefficient of friction of the road surface,
Figure 440700DEST_PATH_IMAGE024
is a viscous friction coefficient;
mechanical braking force
Figure 749322DEST_PATH_IMAGE025
，
Figure 964403DEST_PATH_IMAGE026
Is the braking torque;
the running resistance suffered by the vehicle is obtained as follows:
Figure 725685DEST_PATH_IMAGE027
（7）；
2.1.2 drive train modeling
During modeling, rigidity assumption is carried out on the clutch, the transmission shaft and the driving shaft, and meanwhile, torque loss transmitted between the main speed reducer and the gear transmission is ignored.
2.1.2.1 Clutch
From the rigid assumption, the torque it transmits is:
Figure 815739DEST_PATH_IMAGE028
（8）
wherein
Figure 978867DEST_PATH_IMAGE029
In order to output the torque to the motor,
Figure 99270DEST_PATH_IMAGE030
in order to output the torque for the clutch,
Figure 879007DEST_PATH_IMAGE031
the rotating speed of the motor is output,
Figure 743057DEST_PATH_IMAGE032
the rotational speed is output to the clutch.
2.1.2.2 speed changer
Here we unify the torque output of the variator as it is modelled, since we neglect the torque losses transmitted between the final drive and the range variator
Figure 26271DEST_PATH_IMAGE033
The following formula is modeled:
Figure 583155DEST_PATH_IMAGE034
（9）
wherein
Figure 319029DEST_PATH_IMAGE035
In order to be a torsional damping coefficient,
Figure 252350DEST_PATH_IMAGE036
the product of the torsional damping coefficient and the output rotational speed is used to approximate the friction torque loss,
Figure 124491DEST_PATH_IMAGE037
in order to achieve the above-mentioned transmission ratio,
Figure 616390DEST_PATH_IMAGE038
in order to achieve the gear transmission ratio,
Figure 839561DEST_PATH_IMAGE039
is a main reduction ratio.
2.1.2.3 drive shaft
Figure 310994DEST_PATH_IMAGE040
（10）
Wherein
Figure 303221DEST_PATH_IMAGE041
For the output of the driving shaft,
Figure 201907DEST_PATH_IMAGE042
outputting a rotational speed for the drive shaft; i.e. the wheel speed.
Substituting formula 8 and formula 9 for formula 10 to obtain:
Figure 912374DEST_PATH_IMAGE043
（11）
by using
Figure 921918DEST_PATH_IMAGE044
Driving force, by
Figure 299810DEST_PATH_IMAGE045
Representing wheel radius, from the relationship between force and moment
Figure 103818DEST_PATH_IMAGE046
At the same time of vehicle speed
Figure 301581DEST_PATH_IMAGE047
Therefore, the combination formula 11 is obtained:
Figure 347772DEST_PATH_IMAGE048
（12）
wherein the radius of the wheel
Figure 580170DEST_PATH_IMAGE045
Is obtained from the following formula, wherein
Figure 555079DEST_PATH_IMAGE049
Is the radius of the wheel hub,
Figure 505718DEST_PATH_IMAGE050
in order to obtain the flat ratio of the tire,
Figure 857065DEST_PATH_IMAGE051
is the tire width;
Figure 943969DEST_PATH_IMAGE052
（13）；
2.2 joint observer:
to accurately estimate vehicle mass, we design a mass and drive axle torque joint observer. The coupling relation between the mass and the driving shaft torque is considered, namely the driving shaft torque information is required to be used for estimating the mass, and the vehicle mass information is required to be used for estimating the torque. Therefore, when an estimation scheme is determined, the respective characteristics of the two to-be-observed quantities are analyzed, the change of the mass of the automobile is mainly caused by the number of passengers, the amount of oil in an oil tank and the amount of loaded goods, the mass is relatively stable in the driving process of the automobile, the mass of the automobile is a slow variable and can be identified by an identification method, and the estimation of the moment at the next moment by using the mass identification result at the current moment is considered to have little influence on the moment estimation result, so that the efficiency of an algorithm can be improved, and the coupling relation between the two is solved.
2.2.1 recursive least squares quality identification
Analyzing a dynamic model of the vehicle running on the slope, combining the formula 1 and the formula 7, obtaining the following equation:
Figure 355359DEST_PATH_IMAGE053
Figure 262135DEST_PATH_IMAGE054
（14）
meanwhile, the combined formulas (2), (3), (4), (5) and (6) are arranged into a least square format, and meanwhile, the driving shaft moment estimation value is used when the quality is estimated by combining analysis
Figure 417173DEST_PATH_IMAGE055
Converted into driving force
Figure 358584DEST_PATH_IMAGE056
To obtain
Figure 439411DEST_PATH_IMAGE057
Figure 833483DEST_PATH_IMAGE058
（15）
Wherein
Figure 526632DEST_PATH_IMAGE059
Which is indicative of the longitudinal acceleration of the vehicle,
Figure 588129DEST_PATH_IMAGE060
equivalent rotating mass, value thereof
Figure 341322DEST_PATH_IMAGE061
Wherein
Figure 488269DEST_PATH_IMAGE062
Is the inertia of one wheel of the vehicle,
Figure 719530DEST_PATH_IMAGE063
is the wheel radius;
Figure 635534DEST_PATH_IMAGE064
the representation contains a drive-axis estimator system output,
Figure 559627DEST_PATH_IMAGE065
representing the available vector of data that is available,
Figure 193871DEST_PATH_IMAGE066
as the amount to be identified,
Figure 727358DEST_PATH_IMAGE067
is the process white noise of the system; it is noted that in identifying the mass of the vehicle we consider the drive axle torque as a measurable quantity, and thus the drive force, based on the relationship between force and torque, as well as the longitudinal acceleration and mechanical braking torque as real-time measurable parameters.
According to the principle of least square method described above, the mass of the whole vehicle obtained by system identification at K-1 and K moments is defined as
Figure 232289DEST_PATH_IMAGE068
、
Figure 592863DEST_PATH_IMAGE069
And obtaining a quality identification model:
Figure 448824DEST_PATH_IMAGE070
（16）
in the formula (I), the compound is shown in the specification,
Figure 287467DEST_PATH_IMAGE071
is the forgetting factor at the K-th moment.
The vehicle mass is a slow variable, and as the set initial mass value and the actual mass possibly have larger deviation, a larger confidence attenuation needs to be set when the identification is just started, namely the value of the selected forgetting factor is smaller, and as the identification is continuously carried out, the identification result can be converged near the actual vehicle mass, a larger forgetting factor is needed to obtain the smaller confidence attenuation, so that the forgetting factor selected in the text has the advantage of being capable of obtaining the smaller confidence attenuation
Figure 912483DEST_PATH_IMAGE072
The rule is as follows:
Figure 443959DEST_PATH_IMAGE073
（17）；
2.2.2 drive shaft moment observer
The technology designs a closed-loop driving shaft torque observer, generates a model open-loop calculation result through torque, and corrects the open-loop calculation result through vehicle speed deviation, so that the driving shaft torque observation problem is converted into a rotating speed tracking problem. The estimation scheme is shown in fig. 4. The nonlinear characteristic of the load moment is considered, and the advantages of the feedback linearization method are combined, so that the feedback linearization method is adopted to design the moment observer.
If the rotating speeds of two ends of the driving shaft can be measured, a driving shaft torque generation model is built:
Figure 521636DEST_PATH_IMAGE074
（18）
wherein
Figure 163970DEST_PATH_IMAGE075
The drive shaft torque is calculated for an open loop,
Figure 909072DEST_PATH_IMAGE076
、
Figure 844405DEST_PATH_IMAGE077
for the rotating speed of the two ends of the driving shaft,
Figure 674958DEST_PATH_IMAGE076
in order to output the rotating speed of the gearbox,
Figure 589824DEST_PATH_IMAGE077
as the rotational speed of the wheels,
Figure 189433DEST_PATH_IMAGE078
for the equivalent stiffness coefficient of the drive shaft,
Figure 62711DEST_PATH_IMAGE079
is the drive shaft equivalent damping coefficient.
According to the Lagrange kinetic equation, and assuming that the wheels do pure rolling non-sliding motion during the running of the automobile, an equivalent wheel kinetic model is obtained:
Figure 114980DEST_PATH_IMAGE080
（19）
wherein
Figure 833538DEST_PATH_IMAGE081
As an estimate of the rotational speed of the wheel,
Figure 553232DEST_PATH_IMAGE082
in order to drive the torque of the motor vehicle,
Figure 331832DEST_PATH_IMAGE083
in order to obtain the moment of resistance,
Figure 136977DEST_PATH_IMAGE084
the moment of inertia for driving the ends of the axle shafts is approximated by equation 20
Figure 181198DEST_PATH_IMAGE085
（20）
While taking into account the relationship between force and torque, the driving torque passing through the resistance
Figure 755399DEST_PATH_IMAGE086
The mathematical model being solved, i.e.
Figure 704900DEST_PATH_IMAGE087
Combined type 7 di
Figure 731762DEST_PATH_IMAGE088
（21）
Wherein
Figure 57701DEST_PATH_IMAGE089
The slope-resisting moment is represented by,
Figure 220829DEST_PATH_IMAGE090
the air resistance torque is represented by the air resistance torque,
Figure 606811DEST_PATH_IMAGE091
the resistance torque to friction is represented by,
Figure 120969DEST_PATH_IMAGE092
in order to provide a mechanical braking torque,
Figure 250599DEST_PATH_IMAGE093
representing driving resistance moment; taking into account that the last-time mass representation used in the drive-shaft torque estimation, i.e. in the calculation
Figure 268234DEST_PATH_IMAGE093
When using
Figure 323652DEST_PATH_IMAGE155
Therefore, it is used in designing the driving shaft moment estimator
Figure 325106DEST_PATH_IMAGE094
Instead of the former
Figure 727269DEST_PATH_IMAGE093
。
Defining a deviation
Figure 130568DEST_PATH_IMAGE095
I.e. the estimated wheel speed minus the actual value, for deviations
Figure 858353DEST_PATH_IMAGE096
Derivation, combining equations 19 and 21 together:
Figure 815944DEST_PATH_IMAGE097
（22）
get control input
Figure 552956DEST_PATH_IMAGE098
In the form:
Figure 545183DEST_PATH_IMAGE099
（23）
wherein the content of the first and second substances,
Figure 443869DEST_PATH_IMAGE100
for virtual control input, a feedback linearization method is used, equation 22 linearized form
Figure 154336DEST_PATH_IMAGE101
（24）
The virtual control input is designed to be in the form of PI, namely:
Figure 662416DEST_PATH_IMAGE102
（25）
wherein
Figure 509149DEST_PATH_IMAGE103
，
Figure 844315DEST_PATH_IMAGE104
Is a coefficient of proportionality that is,
Figure 776499DEST_PATH_IMAGE105
is an integral coefficient;
combining equations 23 and 25, we get the controller:
Figure 855314DEST_PATH_IMAGE106
（26）
defining the Lyapunov function as formula (27):
Figure 822133DEST_PATH_IMAGE107
（27）
the two sides are derived:
Figure 62621DEST_PATH_IMAGE108
（28）
substituting formula (25) into the above formula and finishing to obtain:
Figure 747680DEST_PATH_IMAGE109
（29）
thus is provided with
Figure 99027DEST_PATH_IMAGE110
When it is, thus
Figure 185932DEST_PATH_IMAGE111
；
In summary, the estimated drive shaft torque is:
Figure 830278DEST_PATH_IMAGE112
（30）。
the invention discloses a longitudinal vehicle speed controller based on an observer, which comprises the following steps:
the control target of the system is to realize the tracking control of the longitudinal speed in the autonomous driving process, optimize expected driving requirements through a controller under different working conditions, and then realize the tracking of the actual speed to the expected speed through executing mechanisms such as a motor, a mechanical brake and the like.
In order to facilitate design optimization of a controller, mechanical braking torque and motor braking torque are optimized in a unified mode, namely under the condition that the motor is considered to be an ideal motor, the requirement of a driver when the expected speed is achieved is optimized
Figure 2633DEST_PATH_IMAGE157
Then, the motor torque demand and the mechanical torque demand are divided through a certain control strategy.
In order to achieve a uniform optimization of the driver torque demand, a relationship between the mechanical braking torque and the electric machine torque needs to be established, here we ignore the inertia losses of the drive train, and the electric machine torque and the mechanical braking torque are converted in value by the transmission ratio, namely:
Figure 157671DEST_PATH_IMAGE113
（31）
i.e. the braking torque and the driving torque are uniformly expressed as
Figure 99082DEST_PATH_IMAGE114
In conclusion, the system model established in the front is collected through analysis, and meanwhile, in order to accurately realize vehicle speed control, the quality of the whole vehicle is estimated by adopting the joint estimator when the controller is designed.
The following relationships are obtained by the arrangement of the formulae (1), (7) and (12):
Figure 681373DEST_PATH_IMAGE115
（32）
wherein the longitudinal speed is selected as the quantity of state, i.e.
Figure 75445DEST_PATH_IMAGE116
Selecting driving demand as a control variable, i.e.
Figure 34174DEST_PATH_IMAGE117
The longitudinal speed is also selected as an output, i.e.
Figure 830092DEST_PATH_IMAGE118
(ii) a Discretizing the state space equation by Euler method
Figure 848863DEST_PATH_IMAGE119
Indicating the sampling step size, then
Figure 464652DEST_PATH_IMAGE120
At the moment, the discretization obtains a system discrete model as follows:
Figure 227072DEST_PATH_IMAGE121
（33）
Figure 376031DEST_PATH_IMAGE122
for outputting the coefficient matrix, according to the model predictive control theory, defining the predictive time domain of the system as
Figure 565704DEST_PATH_IMAGE123
Controlling the time domain to be
Figure 934369DEST_PATH_IMAGE124
Need to satisfy
Figure 969321DEST_PATH_IMAGE125
Then is at
Figure 739831DEST_PATH_IMAGE120
The predicted output sequence of time instants is represented as:
Figure 834826DEST_PATH_IMAGE126
（34）
simultaneously, optimizing control input sequence at time k
Figure 690786DEST_PATH_IMAGE127
Expressed as:
Figure 529429DEST_PATH_IMAGE128
（35）。
at the sampling time
Figure 420025DEST_PATH_IMAGE120
The value of the state quantity is
Figure 685921DEST_PATH_IMAGE129
According to the basic principle and the related theory of model predictive control, the prediction process of deriving the state quantity and the output quantity is shown in equations (19) and (20):
Figure 527713DEST_PATH_IMAGE130
（36）
Figure 904468DEST_PATH_IMAGE131
（37）。
and (3) optimizing and solving the control quantity sequence by analyzing and calculating the state variable value of the current moment and the system input value of the last moment, and only applying the first quantity of the optimized and solved control quantity sequence to the system. And at the next sampling moment, the electric automobile model feeds back new input variables and state quantities, and the controller re-optimizes and solves the control problem.
At the same time, the reference input is the desired vehicle speed
Figure 649570DEST_PATH_IMAGE132
So, a reference input sequence is obtained:
Figure 86367DEST_PATH_IMAGE133
（38）
in the autonomous driving process, in order to ensure driving safety, the state quantity needs to be restrained, the characteristics of the motor are considered, the output torque of the motor has restraint, the limitation of a mechanical structure is considered, and the restraint of the maximum braking torque is also considered, so that the following restraint needs to be considered in the design of the controller:
Figure 651341DEST_PATH_IMAGE134
（39）
where the maximum torque provided by the motor is found by the MAP lookup table in figure 5.
Meanwhile, in order to ensure the tracking of the longitudinal speed and improve the riding comfort (namely, the control action is as small as possible in the acceleration and braking processes), the controller selects a performance index target as follows:
Figure 97366DEST_PATH_IMAGE135
（40）
wherein
Figure 696974DEST_PATH_IMAGE136
；
Further describing the optimization problem as equation (41), i.e., the objective function
Figure 304673DEST_PATH_IMAGE137
The value is minimum:
Figure 91363DEST_PATH_IMAGE138
（41）
in the formula (41), the compound represented by the formula,
Figure 308456DEST_PATH_IMAGE139
reflects the deviation of the actual output vehicle speed from the desired vehicle speed,
Figure 762571DEST_PATH_IMAGE140
reflects the strength of the driving requirements,
Figure 806750DEST_PATH_IMAGE141
and
Figure 80737DEST_PATH_IMAGE142
the weighting factors of the output signal sequence and the control signal sequence, respectively.
Figure 602985DEST_PATH_IMAGE141
The size of (a) reflects the requirement for speed tracking accuracy,
Figure 911607DEST_PATH_IMAGE141
the larger the deviation of the velocity tracking is, the closer to zero.
Figure 861108DEST_PATH_IMAGE142
The requirements for the control action are reflected,
Figure 887970DEST_PATH_IMAGE142
the larger the control action, the smaller the ride comfort. Using NAG (a rolling time domain optimization algorithm MATLAB solving tool box) to solve the optimization problem of the formula 41, optimizing a control input sequence of a system, and then enabling a first element in the sequence to be the first element
Figure 446865DEST_PATH_IMAGE143
Acting on the system; at the next moment, the optimization solving process is repeated, namely the closed-loop optimization control of the longitudinal speed of the autonomous driving is carried out,
Figure 875572DEST_PATH_IMAGE144
（42）；
the flow of the design of the rolling time domain optimization controller is shown in FIG. 6: rolling time domain optimization controller optimizes driver torque demand
Figure DEST_PATH_IMAGE158
However, the control signals required for the control are the motor torque demand and the mechanical braking signal, so that a torque distribution of the optimized torque is required, with the driver torque demand being divided into a portion greater than 0 as the drive torque and a portion less than or equal to 0 as the braking torque, i.e. the torque distribution is divided into a portion greater than 0 as the braking torque
Figure 464817DEST_PATH_IMAGE145
（43）
Wherein
Figure 978975DEST_PATH_IMAGE146
In order to optimize the driving torque demand,
Figure 843025DEST_PATH_IMAGE147
in order to expect the torque for the motor,
Figure 126239DEST_PATH_IMAGE148
for the braking torque, the braking torque is converted into a mechanical braking signal by the following formula
Figure 417543DEST_PATH_IMAGE149
：
Figure 153418DEST_PATH_IMAGE150
（44）。
Experimental verification
Repeatedly adjusting control parameters, and respectively selecting the weight factor gamma of the output signal sequence and the control signal sequencey=100，Γu= 2, sample time 0.01sWe select the city working condition with frequent acceleration and deceleration, expecting the vehicleThe speed is as in figure 7. The mass of the whole vehicle is set to be 1500kg, and the controller is verified under the working conditions of a flat road, a constant large gradient and a variable gradient close to a real road surface.
1) Simulation verification of flat road working condition
Firstly, selecting a horizontal road surface for verification, setting the road gradient to be 0, setting the wind speed to be 0 and the vehicle mass to be 1500 during simulationKgThat is, the vehicle runs in no-load, the simulation result is shown in fig. 8, the mass identification comparison curve, the driving shaft moment estimation comparison curve and the vehicle speed tracking curve are given in sequence from top to bottom, and it can be seen from the graph that both the estimator and the controller have good effects.
2) Simulation verification of constant large-gradient working condition
In a simulation environment, we set the road gradient to 10%, that is, on a constant large slope, verify whether the control effect of the controller is stable when the controller is running on the large slope for a long time, and the simulation result is shown in fig. 9. The figure shows a mass identification contrast curve, a drive shaft torque estimation contrast curve and a vehicle speed tracking curve in sequence from top to bottom. Simulation results show that when the vehicle runs on a large slope, the estimation effect of the estimator is good, the actual vehicle speed can track the expected vehicle speed in most of time, but the actual vehicle speed does not track the expected vehicle speed but maintains 20 m/s around 200-300 s, because the expected motor torque is constrained by the maximum motor torque of the motor in the design process of the controller, the expected motor torque is just equal to the maximum motor torque as shown in the maximum motor torque map of FIG. 5. The controller has good effect, and the system constraint plays a good role.
3) Slope-variable working condition simulation verification
In an actual vehicle operating environment, the road gradient does not remain constant, so we set a gradient closer to the actual operating condition (road gradient as in fig. 10) for verification. The simulation result is shown in fig. 10, and it can be seen from the simulation result that the joint estimator has a good estimation effect, and the longitudinal vehicle speed of the vehicle can track the expected vehicle speed well under the variable-gradient working condition.
The invention designs a longitudinal vehicle speed controller based on a rolling time domain optimization method aiming at a pure electric intelligent vehicle, and the method well realizes online optimization and explicit processing constraint. In order to verify the effectiveness of the longitudinal vehicle speed optimization controller, a centralized electric vehicle model is built in AMESim advanced simulation software, and the performance of the controller is verified under a flat road working condition, a constant large-gradient working condition and a variable-gradient working condition close to an actual road surface on a complex urban road. Simulation results show that the rolling time domain optimized longitudinal vehicle speed controller has good control performance under different driving conditions.

Claims (1)

1. A method for controlling the longitudinal speed of a pure electric intelligent automobile based on an observer is characterized by comprising the following steps: realizes the joint simulation of Matlab/Simulink and AMESim,
① setting the environment variables of PC computer to be related to each other;
②, adding an interface module for communicating with Simulink in an AMESim interface, and connecting variables needing communication between Matlab/Simulink and AMESim to the module;
③, after the system is compiled, the model information in AMESim is kept in Simulink in the form of S-function, thereby realizing the joint simulation and communication of the two;
the detailed process is as follows:
firstly, building a centralized electric automobile simulation model:
the electric automobile simulation model comprises an electric drive module, a transmission module, a tire module and vehicle longitudinal dynamics, and parameters of the whole automobile model are shown in a table I
Electric vehicle parameter meter
Parameter(s) Numerical value The mass of the whole vehicle, 1500kg Coefficient of air resistance 0.36 Frontal area 2.08m2 Coefficient of viscous friction 1.2258kg/m3 Radius of wheel 0.301m The moment of inertia of the tire, 0.75kg/m2 Maximum braking moment 1000N Coefficient of friction of road surface 0.01064 Armature resistance of motor 0.0001Ω Motor inductor 0.02H Permanent magnetic flux of motor 0.9Wb Acceleration by gravityDegree of rotation 9.8066m/s2 Final reduction ratio 2.2786 Gear reduction ratio 3.9431
Secondly, a rolling time domain optimization controller based on an observer:
2.1 controller-oriented design model building
2.1.1 vehicle longitudinal dynamics model
Without considering the lateral force, the longitudinal force of the vehicle on the slope is as follows according to Newton's second law:
Figure FDA0002353002090000021
wherein: m is the running mass, FwIs a driving force, FresIs the running resistance; fresIncluding air resistance FaRoad surface frictional resistance FfSlope resistance FclAnd a mechanical braking force;
vehicle weight mvThe relationship with the traveling mass m is represented by the following formula:
Figure FDA0002353002090000022
wherein JwIs the inertia of a wheel, r is the wheel radius;
the vehicle travelling on a slope is subjected to a gradient resistance FclComprises the following steps:
Fcl＝mv·g·sinθ (3)
wherein g is the acceleration of gravity;
air resistance F experienced by a vehicle travelling on a road surfaceaComprises the following steps:
Fa＝0.5·ρair·Cx·S·(v+vwind)2(4)
where ρ isairIs air viscosity density, CxIs the wind resistance coefficient, S is the frontal area of the vehicle, vwindIs wind speed, v is vehicle speed; neglecting the influence of the wind speed of the automobile, the air resistance is expressed as:
Fa＝0.5·ρair·Cx·S·v2(5)
frictional resistance FfIs the friction between the road and the tire, as determined by the following equation:
Ff＝mv·g·(f+fk·v) (6)
wherein f is the road surface friction coefficient, fkIs a viscous friction coefficient;
mechanical braking force Fk＝Tk/r，TkIs the braking torque;
the running resistance suffered by the vehicle is obtained as follows:
Fres＝Fcl+Fa+Ff+Fk
＝mv·g·sinθ+0.5·ρair·Cx·S·v2+mv·g·(f+fk·v)+Tk/r (7)；
2.1.2 drive train modeling
2.1.2.1 Clutch
From the rigid assumption, the torque it transmits is:
Tc＝Te，ωe＝ωc(8)
wherein T iseFor output of torque of the motor, TcFor clutch output torque, omegaeFor outputting the rotational speed, omega, of the motorcOutputting the rotating speed for the clutch;
2.1.2.2 speed changer
Transmission output torque TpThe following formula is modeled:
Tp＝Tci0-dtωt(9)
wherein d istTo turn roundDamping coefficient, ωtTo output the rotational speed i0＝αi·αmTo a transmission ratio of αiα for gear ratiomIs a main reduction ratio;
2.1.2.3 drive shaft
Tw＝Tp，ω＝ωt(10)
Wherein T iswIs the output of the driving shaft, and omega is the output rotating speed of the driving shaft;
substituting formula 8 and formula 9 for formula 10 to obtain:
Tw＝Tei0-dtω (11)
by FwThe driving force, denoted by r, is determined by the relationship F between force and momentw＝TxR, and at the same time, the vehicle speed v ═ ω · r, so that the combined formula 11 yields:
Fw＝Tei0/r-dtv/r2(12)
wherein the wheel radius r is determined by the following formula, wherein rmIs the radius of the wheel hub, h is the tire aspect ratio, l is the tire width;
r＝0.5·rm+0.01·h·l (13)；
2.2 joint observer:
2.2.1 recursive least squares quality identification
Combining equations 1 and 7, the following equation is obtained:
Figure FDA0002353002090000041
meanwhile, the combined formulas (2), (3), (4), (5) and (6) are arranged into a least square format, and the moment estimated value of the driving shaft
Figure FDA0002353002090000042
Converted into driving force
Figure FDA0002353002090000043
To obtain
Figure FDA0002353002090000044
Wherein
Figure FDA0002353002090000045
Representing vehicle longitudinal acceleration, sigma equivalent rotating mass, values thereof
Figure FDA0002353002090000046
Wherein JwIs the inertia of a wheel, r is the wheel radius;
Figure FDA0002353002090000047
representation of system output including drive axis estimator, BeRepresenting the available data vector, mvα is the process white noise of the system as the amount to be identified;
respectively defining the mass of the whole vehicle obtained by the system identification at K-1 and K moments as
Figure FDA0002353002090000048
Obtaining a quality identification model:
Figure FDA0002353002090000049
R(k)＝P(k-1)Be(k)[Be(k)P(k-1)Be(k)+μ(k)]-1
P(k)＝μ(k)-1[I-R(k)Be(k)]P(k-1) (16)
wherein u (K) is a forgetting factor at the K-th moment;
the forgetting factor μ (t) rule is:
μ(t)＝1-0.05·0.98t(17)；
2.2.2 drive shaft moment observer
If the rotating speeds of two ends of the driving shaft can be measured, a driving shaft torque generation model is built:
Ttw0＝ks∫(ωt-ωω)dt+bs(ωt-ωω) (18)
wherein T istw0Calculating drive shaft torque, omega, for open loopt、ωωFor the rotational speed, omega, at both ends of the drive shafttFor outputting speed, omega, to the gearboxωAs the wheel speed, ksFor the equivalent stiffness coefficient of the drive shaft, bsIs the equivalent damping coefficient of the driving shaft;
obtaining an equivalent wheel dynamics model:
Figure FDA0002353002090000051
wherein
Figure FDA0002353002090000052
As wheel speed estimate, TtwFor driving torque, TresIs moment of resistance, JtwTo drive the moment of inertia at the ends of the axle shafts,
Figure FDA0002353002090000053
driving torque passing resistance FresThe mathematical model being found to be Tres＝FresR, combined with formula 7
Tres＝Tcl+Ta+Tf+Tk
＝Tload+Tk
＝{mv·g·sin[arctan(0.01·i)]+0.5·ρair·Cx·S·v2+mv·g·(f+fk·v)}·r+Tk(21)
Wherein T isclIndicating the moment of resistance of the slope, TaIndicating air resistance moment, TfRepresenting frictional resistance torque, TkFor mechanical braking torque, TloadRepresenting driving resistance moment; for driving shaft moment estimator
Figure FDA0002353002090000054
Instead of Tload；
Defining a deviation
Figure FDA0002353002090000055
That is, the estimated wheel speed value is subtracted from the actual value, and the deviation e is derived by combining the following equations 19 and 21:
Figure FDA0002353002090000056
taking the control input u as follows:
Figure FDA0002353002090000057
where v is the virtual control input, using a feedback linearization method, the form of linearization of equation 22
Figure FDA0002353002090000058
The virtual control input is designed to be in the form of PI, namely:
v＝-kpe-kiθe(25)
wherein
Figure FDA0002353002090000059
kpGreater than 0 is a proportionality coefficient, kiThe integral coefficient is more than 0;
combining equations 23 and 25, we get the controller:
Figure FDA00023530020900000510
defining the Lyapunov function as formula (27):
Figure FDA0002353002090000061
the two sides are derived:
Figure FDA0002353002090000062
substituting formula (25) into the above formula and finishing to obtain:
Figure FDA0002353002090000063
thus is provided with
Figure FDA0002353002090000064
t → ∞ time, thus
Figure FDA0002353002090000065
t→∞；
The drive shaft torque is therefore:
Figure FDA0002353002090000066
observer-based longitudinal vehicle speed controller:
the conversion between the motor torque and the mechanical braking torque is carried out numerically by means of a transmission ratio, namely:
Tk＝Te·i0(31)
i.e. braking torque and driving torque are uniformly denoted Tdr，
The following relationships are obtained by the arrangement of the formulae (1), (7) and (12):
Figure FDA0002353002090000067
wherein the longitudinal speed is selected as a state quantity, i.e. x ═ v]Selecting the driving demand as a control variable, i.e. u ═ Tdr]Likewise, the longitudinal speed is selected as the output, i.e., y ═ v](ii) a Discretizing the state space equation by an Euler method, and expressing a sampling step length by delta t, wherein at the moment k, discretizing to obtain a system discrete model as follows:
x(k+1)＝f(x(k)，u(k))·Δt+x(k)
y(k+1)＝Cv·x(k)，k≥0. (33)
Cyfor the output coefficient matrix, defining the prediction time domain of the system as NpControl time domain as NuIt is necessary to satisfy 1. ltoreq. Nu≤NpThen the prediction output sequence at time k is represented as:
Figure FDA0002353002090000071
meanwhile, the optimal control input sequence u (k) at time k is represented as:
Figure FDA0002353002090000072
at the sampling instant k, the state quantity has a value x (k | k), and the prediction process for deriving the state quantity and the output quantity is as shown in equations (19) and (20):
Figure FDA0002353002090000073
Figure FDA0002353002090000074
at the same time, the reference input is the desired vehicle speed
Figure FDA0002353002090000075
A reference input sequence is thus obtained:
Figure FDA0002353002090000076
the following constraints need to be considered in the controller design:
0≤v(k)≤35m/s
-1000/i0≤Tdr(k)≤TMmax， (39)
meanwhile, in order to ensure the tracking of longitudinal speed and improve the riding comfort, the controller selects a performance index target as follows:
Figure FDA0002353002090000081
wherein Δ U (k) ═ U (k +1) -U (k);
further described as the optimization problem of equation (41), even though the objective function J (y (k), u (k), Nu, Np) has the smallest value:
minU(k)J(yc(k)，U(k)，Nu，Np) (41)
in the formula (41), the compound represented by the formula,
Figure FDA0002353002090000082
reflecting the deviation of the actual output vehicle speed from the desired vehicle speed, J2＝||ΓuΔU(k)||2Strength, gamma, reflecting the driving requirementsyAnd ΓuWeighting factors of the output signal sequence and the control signal sequence respectively; solving the optimization problem of the formula 41 by using NAG, optimizing a control input sequence of the system, and then acting a first element u (k) in the sequence on the system; at the next moment, the optimization solving process is repeated, namely the closed-loop optimization control of the longitudinal speed of the autonomous driving is carried out,
u(k)＝[1 0…0]U(k) (42)；
the torque distribution required for the optimized torque divides the driver torque demand by a drive torque greater than 0 and a brake torque less than or equal to 0, i.e. the torque distribution is divided into
Figure FDA0002353002090000083
Wherein T isdr(k) For optimized driving torque demand, Te(k) For motor desired torque, Tk(k) For braking torque, the braking torque is converted into a mechanical braking signal sig by the following formulabr：
sigbr＝Tk(k)/1000 (44)。
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