CN109849896B

CN109849896B - Hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation

Info

Publication number: CN109849896B
Application number: CN201910150927.6A
Authority: CN
Inventors: 陈龙; 汪佳佳; 汪若尘; 蔡英凤; 施德华
Original assignee: Jiangsu University
Current assignee: Jiangsu University
Priority date: 2019-02-28
Filing date: 2019-02-28
Publication date: 2020-11-03
Anticipated expiration: 2039-02-28
Also published as: CN109849896A

Abstract

The invention discloses a hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation, and belongs to the field of vehicle dynamic control. Aiming at the longitudinal impact problem and the perturbation phenomenon of system parameters generated in the mode switching process of a Hybrid Electric vehicle from an E (Electric drive mode) to an H (Hybrid drive mode), the invention introduces a multi-power-source coordination control strategy which integrates a parameter uncertainty observer, and timely corrects the parameters of a coordination controller by monitoring the change of the system parameters in real time, so that the vehicle always automatically works in an optimal or sub-optimal state. Meanwhile, due to the self-correcting characteristic of the coordination controller, high-level switching control suitable for different roads and different drivers can be realized. The invention can effectively reduce the mode switching impact and make the coordination control strategy thereof have certain anti-interference and self-adaptability.

Description

Hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation

Technical Field

The invention relates to a hybrid electric vehicle self-adaptive E-H switching coordination control strategy based on parameter observation, belonging to the field of vehicle dynamic control.

Background

As is well known, a hybrid vehicle has a plurality of driving modes, and can select a suitable driving/braking mode according to different driving conditions to achieve good fuel economy and power performance, which inevitably involves mode switching, and the corresponding power source demand torque also changes abruptly, and if no reasonable control is applied, the vehicle is prone to causing an obvious impact feeling, and even a power interruption phenomenon. The engine, the motor and the clutch are used as main impact sources, and output responses of the engine, the motor and the clutch are reasonably coordinated, so that the mode switching quality of the whole vehicle is improved. In the current dynamic coordination control research of the hybrid electric vehicle, the shortage of the torque response speed of the engine is mainly compensated by adopting the dynamic torque of the motor, but the uncertainty and the external interference of the model are rarely considered, and in addition, the coordination control strategy can adapt to the change of various state variables because the actual working environment of the vehicle is complex and changeable.

Disclosure of Invention

In order to overcome the technical defects, the invention provides a hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation, which can adapt to various state variable changes, and the technical scheme comprises the following steps:

a hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation comprises the following steps:

step 1) a hybrid electric vehicle runs in a pure electric mode in an initial state, at the moment, a brake CB1 is locked, an engine is closed, and a motor MG2 completely bears torque required by vehicle driving; meanwhile, a speed sensor and an accelerator pedal position sensing device on the hybrid electric vehicle monitor the current speed information and the position signals of an accelerator pedal and a brake pedal in real time, input the current speed information and the position signals of the accelerator pedal and the brake pedal to a vehicle controller VCU, and switch a speed threshold v according to the set speed threshold v_thrThe VCU judges whether to switch the mode;

step 2) if the vehicle speed v is more than or equal to v_thrWhen the current is over;

at the moment, the hybrid electric vehicle meets the mode switching condition, the mode switching is required, the VCU controls the brake CB1 to be switched off rapidly, and the vehicle enters the engine to be dragged and rotated from the pure electric mode; the control targets in the engine dragging stage are as follows: as soon as the pressure in the clutch CR1 is increased, the motor MG1 is required to drag the engine via the clutch in a short time to the idle speed w_idleWhile reducing longitudinal shock. Considering that the number of control targets and control objects is large at the stage, an optimal coordination controller based on dynamic programming is designed, the target function and the variable range are discretized, and a dynamic programming global optimization algorithm is used for solving the optimal control quantity (T)_MG1,T_CR1,T_MG2) (ii) a Wherein, T_CR1For transmitting torque to the clutch, T_MG1And T_MG2Output torques of the motors MG1 and MG2, respectively; the motor MG2 torque PID compensation module may be expressed by:

wherein k is_p、k_dAnd k_iIs the proportional, derivative and integral coefficient, k ', of the vehicle speed tracking error Deltav'_p、k_d' and k_i' is the proportional, derivative and integral coefficient of the acceleration error Delta alpha, byAdjusting the proportional, integral and differential coefficients of the vehicle speed tracking error delta v and the acceleration error delta alpha, and outputting the current torque compensation signal of the motor MG2_T；

Step 3) when the rotating speed w of the engine_e≥w_idleWhen the current is over;

at this time, the vehicle enters a rotation speed synchronization stage, the engine starts to ignite, and the optimal coordination controller controls (T)_e,T_MG1,T_CR1,T_MG2) Wherein, T_eFor engine output torque, to ensure clutch end speed difference | w_cl-in-w_cl-out| is less than a set threshold₀The synchronization of the rotating speed is realized;

step 4) when | w_cl-in-w_cl-out|≤₀When the current is over; at the moment, the clutch is considered to enter a slip stage, the optimal coordination controller control target in the stage is to further reduce the end speed difference and the slip work, and the target function and the variable limiting conditions of the stage are the same as those in step 3);

step 5) when | w_cl-in-w_cl-out|≤₁When the current is over; at the moment, the end speed difference of the clutch is small enough, the clutch is considered to be completely engaged, the vehicle enters a hybrid driving mode, the motor MG1 regulates the speed of the engine to be in the optimal rotating speed, the whole vehicle is driven by the engine and the motor MG2 together, the optimal torque distribution of multiple power sources is determined by energy management, and the mode switching process is finished;

and 6) designing a corresponding uncertainty parameter observer in the mode switching process of the hybrid electric vehicle, and establishing a parameter change prediction model by performing multiple groups of data input in the Cruise model and applying a data driving theory so as to identify the current system parameter change rule.

The invention has the beneficial effects that: the method is only based on the input and output data of the Cruise model to directly build the data driving predictor, can properly process the multi-constraint problem and high-order nonlinearity in the system, and is very suitable for parameter perturbation prediction in the switching process of the HEV mode with quick and complex dynamic characteristics. By observing the system parameters and the change rate thereof in real time, the parameters of the coordinated controller in the vehicle mode switching process can be correspondingly adjusted, and the mode switching quality with higher smoothness and better robustness can be further obtained.

Drawings

FIG. 1 is a layout diagram of a hybrid vehicle powertrain according to the present invention.

FIG. 2 is a flow chart of E-H mode switching of the hybrid electric vehicle according to the present invention.

FIG. 3 is a diagram of the overall control scheme of the adaptive E-H switching coordination control strategy of the hybrid electric vehicle based on parameter observation according to the invention.

FIG. 4 is a diagram of the design architecture of the uncertain parameter observer of the system according to the present invention.

Detailed Description

The invention is further described with reference to the following drawings and specific embodiments. Fig. 1 shows a double planetary-row hybrid power system researched by the present patent, which mainly includes a front-row ring gear R1, a front-row planet carrier C1, a front-row sun gear S1, a rear-row ring gear R2, a rear-row planet carrier C2, and a rear-row sun gear S2. Wherein the engine is connected to front carrier C1 through clutch CR1 and brake CB1, the rotor shaft of motor MG1 is connected to front sun gear S1 through brake CB2, and the rotor shaft of motor MG2 is connected to rear sun gear S2. In addition, the front row planet carrier C1 is connected with the rear row gear ring R2, and the front row gear ring R1, the rear row planet carrier C2 and the output shaft are connected. The hybrid electric vehicle initially runs in an electric-only mode, the brake CB1 is locked, the engine is turned off, and the motor MG2 fully bears the torque required for driving the vehicle. Meanwhile, a speed sensor and an accelerator pedal position sensing device on the hybrid electric vehicle monitor the current speed information and the position signals of an accelerator pedal and a brake pedal in real time, input the current speed information and the position signals of the accelerator pedal and the brake pedal into a Vehicle Controller (VCU), and switch a speed threshold v according to the set speed threshold v_thrThe VCU judges whether to switch the mode;

if v is>v_thrWhen the hybrid electric vehicle meets the mode switching condition, the mode switching is required, the VCU control brake CB1 is rapidly disconnected, and the vehicle enters the engine to be dragged and rotated from the pure electric mode. The control targets in the engine dragging stage are as follows: as soon as the pressure in the clutch CR1 is increased, the motor MG1 must drive the engine via the clutch within 0.5s until the idle speed w_idleWhile reducing longitudinal shock. Taking into account the orderThe number of segment control targets and control objects is large, and an optimal coordination controller based on dynamic programming is designed.

Objective function

The corresponding variable constraints are:

solving the optimal control quantity (T) by discretizing the objective function and the variable range and applying a dynamic programming global optimization algorithm_MG1,T_CR1,T_MG2). Because the obvious torque fluctuation exists when the engine rotates at a low speed and the discontinuity existing in the process of transmitting the torque by the clutch is transmitted to the driving shaft to bring impact, the torque PID compensation module of the motor MG2 is added to offset the torque fluctuation. The motor MG2 torque PID compensation module may be expressed by:

wherein k is_p、k_dAnd k_iIs the proportional, derivative and integral coefficient, k ', of the vehicle speed tracking error Deltav'_p、k_d' and k_i' the proportional, differential and integral coefficients of the acceleration error delta alpha are adjusted by the vehicle speed tracking error delta v and the proportional, integral and differential coefficients of the acceleration error delta alpha, and the torque compensation signal of the motor MG2 at the current moment is output_T。

When w is_e≥w_idleAt this time, when the vehicle enters a rotation speed synchronization stage, the engine starts to ignite, and the optimal coordination controller controls (T)_e,T_MG1,T_CR1,T_MG2) To reduce the clutch end speed difference | w_cl-in-w_cl-outAnd I, realizing the synchronization of the rotating speed. Likewise, the motor MG2 torque compensation module is designed to counteract the torque ripple after engine firing.

The corresponding objective function is

Subject to the limitation that

When | w is shown in FIG. 2_cl-in-w_cl-out|≤₀When the clutch end speed difference is lower than the set threshold value₀(set to 0.1rad/s herein), the clutch is considered to enter a slip phase where the optimal coordinated controller control objective is to further reduce the end speed differential and the slip work. The stage objective function and its variable constraints are the same as above.

When | w_cl-in-w_cl-out|≤₁When the clutch end speed difference is small enough, that is₁Equal to 0, the clutch is considered fully engaged and the vehicle enters a hybrid drive mode. The motor MG1 regulates the speed of the engine to the optimal rotating speed, the whole vehicle is driven by the engine and the motor MG2 together, the optimal torque distribution of the multiple power sources is determined by an energy management strategy, and the E-H mode switching process is completed.

The overall control scheme of the coordinated control strategy involved in the whole handover process is shown in fig. 3. When the speed of the hybrid electric vehicle exceeds a set threshold value v_thrThe vehicle controller receives a mode switching signal for switching pure electric to hybrid driving, and the energy management strategy determines the target torque T of the engine in the steady state of the hybrid driving mode according to the running condition of the vehicle and the fuel economy requirement at the moment_e-setMotor target torque T_m-setTarget clutch torque T_c-setAnd then the actuating mechanisms of the engine, the clutch and the motor drive the power sources to be transited to the target torque by adjusting the opening degree of the throttle valve, the engaging pressure of the clutch and the current of the three-phase winding respectively. In order to reduce the impact vibration of the driving shaft caused by the sudden change of the torque, a staged coordination control strategy according to claim 1 is designed, and the whole switching process is comprehensively solved through optimal control and motor compensationRequired torque T of middle engine, clutch and motor_e-dem、T_c-dem、T_m-demAnd considering the practical operation limit of the actuator, a torque limiting module and a hysteresis module are respectively designed, wherein the torque limiting module of the engine is

T_e-min(ω)≤T_e(ω)≤T_e-max(ω)

The engine hysteresis module is

T_e(omega) is obtained through a steady state table look-up model according to the current rotating speed of the engine, the table look-up model is established based on experimental data of an engine rack, and tau_eIs the time constant of the first-order inertia link of the engine.

Similarly, the torque limit and hysteresis module of the clutch actuator is

T_CR1-min≤T_CR1≤T_CR1-max

The torque limiting and hysteresis module of the motor is

T_M-m _in(ω)≤T_M(ω)≤T_M-max(ω)

τ_c、τ_mThe time constants of the first-order inertia links of the clutch and the motor are respectively. Engine execution torque T after "actual" processing_e-inClutch actuation torque T_c-inMotor execution torque T_m-inInputting the torque to the whole HEV model (i.e. the system architecture shown in FIG. 1), and finally outputting a torque truth signal T_e-act、T_c-act、T_m-actFeedback into the coordinating controller via sensor measurements. The sensor measuring module mainly simulates the measuring error thereof, and the correspondingThe mathematics of the earth are expressed as

Wherein, Delta_e、Δ_c、Δ_mThe error proportionality coefficients of the engine, the clutch and the motor are respectively. It is worth noting that the whole E-H mode switching process is relatively dynamic, and various time-varying system parameters exist, such as rotational inertia of each component, internal resistance of a motor, starting resistance moment of an engine, friction coefficient of a clutch and the like. Because the effect of the controller is deteriorated due to the fact that system parameters are prone to change, a corresponding uncertain parameter observer is designed based on the fact, as shown in fig. 4, firstly, a whole vehicle model of the double-planet-row type hybrid electric vehicle, which is researched by the patent and is shown in fig. 1, is built by application software Cruise, and the model can better depict the transient dynamic characteristics of the vehicle. Then, by inputting multiple groups of data in the Cruise model, applying a data driving theory and considering a discrete state equation of an E-H switching process, at the kth sampling moment, the following state space expression is provided

x(k+1)＝Ax(k)+Bu(k)

y(k)＝Cx(k)

Where x (k) is the state variable of the system, u (k) is the input variable of the system, y (k) is the output variable of the system, and A, B, C are the state, input, and output gain matrices of the system, respectively.

The inputs to the system are engine, clutch, motor torque and motor operating temperature, i.e., u (k) [ T ]_e(k) T_c(k)T_m(k) Q_m(k)]^TThe system output is clutch end speed difference, clutch friction coefficient and motor internal resistance, namely y (k) ═ delta omega (k) mu_c(k) R_m(k)]^TBy iterative operation has

Then for discrete time, there is the following matrix equation

When k is 1, 0,1,2,.., j-1, there is the following matrix equation

Y_s＝ψ_iX_s+φ_iU_s

Wherein the content of the first and second substances,

X_s＝[x(1) x(2) x(3) … x(j)]

ψ_i＝[C CA CA²… CA^i-1]^T

when k is 1, 0,1,2,.., j-1, there is the following matrix equation

Y_f＝ψ_iX_f+φ_iU_f

Wherein

X_f＝[x(i+1) x(i+2) x(i+3 )… x(i+j)]

Due to the fact that

X_f＝AⁱX_s+σ_iU_s

σ_i＝[A^i-1B A^i-2B … B 0]

Namely have

Y_f＝ψ_iAⁱψ_i ^-1Y_s+ψ_i(σ_i-Aⁱψ_i ^-1φ_i)U_s+φ_iU_f

The iterative recursion method can be used for establishing an input-output inter-control prediction equation, and a parameter change prediction model can be constructed by acquiring enough measurement data, so that the parameter change rule of the current system is identified.

As shown in FIG. 3, through the above uncertainty observer identification, under the condition of system parameter perturbation, the optimal coordination controller parameters α, β, γ, λ and PID parameter k can be performed simultaneously_p、k_d、k_i、k′_p、k_d' and k_i' modified so that the system always operates automatically in optimal or suboptimal operating conditions.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A hybrid electric vehicle self-adaptive E-H switching coordination control method based on parameter observation is characterized by comprising the following steps:

at the moment, the hybrid electric vehicle meets the mode switching condition, the mode switching is required, the VCU controls the brake CB1 to be switched off rapidly, and the vehicle enters the engine to be dragged and rotated from the pure electric mode; the control targets in the engine dragging stage are as follows: as soon as the pressure in the clutch CR1 is increased, the motor MG1 is required to drag the engine via the clutch in a short time to the idle speed w_idleSimultaneously, longitudinal impact is reduced; considering that the number of control targets and control objects is large at the stage, an optimal coordination controller based on dynamic programming is designed, the target function and the variable range are discretized, and a dynamic programming global optimization algorithm is used for solving the optimal control quantity (T)_MG1,T_CR1,T_MG2) (ii) a Wherein, T_CR1For transmitting torque to the clutch, T_MG1And T_MG2Output torques of the motors MG1 and MG2, respectively;

step 4) when | w_cl-in-w_cl-out|≤₀When the current is over; at the moment, the clutch is considered to enter a sliding friction stage, and the optimal coordination controller control target in the stage is oneStep-down the end speed difference and the sliding grinding work, and the objective function and the variable limiting conditions of the stage are the same as those in step 3);

2. The method for controlling the hybrid electric vehicle self-adaptive E-H switching coordination based on the parameter observation according to claim 1, wherein in the step 2), the optimal coordination controller based on the dynamic programming solves the optimal control quantity by applying a dynamic programming global optimization algorithm as follows:

the objective function of the controller:

the corresponding variable constraints are:

wherein, t₀And t₁The moment when the brake CB1 is completely switched off and the engine speed is equal to the idle speed, j is the longitudinal impact of the vehicle, alpha and beta are the weight coefficients of the impact and the engine dragging time, omega_eIs the engine speed, T_CR1For transmitting torque to the clutch, T_efFor engine starting moment of resistance, T_MG1And T_MG2Output torques, T, of the motors MG1 and MG2, respectively_MG1-minAnd T_MG1-maxOutput minimum and maximum torque limits for the motor MG1, respectively, and, similarly, T_MG2-minAnd T_MG2-maxThe minimum torque and the maximum torque of the motor MG 2.

3. The method for controlling the hybrid electric vehicle to switch in the coordinated manner based on the parameter observation of claim 1, wherein in the step 2), because the engine rotates at a low speed, significant torque fluctuation exists, and discontinuity existing in the process of transmitting the torque by the clutch can be transmitted to the driving shaft to bring impact, and the torque PID compensation module of the motor MG2 is added to counteract the torque fluctuation.

4. The method for controlling the hybrid electric vehicle self-adaption E-H switching coordination based on parameter observation according to claim 1, wherein in the step 3), the objective function controlled by the optimal coordination controller is as follows:

subject to the limitation that

Wherein, T_e-maxIs the maximum value of engine output torque, omega_{cl_in}And ω_{cl_out}The rotating speeds of the driving disk and the driven disk of the clutch are t₂For the clutch end speed difference to be equal to₀Gamma and lambda are the weight coefficients of the clutch end speed difference and the sliding friction work, T_CR1-minAnd T_CR1-maxThe motor MG2 torque PID compensation module is also designed to counteract the torque ripple after engine ignition, for the minimum and maximum clutch transfer torque respectively.

5. The method for controlling the hybrid electric vehicle self-adaption E-H switching coordination based on parameter observation according to claim 1, characterized in that the specific process of the step 6) is as follows: firstly, establishing a whole vehicle model of the double-planet-row hybrid electric vehicle by using software Cruise, then inputting a plurality of groups of data in the Cruise model, applying a data driving theory, considering a discrete state equation in an E-H switching process, and at a kth sampling moment, having the following state space expression

x(k+1)＝Ax(k)+Bu(k)

y(k)＝Cx(k)

Where x (k) is the state variable of the system, u (k) is the input variable of the system, y (k) is the output variable of the system, A, B, C are the state, input, output gain matrices of the system, respectively;

the inputs to the system are engine, clutch, motor torque and motor operating temperature, i.e., u (k) [ T ]_e(k) T_c(k) T_m(k) Q_m(k)]^TThe system output is clutch end speed difference, clutch friction coefficient and motor internal resistance, namely y (k) ═ delta omega (k) mu_c(k) R_m(k)]^TAnd obtaining a parameter change prediction model through iterative operation:

Y_f＝ψ_iAⁱψ_i ^-1Y_s+ψ_i(σ_i-Aⁱψ_i ^-1φ_i)U_s+φ_iU_f

wherein the content of the first and second substances,

ψ_i＝[C CA CA²…CA^i-1]^T；

6. the method for controlling the hybrid electric vehicle self-adaption E-H switching coordination based on the parameter observation as claimed in claim 5, wherein the uncertainty parameter observer is designed by emphasizing consideration of the disturbance of the internal resistance of the motor, the friction coefficient of the clutch, the rotational inertia of each component, the equivalent rigidity of a transmission shaft and the damping, and the corresponding Cruise model data are input into the internal working temperature of the motor, and the torques of the engine, the motor MG1 and the motor MG 2.

7. The method for controlling the hybrid electric vehicle to adaptively switch E-H based on parameter observation according to claim 3, wherein the motor MG2 torque PID compensation module is expressed by the following formula:

8. The method for controlling the hybrid electric vehicle self-adaption E-H switching coordination based on the parameter observation is characterized in that a time lag and a limit inevitably exist in a bottom actuator when an upper-layer command is executed, and the design of a coordination controller is more reasonable by respectively introducing an engine torque limiting module, a torque hysteresis module, a clutch torque limiting and torque hysteresis module, a motor torque limiting module and a hysteresis module.