CN103384014A - Maximum net power strategy based proton exchange membrane fuel cell air-supply system control - Google Patents

Abstract

The invention discloses a maximum net power strategy based proton exchange membrane fuel cell air-supply system control. According to the invention, optimized characteristic of net output power based on electric pile operation temperature, OER and load current is analyzed, a self-adaption particle swarm optimization algorithm based on effective information is adopted to solve the optimum operation condition, and according to the optimum operation condition restriction range under different load currents, the optimum predictive control law is solved by the effective information based self-adaption particle swarm optimization algorithm during rolling optimization. By the method provided by the invention, the problem that traditional PEMFC system net-power control is too dependent on an accurate mathematical model of a system and is easily influenced by disturbance, noise and nondeterminacy can be solved, net power of the system is maximized, parasitic power consumption of the system is reduced, and system reliability is raised.

Description

Proton Exchange Membrane Fuel Cells air supply system based on the maximum net power policy is controlled

Technical field

The present invention relates to the Proton Exchange Membrane Fuel Cells technology, especially Proton Exchange Membrane Fuel Cells air supply system control technology field.

Background technology

fuel cell technology is a kind of clean energy technology, have efficient, the characteristics such as environmental protection, 21 century the most promising " green energy resource " technology of being known as, be subject at present the great attention of countries in the world, the emphasis research and development technology that belongs to the national energy field in China, especially Proton Exchange Membrane Fuel Cells (PEMFC) is low except having the total pollution of fuel cell, outside the fuel availability advantages of higher, also has power density high, working temperature is low, start fast, the advantages such as modularity is strong, in portable power source, motor vehicle driven by mixed power and middle-size and small-size distributed generation system field have obtained great attention.

In the PEMFC system, because air supply system is compared with the hydrogen supply system, has dynamic characteristic relatively slowly, cause pile inner air flow to be difficult to respond fast according to loading demand, and because the different system peroxide can cause that than OER (OER is the oxygen quality flow F of cathode inlet supply to " air hunger " and " oxygen saturation " phenomenon _{O2, in}With the oxygen quality flow F that consumes _{O2, ret}Ratio), the system net power of impact is exported to greatest extent, therefore adopting effective control strategy to keep the OER of system is relative optimal value, for the net power output that improves the PEMFC system, keep system stability, extend system and have great importance useful life.

The pemfc stack temperature can be subject to the impact of the factors such as ambient temperature, load current.If stack temperature is too high, will cause the water vapour degree of saturation to descend, " dehydration " phenomenon appears in proton membrane, increases the difficulty of water management, if improve the radiator power output this moment, will cause the parasitic power consumption of system to increase, and system's net power output is reduced; If stack temperature is too low, can cause that system's power output reduces, output performance descends, and can't satisfy loading demand.Therefore, system's net power output can be subject to the impact of pile operating temperature.

At present, relatively less for the achievement in research of PEMFC system net power control problem.the control method major part that has proposed all is based on " air hunger " problem dynamic model and carries out the control strategy design, control target and usually be set as normal value OER, be difficult to realize the maximization output of system's net power, and the control method that adopts is based on working point approximately linear design mostly, only for the part intrinsic property of non linear system, do not consider fully that the PEMFC system is subject to external interference and probabilistic impact in practical work process yet, as ambient noise and the perturbation of the caused system parameters of nonlinear characteristic have been ignored, cause designed control system too to rely on accurate system mathematic model, be difficult to satisfy and have interference, follow-up control under measurement noise and condition of uncertainty and disturbance suppression Capability Requirement.

Summary of the invention

Above deficiency in view of prior art, the present invention aims to provide a kind of PEMFC air supply system prediction automatic correction controling method based on the maximum net power policy, the system that realizes exists in external interference, noise and uncertain situation, and the parasitic power consumption of minimizing system improves system's net power output.

Purpose of the present invention realizes by following means.

Optimization features and the Optimization Solution " optimized operation condition " of analysis Proton Exchange Membrane Fuel Cells PEMFC system maximum net power output.At first, according to the experimental test data of 1.2kW Nexa PEMFC system, analyze based on the net power output optimization features between pile operating temperature, OER and load current, set up the hypersurface model.Then, according to analysis result, sum up the optimized running boundary condition of pile operating temperature and OER, propose " optimized operation condition " restriction range under the different loads electric current.At last, according to " optimized operation condition " restriction range, set up " optimized operation condition " target function, and adopt a kind of adaptive particle swarm optimization Algorithm for Solving based on effective information " optimized operation condition " [OER ^*, T _st ^*, I _net].

A kind of Proton Exchange Membrane Fuel Cells air supply system based on the maximum net power policy is controlled, and realizes the maximization output of system's net power, comprises following means:

A, based on the air supply control system of improving Implicit Generalized prediction self-correcting and control IGPC, adopt rolling optimization, repeatedly online Controlling object function F is optimized constantly at each, and according to predictor parameter matrix G arranged side by side, devise optimum control law; Simultaneously, introduce softening and control reference locus w when the design object function F, concrete form is as follows:

$F=\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{[y(k+j)-w(k+j)]}^2+\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\λ}\left[\mathrm{\Δu}(k+j-1)\right]}^2---\left(1\right)$

Wherein, n is maximum predicted length, and m controls length, and λ controls weight coefficient, and w is that reference locus is controlled in softening, is expressed as follows:

w(k+j)＝α ^jy(k)+(1-α ^j)y _r （2）

Wherein, α is the softening coefficient, y _rWith reference to optimum OER track.

B, reference regulator are by calculating the clean output current I of system under certain operating condition _net, obtain optimum with reference to U ^*Output, then the pile operating temperature T that arrives with actual acquisition _stWith the OER value input variable of conduct improvement Implicit Generalized PREDICTIVE SELF TUNING CONTROLLER IGPC together; Optimum with reference to U ^*With OER ^*, T _st ^*And I _netBe expressed as functional relation:

$U^{*}=f({\mathrm{OER}}^{*},T_{\mathrm{st}}^{*},I_{\mathrm{net}})---\left(3\right)$

Then by feedback compensation, revise the uncertainty of prediction, improve system robustness; At last, obtain to improve the output controlled quentity controlled variable of Implicit Generalized PREDICTIVE SELF TUNING CONTROLLER IGPC, this controlled quentity controlled variable is the control voltage of follow-up air compressor system and the control voltage of radiator fan.

C, IGPC adopt based on the constrained optimum prediction control law of the adaptive particle swarm optimization Algorithm for Solving of effective information when rolling optimization according to " optimized operation condition " restriction range under the different loads electric current.

Adopt PEMFC air supply control system of the present invention, have following advantage:

(1) the present invention is according to actual PEMFC air supply system, set up the hypersurface model, and when " parasitic drain " problem of analytical system, not only considered the power consumption of air supply system, also taken into account the consumption of cooling system, carried out effective analysis of net power output, stack temperature, OER and load current coupled relation, and then found the solution system's " optimized operation condition ", solved the optimized problem of system's net power output, for laying a good foundation based on the Control System Design of maximum net power.

(2) the present invention is directed to conventional P EMFC net power control method and too rely on accurate system mathematic model, be subject to disturbance in practical application, the limitation of the factors such as noise and uncertainty impact, a kind of system prediction automatic correction controling method based on the maximum net power policy is proposed, and employing is based on the adaptive particle swarm optimization Algorithm for Solving optimum prediction control law of effective information, can solve and exist " optimized operation condition " constraint requirements target function and the constraints can be little, and the problem that obtains locally optimal solution, there is interference in the system that realizes, in noise and uncertain situation, follow the tracks of optimum OER running orbit, the parasitic power consumption of minimizing system, raising system net power.

Description of drawings is as follows:

Fig. 1 is system's net power under different clean output currents and the graph of relation of OER.

Fig. 2 is the process flow diagram of the inventive method.

Fig. 3 a is clean output current I _netIn 50s from 11A to 20A on a large scale in carry out continuous step disturbance change curve.Fig. 3 b is the OER response curve in the first process control simulation experiment (nominal condition).Fig. 3 c is for controlling the OER response curve in emulation experiment (containing disturbance, noise and time delay condition) at second process.

Embodiment

Details are as follows for specific implementation process of the present invention.

At first, analyze optimization features and the Optimization Solution " optimized operation condition " of Proton Exchange Membrane Fuel Cells PEMFC system maximum net power output.According to the experimental test data of 1.2kW Nexa PEMFC system, analyze based on the net power output optimization features between pile operating temperature, OER and load current, set up the hypersurface model.Then, according to analysis result, sum up the optimized running boundary condition of pile operating temperature and OER, propose " optimized operation condition " restriction range under the different loads electric current.At last, according to " optimized operation condition " restriction range, set up " optimized operation condition " target function, and adopt a kind of adaptive particle swarm optimization Algorithm for Solving based on effective information " optimized operation condition " [OER ^*, T _st ^*, I _net].

Because the CARIMA forecast model is applicable to the Non-Stationary random noise process, and modeling is not dynamically had better robustness, therefore adopt this model realization to the prediction of the following output of PEMFC air supply system OER, concrete form is as follows:

$A\left(z^{-1}\right)y\left(k\right)=B\left(z^{-1}\right)u(k-1)+C\left(z^{-1}\right)\frac{\mathrm{\ξ}\left(k\right)}{1-z^{-1}}---\left(1\right)$

Wherein, A (z ^-1), B (z ^-1) and C (z ^-1) be respectively z ^-1Multinomial, y (k), u (k) and ξ (k) are respectively that output, input and average are 0 white noise sequence.

Definition OER is the oxygen quality flow F of cathode inlet supply _{O2, in}With the oxygen quality flow F that consumes _{O2, ret}The ratio:

$\mathrm{OER}=\frac{{\mathrm{<figure-callout\; id="2"\; label="F\; O"\; filenames="HDA00003268169000011.png"\; state="\{\{state\}\}">F</figure-callout>}}_{O_{}}}{{\mathrm{<figure-callout\; id="2"\; label="F\; O"\; filenames="HDA00003268169000011.png"\; state="\{\{state\}\}">F</figure-callout>}}_{O_{}}}---\left(2\right)$

The net power output P of PEMFC system _netBe to be determined by pemfc stack power output and parasitic power, and parasitic power is mainly produced by air compressor, so the suitable air mass flow of how to confirm is for realizing that system's maximum net power is of crucial importance.The present embodiment carries out modeling according to 1.2kWNexa PEMFC electricity generation system characteristic, obtains different clean output current I _netUnder condition, the curve relation figure of system's net power output and OER, as shown in Figure 1, the optimum efficiency track represents with the round dot line, wherein each round dot has represented in different I _netUnder system P _netMaximum, corresponding OER value is the optimized operation point, and dotted line represents the border, saturation region of air pump.Can find from figure, for different clean output currents, all exist different OER can make net power output P _netReach maximum, in case system reaches best OER, will cause that parasitic power consumption increases if continue again to increase, and then worsen system's net power output.Clean output current is larger, and these characteristics are more outstanding.

The present invention adopts the IGPC control method to realize the maximum net power control of PEMFC system.When design IGPC controller, in order to strengthen system robustness, in the objective function F of rolling optimization, considered that current k moment controlled quentity controlled variable u (k) on the following k+1 of system impact constantly, sets up following target function:

$F=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y(k+j)-w(k+j)]}^2+\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{\mathrm{\&lambda;}\left[\mathrm{\&Delta;u}(k+j-1)\right]}^2---\left(3\right)$

Wherein, n is maximum predicted length, and m controls length, and λ controls weight coefficient, and w is that reference locus is controlled in softening, is expressed as follows:

w(k+j)＝α ^jy(k)+(1-α ^j)y _r （4）

Wherein, α is the softening coefficient, y _rWith reference to optimum OER track.

The optimal control law of IGPC is expressed as follows:

ΔU＝(G ^TG+λI) ^-1G ^T(W-f) （5）

Wherein, Δ U=[Δ u (k), Δ u (k+1) ..., Δ u (k+n-1)] ^T, f is the open-loop prediction vector, W=[w (k+1), and w (k+2) ..., w (k+n)] ^T, G is predictor parameter matrix arranged side by side.

Traditional explicit GPC control method adopts the method for finding the solution diophantus (Dioaphantine) equation to determine matrix G, because needs carry out multi-step prediction, just must repeatedly find the solution online Diophantine equation, and intermediate computations is loaded down with trivial details, and holding time is long.For reducing amount of calculation and on-line operation time, the present invention adopts the IGPC method according to inputoutput data, adopts the adaptive particle swarm optimization Algorithm for Solving matrix G based on effective information when rolling optimization, and then definite optimal control law.

Reference regulator is by calculating the clean output current I of system under certain operating condition _net, obtain optimum with reference to U ^*Output, then the pile operating temperature T that arrives with actual acquisition _stWith the OER value input variable of conduct improvement Implicit Generalized PREDICTIVE SELF TUNING CONTROLLER IGPC together; Optimum with reference to U ^*With OER ^*, T _st ^*And I _netBe expressed as functional relation:

$U^{*}=f({\mathrm{OER}}^{*},T_{\mathrm{st}}^{*},I_{\mathrm{net}})---\left(3\right)$

At last, by rolling optimization, repeatedly carry out online each control objective optimization constantly, the devise optimum control law through feedback compensation, compares actual measured value and predicted value, revises the uncertainty of prediction, improves the robustness of control system.Fig. 2 is the detailed process flow process of the inventive method.

Below in conjunction with specific embodiment, the present invention is further detailed explanation.

The present invention is directed to 2 kinds of different situations of PEMFC system appearance in process control is used, compare by emulation experiment and from the Tuning PID Controller method, carry out performance test and evaluation under different situations, as Fig. 3 a, Fig. 3 b and Fig. 3 c.

Emulation experiment one

Fig. 3 a be shown in clean output current I _netIn 50s from 11A to 20A on a large scale in carry out continuous step disturbance and change.Optimum OER track y _rSearch for optimal trajectory in 5.88 to 7.93 response range, as shown in Fig. 3 b.

In the experiment of the first process control simulation, all can there be the I shown in Fig. 3 a in IGPC and PID _net(do not consider noise and uncertain impact, i.e. nominal condition) under the shock wave condition continuously, realize optimum OER track y _rTracking, as shown in Fig. 3 b.By relatively finding, although the adjusting time of 2 kinds of control methods is basic identical, the OER of PID method response vibration is larger, and has certain steady-state error, and the vibration of IGPC method is less, can realize more smoothly the floating tracking.

Emulation experiment two

Control in emulation experiment at second process, be the impact of the factors such as disturbance and uncertainty on system in the simulation practical application, the present invention contains under disturbance, noise and time delay condition in system, environmental interference signal (mean value is 0, variance be 20) is joined system input, measurement noise signal (mean value is 0, variance be 0.5) is joined the controller input, time delay delay signal (time-delay is 0.2s) is joined the conditioner outlet end with reference to OER, and result is as shown in Fig. 3 c.The IGPC method can be according to I under such condition _netShock wave, realize substantially to optimum OER track y continuously _rTracking, embody tracing property, vulnerability to jamming and noise immunity preferably.And the PID method can't overcome the impact of these external interference, uncertainty and noise, and its OER response has departed from optimum OER track, is difficult to realize system's maximum net power stage.

The PEMFC air supply system control method based on maximum net power that the present invention proposes, can solve conventional P EMFC system net power controls and too relies on the limitation that is subject to disturbance, noise and the factor impact such as uncertain in accurate system mathematic model, practical application, realize preferably the system optimal operation, and then the maximization that realizes system's net power is exported, improve the systematic steady state performance, improve system reliability.

Claims (2)

1. control based on the Proton Exchange Membrane Fuel Cells air supply system of maximum net power policy, analyze optimization features and the Optimization Solution " optimized operation condition " of Proton Exchange Membrane Fuel Cells PEMFC system maximum net power output:

At first, according to the experimental test data of 1.2kW Nexa PEMFC system, analyze based on the net power output optimization features between pile operating temperature, OER and load current, set up the hypersurface model; Then, according to analysis result, sum up pile operating temperature T _stWith the optimized running boundary condition of OER, " optimized operation condition " restriction range under the different loads electric current is proposed; At last, according to " optimized operation condition " restriction range, set up " optimized operation condition " target function, and adopt a kind of adaptive particle swarm optimization Algorithm for Solving based on effective information " optimized operation condition " [OER ^*, T _st ^*, I _net].

2. the Proton Exchange Membrane Fuel Cells air supply system based on the maximum net power policy is controlled, and realizes the maximization output of system's net power, comprises following means:

A, based on the air supply control system of improving Implicit Generalized prediction self-correcting and control IGPC, adopt rolling optimization, repeatedly online Controlling object function F is optimized constantly at each, and according to predictor parameter matrix G arranged side by side, devise optimum control law; Simultaneously, introduce softening and control reference locus w when the design object function F, concrete form is as follows:

$F=\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{[y(k+j)-w(k+j)]}^2+\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\mathrm{\&lambda;}{\left[\mathrm{\&Delta;u}(k+j-1)\right]}^2---\left(1\right)$

Wherein, n is maximum predicted length, and m controls length, and λ controls weight coefficient, and w is that reference locus is controlled in softening, is expressed as follows:

w(k+j)=α ^jy(k)+(1-α ^j)y _r （2）

Wherein, α is the softening coefficient, y _rIt is optimum reference locus;

B, reference regulator are by calculating the clean output current I of system under certain operating condition _net, obtain optimum with reference to U ^*Output, then the pile operating temperature T that arrives with actual acquisition _stWith the OER value input variable of conduct improvement Implicit Generalized PREDICTIVE SELF TUNING CONTROLLER IGPC together; Optimum with reference to U ^*With OER ^*, T _st ^*And I _netBe expressed as functional relation:

$U^{*}=f({\mathrm{OER}}^{*},T_{\mathrm{st}}^{*},I_{\mathrm{net}})---\left(3\right)$

Then by feedback compensation, revise the uncertainty of prediction, improve system robustness; At last, obtain to improve the output controlled quentity controlled variable of Implicit Generalized PREDICTIVE SELF TUNING CONTROLLER IGPC, this controlled quentity controlled variable is the control voltage of follow-up air compressor system and the control voltage of radiator fan;

C, IGPC adopt based on the constrained optimum prediction control law of the adaptive particle swarm optimization Algorithm for Solving of effective information when rolling optimization according to " optimized operation condition " restriction range under the different loads electric current.

Images