CN111261909B - Maximum net power tracking control device and method for fuel cell system - Google Patents

Maximum net power tracking control device and method for fuel cell system Download PDF

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CN111261909B
CN111261909B CN202010039750.5A CN202010039750A CN111261909B CN 111261909 B CN111261909 B CN 111261909B CN 202010039750 A CN202010039750 A CN 202010039750A CN 111261909 B CN111261909 B CN 111261909B
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CN111261909A (en
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陈启宏
付志超
章子祎
邓志华
张立炎
周克亮
肖朋
刘莉
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Wuhan University of Technology WUT
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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
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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
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Abstract

The invention discloses a maximum net power tracking control device of a fuel cell system, which comprises an adjusting controller, a comparator and an adaptive fuzzy logic PID controller, wherein the adjusting controller, the comparator and the adaptive fuzzy logic PID controller are sequentially connected, the adaptive fuzzy logic PID controller can adopt fuzzy logic rules and proportional-integral-derivative control rules optimized by a particle swarm algorithm to process an error value between a maximum net power reference value and an actual net power value of a fuel cell stack to obtain an output signal, and the output signal is used for controlling the rotating speed of an air compressor so as to control the maximum net power of the fuel cell stack. The control device can realize the quick tracking of the maximum net power under the condition that the external load suddenly and violently changes, and improve the performance, the efficiency and the service life of the fuel cell system. In addition, the invention also discloses a control method for tracking the maximum net power of the fuel cell system.

Description

Maximum net power tracking control device and method for fuel cell system
Technical Field
The invention relates to the field of fuel cell system control, in particular to a maximum net power tracking control device and a maximum net power tracking control method for a fuel cell system.
Background
With the rapid development of global industry and economy, the problems of energy crisis and environmental pollution become more serious, and strong attention of governments of various countries is drawn. Therefore, governments of various countries have strongly supported the development and utilization of clean energy sources, such as solar energy, tidal energy, geothermal energy, wind energy, hydrogen energy, etc., which all have common advantages, i.e., zero pollutant emission, high efficiency, etc. Among them, the fuel cell is the most typical representative of these clean energy sources, and its main raw materials are hydrogen and air, and the chemical reaction of hydrogen and oxygen in air under the condition of catalyst can produce heat, electricity and water. Typically, a fuel cell system includes a fuel cell stack, a hydrogen subsystem, an air subsystem, and a fuel cell auxiliary system.
Because the fuel cell has the advantages of high power density, zero pollution emission, low-temperature cold start, low noise and the like, a novel clean energy product which is pursued by governments and enterprises in various countries is rapidly obtained within a few years. And in particular Proton Exchange Membrane Fuel Cells (PEMFCs), which have all the advantages of a fuel cell system, are considered to be the most promising potential and promising power generation devices. Therefore, it has its wide application in various fields, especially in the automotive field and the field of backup power stations, and is widely used and rapidly developed.
In the PEMFC system, the net power of the PEMFC system reflects the power generation performance of the fuel cell system. Specifically, the net output power of the fuel cell system (referred to as net power) is the output power of the fuel cell stack minus the consumed power of the auxiliary system (mainly referred to as air compressor). However, due to the relatively slow dynamics of the air subsystem as compared to the hydrogen subsystem, oxygen flow inside the PEMFC stack is difficult to respond quickly with load demand, therefore, when the load changes rapidly, the PEMFC system is not only easy to generate oxygen starvation, which causes insufficient oxygen supply of the galvanic pile, reduces the output power of the system and can not meet the load requirement, at the same time, the surface of the proton exchange membrane can generate 'hot spots', which causes the problems of short circuit of the galvanic pile, reduction of the service life of the galvanic pile and the like, thereby seriously impairing the performance of the stack and also making the Oxygen Excess Ratio (OER) of the PEMFC system unable to meet the demand for rapid load changes during transient conditions of rapid load changes, thereby easily overshooting (i.e., overshooting) the tracking net power reference and resulting in a longer response time for the system, which is very fatal to the PEMFC system. Therefore, it is economically important to extend the life of the PEMFC system and avoid the degradation of the system performance through an advanced net power tracking control method.
At present, the most common net power tracking control method is a traditional PID control method, which compares the net power actual value of the fuel cell stack in the fuel cell system with a net power reference value through a PID controller, and obtains a control quantity by processing the difference between the net power actual value and the net power reference value through a proportional-integral-derivative control law, so as to adjust the actual net power of the fuel cell system to reach or keep the net power reference value, thereby making the system more accurate and more stable.
However, since the PEMFC system is a system with strong nonlinearity, strong coupling and time-varying parameters, and the PID control algorithm adopted by the conventional PID controller is mainly suitable for a system with substantially linear and dynamic characteristics that do not change with time, the conventional PID control method cannot adjust the rotation speed of the air compressor in time when the load changes rapidly, so that the oxygen starvation phenomenon cannot be effectively avoided and the problem of slow response speed is solved, and the large pressure difference between hydrogen and oxygen inside the fuel cell stack is easily caused, which damages the proton exchange membrane inside the stack, thereby affecting the performance, efficiency and life of the fuel cell system.
Disclosure of Invention
The present invention provides a maximum net power tracking control device and method for a fuel cell system, which can effectively avoid the oxygen starvation phenomenon of the fuel cell and enable the maximum net power tracking to have good dynamic response, i.e. low overshoot and short adjustment time, under the condition of sudden and severe change of an external load, thereby realizing the rapid tracking of the optimal net power and further improving the performance, efficiency and service life of the fuel cell system.
In order to achieve the above object, the present invention provides a maximum net power tracking control device for a fuel cell system, including an adjustment controller, a comparator and an adaptive fuzzy logic PID controller, wherein the adjustment controller is configured to determine the maximum net power of a fuel cell stack under different load currents and an optimal oxygen excess ratio corresponding to the maximum net power according to a curve relation diagram of an oxygen excess ratio obtained at different load currents and the net power of the fuel cell stack in the fuel system, and obtain a maximum net power reference value through curve fitting; the comparator is connected with the adjusting controller and is used for comparing the maximum net power reference value with the actual net power value of the fuel cell stack to obtain an error value; and the self-adaptive fuzzy logic PID controller is connected with the comparator and is used for processing the error value according to a fuzzy logic rule and a proportional-integral-derivative control rule which are optimized by adopting a particle swarm algorithm to obtain an output signal, and sending the output signal to an air compressor of a fuel cell system to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack.
Preferably, the adaptive fuzzy logic PID controller comprises a differentiation unit, a fuzzy logic regulator, a PID controller and a particle swarm optimization unit, wherein the differentiation unit is connected with the comparator and is used for differentiating the error value to obtain an error variation; the fuzzy logic regulator is connected with the comparator and the differentiating unit and is used for respectively transforming the error value and the error variation from a basic domain to a domain of a fuzzy set according to a first proportional factor and a second proportional factor, fuzzifying the error value and the error variation transformed to the domain of the fuzzy set into fuzzy quantities, representing the fuzzy quantities by using corresponding fuzzy languages to obtain a fuzzy set, carrying out fuzzy reasoning on the fuzzy set by adopting a fuzzy control rule to obtain output fuzzy quantities, carrying out deblurring on the output fuzzy quantities to obtain three clear quantities, and carrying out scale transformation on the three clear quantities to obtain three control quantities, wherein the three control quantities are respectively used as a proportional parameter, an integral parameter and a differential parameter of the PID controller; the PID controller is connected with the comparator and the fuzzy logic regulator and is used for processing the error value according to a proportional parameter, an integral parameter and a differential parameter by adopting a proportional-integral-differential control law to obtain the output signal and sending the output signal to an air compressor of a fuel cell system so as to control the rotating speed of the air compressor and further control the maximum net power of the fuel cell stack; the particle swarm optimization unit is connected with the comparator, the fuzzy logic regulator and the PID controller and is used for respectively performing weighted integration on the error value obtained by the comparator and the value of the output signal of the PID controller, establishing a performance function according to the sum of the weighted integration of the error value and the value of the output signal of the PID controller, solving the performance function by adopting a particle swarm optimization to obtain an optimal solution, sending the optimal solution to the fuzzy logic regulator and the PID controller, and updating the first scale factor and the second scale factor of the fuzzy logic regulator and the scale parameter of the PID controller by using the optimal solution.
Understandably, the performance function is solved by adopting a particle swarm algorithm, and the solved result is used for updating the scale factor input by the fuzzy logic regulator and the scale parameter of the PID controller, so that the self-adaptive fuzzy logic PID controller has the capability of self-adaptively tracking the maximum net power reference value and good dynamic regulation capability.
Preferably, the performance function is:
Figure BDA0002367305230000041
wherein Q and R both represent weighting coefficients, and 0< Q.ltoreq.1, Q + R.ltoreq.1; e (t) represents the difference between the actual net power value of the fuel cell stack and the maximum net power reference value, and u (t) represents the output signal of the PID controller.
Understandably, the integral performance index is selected as an optimized performance function, the performance function can comprehensively evaluate the dynamic performance and the static performance of the control system, and the quick response of overshoot, stable time, steady-state error and the like of the system can be ensured.
Preferably, the PID controller is represented by the following equation:
Figure BDA0002367305230000042
in the formula (b), kp，ki，kdProportional, integral and differential parameters respectively representing a PID controller, e (t) representing a fuel cell stackU (t) represents the output signal of the PID controller, e (t) and
Figure BDA0002367305230000043
is in the range of [ -1,1]。
Preferably, in the fuzzy logic regulator, the formula for scaling the three quantities of sharpness to obtain three control quantities is:
Figure BDA0002367305230000044
in the formula (c), k'jRepresenting the amount of sharpness, k, obtained after deblurring in the fuzzy logic regulatorjA proportional parameter, an integral parameter or a derivative parameter representing the PID controller,
Figure BDA0002367305230000045
respectively representing the minimum value and the maximum value of the output of the fuzzy logic regulator, g representing a scale factor, and alpha, beta and gamma representing a proportional parameter scale factor, an integral parameter scale factor and a differential parameter scale factor.
Preferably, the process of solving the performance function by using the particle swarm algorithm specifically comprises:
(i) initializing a particle swarm;
(ii) calculating the fitness value of each particle according to the performance function;
(iii) for each particle, judging whether the current fitness value of the particle is larger than the individual optimal solution of the particle, and if so, replacing the individual optimal solution with the current fitness value;
(iv) for each particle, judging whether the current fitness value of the particle is larger than the global optimal solution of the whole particle swarm, and if so, replacing the global optimal solution with the current fitness value;
(v) updating the velocity and position of each particle according to the formula (d) and the formula (e),
Figure BDA0002367305230000051
Figure BDA0002367305230000052
in the formulae (d) and (e), ω is an inertia factor,
Figure BDA0002367305230000053
representing the current velocity of particle i at iteration t,
Figure BDA0002367305230000054
representing the current velocity of particle i at iteration t +1, c1And c2Represents a learning factor, r1And r2Represents [0,1 ]]Two random numbers in the range, pi，dRepresenting the individual optimal solution, p, of particle i at iteration tg，dRepresents the current global optimal solution and the current global optimal solution,
Figure BDA0002367305230000055
representing the current position of particle i at iteration t,
Figure BDA0002367305230000056
represents the current position of particle i at iteration t + 1;
(vi) if the maximum iteration number is met, outputting the optimal solution and exiting, otherwise, returning to the step (ii).
In addition, the invention also provides a control method for tracking the maximum net power of the fuel cell system, which comprises the following steps:
(1) determining the maximum net power of the fuel cell stack under different load currents and the optimal oxygen excess ratio corresponding to the maximum net power according to a curve relation graph of the oxygen excess ratio obtained under different load currents and the net power of the fuel cell stack in the fuel system, and obtaining a maximum net power reference value through curve fitting;
(2) comparing the maximum net power reference value with an actual net power value of the fuel cell stack to obtain an error value;
(3) and processing the error value according to a fuzzy logic rule and a proportional-integral-derivative control rule which are optimized by adopting a particle swarm algorithm to obtain an output signal, and sending the output signal to an air compressor of a fuel cell system to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack.
Preferably, the step (3) is specifically:
carrying out differential processing on the error value to obtain error variation; the fuzzy logic regulator converts the error value and the error variable quantity from a basic domain to a domain of a fuzzy set according to a first scale factor and a second scale factor respectively, fuzzifies the error value and the error variable quantity which are converted to the domain of the fuzzy set into fuzzy quantities, expresses the fuzzy quantities by using a corresponding fuzzy language to obtain a fuzzy set, performs fuzzy reasoning on the fuzzy set by adopting a fuzzy control rule to obtain an output fuzzy quantity, performs defuzzification on the output fuzzy quantity to obtain three clear quantities, performs scale conversion on the three clear quantities to obtain three control quantities, and the three control quantities are respectively used as a proportional parameter, an integral parameter and a differential parameter of a PID controller; the PID controller processes the error value according to a proportional parameter, an integral parameter and a differential parameter by adopting a proportional-integral-differential control rule to obtain an output signal, and sends the output signal to an air compressor of a fuel cell system to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack; respectively carrying out weighted integration on the error value and the value of the output signal of the PID controller, establishing a performance function according to the sum of the weighted integration of the error value and the value of the output signal of the PID controller, solving the performance function by adopting a particle swarm algorithm to obtain an optimal solution, sending the optimal solution to the fuzzy logic regulator and the PID controller, and updating the first scale factor and the second scale factor of the fuzzy logic regulator and the scale parameter of the PID controller by using the optimal solution.
Preferably, the performance function is:
Figure BDA0002367305230000061
in the formula (a), Q and R both represent weighting coefficients, and 0< Q.ltoreq.1, Q + R.ltoreq.1; e (t) represents the difference between the actual net power value of the fuel cell stack and the maximum net power reference value, and u (t) represents the output signal of the PID controller.
Preferably, the solving of the performance function by the particle swarm algorithm specifically comprises:
(i) initializing a particle swarm;
(ii) calculating the fitness value of each particle according to the performance function;
(iii) for each particle, judging whether the current fitness value of the particle is larger than the individual optimal solution of the particle, and if so, replacing the individual optimal solution with the current fitness value;
(iv) for each particle, judging whether the current fitness value of the particle is larger than the global optimal solution of the whole particle swarm, and if so, replacing the global optimal solution with the current fitness value;
(v) updating the velocity and position of each particle according to the formula (d) and the formula (e),
Figure BDA0002367305230000062
Figure BDA0002367305230000063
in the formulae (d) and (e), ω is an inertia factor,
Figure BDA0002367305230000064
representing the current velocity of particle i at iteration t,
Figure BDA0002367305230000065
representing the current velocity of particle i at iteration t +1, c1And c2Represents a learning factor, r1And r2Represents [0,1 ]]Two randoms within rangeNumber, pi，dRepresenting the individual optimal solution, p, of particle i at iteration tg，dRepresents the current global optimal solution and the current global optimal solution,
Figure BDA0002367305230000066
representing the current position of particle i at iteration t,
Figure BDA0002367305230000067
represents the current position of particle i at iteration t + 1;
(vi) if the maximum iteration number is met, outputting the optimal solution and exiting, otherwise, returning to the step (ii).
The technical scheme provided by the invention has the beneficial effects that:
the self-adaptive fuzzy logic PID controller adopts a fuzzy logic processing mode and a proportional-integral-derivative control rule which are suitable for nonlinear control, and adopts an ion swarm algorithm which can solve nonlinearity, strong coupling and time-varying property to optimize the fuzzy logic processing mode and the proportional-integral-derivative control rule, so that the problems of nonlinearity, strong coupling and time-varying property can be solved, the oxygen starvation phenomenon of the fuel cell can be effectively avoided under the condition that the external load suddenly and violently changes, the maximum net power tracking has good rapid dynamic response (namely the overshoot is low and the adjusting time is short), the rapid tracking of the maximum net power is realized, and the performance, the efficiency and the service life of a fuel cell system are further improved. In addition, the maximum net power tracking control device and the maximum net power tracking control method for the fuel cell system are simple in structure and easy to implement.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a maximum net power tracking control apparatus of a fuel cell system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a configuration of a differentiating unit coupled to a fuzzy logic regulator according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for solving a performance function using a particle swarm algorithm according to an embodiment of the present invention;
FIG. 4 is a graph of net power versus excess oxygen ratio for a fuel cell stack at different load currents, according to an embodiment of the present invention;
fig. 5 is a flowchart of a maximum net power tracking control method for a fuel cell system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the drawings in the embodiments of the present invention.
As shown in fig. 1-2, the present embodiment provides a maximum net power tracking control apparatus for a fuel cell system, which includes: a regulation controller 10, a comparator 20 and an adaptive fuzzy logic PID controller 30. In detail, the adaptive fuzzy logic PID controller 30 includes a differentiation unit 31, a fuzzy logic regulator 32, a PID controller 33, and a particle swarm optimization unit 34. The regulation controller 10 is connected with the input end of the comparator 20, the output end of the comparator 20 is connected with the input end of the differentiating unit 31, the input end of the fuzzy logic regulator 32, the input end of the particle swarm optimization unit 34 and the input end of the PID controller, the output end of the differentiating unit 31 is connected with the input end of the fuzzy logic regulator 32, the output end of the fuzzy logic regulator 32 is connected with the input end of the PID controller 33, the output end of the PID controller 33 is connected with the input end of the particle swarm optimization unit 34, the output end of the particle swarm optimization unit 34 is connected with the input end of the fuzzy logic regulator 32 and the input end of the PID controller 33, the output end of the PID controller 33 is connected with the air compressor of the fuel cell system 40, and the air compressor of the fuel cell system is connected with the fuel cell.
Specifically, the controller 10 is adapted to adjust the oxygen excess ratio obtained at different load currents to the fuel cell in the fuel systemAnd determining the maximum net power of the fuel cell stack under different load currents and the optimal oxygen excess ratio corresponding to the maximum net power, and obtaining the maximum net power reference value Pr through curve fitting. The comparator 20 is used for comparing the maximum net power reference value Pr with the actual net power value Pnet of the fuel cell stack to obtain an error value e (t). The differentiating unit 31 is used for differentiating the error value e (t) to obtain an error variation
Figure BDA0002367305230000081
The fuzzy logic regulator 32 is operable to adjust the first scaling factor K11 (i.e., NQ)1) And a second scaling factor K22 (or NQ)2) Respectively converting the error value e (t) and the error variation
Figure BDA0002367305230000082
Transforming from the basic domain to the domain of the fuzzy set, and transforming the error value e (t) and the error variation of the domain of the fuzzy set
Figure BDA0002367305230000083
Fuzzification is carried out to obtain fuzzy quantities, the fuzzy quantities are expressed by corresponding fuzzy languages to obtain fuzzy sets, fuzzy reasoning is carried out on the fuzzy sets by adopting a fuzzy control rule to obtain output fuzzy quantities, and the output fuzzy quantities are subjected to defuzzification to obtain three clear quantities k'p、k’iAnd k'dAnd for three clear quantities k'p、k’i、k’dThe three control quantities are obtained by scale conversion and are respectively used as the proportional parameters k of the PID controller 33pIntegral parameter kiAnd a differential parameter kd. The PID controller 33 is used for controlling the proportional parameter kpIntegral parameter kiAnd a differential parameter kdThe error value e (t) is processed by using a proportional-integral-derivative control law to obtain an output signal u (t), and the output signal u (t) is sent to an air compressor (not shown) of the fuel cell system 40 to control the rotating speed of the air compressor, so that the maximum net power of the fuel cell stack (not shown) is controlled. The particle swarm optimization unit 34 is used for comparing the error value obtained by the comparator 20e (t) and the output signal u (t) of the PID controller 33 are respectively subjected to weighted integration, a performance function J is established according to the sum of the weighted integration of the two, the performance function J is solved by adopting a particle swarm algorithm to obtain an optimal solution, the optimal solution is sent to the fuzzy logic regulator 32 and the PID controller 33, and the optimal solution is used for updating the first scale factor K11 and the second scale factor K22 of the fuzzy logic regulator 32 and the scale parameter K of the PID controller 32p。
In the tuning controller 10, a graph (as shown in fig. 4) of the oxygen excess ratio at different load currents of the specific type of the stack, during which the load changes rapidly, and the net power of the fuel cell stack in the fuel system can be obtained by a bench test (system characteristic modeling test) for each type of the fuel cell stack. From this graph, the maximum net power of the fuel cell stack at different load currents and the optimum oxygen excess ratio corresponding to the maximum net power can be obtained. According to different selected functions, the curve fitting can be performed by fitting the load current and the optimal oxygen excess ratio, fitting the load current and the maximum net power or fitting the optimal oxygen excess ratio and the maximum net power, so that the maximum net power reference value can be obtained according to a relation curve obtained after fitting. The curve fitting may employ polynomial fitting or least squares fitting.
In the fuzzy logic regulator 32, three clear quantities k'p、k’iAnd k'dCarrying out scale transformation to obtain three control quantities kp、kiAnd kdThe formula of (1) is:
Figure BDA0002367305230000091
in formula (c), k'jRepresents the amount of sharpness, k, obtained after deblurring in the fuzzy logic modifier 32jA proportional parameter, an integral parameter or a differential parameter representing the PID controller 33,
Figure BDA0002367305230000092
individual watchThe output of the fuzzy logic regulator 32 is shown as a minimum and maximum value, g represents a scale factor, and α, β, γ represent a proportional parameter scale factor, an integral parameter scale factor, and a derivative parameter scale factor, respectively.
In the PID controller 33, the PID controller 33 can be represented by the following equation:
Figure BDA0002367305230000093
in the formula (b), kp，ki，kdRespectively representing the proportional, integral and derivative parameters of the PID controller 33, e (t) representing the difference between the actual net power value Pnet and the maximum net power reference value Pr of the fuel cell stack, u (t) representing the output signal of the PID controller 33, e (t) and
Figure BDA0002367305230000094
is in the range of [ -1,1]。
In the particle swarm optimization unit 34, the performance function is:
Figure BDA0002367305230000095
in formula (a), Q and R both represent weighting coefficients, and 0< Q ≦ 1, Q + R ═ 1; e (t) represents the difference between the actual net power value Pnet of the fuel cell stack and the maximum net power reference value Pr, and u (t) represents the output signal of the PID controller 33. Understandably, the integral performance index is selected as an optimized performance function, the performance function can comprehensively evaluate the dynamic performance and the static performance of the control system, and the quick response of overshoot, stable time, steady-state error and the like of the system can be ensured.
Referring to fig. 3, the process of solving the performance function by using the particle swarm optimization specifically includes:
(i) initializing a particle swarm;
(ii) calculating the fitness value of each particle according to the performance function;
(iii) for each particle, judging whether the current fitness value of the particle is larger than the individual optimal solution of the particle, and if so, replacing the individual optimal solution with the current fitness value;
(iv) for each particle, judging whether the current fitness value of the particle is larger than the global optimal solution of the whole particle swarm, and if so, replacing the global optimal solution with the current fitness value;
(v) updating the velocity and position of each particle according to the formula (d) and the formula (e),
Figure BDA0002367305230000101
Figure BDA0002367305230000102
in the formulae (d) and (e), ω is an inertia factor,
Figure BDA0002367305230000103
representing the current velocity of particle i at iteration t,
Figure BDA0002367305230000104
representing the current velocity of particle i at iteration t +1, c1And c2Represents a learning factor, r1And r2Represents [0,1 ]]Two random numbers in the range, pi，dRepresenting the individual optimal solution, p, of particle i at iteration tg，dRepresents the current global optimal solution and the current global optimal solution,
Figure BDA0002367305230000105
representing the current position of particle i at iteration t,
Figure BDA0002367305230000106
represents the current position of particle i at iteration t + 1;
(vi) if the maximum iteration number is met, outputting the optimal solution and exiting, otherwise, returning to the step (ii).
In detail, in step (i), initializing the particle group includes giving the particle group size the maximum number of iterations and the like and randomly generating the position and velocity of each particle. In step (ii), the fitness value of the particle is a function value calculated according to the performance function, and the function value is the fitness value.
Understandably, the invention adopts the particle swarm algorithm to solve the performance function, and the solved result is used for updating the scale factor input by the fuzzy logic regulator and the scale parameter of the PID controller, so that the self-adaptive fuzzy logic PID controller has the capability of self-adaptively tracking the maximum net power reference value and good dynamic regulation capability.
In addition, referring to fig. 5, the present invention further provides a method for controlling maximum net power tracking of a fuel cell system, comprising the following steps:
step S101: determining the maximum net power of the fuel cell stack under different load currents and the optimal oxygen excess ratio corresponding to the maximum net power according to a curve relation graph of the oxygen excess ratio obtained under different load currents and the net power of the fuel cell stack in a fuel system, and obtaining a maximum net power reference value Pr through curve fitting;
step S102: comparing the maximum net power reference value Pr with the actual net power value Pnet of the fuel cell stack to obtain an error value e (t);
step S103: and processing the error value e (t) to obtain an output signal u (t) according to a fuzzy logic rule and a proportional-integral-derivative control rule which are optimized by a particle swarm algorithm, and sending the output signal u (t) to an air compressor of the fuel cell system and controlling the rotating speed of the air compressor, so that the maximum net power of the fuel cell stack is controlled.
Specifically, step S103 is specifically: differentiating the error value e (t) to obtain the error variation
Figure BDA0002367305230000111
The fuzzy logic regulator 32 adjusts the error value e (t) and the error variance according to the first and second scaling factors K11 and K22, respectively
Figure BDA0002367305230000112
From basic to basicThe domain is converted to the domain of the fuzzy set, the error value and the error variable quantity of the domain converted to the fuzzy set are fuzzified to be changed into fuzzy quantity, the fuzzy quantity is expressed by corresponding fuzzy language to obtain the fuzzy set, fuzzy inference is carried out on the fuzzy set by adopting a fuzzy control rule to obtain an output fuzzy quantity, and the output fuzzy quantity is defuzzified to obtain three clear quantities k'p、k’iAnd k'dAnd for three clear quantities k'p、k’iAnd k'dThe three control quantities are obtained by scale conversion and are respectively used as the proportional parameters k of the PID controller 33pIntegral parameter kiAnd a differential parameter kd(ii) a PID controller 33 is based on the proportional parameter kpIntegral parameter kiAnd a differential parameter kdProcessing the error value e (t) by adopting a proportional-integral-derivative control law to obtain an output signal u (t), and sending the output signal u (t) to an air compressor of the fuel cell system 40 to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack; respectively carrying out weighted integration on the error value e (t) and the value of the output signal u (t) of the PID controller 33, establishing a performance function according to the sum of the weighted integration of the error value e (t) and the output signal u (t), solving the performance function by adopting a particle swarm algorithm to obtain an optimal solution, sending the optimal solution to the fuzzy logic regulator 32, updating the first scale factor K11 and the second scale factor K22 of the fuzzy logic regulator 32, sending the optimal solution to the PID controller 33, and updating the scale parameter K of the PID controller 33p。
In detail, three clear quantities k 'are processed in step S103'p、k’iAnd k'dCarrying out scale transformation to obtain three control quantities kp、kiAnd kdThe formula, the expression formula of the PID controller 33, the flow of the performance function and the particle swarm algorithm are the same as those in the corresponding control device for tracking the maximum net power of the fuel cell system, and are not repeated here. Understandably, the control process ends when the actual net power value Pnet of the fuel cell stack reaches the maximum net power reference value Pr.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A maximum net power tracking control apparatus for a fuel cell system, comprising:
the adjusting controller is used for determining the maximum net power of the fuel cell stack under different load currents and the optimal oxygen excess ratio corresponding to the maximum net power according to a curve relation graph of the oxygen excess ratio obtained under different load currents and the net power of the fuel cell stack in the fuel system, and obtaining a maximum net power reference value through curve fitting;
the comparator is connected with the adjusting controller and is used for comparing the maximum net power reference value with the actual net power value of the fuel cell stack to obtain an error value;
the adaptive fuzzy logic PID controller comprises a differentiation unit, a fuzzy logic regulator, a PID controller and a particle swarm optimization unit, wherein:
the differential unit is connected with the comparator and is used for carrying out differential processing on the error value to obtain an error variation;
the fuzzy logic regulator is connected with the comparator and the differentiating unit and is used for respectively transforming the error value and the error variation from a basic domain to a domain of a fuzzy set according to a first proportional factor and a second proportional factor, fuzzifying the error value and the error variation transformed to the domain of the fuzzy set into fuzzy quantities, representing the fuzzy quantities by using corresponding fuzzy languages to obtain a fuzzy set, carrying out fuzzy inference on the fuzzy set by adopting a fuzzy control rule to obtain output fuzzy quantities, carrying out deblurring on the output fuzzy quantities to obtain three clear quantities, and carrying out scale transformation on the three clear quantities to obtain three control quantities, wherein the three control quantities are respectively used as a proportional parameter, an integral parameter and a differential parameter of the PID controller;
the PID controller is connected with the comparator and the fuzzy logic regulator and is used for processing the error value by adopting a proportional-integral-derivative control rule according to a proportional parameter, an integral parameter and a derivative parameter to obtain an output signal and sending the output signal to an air compressor of a fuel cell system to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack;
the particle swarm optimization unit is connected with the comparator, the fuzzy logic regulator and the PID controller, and is configured to perform weighted integration on the error value obtained by the comparator and the value of the output signal of the PID controller respectively, establish a performance function according to the sum of the weighted integration of the error value and the value of the output signal of the PID controller, solve the performance function by using a particle swarm optimization to obtain an optimal solution, send the optimal solution to the fuzzy logic regulator and the PID controller, and update the first scale factor and the second scale factor of the fuzzy logic regulator and the scale parameter of the PID controller by using the optimal solution.
2. The fuel cell system maximum net power tracking control apparatus of claim 1, wherein the performance function is:
Figure FDA0002911044590000021
in formula (a), Q and R both represent weighting coefficients, and 0< Q ≦ 1, Q + R ═ 1; e (t) represents the difference between the actual net power value of the fuel cell stack and the maximum net power reference value, and u (t) represents the output signal of the PID controller.
3. The fuel cell system maximum net power tracking control apparatus of claim 1, wherein the PID controller is represented by the following equation:
Figure FDA0002911044590000022
in the formula (b), kp，ki，kdRespectively representing the proportional, integral and derivative parameters of the PID controller, e (t) representing the difference between the actual net power value and the maximum net power reference value of the fuel cell stack, u (t) representing the output signal of the PID controller, e (t) and
Figure FDA0002911044590000023
is in the range of [ -1,1]。
4. The fuel cell system maximum net power tracking control apparatus of claim 1, wherein in the fuzzy logic regulator, the three quantities of resolution are scaled to obtain three control quantities according to the formula:
Figure FDA0002911044590000024
in formula (c), k'jRepresenting the amount of sharpness, k, obtained after deblurring in the fuzzy logic regulatorjA proportional parameter, an integral parameter or a derivative parameter representing the PID controller,
Figure FDA0002911044590000025
respectively representing the minimum value and the maximum value of the output of the fuzzy logic regulator, g representing a scale factor, and alpha, beta and gamma representing a proportional parameter scale factor, an integral parameter scale factor and a differential parameter scale factor.
5. The maximum net power tracking control device of the fuel cell system according to claim 1, wherein the procedure for solving the performance function by using the particle swarm algorithm is specifically as follows:
(i) initializing a particle swarm;
(ii) calculating the fitness value of each particle according to the performance function;
(iii) for each particle, judging whether the current fitness value of the particle is larger than the individual optimal solution of the particle, and if so, replacing the individual optimal solution with the current fitness value;
(iv) for each particle, judging whether the current fitness value of the particle is larger than the global optimal solution of the whole particle swarm, and if so, replacing the global optimal solution with the current fitness value;
(v) updating the velocity and position of each particle according to the formula (d) and the formula (e),
Figure FDA0002911044590000031
Figure FDA0002911044590000032
in the formulae (d) and (e), ω is an inertia factor,
Figure FDA0002911044590000033
representing the current velocity of particle i at iteration t,
Figure FDA0002911044590000034
representing the current velocity of particle i at iteration t +1, c1And c2Represents a learning factor, r1And r2Represents [0,1 ]]Two random numbers in the range, pi，dRepresenting the individual optimal solution, p, of particle i at iteration tg，dRepresents the current global optimal solution and the current global optimal solution,
Figure FDA0002911044590000035
representing the current position of particle i at iteration t,
Figure FDA0002911044590000036
represents the current position of particle i at iteration t + 1;
(vi) if the maximum iteration number is met, outputting the optimal solution and exiting, otherwise, returning to the step (ii).
6. A method for controlling maximum net power tracking of a fuel cell system, comprising the steps of:
(1) determining the maximum net power of the fuel cell stack under different load currents and the optimal oxygen excess ratio corresponding to the maximum net power according to a curve relation graph of the oxygen excess ratio obtained under different load currents and the net power of the fuel cell stack in the fuel system, and obtaining a maximum net power reference value through curve fitting;
(2) comparing the maximum net power reference value with an actual net power value of the fuel cell stack to obtain an error value;
(3) carrying out differential processing on the error value to obtain error variation; the fuzzy logic regulator converts the error value and the error variable quantity from a basic domain to a domain of a fuzzy set according to a first scale factor and a second scale factor respectively, fuzzifies the error value and the error variable quantity which are converted to the domain of the fuzzy set into fuzzy quantities, expresses the fuzzy quantities by using a corresponding fuzzy language to obtain a fuzzy set, performs fuzzy reasoning on the fuzzy set by adopting a fuzzy control rule to obtain an output fuzzy quantity, performs defuzzification on the output fuzzy quantity to obtain three clear quantities, performs scale conversion on the three clear quantities to obtain three control quantities, and the three control quantities are respectively used as a proportional parameter, an integral parameter and a differential parameter of a PID controller; the PID controller processes the error value according to a proportional parameter, an integral parameter and a differential parameter by adopting a proportional-integral-differential control rule to obtain an output signal, and sends the output signal to an air compressor of a fuel cell system to control the rotating speed of the air compressor so as to control the maximum net power of the fuel cell stack; respectively carrying out weighted integration on the error value and the value of the output signal of the PID controller, establishing a performance function according to the sum of the weighted integration of the error value and the value of the output signal of the PID controller, solving the performance function by adopting a particle swarm algorithm to obtain an optimal solution, sending the optimal solution to the fuzzy logic regulator and the PID controller, and updating the first scale factor and the second scale factor of the fuzzy logic regulator and the scale parameter of the PID controller by using the optimal solution.
7. The method of controlling maximum net power tracking for a fuel cell system of claim 6, wherein the performance function is:
Figure FDA0002911044590000041
in the formula (a), Q and R both represent weighting coefficients, and 0< Q.ltoreq.1, Q + R.ltoreq.1; e (t) represents the difference between the actual net power value of the fuel cell stack and the maximum net power reference value, and u (t) represents the output signal of the PID controller.
8. The method for controlling maximum net power tracking of a fuel cell system according to claim 6, wherein the procedure for solving the performance function by using the particle swarm algorithm is specifically as follows:
(i) initializing a particle swarm;
(ii) calculating the fitness value of each particle according to the performance function;
(iii) for each particle, judging whether the current fitness value of the particle is larger than the individual optimal solution of the particle, and if so, replacing the individual optimal solution with the current fitness value;
(iv) for each particle, judging whether the current fitness value of the particle is larger than the global optimal solution of the whole particle swarm, and if so, replacing the global optimal solution with the current fitness value;
(v) updating the velocity and position of each particle according to the formula (d) and the formula (e),
Figure FDA0002911044590000042
Figure FDA0002911044590000051
in the formulae (d) and (e), ω is an inertia factor，
Figure FDA0002911044590000052
Representing the current velocity of particle i at iteration t,
Figure FDA0002911044590000053
representing the current velocity of particle i at iteration t +1, c1And c2Represents a learning factor, r1And r2Represents [0,1 ]]Two random numbers in the range, pi，dRepresenting the individual optimal solution, p, of particle i at iteration tg，dRepresents the current global optimal solution and the current global optimal solution,
Figure FDA0002911044590000054
representing the current position of particle i at iteration t,
Figure FDA0002911044590000055
represents the current position of particle i at iteration t + 1;
(vi) if the maximum iteration number is met, outputting the optimal solution and exiting, otherwise, returning to the step (ii).
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