CN109849896B - A Coordinated Control Method for Adaptive E-H Switching of Hybrid Electric Vehicles Based on Parameter Observation - Google Patents

Abstract

The invention discloses an adaptive E-H switching coordination control method for a hybrid electric vehicle based on parameter observation, which belongs to the field of vehicle dynamic control. Aiming at the longitudinal impact problem and the system parameter perturbation phenomenon generated during the mode switching process of the hybrid electric vehicle from E (Electric drive mode, pure electric mode)-H (Hybrid drive mode, hybrid drive mode), the present invention introduces a fusion parameter The multi-power source coordinated control strategy of uncertainty observer, through real-time monitoring of system parameter changes and timely correction of coordinated controller parameters, makes the vehicle always automatically work in an optimal or sub-optimal state. At the same time, due to the self-correction characteristics of the coordinated controller, high-level switching control suitable for different road surfaces and different drivers can be realized. The invention can effectively reduce the impact of mode switching, and make its coordinated control strategy have certain anti-interference and self-adaptability.

Description

A Coordinated Control of Adaptive E-H Switching for Hybrid Electric Vehicles Based on Parameter Observation method

technical field

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

Background technique

As we all know, hybrid vehicles have a variety of driving modes, and the appropriate driving/braking mode can be selected according to different driving conditions to achieve good fuel economy and dynamic performance, which inevitably involves mode switching, corresponding The torque required by the power source will also undergo sudden changes. If reasonable control is not applied, it will easily cause a significant impact, and even power interruption will occur. The engine, motor and clutch are the main shock sources, and a reasonable coordination of the output responses of the three is conducive to improving the quality of vehicle mode switching. The current research on the dynamic coordinated control of hybrid electric vehicles mainly uses the dynamic torque of the motor to compensate for the lack of torque response speed of the engine, but rarely considers the uncertainty of the model itself and external interference. The control strategy must be able to adapt to changes in various state variables.

SUMMARY OF THE INVENTION

In order to overcome the above technical defects, the present invention proposes a hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation, which can adapt to the changes of various state variables. The technical scheme includes the steps:

A hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation, comprising the following steps:

Step 1) The initial state of the HEV runs in pure electric mode. At this time, the brake CB1 is locked, the engine is turned off, and the motor MG2 is fully responsible for the torque required for driving the vehicle; at the same time, the vehicle speed sensor and the accelerator pedal position sensing device on the HEV Real-time monitoring of current vehicle speed information and accelerator pedal, brake pedal position signals, and input to the vehicle controller VCU, according to the preset switching speed threshold v _thr , the VCU judges whether to switch the mode;

Step 2) If the vehicle speed v ≥ v _thr ;

At this time, the hybrid electric vehicle meets the mode switching conditions and needs to perform mode switching. The VCU controls the brake CB1 to quickly disconnect, and the vehicle enters the engine towing from the pure electric mode. It is necessary to drag the engine through the clutch to the idle speed _widle in a short time, while reducing the longitudinal shock. Considering the large number of control objectives and control objects at this stage, an optimal coordinated controller based on dynamic programming is designed. By discretizing the above objective function and variable range, the dynamic programming global optimization algorithm is used to solve the optimal control variables (T _MG1 , T _CR1 , T _MG2 ); wherein, T _CR1 is the clutch transmission torque, T _MG1 and T _MG2 are the output torques of the motor MG1 and MG2 respectively; the motor MG2 torque PID compensation module can be expressed by the following formula:

Among them, k _p , k _d and _ki are the proportional, differential and integral coefficients of the vehicle speed tracking error Δv, and k' _p , k _d ' and _ki ' are the proportional, differential and integral coefficients of the acceleration error Δα. By adjusting the vehicle speed tracking The proportional, integral and differential coefficients of the error Δv and the acceleration error Δα, and output the motor MG2 torque compensation signal δ _T at the current moment;

Step 3) When the rotational speed of the engine w _e ≥ w _idle ;

At this time, the vehicle enters the speed synchronization stage, the engine starts to ignite, and the optimal coordinated controller controls (T _e , T _MG1 , T _CR1 , T _MG2 ), where T _e is the engine output torque to ensure the clutch end speed difference| w _cl-in -w _cl-out | is less than the set threshold ε ₀ to achieve speed synchronization;

Step 4) When |w _cl-in -w _cl-out |≤ε ₀ ; at this time, it is considered that the clutch enters the slippage stage, and the control objective of the optimal coordinated controller at this stage is to further reduce the end speed difference and slippage work. The stage objective function and its variable constraints are the same as step 3);

Step 5) When |w _cl-in -w _cl-out |≤ε ₁ ; at this time, the clutch end speed difference is small enough, it is considered that the clutch is fully engaged, the vehicle enters the hybrid drive mode, the motor MG1 adjusts the speed of the engine at the optimal speed, The whole vehicle is jointly driven by the engine and the motor MG2, the optimal torque distribution of multiple power sources is determined by energy management, and the mode switching process ends;

Step 6) In the process of hybrid electric vehicle mode switching, design the corresponding uncertainty parameter observer, through inputting multiple sets of data in the Cruise model, and applying data-driven theory, a parameter change prediction model is constructed to identify the current system parameter change. law.

The beneficial effects of the present invention are: the present invention directly builds a data-driven predictor based only on the input and output data of the Cruise model, can properly handle the multi-constraint problem and high-order nonlinearity in the system, and is very suitable for the HEV mode with fast and complex dynamic characteristics Parameter perturbation prediction during switching. Through the real-time observation of the system parameters and their rate of change, the parameters of the coordinated controller in the vehicle mode switching process can be adjusted accordingly, and the mode switching quality with high smoothness and robustness can be obtained.

Description of drawings

FIG. 1 is a layout diagram of a hybrid electric vehicle power system according to the present invention.

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

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

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

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and specific embodiments. As shown in Figure 1, the dual-planetary hybrid power system studied in this patent mainly includes a front-row ring gear R1, a front-row planetary carrier C1, a front-row sun gear S1, a rear-row ring gear R2, a rear-row planetary carrier C2, Rear sun gear S2. The engine is connected to the front planet carrier C1 through the clutch CR1 and the brake CB1, the rotor shaft of the motor MG1 is connected to the front sun gear S1 through the brake CB2, and the rotor shaft of the motor MG2 is connected to the rear sun gear S2. In addition, the front-row planet carrier C1 is connected with the rear-row ring gear R2, and the front-row ring gear R1, the rear-row planet carrier C2, and the output shaft are connected. The hybrid vehicle initially runs in pure electric mode, the brake CB1 is locked, the engine is turned off, and the motor MG2 is fully responsible for the torque required for driving the vehicle. At the same time, the vehicle speed sensor and accelerator pedal position sensing device on the hybrid vehicle monitors the current vehicle speed information and the position signals of the accelerator pedal and the brake pedal in real time, and inputs them to the vehicle controller (VCU), according to the preset switching speed threshold v _thr , the VCU determines whether to switch the mode;

If v>v _thr , it indicates that the hybrid vehicle meets the mode switching conditions and needs to perform mode switching. The VCU controls the brake CB1 to quickly disconnect, and the vehicle enters the engine tow from the pure electric mode. The control objective of the engine towing stage is to increase the pressure of the clutch CR1 as soon as possible, and the motor MG1 needs to drag the engine through the clutch to the _idle speed widle within 0.5s, while reducing the longitudinal impact. Considering the large number of control objectives and control objects at this stage, an optimal coordinated controller based on dynamic programming is designed.

objective function

The corresponding variable constraints are:

By discretizing the above objective function and variable range, the dynamic programming global optimization algorithm is used to solve the optimal control variables (T _MG1 , T _CR1 , T _MG2 ). Due to the significant torque fluctuations when the engine rotates at low speed, and the discontinuity in the torque transmission process of the clutch, it will be transmitted to the drive shaft to bring shocks. The addition of the motor MG2 torque PID compensation module offsets this part of the torque fluctuations. The motor MG2 torque PID compensation module can be expressed by the following formula:

Among them, k _p , k _d and _ki are the proportional, differential and integral coefficients of the vehicle speed tracking error Δv, and k' _p , k _d ' and _ki ' are the proportional, differential and integral coefficients of the acceleration error Δα. By adjusting the vehicle speed tracking The proportional, integral and differential coefficients of the error Δv and the acceleration error Δα are used to output the motor MG2 torque compensation signal δ _T at the current moment.

When w _e ≥ w _idle , the vehicle enters the 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-out | for speed synchronization. Similarly, the motor MG2 torque compensation module is designed to offset the torque fluctuation after the engine is fired.

The corresponding objective function is

The constraints are

As shown in Figure 2, when |w _cl-in -w _cl-out |≤ε ₀ , the clutch end speed difference is lower than the set threshold ε ₀ (this paper is set to 0.1rad/s), it is considered that the clutch enters In the slip-grinding stage, the control objective of the optimal coordinated controller at this stage is to further reduce the end speed difference and slip-grinding work. The objective function and its variable constraints in this stage are the same as above.

When |w _cl-in -w _cl-out |≤ε ₁ , the clutch end speed difference is small enough, that is, ε ₁ is equal to 0, it is considered that the clutch is fully engaged, and the vehicle enters the hybrid drive mode. The motor MG1 regulates the speed of the engine at the optimal speed, the whole vehicle is jointly driven by the engine and the motor MG2, and the optimal torque distribution of multiple power sources is determined by the energy management strategy to complete the EH mode switching process.

The overall control scheme of the coordinated control strategy involved in the entire handover process is shown in Figure 3. When the vehicle speed of the HEV exceeds the set threshold v _thr , the vehicle controller receives the mode switching signal for switching from pure electric to hybrid drive. At this time, the energy management strategy determines the steady state of the hybrid drive mode according to the vehicle driving conditions and fuel economy requirements. Lower the engine target torque T _e-set , the motor target torque T _m-set , and the clutch target torque T _c-set , so the actuators of the engine, clutch, and motor respectively adjust the throttle opening, clutch engagement pressure, three phase winding current to drive each power source to transition to the target torque. In order to reduce the shock and vibration of the drive shaft caused by the above-mentioned sudden change in torque, a phased coordinated control strategy as claimed in claim 1 is designed, and the optimal control and motor compensation are used to comprehensively solve the relationship between the engine, clutch and motor during the entire switching process. The required torques T _e-dem , T _c-dem , T _m-dem , and considering the actual operating limitations of the actuators, a torque limit module and a hysteresis module are designed respectively. The torque limit module of the engine is

T _e-min (ω)≤T _e (ω)≤T _e-max (ω)

The engine hysteresis module is

T _e (ω) is obtained through the steady-state look-up table model according to the current engine speed. The look-up table model is established based on the experimental data of the engine bench. τ _e is the time constant of the first-order inertial link of the engine.

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

T _CR1-min ≤T _CR1 ≤T _CR1-max

The torque limit and hysteresis module for the motor is

T _{Mm in} (ω)≤T _M (ω)≤T _M-max (ω)

τ _c , τ _m are the time constants of the first-order inertial elements of the clutch and the motor, respectively. The "actualized" engine execution torque T _e-in , clutch execution torque T _c-in , and motor execution torque T _m-in are input into the HEV vehicle model (ie, the system architecture shown in Figure 1 ), and the final The output torque true value signals T _e-act , T _c-act , T _m-act are fed back to the coordinated controller via sensor measurements. The sensor measurement module mainly simulates its measurement error, which is mathematically expressed as

Among them, Δ _e , Δ _c , and Δ _m are the error proportional coefficients of the engine, clutch, and motor, respectively. It is worth noting that the entire EH mode switching process is relatively dynamic, and there are various time-varying system parameters, such as the rotational inertia of each component, the internal resistance of the motor, the engine starting resistance torque, and the clutch friction coefficient. Due to the time-varying system parameters, it is easy to cause the controller effect to deteriorate. Based on this, a corresponding uncertainty parameter observer is designed, as shown in Figure 4. First, the double planetary array as shown in Figure 1 studied in this patent is built using the software Cruise. The whole vehicle model of the hybrid electric vehicle can better describe the transient dynamic characteristics of the vehicle. Then, by inputting multiple sets of data in the Cruise model, applying data-driven theory, and considering the discrete state equation of the EH switching process, at the kth sampling time, there is 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, and A, B, and C are the state, input, and output gain matrices of the system, respectively.

The input of the system is the engine, clutch, motor torque and motor operating temperature, that is, u(k)=[T _e (k) T _c (k) T _m (k) Q _m (k)] ^T , the system output is the clutch Terminal speed difference, clutch friction coefficient, motor internal resistance, namely y(k)=[Δω(k)μ _c (k) R _m (k)] ^T , through iterative operation, we have

Then for discrete time δ, there is the following matrix equation

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

Y _s =ψ _i X _s +φ _i U _s

in,

X _s = [x(1) x(2) x(3) … x(j)]

ψ _i = [C CA CA ² ... CA ^i-1 ] ^T

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

Y _f =ψ _i X _f +φ _i U _f

in

X _f =[x(i+1) x(i+2) x(i+3 )... x(i+j)]

because

X _f =A ⁱ X _s +σ _i U _s

σ _i = [A ^i-1 BA ^i-2 B ... B 0]

that is

Y _f =ψ _i A ⁱ ψ _i ^-1 Y _s +ψ _i (σ _i -A ⁱ ψ _i ^-1 φ _i )U _s +φ _i U _f

The input-output inter-control prediction equation can be established by applying the above iterative recursion method, and the parameter change prediction model can be constructed by collecting enough measurement data, so as to identify the current system parameter change law.

As shown in Figure 3, through the above-mentioned uncertainty observer identification, under the condition of system parameter perturbation, the optimal coordinated controller parameters α, β, γ, λ and PID parameters k _p , k _d , k can be simultaneously performed. _i , k′ _p , k _d ′ and k _i ′ are modified so that the system always automatically works under optimal or sub-optimal operating conditions.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples," or the like, is meant to incorporate the embodiment. A particular feature, structure, material, or characteristic described by an example or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms 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.

Although embodiments of the present 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 in these embodiments without departing from the principles and spirit of the invention, The scope of the invention is defined by the claims and their equivalents.

Claims (8)

1. a hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation, is characterized in that, comprises the following steps: Step 1) The initial state of the HEV runs in pure electric mode. At this time, the brake CB1 is locked, the engine is turned off, and the motor MG2 is fully responsible for the torque required for driving the vehicle; at the same time, the vehicle speed sensor and the accelerator pedal position sensing device on the HEV Real-time monitoring of current vehicle speed information and accelerator pedal, brake pedal position signals, and input to the vehicle controller VCU, according to the preset switching speed threshold v _thr , the VCU judges whether to switch the mode; Step 2) If the vehicle speed v ≥ v _thr ; At this time, the hybrid electric vehicle meets the mode switching conditions and needs to perform mode switching. The VCU controls the brake CB1 to quickly disconnect, and the vehicle enters the engine towing from the pure electric mode. It is necessary to drag the engine through the clutch until the idle speed w _idle in a short time, and at the same time reduce the longitudinal impact; considering the large number of control objectives and control objects at this stage, an optimal coordinated controller based on dynamic programming is designed. By discretizing the above The objective function and variable range, and the dynamic programming global optimization algorithm is used to solve the optimal control variables (T _MG1 , T _CR1 , T _MG2 ); among them, T _CR1 is the clutch transmission torque, and T _MG1 and T _MG2 are the outputs of the motors MG1 and MG2 respectively. torque; Step 3) When the rotational speed of the engine w _e ≥ w _idle ; At this time, the vehicle enters the speed synchronization stage, the engine starts to ignite, and the optimal coordinated controller controls (T _e , T _MG1 , T _CR1 , T _MG2 ), where T _e is the engine output torque to ensure the clutch end speed difference| w _cl-in -w _cl-out | is less than the set threshold ε ₀ to achieve speed synchronization; Step 4) When |w _cl-in -w _cl-out |≤ε ₀ ; at this time, it is considered that the clutch enters the slippage stage, and the control objective of the optimal coordinated controller at this stage is to further reduce the end speed difference and slippage work. The stage objective function and its variable constraints are the same as step 3); Step 5) When |w _cl-in -w _cl-out |≤ε ₁ ; at this time, the clutch end speed difference is small enough, it is considered that the clutch is fully engaged, the vehicle enters the hybrid drive mode, the motor MG1 adjusts the speed of the engine at the optimal speed, The whole vehicle is jointly driven by the engine and the motor MG2, the optimal torque distribution of multiple power sources is determined by energy management, and the mode switching process ends; Step 6) In the process of hybrid electric vehicle mode switching, design the corresponding uncertainty parameter observer, through inputting multiple sets of data in the Cruise model, and applying data-driven theory, a parameter change prediction model is constructed to identify the current system parameter change. law. 2. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 1, is characterized in that, in step 2), the optimal coordination controller based on dynamic programming uses dynamic programming global optimization algorithm The optimal control quantity is solved as: The objective function of this controller:

The corresponding variable constraints are:

Among them, t ₀ and t ₁ are the moment when the brake CB1 is completely disconnected and the engine speed is equal to the idle speed, j is the longitudinal shock degree of the vehicle, α and β are the weight coefficients of the shock degree and the engine drag time, respectively, ω _e is Engine speed, T _CR1 is the clutch transmission torque, T _ef is the engine starting resistance torque, T _MG1 and T _MG2 are the output torque of the motor MG1 and MG2 respectively, T _MG1-min and T _MG1-max are the minimum output of the motor MG1 respectively Torque and maximum torque limit, in the same way, T _MG2-min and T _MG2-max are the minimum torque and maximum torque of the motor MG2. 3. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 1, is characterized in that, in step 2), because there is significant torque fluctuation when the engine rotates at low speed, and clutch transmission The discontinuity in the torque process will be transmitted to the drive shaft to bring shocks. The addition of the motor MG2 torque PID compensation module offsets this part of the torque fluctuation. 4. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 1, is characterized in that, in step 3), the objective function of optimal coordination controller control is:

The constraints are

Among them, T _e-max is the maximum output torque of the engine, ω _{cl_in} and ω _{cl_out} are the speed of the clutch driving disc and the driven disc respectively, t ₂ is the instantaneous moment when the speed difference at the clutch end is equal to ε ₀ , γ and λ are respectively The weight coefficients of the clutch end speed difference and slipping work, T _CR1-min and T _CR1-max are the minimum and maximum values of the clutch transmission torque, respectively. Similarly, the motor MG2 torque PID compensation module is designed to offset the rotational speed of the engine after ignition. moment fluctuations. 5. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 1, is characterized in that, the concrete process of step 6) is: at first use software Cruise to build double planetary hybrid electric vehicle Then, by inputting multiple sets of data in the Cruise model, applying data-driven theory and considering the discrete state equation of the E-H switching process, at the kth sampling time, there is 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, and A, B, and C are the state, input, and output gain matrices of the system, respectively; The input of the system is the engine, clutch, motor torque and motor operating temperature, namely u(k)=[T _e (k) T _c (k) T _m (k) Q _m (k)] ^T , the system output is the clutch Terminal speed difference, clutch friction coefficient, motor internal resistance, namely y(k)=[Δω(k) μ _c (k) R _m (k)] ^T , the parameter change prediction model is obtained by iterative operation: Y _f =ψ _i A ⁱ ψ _i ^-1 Y _s +ψ _i (σ _i -A ⁱ ψ _i ^-1 φ _i )U _s +φ _i U _f in,

ψ _i = [C CA CA ² ... CA ^i-1 ] ^T ;

6. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 5, is characterized in that, in the design of uncertainty parameter observer, should focus on considering motor internal resistance, clutch friction coefficient, The rotational inertia of each component, the equivalent stiffness of the transmission shaft and the perturbation of the damping, the corresponding Cruise model data input is the internal working temperature of the motor, the torque of the engine, and the motors MG1 and MG2. 7. a kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 3 is characterized in that, motor MG2 torque PID compensation module can be expressed by the following formula:

Among them, k _p , k _d and _ki are the proportional, differential and integral coefficients of the vehicle speed tracking error Δv, and k' _p , k _d ' and _ki ' are the proportional, differential and integral coefficients of the acceleration error Δα. By adjusting the vehicle speed tracking The proportional, integral and differential coefficients of the error Δv and the acceleration error Δα are used to output the motor MG2 torque compensation signal δ _T at the current moment. 8. A kind of hybrid electric vehicle adaptive E-H switching coordination control method based on parameter observation according to claim 1, it is characterized in that, when the bottom-level actuators execute the upper-level commands, there are inevitably time delays and restrictions, by introducing respectively Engine torque limit module and torque hysteresis module, clutch torque limit and torque hysteresis module, motor torque limit module and hysteresis module make the design of the coordinated controller more reasonable.

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