CN112563541A - Fuel cell cathode pressure control method for improving particle swarm PID - Google Patents
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- 239000003054 catalyst Substances 0.000 description 6
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- 239000001257 hydrogen Substances 0.000 description 3
- 229910052739 hydrogen Inorganic materials 0.000 description 3
- 239000001301 oxygen Substances 0.000 description 3
- 229910052760 oxygen Inorganic materials 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 1
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- 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
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- H01M8/04694—Processes for controlling fuel cells or fuel cell systems characterised by variables to be controlled
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- H01M8/00—Fuel cells; Manufacture thereof
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Abstract
The invention discloses a fuel cell cathode pressure control method for improving particle swarm PID, which is characterized in that a gas dynamic model of a proton exchange membrane fuel cell is established based on a Matlab/Simulink simulation platform, and the gas pressure of the fuel cell is obtained through the dynamic model; optimizing PID control parameters by adopting an improved particle swarm algorithm; the optimized control parameters are assigned to the PID controller, the fan voltage is adjusted, and the gas flow is output, so that the gas pressure of the fuel cell is effectively controlled, the overshoot during load current disturbance is reduced, the adjusting speed is increased, the influence of pressure fluctuation on a proton membrane is reduced, and the effective control on the cathode pressure of the proton exchange membrane fuel cell can be realized.
Description
Technical Field
The invention relates to the technical field of fuel cell air inlet system management, in particular to a fuel cell cathode pressure control method for improving particle swarm PID.
Background
The traditional fossil energy causes great pollution to the environment, and hydrogen energy is the world recognized energy technology with the lowest carbon emission and the best power generation efficiency. Therefore, the use of hydrogen energy technology in new energy automobiles and power stations has become one of the hot spots of concern in countries around the world. When the load of the fuel cell changes, the catalyst layer of the fuel cell generates a large amount of water due to hydrogen-oxygen electrochemical reaction, and the gas pressure is reduced due to gas consumption, so that the generated water cannot be discharged in time and is gathered near the membrane electrode to submerge the Pt catalyst, thereby preventing the adsorption and desorption efficiency of hydrogen and oxygen on the catalyst layer, influencing the generation rate of hydrogen-oxygen chemical reaction and reducing the output performance of the fuel cell. In addition, with the operation and use of the fuel cell, the Pt catalyst layer on the proton membrane will inevitably fall off, which causes the mechanical strength of the proton membrane to decrease, and the excessive gas pressure fluctuation will damage the proton exchange membrane in the cell and make the Pt catalyst layer on the catalyst layer fall off more quickly, which causes the service life of the cell to decrease rapidly.
At present, intelligent methods such as a fuzzy controller, a neural network and the like are often combined with a PID controller, but the methods only dynamically adjust the set control parameters of the PID according to the change of the controlled error of the system, and the initial control parameters are set artificially and cannot effectively control the cathode pressure of the proton exchange membrane fuel cell.
Disclosure of Invention
The invention aims to provide a fuel cell cathode pressure control method for improving particle swarm PID, which can effectively control the cathode pressure of a proton exchange membrane fuel cell.
To achieve the above object, the present invention provides a fuel cell cathode pressure control method improving particle swarm PID, comprising the steps of:
establishing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a simulation platform, and calculating an ITAE performance index according to an expected pressure value and an actual pressure value;
optimizing the control parameters of the constructed PID controller according to the ITAE performance index by adopting an improved particle swarm optimization;
and assigning the optimized proportional, integral and differential parameters of the controller to the PID controller, and controlling the gas pressure of the fuel cell based on a PID control law.
The method comprises the following steps of establishing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a simulation platform, and calculating ITAE performance indexes according to expected pressure values and actual pressure values, wherein the ITAE performance indexes comprise:
constructing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a Matlab or Simulink simulation platform according to an ideal state gas equation and a mass conservation law;
and constructing a corresponding PID controller based on the gas pressure dynamic model of the proton exchange membrane fuel cell, and calculating the ITAE performance index according to the expected pressure value and the actual pressure value.
The method comprises the following steps of optimizing control parameters of the constructed PID controller according to the ITAE performance index by adopting an improved particle swarm optimization, wherein the method comprises the following steps:
acquiring the population scale and the iteration threshold of a particle swarm, and initializing the initial position and the initial speed of the particle swarm in a corresponding search space;
and assigning the corresponding numerical value of the particle swarm to the PID controller, and optimizing the control parameters of the PID controller according to the ITAE performance index.
Wherein, optimizing the control parameters of the PID controller according to the ITAE performance index comprises:
obtaining index particles according to the ITAE performance index, and constructing a particle swarm algorithm to update the inertia weight of each particle;
and assigning the value corresponding to each particle to the PID controller, and updating the individual extreme value and the group extreme value until an iteration threshold value is reached to obtain an optimized parameter set.
The invention relates to a fuel cell cathode pressure control method for improving particle swarm PID, which is characterized in that a gas dynamic model of a proton exchange membrane fuel cell is established based on a Matlab/Simulink simulation platform, and the gas pressure of the fuel cell is obtained through the dynamic model; optimizing PID control parameters by adopting an improved particle swarm algorithm; the optimized control parameters are assigned to the PID controller, the fan voltage is adjusted, and the gas flow is output, so that the gas pressure of the fuel cell is effectively controlled, the overshoot during load current disturbance is reduced, the adjusting speed is increased, the influence of pressure fluctuation on a proton membrane is reduced, and the effective control on the cathode pressure of the proton exchange membrane fuel cell can be realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the steps of a fuel cell cathode pressure control method with improved particle group PID provided by the invention.
FIG. 2 is a basic schematic diagram of PID control parameter optimization by particle swarm optimization provided by the invention.
FIG. 3 is a flow chart of a procedure for optimizing PID control parameters by a particle swarm algorithm provided by the invention.
FIG. 4 is a graph of PID controller test signal provided by the present invention.
FIG. 5 is a schematic diagram of the dynamic process of the controlled pressure when the load changes.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Referring to fig. 1 and 2, the present invention provides a method for controlling cathode pressure of a fuel cell with improved particle group PID, comprising the following steps:
s101, establishing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a simulation platform, and calculating an ITAE performance index according to an expected pressure value and an actual pressure value.
Specifically, a gas pressure dynamic model of the proton exchange membrane fuel cell is established based on a Matlab/Simulink simulation platform according to an ideal state gas equation and a mass conservation law to obtain dynamic pressure change of the fuel cell; then, establishing a corresponding PID controller according to the gas pressure dynamic model of the proton exchange membrane fuel cell, and calculating an ITAE performance index according to the error between an expected pressure value and an actual pressure value, wherein the ITAE performance index comprises the following steps:
selecting the expected cathode pressure value P of the fuel cellca,ref(t) and the actual cathode pressure value Pca,stackError e (t) of (t) Pca,ref(t)-Pca,stack(t) as an input to the PID controller, and integrating absolute value of the error | e (t) | with time t
As the performance index of the particle swarm optimization, the smaller the value of the performance index is, the better the control parameter obtained by the optimization of the algorithm is.
And S102, optimizing the control parameters of the constructed PID controller according to the ITAE performance index by adopting an improved particle swarm optimization.
Specifically, as shown in fig. 3, an improved particle swarm algorithm is adopted to optimize the proportional, integral and differential term parameters of the PID controller, and the optimization process includes the following steps:
A. determining the population size S of the particle swarm and the maximum iteration number N of the algorithm, and searching a space range R ═ Xd,min,Xd,max]Initial position X of all particles of internally initialized particle swarmiAnd an initial velocity Vi；d＝{1,2,3}；Xd,minAnd Xd,maxIs the lower and upper limit of the d-th dimension of the particle in the search space R;
B. assigning the particles to a PID controller in a Matlab/Simulink simulation platform, calculating the performance index value of the corresponding particles according to the performance index in the step S101, and finding out the particles with the minimum performance index value, namely index particles;
C. the inertial weight value taking method for each particle in the particle swarm algorithm updating formula is established as follows:
in the formula (f)k(i) Is the k-th iterationPerformance index value f of the ith particlek avgIs the average performance index value for the entire population at the kth iteration. When f isk(i)≤fk avgThe ith particle is shown to belong to a better class in the whole population, and the particle is closer to the local optimum, so that the inertia factor omega can be reducedkSo as to accelerate the convergence speed of the particles to the local optimum; when f isk(i)＞fk avgIt means that the ith particle belongs to a poor class in the whole population, the particle is far from the local optimum value, and the maximum inertia factor omega should be keptkThe particles are prevented from sliding to be locally optimal, so that the particles have an opportunity to step out of the vicinity of the locally optimal position, and the global optimization capability of the particle swarm is improved.
Wherein the content of the first and second substances,
D. updating all the particles, assigning the value of each particle to PID, obtaining the performance index value f (i) corresponding to the particle through simulation, and matching the performance index value of each particle with the historical optimal individual extreme value Pi,bestAnd group extremum GbestComparing the performance index values, and judging and updating individual extremum and group extremum of the particle swarm;
E. judging whether the algorithm is converged, if the iteration times are more than the given maximum iteration times N, namely when the current iteration times t is more than N, then the iteration is converged, and outputting the optimal parameter combination Gbest. If the condition is not satisfied, executing step B.
And S103, assigning the optimized proportional, integral and differential term parameters of the controller to the PID controller, and controlling the gas pressure of the fuel cell based on a PID control law.
Specifically, a PID cathode pressure controller is configured, optimized Kp, Ki and Kd control parameters are assigned to the PID cathode pressure controller, and the input of the cathode pressure PID controller is the error e (t) ═ P of the set cathode pressure of the fuel cell stack and the actual cathode pressure of the stackca,ref(t)-Pca,stack(t)。
And obtaining the accurate control quantity of the appropriate controlled object according to the PID control law. Specifically, when the actual pressure of the cathode of the stack is higher than the set pressure value, the blower voltage needs to be reduced to reduce the rotation speed of the blower so as to achieve the purpose of reducing the pressure, and conversely, when the actual pressure of the cathode of the stack is higher than the set pressure value, the blower voltage needs to be increased.
Fig. 4 shows the load current test signal for the stack over the operating range, primarily as a step up and down signal, with an initial load current of 100A, a step up to 250A at 15 seconds, a step down to 150A at 25 seconds, and a step up to 300A at 35 seconds. The effect of each step change of the load current on the pressure and the obtained control effect of the improved particle swarm algorithm PID are compared with the standard particle swarm algorithm PID control and the traditional PID control, and the comparison graph is shown in FIG. 5. In FIG. 5, the diamond shape is a conventional PID control effect curve, the star shape is a LIPSO-PID control effect curve, and the quadrilateral shape is an improved LIWPSO-PID control effect curve. The control overshoot of the traditional PID controller is 16.65%, and the adjusting time is 3.542 seconds; the control overshoot of the LIPSO-PID controller is 10.15%, and the adjusting time is 0.992 seconds; the control overshoot of the improved LIWPSO-PID controller is 7.85%, and the adjusting time is 0.883 seconds. Therefore, under the action of the improved controller, the overshoot and the adjusting time of the system are further improved, and the impact of gas pressure fluctuation on a proton membrane can be better reduced, so that the system can stably run.
Compared with the prior art, the invention has the beneficial effects that:
(1) the improved particle swarm algorithm is combined with the traditional PID control, the defect that the traditional PID parameter setting method depends on expert experience and engineering experience is overcome, the requirement of stable control is met, and the control effect is improved;
(2) compared with a standard particle swarm algorithm, the improved algorithm improves the convergence precision and the convergence speed of the algorithm.
The invention relates to a fuel cell cathode pressure control method for improving particle swarm PID, which is characterized in that a gas dynamic model of a proton exchange membrane fuel cell is established based on a Matlab/Simulink simulation platform, and the gas pressure of the fuel cell is obtained through the dynamic model; optimizing PID control parameters by adopting an improved particle swarm algorithm; the optimized control parameters are assigned to the PID controller, the fan voltage is adjusted, and the gas flow is output, so that the gas pressure of the fuel cell is effectively controlled, the overshoot during load current disturbance is reduced, the adjusting speed is increased, the influence of pressure fluctuation on a proton membrane is reduced, and the effective control on the cathode pressure of the proton exchange membrane fuel cell can be realized.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (4)
1. A fuel cell cathode pressure control method with improved particle swarm PID, comprising the steps of:
establishing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a simulation platform, and calculating an ITAE performance index according to an expected pressure value and an actual pressure value;
optimizing the control parameters of the constructed PID controller according to the ITAE performance index by adopting an improved particle swarm optimization;
and assigning the optimized proportional, integral and differential parameters of the controller to the PID controller, and controlling the gas pressure of the fuel cell based on a PID control law.
2. The method for controlling the cathode pressure of the fuel cell with the improved particle swarm PID as claimed in claim 1, wherein the establishing of the gas pressure dynamic model of the proton exchange membrane fuel cell based on the simulation platform and the calculation of the ITAE performance index according to the expected pressure value and the actual pressure value comprise:
constructing a gas pressure dynamic model of the proton exchange membrane fuel cell based on a Matlab or Simulink simulation platform according to an ideal state gas equation and a mass conservation law;
and constructing a corresponding PID controller based on the gas pressure dynamic model of the proton exchange membrane fuel cell, and calculating the ITAE performance index according to the expected pressure value and the actual pressure value.
3. The method for controlling cathode pressure of a fuel cell with improved particle swarm PID as claimed in claim 1, wherein the optimization of the control parameters of the constructed PID controller according to the ITAE performance index by adopting the improved particle swarm optimization comprises:
acquiring the population scale and the iteration threshold of a particle swarm, and initializing the initial position and the initial speed of the particle swarm in a corresponding search space;
and assigning the corresponding numerical value of the particle swarm to the PID controller, and optimizing the control parameters of the PID controller according to the ITAE performance index.
4. The method of claim 3, wherein optimizing control parameters of the PID controller based on the ITAE performance index comprises:
obtaining index particles according to the ITAE performance index, and constructing a particle swarm algorithm to update the inertia weight of each particle;
and assigning the value corresponding to each particle to the PID controller, and updating the individual extreme value and the group extreme value until an iteration threshold value is reached to obtain an optimized parameter set.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114551944A (en) * | 2022-01-07 | 2022-05-27 | 国网浙江省电力有限公司电力科学研究院 | Method and system for rapidly controlling water content in proton exchange membrane fuel cell |
| CN117784590A (en) * | 2024-02-28 | 2024-03-29 | 齐鲁工业大学(山东省科学院) | PID control method and system for microbial fuel cell |
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| US20140120443A1 (en) * | 2012-10-25 | 2014-05-01 | GM Global Technology Operations LLC | Cathode pressure control utilizing a capacitance model |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114551944A (en) * | 2022-01-07 | 2022-05-27 | 国网浙江省电力有限公司电力科学研究院 | Method and system for rapidly controlling water content in proton exchange membrane fuel cell |
| CN114551944B (en) * | 2022-01-07 | 2023-10-10 | 国网浙江省电力有限公司电力科学研究院 | Method and system for rapidly controlling internal water content of proton exchange membrane fuel cell |
| CN117784590A (en) * | 2024-02-28 | 2024-03-29 | 齐鲁工业大学(山东省科学院) | PID control method and system for microbial fuel cell |
| CN117784590B (en) * | 2024-02-28 | 2024-05-10 | 齐鲁工业大学(山东省科学院) | PID control method and system for microbial fuel cell |
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