CN115347218A - Cascade control method of air supply system of proton exchange membrane fuel cell - Google Patents

Cascade control method of air supply system of proton exchange membrane fuel cell Download PDF

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CN115347218A
CN115347218A CN202210871409.5A CN202210871409A CN115347218A CN 115347218 A CN115347218 A CN 115347218A CN 202210871409 A CN202210871409 A CN 202210871409A CN 115347218 A CN115347218 A CN 115347218A
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air compressor
fuel cell
oxygen excess
excess ratio
supply system
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徐鸣
马龙华
汪依宁
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Zhejiang University of Science and Technology ZUST
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04082—Arrangements for control of reactant parameters, e.g. pressure or concentration
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
    • H01M8/04082—Arrangements for control of reactant parameters, e.g. pressure or concentration
    • H01M8/04089—Arrangements for control of reactant parameters, e.g. pressure or concentration of gaseous reactants
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M8/00—Fuel cells; Manufacture thereof
    • H01M8/06—Combination of fuel cells with means for production of reactants or for treatment of residues
    • H01M8/0606—Combination of fuel cells with means for production of reactants or for treatment of residues with means for production of gaseous reactants
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract

The invention provides a cascade control method of an air supply system of a proton exchange membrane fuel cell, aiming at adjusting the oxygen excess ratio of the proton exchange membrane fuel cell system to enable the oxygen excess ratio to track the optimal value quickly, thereby avoiding the oxygen starvation phenomenon of the air supply system and ensuring the stable and safe operation of the fuel cell system. The method comprises the following steps: 1) Establishing a high-precision control model of an air supply system; 2) Establishing an air compressor neural network model; 3) Fitting an optimal oxygen excess ratio curve of the proton exchange membrane fuel cell system; 4) And designing an oxygen excess ratio cascade controller of the proton exchange membrane fuel cell system. Compared with the prior art, the invention can effectively improve the dynamic response speed of the system and improve the output power of the power grid side of the system.

Description

Cascade control method of air supply system of proton exchange membrane fuel cell
Technical Field
The invention relates to a cascade control method of an air supply system of a proton exchange membrane fuel cell, belonging to the technical field of fuel cell control.
Background
Proton exchange membrane fuel cells have been used in the fields of new energy vehicles, new energy power stations, cogeneration, and the like, due to their advantages of high efficiency, high specific power, low operating temperature, low noise, and the like. The air supply system of the proton exchange membrane fuel cell is an important component of a fuel cell stack, and the power consumed by a fan accounts for most of the energy consumed by the whole auxiliary system. The flow of cathode oxygen in the galvanic pile needs to be accurately controlled, and if the flow of the oxygen is too low, the oxygen supply of the fuel cell pile is insufficient, so that the output voltage of the pile is reduced, namely the phenomenon of oxygen starvation is generated; however, when the flow rate of the cathode oxygen exceeds a certain limit, the output voltage of the stack cannot be increased, and if the flow rate of the oxygen is continuously increased, the power consumed by the fan is increased, so that the net output power of the whole galvanic pile (the power consumed by the galvanic pile auxiliary device subtracted from the galvanic pile output power) is reduced. Therefore, the method has very important significance for reasonably modeling and effectively controlling the air supply system of the proton exchange membrane fuel cell.
The air supply system of the proton exchange membrane fuel cell is a strong-hysteresis, multi-coupling multivariable nonlinear system. Currently, researchers have proposed various control strategies for the adjustment of the oxygen excess ratio of the air supply system, which are typically fuzzy control, model predictive control neural network control, and the like. Fuzzy control is robust and does not depend on a particularly accurate system model, but the step of establishing a fuzzy rule base requires a great deal of design experience for designers, otherwise it is difficult to design a high-performance fuzzy controller. Predictive control has numerous advantages, but predictive models consume a certain amount of computational resources. In addition, theoretical research in the field of nonlinear predictive control is still incomplete, and therefore, certain thresholds exist in the aspects of system stability, robustness analysis and the like. The neural network controller is a "black box" for the user. Although it has high fault tolerance and adaptability, it relies on a large amount of measured data for training. And most of them do not consider external interference and internal parameter uncertainty in the actual operation process of the system, so that the robustness and the interference resistance are not good.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides a cascade control method which has strong anti-interference performance and can improve the operation efficiency of an air supply system of a proton exchange membrane fuel cell.
The purpose of the invention is realized by the following technical scheme: a cascade control method of air supply system of proton exchange membrane fuel cell includes the following steps:
the method comprises the following steps: according to a material conservation equation, an electrochemical reaction principle and a heat conduction law mechanism, a system mechanism model capable of reflecting the internal reaction of the proton exchange membrane fuel cell is established, and a controller-oriented four-order nonlinear state space model of the simplified air supply system is obtained.
Step two: collecting air compressor outlet flow, air compressor rotating speed and overpressure ratio data in a fuel cell air supply system, inputting the overpressure ratio and the air compressor rotating speed as inputs, inputting the air compressor outlet flow as an output into a radial basis function neural network, and adding an air compressor model obtained through network training into a four-order nonlinear state space model;
step three: and fitting an optimal oxygen excess ratio change curve by taking the maximum electric pile output power as a target according to the relation between the maximum electric pile output power and the load current and the oxygen excess ratio.
Step four: the cascade control is carried out based on a simplified fourth-order nonlinear state space model, and specifically comprises the following steps: the oxygen excess ratio adjusting outer ring takes tracking and fitting the optimal oxygen excess ratio as a control target, and outputs an air compressor rotating speed set value under the optimal oxygen excess ratio condition for the air compressor rotating speed adjusting inner ring on the basis of a second-order active disturbance rejection control strategy according to the deviation of the actual oxygen excess ratio of the system and the set value; the inner ring for adjusting the rotating speed of the air compressor takes the driving voltage of the air compressor as a control variable, and a second-order sliding mode control strategy is selected to quickly adjust the rotating speed of the air compressor to reach a given value.
Further, the fourth-order nonlinear state space model of the air supply system is as follows:
Figure BDA0003760877370000021
in the formula, x 1 And x 2 Are respectively fuelCathode oxygen and nitrogen partial pressures, x, in the air supply system of the cell 3 Is the speed of the air compressor, x 4 Supplying the cathode with lumen pressure; u = u cp The driving voltage of the air compressor is the control variable; i is st Is the stack load current, it is considered a measurable external disturbance. (ii) a W is a group of cp Representing the outlet flow of the air compressor, which is a nonlinear function to be identified about the rotating speed and the overpressure ratio of the air compressor; the expressions for the intermediate variables X and α are as follows:
X＝x 1 +x 2 +c 2
Figure BDA0003760877370000022
wherein c is 1 ,…c 20 Is a constant coefficient.
Further, the oxygen excess ratio is:
Figure BDA0003760877370000031
wherein c is 23 ,c 24 Is a constant coefficient.
The oxygen excess ratio can be influenced by the load current and changes in real time, and in order to improve the output power of the galvanic pile, an optimal oxygen excess ratio curve needs to be fitted through experiments. Selecting a current range of [100A 300A ], taking a test point every 50A, observing and recording the power output condition of the galvanic pile of a fourth-order nonlinear model, then respectively fitting an output power change curve under each current, determining a maximum power output point under each current, and finally fitting the maximum power output point under each current into a relation curve of an optimal oxygen excess ratio and the galvanic pile load current by a least square method, wherein the relation curve is as follows:
Figure BDA0003760877370000032
wherein z is 2,opt The optimal oxygen excess ratio is obtained for the fit.
Further, the cascade control method can quickly track the optimal oxygen excess ratio through an outer ring, and the implementation specific process comprises the following steps: firstly, finding a steady-state working point of a fourth-order nonlinear state space model added into an air compressor model through a simulation experiment, linearizing the fourth-order nonlinear state space model added into the air compressor model at the steady-state working point, and obtaining a transfer function between the excess oxygen ratio and the rotating speed of the air compressor:
Figure BDA0003760877370000033
transforming the two-dimensional differential equation into a differential equation form, and integrating the two sides to obtain a second-order differential standard form:
Figure BDA0003760877370000034
wherein y represents the oxygen excess ratio.
Further, the cascade control method adjusts the rotating speed of the inner ring air compressor through a second-order sliding mode control strategy, and the specific implementation process comprises the following steps:
selecting a sliding plane as follows:
8＝ω cp -ω cp，ref
s represents a sliding plane; omega cp,ref The method comprises the following steps of outputting a given rotating speed value of the air compressor by an outer ring, and deriving a sliding plane s:
Figure BDA0003760877370000035
the following feedback control rate is designed:
Figure BDA0003760877370000036
such that:
Figure BDA0003760877370000037
wherein the virtual control quantity v is calculated by a supercoiling algorithm.
The invention has the advantages that: the cascade control method of the air supply system of the proton exchange membrane fuel cell designed by the invention consists of an oxygen excess ratio tracking (outer ring) loop and an air compressor rotating speed adjusting (inner ring) loop, and the control target is to adjust the oxygen excess ratio under the working state of the fuel cell with frequently changed load, so that the oxygen excess ratio can quickly track a real-time optimal value, thereby maximizing the output power of the fuel cell stack grid side while avoiding insufficient oxygen supply of the stack.
Real-time values of internal and external disturbances of the system are reconstructed through an extended state observer in the outer ring active disturbance rejection controller, and then the influence of the disturbances is counteracted through the control rate, so that the designed cascade controller has good robustness and disturbance rejection. The buffeting phenomenon of the air compressor is improved through a supercoiling algorithm of the inner-ring second-order sliding mode controller, so that the controller can be suitable for an actual fuel cell stack system.
Drawings
FIG. 1 is a schematic view of the fuel cell air supply system of the present invention;
FIG. 2 is a control block diagram of a cascade control method of an air supply system of a PEM fuel cell according to the present invention;
FIG. 3 is a fitted optimal oxygen excess ratio curve;
FIG. 4 is a graph comparing the control effect of cascade control with other control methods;
FIG. 5 is a diagram of stack output power under a cascade control method;
fig. 6 is a control effect diagram under the condition that the parameters are not determined.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive efforts based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
As shown in fig. 1 and fig. 2, the embodiment of the present invention provides a cascade control method for an air supply system of a proton exchange membrane fuel cell, in which a tracking real-time optimal oxygen excess ratio is used as a control target, and an input voltage of an air compressor is used as a control variable. On the basis of considering that the air supply system of the fuel cell is a nonlinear system with the relative order of 2, a cascade control method which takes an active disturbance rejection control strategy as an outer ring oxygen ratio controller and a second-order sliding mode control strategy as an inner ring air compressor rotating speed controller is designed. Aiming at external disturbance and internal parameter uncertainty existing in the system operation process, the extended state observer is used for real-time estimation of system interference, and the influence of the system interference is compensated through a control rate. The fuel cell control method of the embodiment can realize the rapid unbiased tracking of the optimal oxygen excess ratio, and can effectively improve the output power of the electric pile while avoiding the phenomenon of oxygen starvation.
Specifically, the cascade control method of the air supply system of the proton exchange membrane fuel cell comprises the following steps:
the method comprises the following steps: according to mechanisms such as a material conservation equation, an electrochemical reaction principle, a heat conduction law and the like, a system mechanism model capable of reflecting the internal reaction of the proton exchange membrane fuel cell is established, and a controller-oriented four-order nonlinear state space model of the simplified air supply system is obtained through deduction.
Step two: collecting air compressor outlet flow, air compressor rotating speed and overpressure ratio data in a fuel cell air supply system, inputting the overpressure ratio and the air compressor rotating speed as inputs, inputting the air compressor outlet flow as an output into a radial basis function neural network, and adding an air compressor model obtained through network training into a four-order nonlinear state space model;
step three: and fitting an optimal oxygen excess ratio change curve by taking the maximum electric pile output power as a target according to the relationship between the electric pile maximum output power, the load current and the oxygen excess ratio.
Step four: the cascade control is carried out based on a simplified fourth-order nonlinear state space model, and specifically comprises the following steps: the oxygen excess ratio adjusting outer ring takes tracking and fitting the optimal oxygen excess ratio as a control target, and outputs an air compressor rotating speed set value under the optimal oxygen excess ratio condition for the air compressor rotating speed adjusting inner ring on the basis of a second-order active disturbance rejection control strategy according to the deviation of the actual oxygen excess ratio of the system and the set value; the inner ring for adjusting the rotating speed of the air compressor takes the driving voltage of the air compressor as a control variable, and a second-order sliding mode control strategy is selected to quickly adjust the rotating speed of the air compressor to reach a given value.
The following steps are described in detail:
the first step is specifically that the fourth-order nonlinear model of the fuel cell air supply system is as follows:
Figure BDA0003760877370000051
in the formula, the state variable x 1 And x 2 Cathode oxygen and nitrogen partial pressures, x, in separate fuel cell air supply systems 3 Is the speed of the air compressor, x 4 Supplying lumen pressure to the cathode. u = u cp The driving voltage of the air compressor is the control variable. I.C. A st For stack load currents, it can generally be considered as a measurable external disturbance. The expressions for X and α are as follows:
X＝x 1 +x 2 +c 2
Figure BDA0003760877370000052
W cp representing the outlet flow of the air compressor, is a nonlinear function to be identified about the rotating speed and the overpressure ratio of the air compressor, c 1 ,...c 20 The values of (a) are calculated from the model parameters in table 2 according to the formula in table 1.
TABLE 1 coefficient calculation formula table of fourth-order nonlinear state space model
Figure BDA0003760877370000061
TABLE 2 fourth-order nonlinear state space model parameter table
Figure BDA0003760877370000071
Figure BDA0003760877370000081
The third step is specifically as follows:
the oxygen excess ratio is:
Figure BDA0003760877370000082
wherein c is 23 ,c 24 Calculated from tables 1 and 2.
The oxygen excess ratio can be influenced by the load current and changes in real time, and in order to improve the output power of the galvanic pile, an optimal oxygen excess ratio curve needs to be fitted through experiments. Selecting a current range of [100A 300A ], taking a test point every 50A, as shown in FIG. 3, selecting an oxygen excess ratio of 1.8,2.0,2.2,2.4,2.6 and 2.8 at each test current, observing and recording the power output condition of the galvanic pile of a fourth-order nonlinear model, respectively fitting an output power change curve under each current, determining a maximum power output point under each current, and finally fitting the maximum power output points under each current into a final optimal oxygen excess ratio curve by a least square method, wherein the expression is as follows:
Figure BDA0003760877370000083
wherein z is 2,opt For optimal oxygen excess ratio.
4. Step four
The fourth step is specifically as follows:
the actual oxygen excess ratio can quickly track the current optimal oxygen excess ratio and is realized by an outer ring based on a second-order active disturbance rejection controller, and the specific process of the design of the outer ring second-order active disturbance rejection controller is as follows:
the second-order differential standard form of the second-order active disturbance rejection controller design is as follows:
Figure BDA0003760877370000091
wherein y = z 2 Is the output oxygen excess ratio, f anti Is the total disturbance of the system, b 0 Is a system parameter.
Let w 1 ＝y,
Figure BDA0003760877370000092
w 3 ＝f anti ,
Figure BDA0003760877370000093
Then a state space expression can be derived:
Figure BDA0003760877370000094
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003760877370000095
B＝[0 b 0 0] T ,E＝[0 0 1] T ,C＝[1 0 0]。
the linear extended observer equation corresponding to the active disturbance rejection controller employed is:
Figure BDA0003760877370000096
wherein G = [ beta ] 1 β 2 β 3 ] T Is the observer parameter to be determined, and is selected in such a way that it is ensured that the error matrix A-GC is HullAll characteristic roots of Witz, i.e., A-GC, are in the left half plane of the complex plane.
β 1 ，β 2 And beta 3 Can be calculated according to the following formula:
β 1 ＝3ω o ；β 2 ＝3ω o 2 ；β 3 ＝ω o 3
wherein ω is o Representing the observer bandwidth. Coefficient of controller k p And k d The dynamic performance of the system is affected, and the following settings can be adopted:
k p ＝ω c 3 ；k d ＝2ω c
wherein ω is c Representing the controller bandwidth.
Parameter b 0 It has a great influence on the stability margin of the system, and therefore, the value of b must be as close as possible to the true value in order to find the value of b close to the true value 0 Finding a steady-state working point of a four-order nonlinear state space model added into the air compressor model through a simulation experiment, and finding a load current I after multiple tests st At 220A, the control voltage u of the air compressor cp At 180V, four state quantities [ x ] of the system 1 ,x 2 ,x 3 ,x 4 ]Can be stabilized at [103300,187900,11110,372900 ]]And the characteristic vectors of the linear state space model after linearization are all positioned in the left half plane of the complex plane, namely the Helvelz condition of system stability is met. Linearizing a four-order nonlinear state space model added into an air compressor model at a stable working point to obtain a transfer function between the excess oxygen ratio and the rotating speed of the air compressor:
Figure BDA0003760877370000101
transforming the two-dimensional differential into a differential equation form, and integrating the two sides to obtain a second-order differential standard form:
Figure BDA0003760877370000102
then b can be determined 0 ＝-35.0247。
Selecting omega o ＝800，ω c And =100, the design of the outer ring active disturbance rejection controller is completed.
The rotating speed of the air compressor is realized by an air compressor rotating speed adjusting loop of which the inner ring is controlled based on a second-order sliding mode, so that the actual rotating speed of the air compressor can be adjusted to the rotating speed of the air compressor given by the outer ring, and the design process of the inner ring second-order sliding mode controller is as follows:
for a given nonlinear system:
Figure BDA0003760877370000103
the second-order sliding mode control means that the second-order guide of a given sliding mode surface is subjected to discontinuous switching control, so that the second-order sliding mode surface meets the condition
Figure BDA0003760877370000104
Obtaining the following by differentiating the sliding mode surface twice:
Figure BDA0003760877370000105
Figure BDA0003760877370000106
note that u appears in
Figure BDA0003760877370000107
So that the system can pass the control rate
Figure BDA0003760877370000108
And (5) calming.
Selecting a sliding plane s = omega cp -ω cp,ref ，ω cp,ref The outer ring gives the rotating speed of the air compressor. And (3) deriving the sliding plane, and finishing to obtain:
Figure BDA0003760877370000109
the following control rates were designed:
Figure BDA00037608773700001010
as a result of this, the number of the,
Figure BDA00037608773700001011
the expression of (c) can be simplified as:
Figure BDA00037608773700001012
to make s and
Figure BDA00037608773700001013
can converge to zero in a limited time, and can calculate v by adopting a supercoiled algorithm:
Figure BDA0003760877370000111
wherein α, β and γ are parameters to be determined, and the finite time convergence condition of the sliding plane is:
Figure BDA0003760877370000112
let γ =0.5, parameter Γ m ，Γ M And φ may be determined according to the following rules:
1) For a given
Figure BDA0003760877370000115
Presence of a normal number s 0 ，Γ m And gamma M Such that when s < | s 0 If | the following holds:
Figure BDA0003760877370000113
suppose s 0 ＝e -4 Selecting gamma through repeated simulation test m ＝0.5，Γ M ＝0.9。
2) The normal phi must be guaranteed when s < | s 0 If | the following inequality holds:
Figure BDA0003760877370000114
choosing phi =0.01, one can deduce that alpha and beta are: α =2 and β =2.
And finishing the design of the inner-ring second-order sliding mode controller.
A comparison of the cascade controller of the present invention with a PI controller, a model predictive controller and a pure active disturbance rejection controller is shown in fig. 4. From the 10s and 80s partial enlarged views, the MPC showed excessively long settling times when the load current abruptly changes, although the control curve did not show any overshoot. The ADRC can effectively accelerate the adjustment time, but the ADRC has larger overshoot and simultaneously shows a slight control signal jitter phenomenon, which is not beneficial to the stable operation of the air compressor. Compared with MPC and ADRC, the PI controller with well-set parameters shows more satisfactory dynamic performance. Among the four controllers, the cascade controller has the minimum overshoot, the shortest adjusting time and the optimal control effect.
Fig. 5 is a variation curve of the output power of the fuel cell stack, and it can be seen that the optimal oxygen excess ratio control can effectively improve the grid-side output power compared with the fixed oxygen excess ratio control, thereby achieving the effect of improving the system working efficiency.
FIG. 6 is a control effect curve in the presence of model internal parameter uncertainty. It can be seen from the figure that the oxygen excess ratio cannot be maintained at a constant value due to real-time changes in the internal parameters of the system, but fluctuates within an acceptable range around a given optimum value. In addition, when the system starts to operate, the control curve with fixed parameters tends to be stable after multiple oscillations, and when the internal disturbance of the system exists, the extended state observer can quickly reconstruct a real-time disturbance value and accelerate the response speed.
The method improves the dynamic characteristic and the anti-interference performance of the air supply system of the fuel cell. The invention adopts a double closed-loop control structure: namely an outer ring for adjusting the oxygen excess ratio based on a second-order active disturbance rejection controller and an inner ring for adjusting the rotating speed of the air compressor based on a second-order sliding mode controller. The simulation result of the controller shows that the controller can effectively accelerate the dynamic response speed of the system and can deal with external interference and internal parameter uncertainty.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.

Claims (5)

1. A cascade control method of an air supply system of a proton exchange membrane fuel cell is characterized in that: the method comprises the following steps:
the method comprises the following steps: according to a material conservation equation, an electrochemical reaction principle and a heat conduction law mechanism, a system mechanism model capable of reflecting the internal reaction of the proton exchange membrane fuel cell is established, and a controller-oriented four-order nonlinear state space model of the simplified air supply system is obtained.
Step two: collecting air compressor outlet flow, air compressor rotating speed and overpressure ratio data in a fuel cell air supply system, inputting the overpressure ratio and the air compressor rotating speed as inputs, inputting the air compressor outlet flow as an output into a radial basis function neural network, and adding an air compressor model obtained through network training into a four-order nonlinear state space model;
step three: and fitting an optimal oxygen excess ratio change curve by taking the maximum electric pile output power as a target according to the relation between the maximum electric pile output power and the load current and the oxygen excess ratio.
Step four: the cascade control is carried out based on a simplified fourth-order nonlinear state space model, and specifically comprises the following steps: the oxygen excess ratio adjusting outer ring takes tracking and fitting the optimal oxygen excess ratio as a control target, and outputs an air compressor rotating speed set value under the optimal oxygen excess ratio condition for the air compressor rotating speed adjusting inner ring on the basis of a second-order active disturbance rejection control strategy according to the deviation of the actual oxygen excess ratio of the system and the set value; the inner ring for adjusting the rotating speed of the air compressor takes the driving voltage of the air compressor as a control variable, and a second-order sliding mode control strategy is selected to quickly adjust the rotating speed of the air compressor to reach a given value.
2. The cascade control method of the air supply system of the proton exchange membrane fuel cell as claimed in claim 1, wherein the fourth-order nonlinear state space model of the air supply system is:
Figure FDA0003760877360000011
in the formula, x 1 And x 2 Cathode oxygen and nitrogen partial pressures, x, in the fuel cell air supply system, respectively 3 Is the speed of the air compressor, x 4 Supplying the cathode with lumen pressure; u = u cp The driving voltage of the air compressor is a control variable; I.C. A st Is the stack load current, it is considered a measurable external disturbance. (ii) a W is a group of cp Representing the outlet flow of the air compressor, wherein the outlet flow is a nonlinear function to be identified about the rotating speed and the over-pressure ratio of the air compressor; the expressions for the intermediate variables X and α are as follows:
X＝x 1 +x 2 +c 2
Figure FDA0003760877360000021
wherein c is 1 ,...c 20 Is a constant coefficient.
3. The cascade control method of the air supply system of the proton exchange membrane fuel cell according to claim 1, wherein the oxygen excess ratio is:
Figure FDA0003760877360000022
wherein c is 23 ,c 24 Is a constant coefficient.
The oxygen excess ratio can be influenced by the load current and changes in real time, and in order to improve the output power of the galvanic pile, an optimal oxygen excess ratio curve needs to be fitted through experiments. Selecting a current range of [100A 300A ], taking a test point every 50A, observing and recording the power output condition of the galvanic pile of a fourth-order nonlinear model, then respectively fitting an output power change curve under each current, determining a maximum power output point under each current, and finally fitting the maximum power output point under each current into a relation curve of an optimal oxygen excess ratio and the galvanic pile load current by a least square method, wherein the relation curve is as follows:
Figure FDA0003760877360000023
wherein z is 2,opt The optimal oxygen excess ratio is obtained for the fit.
4. The cascade control method of the air supply system of the proton exchange membrane fuel cell according to claim 1, wherein the cascade control method can quickly track the optimal oxygen excess ratio through an outer loop, and the implementation process comprises:
firstly, finding a steady-state working point of a four-order nonlinear state space model added into an air compressor model through a simulation experiment, linearizing the four-order nonlinear state space model added into the air compressor model at the steady-state working point, and obtaining a transfer function between the excess oxygen ratio and the rotating speed of the air compressor:
Figure FDA0003760877360000024
transforming the two-dimensional differential into a differential equation form, and integrating the two sides to obtain a second-order differential standard form:
Figure FDA0003760877360000025
wherein y represents the oxygen excess ratio.
5. The cascade control method of the air supply system of the proton exchange membrane fuel cell according to claim 1, wherein the cascade control method adjusts the rotating speed of the inner ring air compressor through a second-order sliding mode control strategy, and the specific process of the realization comprises the following steps:
selecting a sliding plane as follows:
s＝ω cp -ω cp，ref
s represents a sliding plane; omega cp,ref The method comprises the following steps of outputting a given rotating speed value of the air compressor by an outer ring, and deriving a sliding plane s:
Figure FDA0003760877360000031
the following feedback control rates were designed:
Figure FDA0003760877360000032
such that:
Figure FDA0003760877360000033
wherein the virtual control quantity v is calculated by a supercoiling algorithm.
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* Cited by examiner, † Cited by third party
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CN116995268A (en) * 2023-06-01 2023-11-03 佛山仙湖实验室 Control method and device for air supply system of proton exchange membrane fuel cell
CN116995268B (en) * 2023-06-01 2024-03-01 佛山仙湖实验室 Control method and device for air supply system of proton exchange membrane fuel cell

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