CN112072631B - A highly robust and self-stabilizing hybrid energy storage system composite power control method - Google Patents

Abstract

The invention discloses a high-robustness self-stabilization type mixtureThe combined energy storage system composite power control method is used for passing the expected current i of the storage battery in the next sampling period in the current sampling period_{batt req}And desired current i of the super capacitor_{cap req}Respectively obtaining the tracking value v of the expected current of the storage battery or the super capacitor through a second-order tracking smoothing preprocessor_x1And tracking the trend v_x2Acquiring the actual current i in the current sampling period of the storage battery and the super capacitor through a sensorless state observer_xIs a feedback value z_x1Trend of change z_x2And system disturbance z_x3And a control quantity u_xThen using the corresponding relation based on the deviation e_x1And differential deviation e_x2By a progressive robust state function s_xFast convergence solving for converter control u_xFinally according to the control quantity u_xControlling the DC/DC converter to output the actual current i of the next sampling period_xThe actual total current i of the next sampling period is realized_totalApproximating the desired current i_{total req}The control process is carried out on the basis of the normal state of charge of the storage battery, and the problems of poor robustness, easiness in oscillation and saturation of control quantity in the traditional control are solved.

Description

A highly robust and self-stabilizing hybrid energy storage system composite power control method

technical field

The invention relates to the field of direct current distribution network control, in particular to a composite power control method of a highly robust self-stabilizing hybrid energy storage system.

Background technique

Due to the increasing scarcity of non-renewable energy such as oil and coal, people are paying more and more attention to the development of renewable energy, and more and more distributed power technologies have emerged one after another. The performance of the hybrid energy storage system composite power control method directly affects the operation characteristics of the DC microgrid, which makes the hybrid energy storage system composite power control method one of the current research focuses and hot spots. Model Predictive Control (MPC) is a nonlinear optimal control method, which has the characteristics of good control effect and strong robustness. At present, the current in the DC microgrid has been compensated and controlled by a layered structure, but the research on the input control quantity of the DC/DC converter is mainly concentrated on the basis of the traditional method. The hybrid energy storage converter based on this method Control strategies are the focus of current research. The energy storage converter control strategy based on the traditional method plays an active role in the coordinated control of the DC microgrid, but there are some shortcomings: poor robustness, easy oscillation and control saturation.

The idea of the present invention is to propose a composite power control method for a highly robust self-stabilizing hybrid energy storage system aiming at the shortcomings of the traditional method.

SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention discloses a composite power control method of a highly _robust and self-stabilizing _hybrid energy storage system. _req obtains the tracking value v _x1 and the tracking trend v _x2 of the expected current of the battery or super capacitor in the next sampling period through a second-order tracking smoothing preprocessor, and obtains the current sampling period of the battery and super capacitor through the sensorless state observer SLESO. The corresponding relationship between the feedback value z _x1 of the actual current i _x , the change trend z _x2 and the system disturbance z _x3 and the control quantity u _x , and then use the asymptotic robust state function s _x based on the deviation e _x1 and the differential deviation e _x2 to quickly converge Solve the converter control quantity u _x of the battery and super capacitor, and finally control the DC/DC converter according to the control quantity u _x to output the actual current i _x of the next sampling period, so that the actual total current i _total approximates the expected current i _{total req} , the above-mentioned control processes are all performed under the condition that the state of charge of the battery is normal, which solves the problems of poor robustness, easy oscillation and saturation of control quantities in traditional control.

In order to realize the above purpose, a kind of technical scheme that the present invention takes is:

A highly robust self-stabilizing hybrid energy storage system composite power control method, comprising the following steps:

S1: obtain the total expected current i _{total req} of the next sampling period on the DC bus side, obtain the expected current i _{batt req} of the battery in the next sampling period and the expected current i _{cap req} of the super capacitor in the next sampling period;

S2: Establish two second-order tracking smoothing pre-processors y(v _x1 , v _x2 )=SONTD(i _{x req} ) to obtain the tracking value v _x1 and the tracking trend v _x2 of the expected current, the second-order tracking smoothing preprocessing The device is defined as:

where x∈{batt,cap}, r _x is a positive real number, i _{x req} represents the expected current of the battery or super capacitor in the next sampling period, as the input value of the second-order tracking smoothing preprocessor, v _x1 represents the battery or super capacitor The tracking value of the expected current, v _x2 represents the tracking trend of the expected current of the battery or super capacitor;

S3: Establish a sensorless state observer SLESO, according to the actual current i _x of the battery or super capacitor and the control value u _x in the current sampling period, obtain the feedback value z _x1 and change trend of the actual current of the battery and super capacitor in the current sampling period Correspondence between z _x2 and system disturbance z _x3 and control quantity u _x , where x∈{batt,cap};

S4: Establish a highly robust controller NLRC and combine the system disturbance z _x3 to solve the control variable u _x , that is, use the asymptotic robust state function s _x based on the deviation e _x1 and differential deviation e _x2 to quickly converge to solve the converter of the battery and super capacitor control quantity u _x , where,

S5: control the DC/DC converter according to the control quantity u _x to output the actual current i _x of the next sampling period, so as to obtain the actual total current i _total =i _batt *u _batt +i _cap *u _cap of the next sampling period ; wherein, i _batt and i _cap represent the actual current of the battery and the actual current of the super capacitor respectively, and u _batt and u _cap respectively represent the control amount of the battery and the control amount of the super capacitor;

S6: Obtain the state of charge of the battery, and determine whether the state of charge is within the normal working range:

If so, go to step S1; if not, send a shutdown signal to disconnect all loads on the battery side.

Further, step S1 includes the following steps:

S11: According to the required power P _{total req} of the next sampling period on the DC bus side, obtain the total expected current i _{total req} in the next sampling period, and the formula for obtaining i _{total req} is defined as:

In the formula, V _bus is the DC bus voltage;

S12: Obtain the high frequency component i _{h req} of the total expected current i _{total req} by using a high frequency filter, thereby obtaining the low frequency component i _{l req} =i _{total req} −i _{h req of the total expected current i total req .}

S13: According to the preset stable terminal voltage V _{cap ref} of the super capacitor, use the battery to provide the super capacitor with a voltage regulation current control amount i _bc to form a self voltage regulation strategy SBVC of the super capacitor, and the self voltage regulation strategy control amount i _bc is calculated as The formula is defined as:

In the formula, v _cap is the instantaneous value of the terminal voltage of the super capacitor, k ₁ and k ₂ are positive real numbers, and T is a sampling period;

S14: Obtain the expected current i _{batt req} of the battery in the next sampling period, and the formula for obtaining i _{batt req} is defined as:

为蓄电池变换器占空比，

为超级电容变换器占空比，V_batt为蓄电池的电压值；In the formula,

is the duty cycle of the battery converter,

is the duty cycle of the supercapacitor converter, and V _batt is the voltage value of the battery;

S15: Obtain the expected current i _{cap req} of the super capacitor in the next sampling period, and the formula for obtaining i _{cap req} is defined as:

求取方程定义为：

Further, the feedback value z _x1 and its differential value of the actual current in the current sampling period of the battery and the super capacitor in the step S3

The equation to find is defined as:

系统扰动z_x3及其微分值

的求取方程定义为：Under the sensorless state observer, the change trend z _x2 and its differential value of the actual current of the battery and the supercapacitor in the current sampling period

System perturbation z _x3 and its differential value

The equation for finding is defined as:

In the formula, β ₁ , β ₂ , β ₃ , c ₁ , c ₂ , δ, b are positive real numbers, and fal() is a power function whose expression is as follows:

In the formula, δ and _c are positive real numbers, and sgn() is a sign function.

Further, step S4 includes the following steps:

S41: Obtain the deviation e _x1 between the tracking value v _x1 of the expected current of the battery and the super capacitor in the next sampling period and the feedback value z _x1 of the actual current of the battery and the super capacitor in the current sampling period, e x1 , and the expectation of the battery and the super capacitor in the next sampling period The differential deviation e _x2 of the current tracking trend v _x2 and the actual current change trend z _x2 of the battery and the supercapacitor in the current sampling period, the formulas for obtaining e _x1 and e _x2 are defined as:

简化后

式中，a₁和a₂满足0＜a₁＜1＜a₂＜2；S42: Establish a highly robust controller NLRC, that is, construct an asymptotic robust state function s _x based on the deviation e _x1 and the differential deviation e _x2 , the s _x is defined as:

after simplification

In the formula, a ₁ and a ₂ satisfy 0<a ₁ <1<a ₂ <2;

S43: Considering the system disturbance z _x3 , use a nonlinear approach function to approximate the first-order derivative of the asymptotic robust state function to obtain the control variable u _x , and the nonlinear approach function is:

Thus u _x =-b ^-1 [ρ(e _x2 ) ^-1 (e _x2 +ks _x +εsgn(s _x ))+z _x3 ], where k and ε are positive real numbers, ρ(e _x2 ) expression for:

Further, it is characterized in that the state of charge of the battery is obtained by using an unscented Kalman filter.

The beneficial effects of the present invention are:

(1) According to the low power density of the battery and the high power density of the super capacitor, the desired current i _{total req} is used to control the DC/DC converter in layers to control the output current i _total , the functional structure is clear, the response speed is fast, and the compensation effect is good;

(2) Aiming at the problem of frequent fluctuations in the voltage at the charging and discharging terminals of the super capacitor, the super capacitor self-stabilizing strategy SBVC is adopted, and the regulated supply current is provided through the battery, no additional regulated power supply is required, the structure is simple, and the self-stabilizing performance is excellent;

(3) The expected current i _{batt req} of the battery in the next sampling period and the expected current i _{cap req} of the super capacitor in the next sampling period are respectively preprocessed by the second-order tracking smoothing preprocessor SONTD, which improves the expected currents i _{batt req} and i _cap The smoothness of _req is obtained through the sensorless state observer SLESO to obtain the feedback value z _x1 of the actual current i _x of the battery and super capacitor, the change trend z _x2 and the corresponding relationship between the system disturbance z _x3 and the control amount u _x , reducing the number of hardware sensors The cost is reduced, the multi-state quantity is introduced, the softness of the control is improved, and the robustness of the control is enhanced.

(4) Use the asymptotic robust state function s _x based on the deviation e _x1 and the differential deviation e _x2 to quickly converge to solve the converter control variable u _x of the battery and super capacitor, and introduce states such as deviation, deviation differentiation, and nonlinear convergence state variables. In addition, the present invention has a high degree of nonlinearity, and also introduces power function, reciprocal and non-integer idempotent operations to ensure fast convergence and strong robustness.

Description of drawings

FIG. 1 is a flowchart of a method for compound power control of a highly robust and self-stabilizing hybrid energy storage system provided in an embodiment of the present invention;

2 is a circuit diagram of a hybrid energy storage system provided in an embodiment of the present invention;

3 is a high-pass filter installed on a bus side provided in an embodiment of the present invention;

4 is a simulation diagram of the expected current i _{cap req} and the actual current i _cap of the supercapacitor corresponding to the step-type total expected current i _{total req} provided in an embodiment of the present invention;

5 is a simulation diagram of the expected current i _{batt req} and the actual current i _batt of the battery corresponding to the step-type total expected current i _{total req} provided in an embodiment of the present invention;

6 is a simulation diagram of the actual total current i _total corresponding to the step-type total expected current i _{total req} provided in an embodiment of the present invention;

7 is a simulation diagram of the expected current i _{cap req} and the actual current i _cap of the supercapacitor corresponding to the random disturbance type total expected current i _{total req} provided in an embodiment of the present invention;

8 is a simulation diagram of the expected current i _{batt req} and the actual current i _batt of the battery corresponding to the random disturbance type total expected current i _{total req} provided in an embodiment of the present invention;

FIG. 9 is a simulation diagram of the actual total current i _total corresponding to the random disturbance type total expected current i _{total req} provided in an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

A method for compound power control of a highly robust self-stabilizing hybrid energy storage system provided by the present invention, as shown in FIG. 1 , includes the following steps:

S1: obtain the total expected current i _{total req} of the next sampling period on the DC bus side, obtain the expected current i _{batt req} of the battery in the next sampling period and the expected current i _{cap req} of the super capacitor in the next sampling period; the next sampling period of the battery The expected current i _{batt req} of the supercapacitor and the expected current i _{cap req} of the next sampling period of the super capacitor can be obtained by passing the total expected current i _{total req} of the next sampling period through a high-pass filter and a subtractor;

S2: Establish two second-order tracking smoothing pre-processors y(v _x1 , v _x2 )=SONTD(i _{x req} ) to obtain the tracking value v _x1 and the tracking trend v _x2 of the expected current, the second-order tracking smoothing preprocessing The device is defined as:

where x∈{batt,cap}, r _x is a positive real number, i _{x req} represents the expected current of the battery or super capacitor in the next sampling period, as the input value of the second-order tracking smoothing preprocessor, v _x1 represents the battery or super capacitor The tracking value of the expected current, v _x2 represents the tracking trend of the expected current of the battery or super capacitor;

S3: Establish a sensorless state observer SLESO, according to the actual current i _x of the battery or super capacitor and the control value u _x in the current sampling period, obtain the feedback value z _x1 and change trend of the actual current of the battery and super capacitor in the current sampling period Correspondence between z _x2 and system disturbance z _x3 and control quantity u _x , where x∈{batt,cap};

S4: Establish a highly robust controller NLRC and combine the system disturbance z _x3 to solve the control variable u _x , which is equivalent to using the asymptotic robust state function s _x based on the deviation e _x1 and differential deviation e _x2 to quickly converge to solve the battery and super capacitor The converter control quantity u _x , where,

S5: control the DC/DC converter according to the control quantity u _x to output the actual current i _x of the next sampling period, so as to obtain the actual total current i _total =i _batt *u _batt +i _cap *u _cap of the next sampling period ; wherein, i _batt and i _cap represent the actual current of the battery and the actual current of the super capacitor respectively, and u _batt and u _cap respectively represent the control amount of the battery and the control amount of the super capacitor;

S6: Estimate the state of charge of the battery according to the unscented Kalman filter, optionally, the state of charge of the battery is obtained by using the unscented Kalman filter to determine whether the state of charge is within the normal working range:

If so, go to step S1; if not, send a shutdown signal to disconnect all loads on the battery side.

As shown in Figure 3, step S1 includes the following steps:

S11: According to the required power P _{total req} of the next sampling period on the DC bus side, obtain the total expected current i _{total req} in the next sampling period, and the formula for obtaining i _{total req} is defined as:

In the formula, V _bus is the DC bus voltage;

S12: Obtain the high frequency component i _{h req} of the total expected current i _{total req} by using a high frequency filter, thereby obtaining the low frequency component i _{l req} =i _{total req} −i _{h req of the total expected current i total req .}

S13: According to the preset stable terminal voltage V _{cap ref} of the super capacitor, use the battery to provide the super capacitor with a voltage regulation current control amount i _bc to form a self voltage regulation strategy SBVC of the super capacitor, and the self voltage regulation strategy control amount i _bc is calculated as The formula is defined as:

In the formula, v _cap is the instantaneous value of the terminal voltage of the super capacitor, k ₁ and k ₂ are positive real numbers, and T is a sampling period;

S14: Obtain the expected current i _{batt req} of the battery in the next sampling period, and the formula for obtaining i _{batt req} is defined as:

为蓄电池变换器占空比，

为超级电容变换器占空比，V_batt为蓄电池的电压值；In the formula,

is the duty cycle of the battery converter,

is the duty cycle of the supercapacitor converter, and V _batt is the voltage value of the battery;

S15: Obtain the expected current i _{cap req} of the super capacitor in the next sampling period, and the formula for obtaining i _{cap req} is defined as:

In Fig. 2, the input terminal of the current controller corresponding to the super capacitor is the expected current i _{cap req} of the next sampling period and the actual current i _cap of the current sampling period, and outputs the control amount u of the PWM of the super capacitor converter _cap , PWM and ₂ control switches T3 and _T4 are the DC/DC converter, the control quantity u _cap outputs the actual current i _cap of the next sampling period through the DC/DC converter, so as to realize the actual current of the next sampling period The current i _cap approaches the expected current i _{cap req} ; the input terminal of the current controller (current controller) corresponding to the battery is the expected current i _{batt req} of the next sampling period and the actual current i _batt of the current sampling period, and outputs the supercapacitor converter PWM The control variable u _batt , PWM and two control switches T ₃ and T ₄ are the DC/DC converter, and the control variable u _cap outputs the actual current i _batt of the next sampling period through the DC/DC converter to realize the next The actual current i _batt in the sampling period approaches the expected current i _batt , and finally, after one sampling period is completed, the actual current i _total = i _batt *u _batt +i _cap *u _cap approaches the total expected current i _total in the next sampling period on the bus side _req .

In Figure 3, the second-order voltage-controlled active high-pass filter is installed on the DC bus side to filter out errors such as high-order harmonics, and distribute the high-frequency component i _{h req} and the low-frequency component i of the desired current i _{total req l req} . According to the virtual short and virtual off characteristics of the amplifier, the KCL equations of two points h and j are selected, and the equations are defined as

In the formula, the capacitance value C ₁ =C ₂ =C ₀ , and the second-order high-pass filter transfer function is obtained:

Take pass-band gain A _up =1+R _n /R _m , quality factor Q=|3-A _up |-1, cut-off frequency f _p =(2πR _t C ₀ ) ^-1 , s=jω, ω=2f, Then the above formula can be rewritten as

Due to the use of second-order high-pass filtering, when the external signal frequency f<<f _p , the amplitude-frequency characteristic curve is attenuated according to the slope of 40dB/dec, the quality factor Q=0.8, the cut-off frequency fp ₌ 159HZ, and the passband gain is A _up =1.75, R _t =1kΩ, _Rm =4kΩ, _Rn =3kΩ, C ₁ =C ₂ =C ₀ =1μF, calculate the input-output transfer function G _hp (s).

求取方程定义为：Further, the feedback value z _x1 and its differential value of the actual current in the current sampling period of the battery and the super capacitor in the step S3

The equation to find is defined as:

系统扰动z_x3及其微分值

的求取方程定义为：The variation trend z _x2 and its differential value of the actual current of the battery and the super capacitor in the current sampling period

System perturbation z _x3 and its differential value

The equation for finding is defined as:

In the formula, β ₁ , β ₂ , β ₃ , c ₁ , c ₂ , δ, b are positive real numbers, and fal() is a power function whose expression is as follows:

In the formula, δ and _c are positive real numbers, and sgn() is a sign function.

Further, step S4 includes the following steps:

S41: Obtain the deviation e _x1 between the tracking value v _x1 of the expected current of the battery and the super capacitor in the next sampling period and the feedback value z _x1 of the actual current of the battery and the super capacitor in the current sampling period, e x1 , and the expectation of the battery and the super capacitor in the next sampling period The differential deviation e _x2 of the current tracking trend v _x2 and the actual current change trend z _x2 of the battery and the supercapacitor in the current sampling period, the formulas for obtaining e _x1 and e _x2 are defined as:

根据当前值远离期望值时，系统快速追踪当前值趋近期望值，此时微分偏差e_x2较大，对渐进鲁棒状态函数s_x简得到简化后S42: Establish a highly robust controller NLRC, that is, construct an asymptotic robust state function s _x based on the deviation e _x1 and the differential deviation e _x2 , the s _x is defined as:

According to when the current value is far away from the expected value, the system quickly tracks the current value and approaches the expected value. At this time, the differential deviation e _x2 is large, and the asymptotic robust state function s _x is simplified after simplified

In the formula, a ₁ and a ₂ satisfy 0<a ₁ <1<a ₂ <2;

S43: Considering the system disturbance z _x3 , use a nonlinear approach function to approximate the first-order derivative of the asymptotic robust state function to obtain the control variable u _x , and the nonlinear approach function is:

利用公式1、2、3、4代入，从而u_x＝-b^-1[ρ(e_x2)^-1(e_x2+ks_x+εsgn(s_x))+z_x3]，式中k和ε为正实数，ρ(e_x2)表达式为：

After derivation of Equation 5, enter into Equation 6, and the derivation of Equation 5 involves

Substitute using formulas 1, 2, 3, and 4, so that u _x =-b ^-1 [ρ(e _x2 ) ^-1 (e _x2 +ks _x +εsgn(s _x ))+z _x3 ], where k and ε is a positive real number, ρ(e _x2 ) is expressed as:

In this implementation, the parameters are selected as follows: in Figure 2, the line inductance L _b =L _c =0.56mH, the line resistance R _b =R _c =0.1Ω, the filter capacitor C = 30μF, the battery voltage V _batt =72V, the DC bus side Voltage V _bus = 220V, super capacitor voltage V _cap = 50V;

Second-order voltage-controlled active high-pass filter R _t =1kΩ, _Rm =4kΩ, _Rn =3kΩ, C ₁ =C ₂ =C ₀ =1μF;

The parameters of the super capacitor terminal voltage SBVC controller are: k ₁ =1, k ₂ =10;

The control parameters of the sensorless state observer SLESO are: r _x =200;

The parameters of the non-singular terminal sliding mode ADRC controller are: β ₁ =100, β ₂ =300, β ₃ =100000, c ₁ =0.5, c ₂ =0.25, k=14, ε=0.00001, δ=0.01, b=1000, a ₁ =0.95, a ₂ =1.02;

The simulation diagrams of the expected current i _{cap req} and the actual current i _cap of the super capacitor, the expected current i _{batt req} and the actual current i _batt of the battery, the total expected current i _{total req} and the actual current i _total on the bus side are shown in Figure 4 to Figure 6 As shown in Figure 6, the total expected current i _{total req} and the actual total current i _total on the bus side have been approximated.

In Fig. 7, Fig. 8 and Fig. 9, the required parameters are the same as the aforementioned parameters, but at this time the total expected current i _{total req} adopts random disturbance current. It can be seen from the figures that the actual total current i _total approximates the total expected current i _{total req} , Control robustness is strong.

The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (5)

1. A composite power control method for a high-robustness self-stabilization type hybrid energy storage system is characterized by comprising the following steps:

s1: obtaining the total expected current i of the next sampling period on the side of the direct current bus_{total req}Obtaining the expected current i of the storage battery in the next sampling period_{batt req}And the expected current i of the super capacitor in the next sampling period_{cap req}；

S2: establishing two second-order tracking smoothing preprocessors y (v)_x1,v_x2)＝SONTD(i_{x req}) Obtaining a tracking value v of the desired current_x1And tracking the trend v_x2The second-order tracking smoothing preprocessor is defined as:

where x ∈ { batt, cap }, r_xIs a positive real number, i_{x req}Representing the expected current of the battery or super capacitor in the next sampling period as the input value of the second-order tracking smoothing preprocessor, v_x1A tracking value, v, representing the desired current of the accumulator or supercapacitor_x2A tracking trend representing a desired current of the battery or supercapacitor;

s3: establishing a sensor-free state observer SLESO, and storing the actual current i of the storage battery or the super capacitor according to the current sampling period_xAnd a control quantity u_xCalculating the feedback value z of the actual current in the current sampling period of the storage battery and the super capacitor_x1Trend of change z_x2And system disturbance z_x3And a control quantity u_xWherein x ∈ { batt, cap };

s4: building a highly robust controller NLRC and combining system disturbance z_x3Solving for the controlled variable u_xUsing a base deviation e_x1And differential deviation e_x2By a progressive robust state function s_xFast convergence solving converter control quantity u of storage battery and super capacitor_xWherein, in the step (A),

x∈{batt,cap}；

s5: according to the control quantity u_xControlling the DC/DC converter to output the actual current i of the next sampling period_xTo obtain the actual total current i of the next sampling period_total＝i_batt*u_batt+i_cap*u_cap(ii) a Wherein i_batt、i_capRespectively representing the actual current of the accumulator and the actual current of the supercapacitor, u_batt、u_capRespectively representing the control quantity of the storage battery and the control quantity of the super capacitor;

s6: acquiring the charge state of the storage battery, and judging whether the charge state is within a normal working range:

if yes, go to step S1; if not, a shutdown signal is sent, and all loads on the storage battery side are disconnected.

2. The method for controlling the composite power of the high-robustness self-stabilized hybrid energy storage system according to claim 1, wherein the step S1 comprises the following steps:

s11: according to the next sampling period required power P of the DC bus side_{total req}Calculating the total expected current i of the next sampling period_{total req}The said i_{total req}The formula is defined as:

in the formula, V_busIs a dc bus voltage;

s12: obtaining a total desired current i using a high frequency filter_{total req}High frequency component i of_{h req}To obtain the total desired current i_{total req}Of the low-frequency component i_{l req}＝i_{total req-ih req}；

S13: according to the preset stable terminal voltage V of the super capacitor_{cap ref}And the storage battery is used for providing voltage stabilization for the super capacitorCurrent control quantity i_bcForming a self-stabilizing strategy SBVC of the super capacitor, wherein the self-stabilizing strategy controls the quantity i_bcThe formula is defined as:

in the formula, v_capIs instantaneous value of terminal voltage of super capacitor, k₁And k₂Is a positive real number, and T is a sampling period;

s14: obtaining expected current i of storage battery in next sampling period_{batt req}The said i_{batt req}The formula is defined as:

in the formula (I), the compound is shown in the specification,

for the duty cycle of the battery converter,

is the duty ratio of the super capacitor converter, V_battThe voltage value of the storage battery;

s15: obtaining expected current i of super capacitor in next sampling period_{cap req}The said i_{cap req}The formula is defined as:

3. the composite power control method for the high-robustness self-stabilization type hybrid energy storage system according to claim 1, wherein the feedback value z of the actual current of the storage battery and the super capacitor in the current sampling period in the step S3 is_x1And differential value thereof

The solving equation is defined as:

under the sensorless state observer SLESO, the variation trend z of the actual current of the battery and the super capacitor in the current sampling period_x2And differential value thereof

System disturbance z_x3And differential value thereof

The solving equation of (2) is defined as:

in the formula, beta₁、β₂、β₃、c₁、c₂δ, b are positive real numbers, and fal () is a power function whose expression is as follows:

where δ and c are positive real numbers and sgn () is a sign function.

4. The composite power control method for the high-robustness self-stabilization type hybrid energy storage system according to claim 3, wherein the step S4 comprises the following steps:

s41: obtaining a tracking value v of the expected current of the next sampling period of the storage battery and the super capacitor_x1Feedback value z of actual current of current sampling period of storage battery and super capacitor_x1Deviation e of_x1Trend v of the expected current for the next sampling period of the accumulator and the supercapacitor_x2Current sampling period of storage battery and super capacitorTrend of change z of actual current of period_x2Differential deviation e of_x2Said e is_x1And e_x2The formula of (2) is defined as:

s42: building a highly robust controller NLRC, i.e. construction based on the deviation e_x1And differential deviation e_x2By a progressive robust state function s_xS of said s_xThe definition formula is:

after simplification

In the formula, a₁And a₂Satisfies 0 < a₁＜1＜a₂＜2；

S43: taking into account the system disturbance z_x3The control quantity u is obtained by approximating the first derivative of the gradual robust state function by a nonlinear approach function_xThe nonlinear approximation function is:

thus u_x＝-b^-1[ρ(e_x2)^-1(e_x2+ks_x+εsgn(s_x))+z_x3]Where k and ε are positive real numbers, ρ (e)_x2) The expression is as follows:

5. the composite power control method for the high-robustness self-stabilization type hybrid energy storage system according to claim 1, wherein the state of charge of the storage battery is obtained by using an unscented kalman filter.

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