CN110778670B

CN110778670B - Comprehensive optimization control strategy for continuously variable transmission based on model predictive control

Info

Publication number: CN110778670B
Application number: CN201911063153.XA
Authority: CN
Inventors: 韩玲; 刘鸿祥; 任磊磊
Original assignee: Changchun University of Technology
Current assignee: Changchun University of Technology
Priority date: 2019-11-01
Filing date: 2019-11-01
Publication date: 2021-09-17
Anticipated expiration: 2039-11-01
Also published as: CN110778670A

Abstract

Disclosed herein is a comprehensive optimization control strategy for a continuously variable transmission based on model predictive control, comprising: according to the kinematic principle of a transmission system, establishing a CVT dynamic equation and a state space expression; the method is characterized in that a combined controller is designed by taking the highest comprehensive transmission efficiency of the whole vehicle as an optimization target and taking the torque and the clamping force of an engine as specific optimization objects; and (4) considering the constraint of the actuator in the optimization process, and solving the optimization problem through online rolling to obtain an optimal control input sequence.

Description

Comprehensive optimization control strategy for continuously variable transmission based on model predictive control

Technical Field

The invention relates to the field of transmission control, in particular to a comprehensive optimization control strategy of a continuously variable transmission based on model predictive control.

Background

In recent years, with the continuous development of social economy and the improvement of living standard of people, the number of automobiles is gradually increased. Although the automobile is convenient for people to go out daily, the exhaust emission seriously affects the air quality, and further aggravates the environmental pollution. Therefore, improving vehicle transmission efficiency and fuel economy plays a crucial role in improving air quality.

In many previous researches, in order to improve the fuel economy of a vehicle, only the engine efficiency is optimized, the influence of the transmission efficiency on the fuel consumption of the vehicle is ignored, and the optimal optimization result cannot be achieved. Therefore, in order to achieve the optimal optimization effect, the optimal efficiency of the engine and the transmission needs to be considered comprehensively, namely the comprehensive efficiency of the whole vehicle transmission system is optimal.

CVTs are receiving worldwide attention as an automatic transmission for automobiles due to their excellent smoothness and good fuel economy. Research shows that under the same conditions, the fuel efficiency of the CVT vehicle can be improved by 10-15%, and the emission of harmful substances can be reduced by more than 10%. Meanwhile, since the continuously variable transmission has a unique belt structure, continuous change of the speed ratio can be realized. Compared with other types of transmissions, the CVT can be better matched with the engine, so that the engine always works in an ideal area, and the transmission loss and the pollution emission of a vehicle are reduced. The transmission mechanism of the stepless speed changer is as follows: the working radius of the belt wheel is changed by clamping force on the metal pulley, and continuous change of transmission speed ratio is realized. Therefore, whether the change of the clamping force is accurate and reasonable or not directly influences the transmission efficiency of the stepless speed changer and the fuel economy of the whole vehicle. The clamping force control system, as a key technology of the continuously variable transmission, has been a focus and hot spot of attention of researchers. Conventional clamping force control strategies are typically multiplied by a constant factor to ensure that the cvt is operating properly under any extreme conditions. However, in the daily driving of the vehicle, the transmission loss is increased due to the excessively large safety margin of the clamping force, the abrasion loss between the metal belt and the pulley is increased, and the transmission efficiency and the service life of the CVT are reduced.

MPC is real-time on-line optimal control, has the characteristics of real-time prediction, rolling optimization, feedback correction and the like, and is suitable for solving the problem of multi-target control of a complex nonlinear system. In recent years, MPC has been widely used in many engineering fields, but there has been no case of combined control of an automobile engine and a transmission. Therefore, an MPC-based engine and CVT combined efficiency optimization strategy is proposed herein for a certain brand of automobile. The optimal comprehensive efficiency of the vehicle transmission system is taken as an optimization target, the engine torque and the CVT clamping force are taken as specific optimization objects, and the optimal input sequence of the engine torque and the clamping force is obtained by solving the optimization problem on line, so that the efficiency of the vehicle transmission system is optimal, and the fuel economy of the vehicle is improved.

Disclosure of Invention

The invention discloses a comprehensive optimization control strategy of a continuously variable transmission based on model predictive control, which comprehensively considers the transmission efficiency of an engine and the transmission, optimizes the efficiency of a vehicle transmission system and improves the fuel economy of a vehicle.

The technical scheme adopted by the invention is as follows:

a comprehensive optimization control strategy of a continuously variable transmission based on model prediction control comprises the following steps:

step S1, establishing a CVT dynamic equation according to the dynamic principle of the transmission system:

in the formula, ω_pIs the angular velocity, omega, of the driving pulley_sAngular velocity, T, of the driven pulley_eIs the output torque of the engine in a steady state, F_axFor axial thrust loading on the driven pulley, R_p、R_sRespectively the working radius of the driving wheel and the driven wheel, mu is the friction factor between the metal belt wheel and the driving belt wheel, T_rFor converting the vehicle running resistance to the resistance torque on the driven shaft of the CVT, C_p、C_sDamping coefficients of a driving shaft and a driven shaft respectively, eta is CVT transmission efficiency, lambda is an included angle between a conical disc bus and a vertical plane of a belt wheel axis, and J_p、J_sThe rotary inertia of the input end and the output end of the CVT are respectively.

Step S2, designing an MPC-based integrated controller, the process includes the following sub-steps:

s2.1, defining the angular speed omega of the driving pulley and the driven pulley according to the CVT dynamic equation and the control requirement_p、ω_sAs state variables, engine steady-state torque T_eAxial thrust F of the driven pulley_axFor input variable, driven pulley angular velocity omega_sFor output variables, T_rThe resistance torque on the driven shaft of the CVT is converted for the vehicle running resistance. Thus, the CVT state space expression can be expressed as:

y＝Cx(4)

in the formula: x ═ ω_p ω_s]^T

u＝[T_e F_ax]^T

y＝ω_s

d＝T_r

C＝[0 1]

S2.2, in order to improve the control precision of the control system, discretizing and increasing the state space expression in the step S2.1, T_sFor the controller sampling period, the incremental model is shown in equations (5) (6):

Δx(k+1)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (5)

y(k)＝CΔx(k)+y(k-1) (6)

in the formula:

step S2.3, predicting time domain N_pAnd control time domain N_uRespectively taking the values of 10 and 2, taking Deltax (k) as a starting point of prediction, and predicting k +1 by the formulaThe states of the etching are as follows:

Δx(k+1|k)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (7)

where k +1| k represents the prediction made at time k for time k +1, and further k + N is predicted_pThe state of the moment:

Δx(k+N_p|k)＝A_cΔx(k+N_p-1|k)+B_cΔu(k+N_p-1)+B_dcΔd(k+N_p-1) (8)

similarly, k +1 to k + N are predicted by the formula_pIs controlled to output as

y(k+1|k)＝CΔx(k+1|k)+y(k) (9)

y(k+N_p|k)＝CΔx(k+N_p|k)+y(k+N_p-1|k) (10)

At time k, the optimal control input sequence Δ U (k) and the prediction output Y (k +1| k) of the system are defined as

Wherein the content of the first and second substances,

then, for the system future N_pThe output prediction of a step can be calculated by the following prediction equation:

Y(k+1|k)＝S_xΔx(k)+I_cy(k)+S_uΔU(k)+S_dΔd(k) (13)

in the formula: i is_c＝[I I…I]^T

And S2.4, optimally controlling the vehicle transmission system, belonging to the multi-target coordination control problem. Herein referred to as engine torque T_eAnd a clamping force F_axThe control method is an optimal control object of the system, and realizes comprehensive optimal control on the whole vehicle engine and the continuously variable transmission. Thus, the optimization problem can be described as a specific objective function:

J＝||Γ_y(Y(k+1|k)-R_e(k+1))||²+||Γ_uΔU(k)||²+ρε² (15)

in the formula:

during the simulation, γ is set_y,i＝0.13，i＝1,2,…N_p；γ_u,i＝1，i＝1,2,…N_u. In order to reduce the complexity of operation and ensure the real-time performance of control, a weight coefficient of a relaxation factor epsilon is introduced into an objective function, rho is epsilon, and a smaller value is taken when the tracking error of the system is larger, otherwise, a larger value is taken.

Let J₁＝||Γ_y(Y(k+1|k)-R_e(k+1))||² (16)

J₂＝||Γ_uΔU(k)||² (17)

In the formula, gamma_y、Γ_uRespectively representing an error weight coefficient and a control weight coefficient; r_e(k +1) is a reference sequence of angular velocities of the driven pulley, since N is common in equation (12)_pA prediction for different time instants, so that R is defined_e(k+1)＝[r(k+1)r(k+2)…r(k+N_p)]^ΤWherein

A desired trajectory for the driven pulley angular velocity; j. the design is a square₁The square weighted value of the difference between the actual output angular speed of the CVT driven pulley and the reference angular speed in the prediction time domain is represented, and in order to enable the system prediction output to track the reference value as much as possible, the minimum value of the difference value of the actual output angular speed and the reference angular speed is used as an optimization target; j. the design is a square₂The square weighted value of the input variable in the control time domain is expressed, and the function of restraining the change amplitude of the input variable is achieved.

Step S2.5, during the actual running process of the vehicle, the engine torque T_eCVT speed ratio i and clamping force F thereof_axCannot be changed arbitrarily, so a constraint range needs to be set to ensure that each variable is always kept within a normal range. The specific constraints are as follows:

T_{e_min}≤T_e≤T_{e_max}

i_{_min}≤i≤i_{_max}

F_{ax_min}≤F_ax≤F_{ax_max}

wherein the engine torque limit and the speed ratio limit are determined by specific parameters of the engine and the continuously variable transmission. In the clamping force optimization, the minimum value of the reliable torque transmission and the maximum value that can be transmitted by the hydraulic line clamping force control valve are respectively used as the lower limit value and the upper limit value.

Therefore, the objective function is converted into a quadratic programming problem, and the constraint factors in the vehicle running process are considered for solving. And performing rolling solution on the optimization problems of different sampling times, and applying the first component of the obtained optimal control sequence to the control system to obtain a final optimization result.

The invention has the following beneficial effects:

an engine and CVT comprehensive efficiency optimization strategy based on the MPC is provided, the optimal comprehensive efficiency of a vehicle transmission system is taken as an optimization target, the engine torque and the CVT clamping force are taken as specific optimization objects, and the optimal input sequence of the engine torque and the clamping force is obtained by solving the optimization problem on line, so that the efficiency of the vehicle transmission system is optimal, and the fuel economy of a vehicle is improved.

Drawings

FIG. 1 is a block diagram of an engine and CVT integrated control according to the present invention

FIG. 2 is a block diagram of a CVT transmission system according to the present invention

FIG. 3 is a schematic diagram of a CVT according to the present invention

FIG. 4 is a structural view of a clamping force test bed according to the present invention

FIG. 5 is a schematic diagram of an engine and CVT integrated control strategy according to the present invention

Detailed Description

The present invention will be described in detail with reference to the accompanying drawings.

The invention discloses a comprehensive optimization control strategy of a continuously variable transmission based on model predictive control, which comprises the following steps as shown in figure 1:

the fuel consumption of the vehicle is different under different driving conditions. Therefore, in order to improve the scientificity and accuracy of the fuel consumption, the fuel consumption of the vehicle under the conditions of constant speed, constant acceleration, constant deceleration and idling is calculated respectively. The calculation formula under different working conditions is as follows:

when the vehicle runs at a constant speed, the effective power of the engine is as follows:

the oil consumption (ml/s) at this time was:

wherein b is the fuel consumption rate [ g/(kw. h)](ii) a Rho is the density (Kg/L) of the fuel oil; g is the acceleration of gravity; v is_aIs the speed of travel.

At the time of acceleration of the vehicle or the like, the power consumed by the engine against the acceleration resistance is as follows:

t₂the oil consumption in the time is the accumulated value of the oil consumption of each short time delta t when the vehicle is in a constant speed state.

In the formula

Is the road grade; c_DIs the wind resistance coefficient; delta is a rotational inertia coefficient; a is the frontal area of the vehicle.

At the time of deceleration of the vehicle or the like, t₃The fuel consumption over time was:

Q₃＝Q_it₃ (5)

in the formula Q_iIs the vehicle idling fuel consumption rate.

During idling of the vehicle, t₄The fuel consumption over time is as follows:

Q₄＝Q_it₄ (6)

the formula can be used for calculating the oil consumption of the vehicle under different working conditions, and theoretical support is provided for the subsequent calculation of the oil consumption of the vehicle.

A CVT dynamic equation is established according to the metal belt CVT tribology principle and the transmission system dynamics principle, and the CVT transmission system block diagram and the structural schematic diagram are respectively shown in figures 2 and 3.

Obtained by the formulas (1) and (2)

F_ax＝F_ncosλ (10)

From the formulas (3) and (4)

In the formula, F_tThe friction force between the metal belt and the driving belt wheel; f_nThe end face of the driving belt wheel is vertically clamped;

T_in,p,T_out,storque acting on the driving wheel and the driven wheel respectively; r_p,R_sRespectively the working radius of the driving wheel and the driven wheel; mu is the friction factor between the metal belt wheel and the driving belt wheel; f_axAxial thrust loaded on the driven belt wheel; lambda is the included angle between the conical disc generatrix and the vertical plane of the axis of the belt wheel.

Establishing a CVT dynamic equation according to the metal belt CVT tribology principle and the transmission system dynamics principle:

in the formula (I), the compound is shown in the specification,ω_pis the angular velocity, omega, of the driving pulley_sAngular velocity, T, of the driven pulley_eIs the output torque of the engine in a steady state, F_axFor axial thrust loading on the driven pulley, R_p、R_sRespectively the working radius of the driving wheel and the driven wheel, mu is the friction factor between the metal belt wheel and the driving belt wheel, T_rFor converting the vehicle running resistance to the resistance torque on the driven shaft of the CVT, C_p、C_sDamping coefficients of a driving shaft and a driven shaft respectively, eta is CVT transmission efficiency, lambda is an included angle between a conical disc bus and a vertical plane of a belt wheel axis, and J_p、J_sThe rotary inertia of the input end and the output end of the CVT are respectively.

And (3) building a clamping force test bed, wherein the structural sketch of the clamping force test bed is shown in figure 4. Wherein, the driving motor and the load motor respectively represent an engine and a road load, and the torque and rotating speed sensor can collect the torque T of the driving wheel and the driven wheel of the CVT_p、T_sAnd a rotational speed omega_p、ω_sThe laser displacement sensor and the pressure sensor can respectively obtain the displacement X of the active movable cylinder_pAnd master and slave cylinder pressures P_p、P_s。

Designing an MPC-based integrated controller, wherein the process comprises the following sub-steps:

substep 1, defining angular speed omega of driving pulley and driven pulley according to CVT dynamic equation and control requirement_p、ω_sAs state variables, engine steady-state torque T_eAxial thrust F of the driven pulley_axFor input variable, driven pulley angular velocity omega_sFor output variables, T_rThe control strategy is schematically illustrated in fig. 4 for converting vehicle driving resistance to the resistive torque on the driven shaft of the CVT. Thus, the CVT state space expression can be expressed as:

y＝Cx(16)

in the formula: x ═ ω_p ω_s]^T

u＝[T_e F_ax]^T

y＝ω_s

d＝T_r

C＝[0 1]

Substep 2, in order to improve the control precision of the control system, discretizing and increasing the state space expression of the substep 1, T_sFor the controller sampling period, the incremental model is shown in equations (17) (18):

Δx(k+1)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (17)

y(k)＝CΔx(k)+y(k-1) (18)

in the formula:

substep 3, predicting the time domain N_pAnd control time domain N_uTaking values of 10 and 2 respectively, and taking Δ x (k) as a starting point of prediction, the state at the time k +1 can be predicted by the following formula:

Δx(k+1|k)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (19)

Δx(k+N_p|k)＝A_cΔx(k+N_p-1|k)+B_cΔu(k+N_p-1)+B_dcΔd(k+N_p-1) (20)

y(k+1|k)＝CΔx(k+1|k)+y(k) (21)

y(k+N_p|k)＝CΔx(k+N_p|k)+y(k+N_p-1|k) (22)

Wherein the content of the first and second substances,

Y(k+1|k)＝S_xΔx(k)+I_cy(k)+S_uΔU(k)+S_dΔd(k) (25)

in the formula: i is_c＝[I I…I]^T

And substep 4, optimally controlling the vehicle transmission system, belonging to the multi-target coordination control problem. Herein referred to as engine torque T_eAnd a clamping force F_axThe control method is an optimal control object of the system, and realizes comprehensive optimal control on the whole vehicle engine and the continuously variable transmission. Thus, the optimization problem can be described as a specific objective function:

J＝||Γ_y(Y(k+1|k)-R_e(k+1))||²+||Γ_uΔU(k)||²+ρε² (27)

in the formula:

Let J₁＝||Γ_y(Y(k+1|k)-R_e(k+1))||² (28)

J₂＝||Γ_uΔU(k)||² (29)

In the formula, gamma_y、Γ_uRespectively representing an error weight coefficient and a control weight coefficient; r_e(k +1) is a reference sequence of angular velocities of the driven pulley, since N is common in equation (12)_pA prediction for different time instants, soMeaning R_e(k+1)＝[r(k+1)r(k+2)…r(k+N_p)]^ΤWherein

Substep 5. during the actual running of the vehicle, the engine torque T_eCVT speed ratio i and clamping force F thereof_axCan not

Any change, and therefore, a constraint range needs to be set to ensure that the variables always remain within the normal range. The specific constraints are as follows:

T_{e_min}≤T_e≤T_{e_max}

i_{_min}≤i≤i_{_max}

F_{ax_min}≤F_ax≤F_{ax_max}

Meanwhile, the effectiveness of the control strategy is verified by means of a Simulink and an AMEslim simulation platform, wherein the AMEslim comprises a CVT module, a whole vehicle module and a system hydraulic module, the Simulink comprises a CVT control module, an engine module, an accelerator pedal module and a brake pedal module, and specific parameters required in the modeling process are shown in a table 1.

TABLE 1 model simulation parameters

While the invention has been described with reference to specific embodiments thereof, it is not intended that the invention be limited to the details of the description and the operation of the embodiments, as many variations thereof are possible to those skilled in the art.

Claims

1. A comprehensive optimization control strategy for a continuously variable transmission based on model predictive control is characterized by comprising the following steps:

step S1, establishing a CVT dynamic equation according to the dynamic principle of a CVT in the transmission system:

in the formula, ω_pIs the angular velocity, omega, of the driving pulley_sAngular velocity, T, of the driven pulley_eIs the output torque of the engine in a steady state, F_axFor axial thrust loading on the driven pulley, R_p、R_sRespectively the working radius of the driving wheel and the driven wheel, mu is the friction factor between the metal belt wheel and the driving belt wheel, T_rFor converting the vehicle running resistance to the resistance torque on the driven shaft of the CVT, C_p、C_sDamping coefficients of the driving and driven shafts, eta is CVT transmissionDynamic efficiency, λ is the included angle between the conical disc generatrix and the vertical plane of the belt wheel axis, J_p、J_sRespectively are the rotational inertia of the input end and the output end of the CVT,

s2.1, defining the angular speed omega of the driving pulley and the driven pulley according to the CVT dynamic equation and the control requirement_p、ω_sFor state variables, engine steady-state torque input torque T_eAxial thrust F of the driven pulley_axFor input variable, driven pulley angular velocity omega_sFor output variables, T_rThe resistance torque converted to the CVT driven shaft for the vehicle running resistance, therefore, the CVT state space expression can be expressed as:

y＝Cx (4)

in the formula: x ═ ω_p ω_s]^T

u＝[T_e F_ax]^T

y＝ω_s

d＝T_r

C＝[0 1]

Step S2.2, in order to improve the control precision of the control system, the state space expression in the step S2.1 is discretized and increased,T_sfor the controller sampling period, the incremental model is shown in equations (5) (6):

Δx(k+1)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (5)

y(k)＝CΔx(k)+y(k-1) (6)

in the formula:

step S2.3, predicting time domain N_pAnd control time domain N_uTaking values of 10 and 2 respectively, with Δ x (k) as a starting point of prediction, the state at the time k +1 can be predicted by the following formula:

Δx(k+1|k)＝A_cΔx(k)+B_cΔu(k)+B_dcΔd(k) (7)

Δx(k+N_p|k)＝A_cΔx(k+N_p-1|k)+B_cΔu(k+N_p-1)+B_dcΔd(k+N_p-1) (8)

y(k+1|k)＝CΔx(k+1|k)+y(k) (9)

y(k+N_p|k)＝CΔx(k+N_p|k)+y(k+N_p-1|k) (10)

Wherein the content of the first and second substances,

then, for the system future N_pThe output prediction of a step can be calculated from the following prediction equation:

Y(k+1|k)＝S_xΔx(k)+I_cy(k)+S_uΔU(k)+S_dΔd(k) (13)

in the formula: i is_c＝[I I…I]^T

S2.4, the optimal control of the vehicle transmission system belongs to the multi-target coordination control problem, and the engine torque Te and the clamping force Fax are taken as the optimal control objects of the system to realize the comprehensive optimal control of the whole vehicle engine and the continuously variable transmission, so that the optimization problem can be described as a specific objective function:

J＝||Γ_y(Y(k+1|k)-R_e(k+1))||²+||Γ_uΔU(k)||²+ρε² (15)

in the formula:

during the simulation, γ is set_y,i＝0.13，i＝1,2,…N_p；γ_u,i＝1，i＝1,2,…N_uIn order to reduce the complexity of operation and ensure the real-time performance of control, a relaxation factor epsilon is introduced into an objective function, rho is a weight coefficient of epsilon, and a smaller value is taken when the tracking error of the system is larger, otherwise, a larger value is taken;

let J₁＝||Γ_y(Y(k+1|k)-R_e(k+1))||² (16)

J₂＝||Γ_uΔU(k)||² (17)

In the formula, gamma_y、Γ_uRespectively representing an error weight coefficient and a control weight coefficient; r_e(k +1) is a reference sequence of angular velocities of the driven pulley, since N is common in equation (12)_pA prediction for different time instants, so that R is defined_e(k+1)＝[r(k+1) r(k+2)…r(k+N_p)]^TWherein

A desired trajectory for the driven pulley angular velocity; j. the design is a square₁A square weight value representing the difference between the actual output angular velocity of the CVT driven pulley and the reference angular velocity in the prediction time domain, for the system to predictThe measured output can track the reference value as much as possible, and the minimum value of the difference value of the measured output and the reference value is used as an optimization target; j. the design is a square₂The square weighted value representing the variation of the clamping force in the control time domain plays a role of restraining the variation range of the clamping force,

step S2.5, in the actual running process of the vehicle, the engine torque Te, the CVT speed ratio i and the clamping force Fax thereof cannot be changed at will, so that a constraint range needs to be set to ensure that all variables are always kept in a normal range, and the specific constraint conditions are as follows:

T_{e_min}≤T_e≤T_{e_max}

i_{_min}≤i≤i_{_max}

F_{ax_min}≤F_ax≤F_{ax_max}

wherein, the torque limit value and the speed ratio limit value of the engine are determined by specific parameters of the engine and the continuously variable transmission, in the clamping force optimization, the minimum value of reliable torque transmission and the maximum value which can be transmitted by a hydraulic pipeline clamping force control valve are respectively used as a lower limit value and an upper limit value,

therefore, the objective function is converted into a quadratic programming problem, the constraint factors in the vehicle running process are considered for solving, the optimization problems of different sampling time are solved in a rolling mode, the first component of the obtained optimal control sequence is applied to the control system, and the final optimization result is obtained.