CN115763908B - Distributed control method for optimizing efficiency of multi-stack fuel cell system - Google Patents
Distributed control method for optimizing efficiency of multi-stack fuel cell system Download PDFInfo
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Abstract
The invention discloses a distributed control method for optimizing efficiency of a multi-stack fuel cell system, which considers the difference of efficiency characteristics caused by different performances of fuel cell stacks, firstly, each single stack cell of the multi-stack fuel cell system obtains an efficiency/power curve of a self stack through identification, incremental hydrogen consumption of the single stack cell is set as a consistency variable, a consistency algorithm is adopted, information interaction iteration between adjacent fuel cells is realized through a distributed communication topology to achieve consistency of incremental hydrogen consumption values, output power of each corresponding stack is obtained, and different given modes of stack control and DC/DC control reference power in a single stack fuel cell control layer are designed. The invention can realize the optimal efficiency operation of the multi-stack fuel cell system when the stack performance of each single-stack system is inconsistent, so as to avoid the dependence on a centralized controller and the damage to the stack performance caused by oxygen starvation, thereby improving the high efficiency and the durability of the multi-stack system operation.
Description
Technical Field
The invention belongs to the field of new energy utilization, and relates to an MFCS efficiency optimization power distribution method based on a consistency algorithm.
Background
The proton exchange membrane fuel cell (Proton exchange membrane fuel cell, PEMFC) is an energy conversion device for generating electric energy through electrochemical reaction of hydrogen and oxygen, and has the characteristics of high power density, high conversion efficiency and low noise. Because the output power of a single-stack fuel cell is limited and the reliability is not high, a Multi-stack fuel cell system (Multi-stack fuel cell system, MFCS) is usually formed by connecting a plurality of single-stack PEMFCs in series and parallel at present to improve the overall power level of the system, and the system has diversified topological structures such as series connection, parallel connection and the like, and the stability and the durability of the system are improved by a Multi-stack cooperative operation mode.
Under the background of high hydrogen energy development cost and limited application scale, the system efficiency is improved, and the reduction of the overall hydrogen consumption is significant for popularization and application of the fuel cell. For a multi-stack fuel cell system with different power levels or performances of the single PEMFCs, how to distribute the output power of each single stack cell, so that the multi-stack system participates in the coordinated operation of the multi-state energy system in a high-efficiency operation state is a problem that needs to be studied in an important way at present. The power distribution mode of the traditional multi-stack fuel cell system has average distribution and chained distribution, and the existing literature mainly optimizes the power distribution problem of the multi-stack fuel cell system in two modes. Firstly, the accuracy and the instantaneity of the efficiency curve identification are improved; and secondly, an intelligent algorithm is adopted to improve the allocation rationality so as to realize the optimization target.
However, the existing power distribution mode of the multi-stack fuel cell system has certain defects and shortcomings, and mainly focuses on:
1. most of the existing power distribution modes are designed on the premise that the characteristics of each single cell stack are consistent. The influence of numerous factors such as power output, stack temperature, gas pressure, relative humidity, hydrogen supply, system operation dynamic characteristics and the like on the actual efficiency characteristics of the stacks is not considered, so that the follow-up power distribution is inaccurate.
2. Most efficiency optimization control methods based on power self-adaptive distribution obtain efficiency power curves of different numbers of galvanic piles through offline data fitting, so as to realize the overall maximum efficiency tracking of the system; however, when power is distributed, the starting number of the electric pile needs to be changed synchronously, the electric pile is not reasonably started frequently in an actual system, and the difference of output characteristics when the electric pile actually operates is not considered.
3. The existing various self-adaptive distribution modes belong to centralized control, measurement data such as voltage, current and the like of a pile are sent to a centralized controller, and after identification, power distribution optimizing is carried out by using an intelligent optimization algorithm, and then an instruction is sent to each single controller. The centralized controller needs to interact information with all the single-stack FC controllers, requires a perfect communication network of the system, has higher construction cost, and simultaneously requires the centralized controller to have strong data processing capability. When the centralized controller or the communication link fails, the centralized control cannot complete the corresponding control target, and thus the reliability is not high.
Disclosure of Invention
The invention provides a distributed control method for optimizing the efficiency of a multi-stack fuel cell system, which aims to realize the optimal efficiency operation of the multi-stack fuel cell system when the performances of the stacks of the single-stack system are inconsistent, so as to avoid the dependence on a centralized controller and the damage to the performances of the stacks caused by oxygen starvation, thereby improving the high efficiency, the durability and the stability of the operation of the multi-stack system.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a distributed control method for optimizing efficiency of a multi-stack fuel cell system, which is applied to the multi-stack fuel cell system, wherein the multi-stack fuel cell system comprises n single-stack fuel cell systems, and any jth single-stack fuel cell system comprises: a jth electric pile, a jth DC/DC converter, a jth DC/DC controller and a jth electric pile controller; the distributed control method is characterized by comprising the following steps of:
step 1: the jth electric pile controller collects the voltage v of the electric pile fc,j Current i fc,j Auxiliary system power Paux, j, thereby obtaining single-stack fuel cell efficiency eta corresponding to the current jth electric stack output power by using the formula (1) fcs,j ：
In the formula (1), P fc,j Represents the output power of the jth electric pile, N is the number of electric pile monomers, eta s,j Indicating the thermal efficiency, eta of the jth stack e,j Represents the jth electricityElectrical efficiency, eta of the stack f,j Indicating the fuel utilization rate of the jth electric pile; e, a coefficient related to the reaction heat of the PEMFC; j=1, 2,3, …, n; n is the total number of stacks; p (P) aux,j Representing the power consumption of auxiliary equipment of a jth electric pile in the PEMFC system;
the fitting relation between the output power and the efficiency of each electric pile in the multi-pile fuel cells is identified through a least square method, so that a fitting function of the characteristic of the jth electric pile characterization curve is obtained through the formula (2):
η fcs,j ＝a 0 +a 1 P fc,j +a 2 (P fc,j ) 2 +a 3 (P fc,j ) 3 +a 4 (P fc,j ) 4 +a 5 (P fc,j ) 5 +a 6 (P fc,j ) 6 (2)
in the formula (2), a 0 ，a 1 ，a 2 ，a 3 ，a 4 ，a 5 ，a 6 Is the coefficient to be identified;
step 2: selecting one electric pile from the multiple stacks of fuel cells as a leading node, and marking the electric pile as a main electric pile; the n-1 electric piles except the leading node are all autonomous nodes;
step 2.1: defining the current update times as k, and initializing k=1; the output power P of the dominant node fc,main Initial output power P as the dominant node for the k-1 th update k-1,main The method comprises the steps of carrying out a first treatment on the surface of the Output power P of any v-th autonomous node fc,v Initial output power P as the dominant node for the k-1 th update k-1,v ；
Step 2.2: the master node receives the current total instruction power P from the energy management center ref After that, the dominant node updates the output power P according to the k-1 th time of the dominant node k-1,main Obtaining the increment hydrogen consumption value h updated by the dominant node at the kth-1 time by using the formula (3) main (k-1)；
In the formula (3), eta fcs,main Representing dominant node efficiency;
step 2.3: the n-1 autonomous nodes also obtain the self-updated incremental hydrogen consumption value at the k-1 time according to the formula (3)Wherein h is v (k-1) represents an incremental hydrogen consumption value updated by the v-th autonomous node at the k-1 th time;
step 2.4: the v-th autonomous node obtains the k-th updated incremental hydrogen consumption value h by using the formula (4) v (k)；
In the formula (4), d v,u Representing the connection condition between the v autonomous node and the u autonomous node as the element of the v row and the u column in the state transition matrix;
step 2.5: the dominant node obtains the increment hydrogen consumption value h updated for the kth time by using the formula (5) main (k)；
In the formula (5), d main,j Representing the connection status between the main leading node main and the jth electric pile, h j (k-1) represents the increment hydrogen consumption value of the jth cell stack of k-1 iterations, ΔP (k-1) represents the difference between the total output power and the command power of the k-1 iterations, and P fc,j (k-1) represents the output power of the jth cell stack of k-1 iterations, P ref Representing total instruction power, wherein mu is a convergence coefficient;
step 2.6: obtaining the output power P of the jth electric pile updated at the kth time by using the method (6) fc,j (k)；
In formula (6), P min Representing galvanic pileMinimum output power, P max Represents the maximum output power of the electric pile, h j (k) Represents the increment hydrogen consumption value, h of the jth galvanic pile in the kth iteration j -1 [·]The expression (2) represents an inverse function of the function;
step 2.7: obtaining a difference value delta P (k) between the total output power of the electric pile and the instruction power in the kth iteration by using the formula (7);
step 2.8: judging increment hydrogen consumption values of n electric stacksEqual or not, and |ΔP (k) |<Epsilon is established, if so, the efficiency of the multi-stack fuel cell is optimal, and the current total instruction power P is calculated ref The output power of the corresponding individual galvanic pile +.>To the respective single stack fuel cell system; otherwise, after assigning k to k-1, returning to the step 2.2 for sequential execution; wherein (1)>Representing the optimal output power of the jth electric pile;
step 3: the j-th single-stack fuel cell system receives the optimal output powerAfter that, the optimal output power from last received +.>Comparing if->Then the command power P transmitted to the jth stack controller is obtained by using equation (8) fc And the j-th DC/DC controllerRate P dc The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the command power P transmitted to the jth cell stack controller is obtained by using the formula (9) fc And command power P of jth DC/DC controller at t time dc (t)；
In the formula (9), τ is an FC dynamic time constant.
The electronic device of the invention comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute the distributed control method, and the processor is configured to execute the program stored in the memory.
The invention relates to a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, performs the steps of the distributed control method.
Compared with the existing method, the invention has the beneficial effects that:
1. the invention aims at optimizing the power distribution problem of the efficiency of the multi-stack battery system, is different from the existing centralized control, adopts a distributed control power distribution mode based on a consistency algorithm, realizes the optimal operation of the system efficiency through information interaction and consistency iteration between topological adjacent stacks, does not depend on a centralized controller, and has higher reliability. In distributed control, each pile node only needs to communicate with adjacent nodes, so that the quantity of information transmission is small, the requirement on communication topology is not high, certain robustness is achieved, data identification work and power calculation are all carried out inside the nodes, and the overall data processing pressure of the system is reduced. The network topology structure is more flexible and changeable, the communication construction cost is reduced, and the system expansibility is higher.
2. Based on the control time scale difference caused by different dynamic response speeds of the fuel cell body and the DC/DC, the invention designs different given modes of reference power of electric pile air inlet control and DC/DC control in a single pile control layer, and avoids damage to electric pile performance caused by oxygen starvation on the basis of completing power instructions of an upper layer.
Drawings
FIG. 1 is a diagram of a road traffic multi-state energy system architecture of the present invention;
FIG. 2 is a block diagram of a fuel cell air supply system of the present invention;
fig. 3 is a block diagram of air compressor air supply control of the present invention;
FIG. 4 is a control block diagram of a fuel cell boost converter of the present invention;
FIG. 5 is a block diagram of the efficiency optimization of the multi-stack fuel cell system of the present invention;
FIG. 6 is a flow chart of a coherency algorithm of the present invention;
FIG. 7 is a multi-stack fuel cell system topology of the present invention;
FIG. 8 is a graph of the efficiency of four different stacks of the present invention;
FIG. 9a is an iteration graph of incremental hydrogen consumption of each node when the scheduling power of the invention is 520 KW;
FIG. 9b is a graph of total output power of the multi-stack system for a scheduled power of 520KW in accordance with the present invention;
FIG. 9c is a graph of the output power of each stack of the multi-stack system for a scheduled power of 520KW in accordance with the present invention;
FIG. 10 is a diagram of three different communication networks of the present invention;
FIG. 11a is a graph of a loop topology consistent iteration process of the invention;
FIG. 11b is a diagram of a star topology consistency iteration process of the present invention;
FIG. 11c is a diagram of a chain topology consistent iteration process of the invention;
FIG. 12 is a graph of a DC/DC reference power set-up pattern of the present invention;
fig. 13 is a graph of the output voltage of the stack of the present invention.
Detailed Description
The following describes the embodiments and working principles of the present invention in further detail with reference to the drawings.
In the embodiment, a distributed control method for optimizing the efficiency of a multi-stack fuel cell system considers the difference of stack efficiency characteristics caused by the operation condition and the stack performance of each single-stack fuel cell, considers each single-stack fuel cell of the multi-stack fuel cell system as an intelligent agent, and after the efficiency/power curve of each single-stack is obtained through identification by each intelligent agent, sets the equivalent increment hydrogen consumption of each single-stack cell as a consistency variable, adopts a consistency algorithm to realize distributed control on the multi-stack system, achieves the consistency of increment hydrogen consumption values through information interaction iteration between topological adjacent fuel cells, obtains the output power corresponding to each single-stack increment hydrogen consumption value, and controls the stacks to complete corresponding output, thereby realizing the optimal efficiency operation of the multi-stack fuel cell system. Different given modes of electric pile air inlet control and DC/DC control reference power in a single pile fuel cell control layer are designed, and the system performance is improved through control cooperation.
Fig. 1 is a diagram of a road traffic multi-state energy system, which is formed by respectively connecting devices such as a photovoltaic cell, a fan, a fuel cell, an electrochemical energy storage device and the like into a direct current bus through a converter, and hydrogen production is carried out on redundant electric energy through an electrolytic tank so as to realize large-scale energy storage. The proton exchange membrane fuel cell has a complex gas supply system and a chemical reaction process, and the dynamic response time is slow, so that a multi-stack fuel cell system consisting of proton exchange membrane fuel cell units operates according to an energy scheduling instruction no matter whether the road traffic multi-state energy system is in a networking state or an island state. The invention selects a multi-stack combined operation mode of a plurality of PEMFCs connected in parallel, improves the power level of the whole system, and simultaneously, each fuel cell is provided with a DC/DC converter to independently control the power output.
Aiming at the problem of optimizing power distribution of a multi-stack fuel cell system with different single-stack output characteristics, the invention designs a distributed power optimizing distribution control framework of the multi-stack fuel cell system based on a consistency algorithm as shown in fig. 5, wherein the distributed power optimizing distribution control framework comprises an equipment layer, a single-stack control layer and a multi-stack distribution layer. The multi-stack fuel cell system adopts a parallel topology model, and a ring-shaped distributed communication architecture is adopted among the stacks of fuel cell systems.
In the multi-stack distribution layer, each stack performs an efficiency-power curve identification operation in the respective fuel cell controller without transmitting measurement data to the centralized controller. Each single-stack cell system is regarded as an intelligent node for distributed control, and when the total scheduling power P of the multi-stack fuel cell systems is ref When the power is changed, a master node (shown as a node 1 in fig. 5) is set to replace a centralized controller to receive a total power instruction, meanwhile, consistent variable parameters are interacted between the master node and other nodes connected with a communication topology by a plurality of autonomous nodes, after a plurality of iterations reach consistency, a power optimization distribution instruction considering each single stack efficiency-power curve is obtained, the aim of optimal efficiency operation of the multi-stack fuel cell system is achieved, and each stack reference power instruction is transmitted to a single stack control layer.
The control of the single-stack fuel cell system comprises stack air inlet control (shown in figure 2), DC/DC control (shown in figure 4), humidity and temperature control and the like, and the invention designs different given modes of reference power of the stack air inlet control and the DC/DC control in a single-stack control layer based on control time scale difference caused by different dynamic response speeds of a fuel cell body and the DC/DC.
Based on the power distribution mode of the multi-stack fuel cells, the distributed control method for optimizing the efficiency of the multi-stack fuel cell system is applied to the multi-stack fuel cell system, wherein the multi-stack fuel cell system comprises n single-stack fuel cell systems, and any jth single-stack fuel cell system comprises: a jth electric pile, a jth DC/DC converter, a jth DC/DC controller and a jth electric pile controller; the distributed control method is characterized by comprising the following steps of:
step 1: the jth electric pile controller collects the voltage v of the electric pile fc,j Current i fc,j Auxiliary system power Paux, j, thereby obtaining single-stack fuel cell efficiency eta corresponding to the current jth electric stack output power by using the formula (1) fcs,j ：
In the formula (1), P fc,j Represents the output power of the jth electric pile, N is the number of electric pile monomers, eta s,j Indicating the heat efficiency of the galvanic pile eta e,j Representing the electrical efficiency, eta f,j Representing the fuel utilization rate, the present invention is set to 100%; e is related to the reaction heat of the PEMFC, if the reaction heat of the electric pile is high, 1.48 is used, and if the reaction heat is low, 1.25 is taken; j=1, 2,3, …, n; n is the total number of stacks; p (P) aux,j The power consumed by the auxiliary equipment in the PEMFC system includes, among other things, the PEMFC controller, the unidirectional DC/DC converter, and the fuel cell auxiliary system power.
The fitting relation between the output power and the efficiency of each electric pile in the multi-pile fuel cells is identified through a least square method, so that a fitting function of the characteristic of the jth electric pile characterization curve is obtained through the formula (2):
η fcs,j ＝a 0 +a 1 P fc,j +a 2 (P fc,j ) 2 +a 3 (P fc,j ) 3 +a 4 (P fc,j ) 4 +a 5 (P fc,j ) 5 +a 6 (P fc,j ) 6 (2)
in the formula (2), a 0 ，a 1 ，a 2 ，a 3 ，a 4 ，a 5 ，a 6 Is the coefficient to be identified;
the overall efficiency of the multi-stack fuel cell system is defined as in formula (3):
in the formula (3), eta MFCS Overall efficiency of the multi-stack fuel cell; p (P) fc,j ，η fcs,j The output power and the stack efficiency of the jth stack are respectively; j=1, 2,3, …, n (n is the total number of stacks); p (P) ref Scheduling power for the total; p (P) H2 Is the total equivalent hydrogen consumption.
Step 2: distributed power optimal allocation control of multi-stack fuel cell system based on consistency algorithm
A first part: consistency algorithm theory:
the graph theory and the matrix theory are the theoretical basis of a consistency algorithm, each distributed control unit realizes local information interaction through interconnected communication lines, and the communication structure among the units can be described by using one graph. For a system with n units, the communication topology is represented by graph g= (V, E). Wherein, the node set v= {1, …, n }, represents the set of all vertices in the graph G; edge setRepresenting a collection of edges that each node connects. The selected graph of the invention is an undirected graph, i.e. the information transfer of two connected units is bidirectional. If paths exist between nodes in the graph G, the graph G is called connected, and the system connectivity can use the premise of a discrete consistency algorithm. Two matrices in graph theory can be used to describe the connection condition between nodes, namely adjacent matrix a= [ a ] ij ] n×n And laplace matrix l= [ L ] ij ] n×n They are defined by formula (4) and formula (5), respectively:
the consistency algorithm has two kinds of discrete and continuous, in the distributed control, the communication transmission has discrete characteristic, so the invention adopts a first-order discrete consistency algorithm for research.
For a distributed control system with n nodes, let x i [k]Represents a consistent state variable for node i, where i=1, 2, …, n, k is the number of iterations. Node state variable x i [k]By and adjacent to node x j [k]Exchanging state information, updating self stateThe state variables of all nodes tend to agree as the number of iterations increases. The discrete consistency algorithm may be described as formula (6):
can be written in a matrix form as shown in formula (7):
X(k+1)＝DX(k) (7)
in the formula (7), X (k) = [ X ] 1 (k)，x 2 (k)，…，x n (k)] T ，D＝[d ij ] n×n For the state transition matrix, D is a random matrix meeting certain conditions [19，20] The state variables of all nodes can finally be consistent through iteration, and particularly, when D is a double-random matrix and the diagonal element is not 0, the state quantity of each node can be converged to the average value of the initial state quantity.
The construction modes of the state transition matrix D are various, and the convergence speed of the consistency iterative algorithm is influenced, and the method selects a Metropolis method to construct the matrix D, wherein the form is shown as a formula (8):
in the formula (8), n i And n j Representing the number of connected nodes of the node i and the node j; n (N) i Representing a collection of nodes connected to node i.
A second part: system efficiency optimization objective function:
by reasonably distributing the output of each single-stack fuel cell, the overall efficiency optimal operation of the fuel cells of multiple stacks is realized. In the process, not only is a scheduling instruction of an upper control center required to be reached, but also the output power constraint of each single-stack battery is satisfied. The objective function and constraint conditions can be summarized as formula (9) by using a mathematical model:
the Lagrangian function may be constructed from the objective of maximum efficiency and the equality constraint of system output as in equation (10):
p pair P fci Obtaining an optimal solution by solving the bias derivative:
definition of equivalent incremental hydrogen consumption h i The method comprises the following steps:
the optimal solution of equation (9) is known as:
h 1 ＝h 2 ＝…＝h n ＝-λ (13)
in the formula (13), n is the number of stacks. I.e., when the equivalent incremental hydrogen consumption of each single stack of fuel cells is equal, the multi-stack fuel cell system achieves the optimum overall efficiency.
Third section: iterative calculation of increment hydrogen consumption consistency:
selecting one electric pile from the multiple stacks of fuel cells as a leading node, and marking the electric pile as a main electric pile; the n-1 electric piles except the leading node are all autonomous nodes;
step 2.1: defining the current update times as k, and initializing k=1; the output power P of the dominant node fc,main Initial output power P as the dominant node for the k-1 th update k-1,main The method comprises the steps of carrying out a first treatment on the surface of the Output power P of any v-th autonomous node fc,v Initial output power P as the dominant node for the k-1 th update k-1,v ；
Step 2.2: the master node receives the current total instruction power P from the energy management center ref The dominant node then updates the output power P according to the k-1 th update of itself k-1,main Is good forObtaining the increment hydrogen consumption value h updated by the dominant node at the kth-1 time by using the formula (14) main (k-1)；
In the formula (14), eta fcs,main Representing dominant node efficiency;
step 2.3: the n-1 autonomous nodes also obtain the self-updated incremental hydrogen consumption value at the k-1 th time according to the formula (14)Wherein h is v (k-1) represents an incremental hydrogen consumption value updated by the v-th autonomous node at the k-1 th time;
step 2.4: the v-th autonomous node obtains the k-th updated incremental hydrogen consumption value h by using the formula (15) v (k)；
In the formula (15), d v,j And the element of the jth column of the v row in the state transition matrix represents the connection condition between the v node and other nodes.
Step 2.5: the dominant node obtains the increment hydrogen consumption value h updated by the kth time by using the formula (16) main (k)；
In the formula (16), d main,j Representing the connection status between the main master node main and other nodes, h j (k-1) represents the increment hydrogen consumption value of each node of k-1 iterations, delta P (k-1) represents the difference value between the total output power and the instruction power of the k-1 iterations, and P fc,j (k-1) represents the output power of each pile for k-1 iterations, P ref Representing total instruction power, wherein mu is a convergence coefficient;
step 2.6: obtaining the output power P of the jth cell stack updated at the kth time by using the formula (17) fc,j (k)；
In the formula (17), P min Representing the minimum output power of the galvanic pile, P max Represents the maximum output power of the electric pile, h j (k) Represents the increment hydrogen consumption value, h of the jth galvanic pile in the kth iteration j -1 [·]The expression (2) represents an inverse function of the function;
step 2.7: obtaining Δp (k) for the kth update using equation (18);
step 2.8: judging increment hydrogen consumption values of n electric stacksEqual or not, and |ΔP (k) |<Epsilon is established, if so, the efficiency of the multi-stack fuel cell is optimal, and the current total instruction power P is calculated ref The output power of the corresponding individual galvanic pile +.>To the respective single stack fuel cell system; otherwise, after assigning k to k-1, returning to the step 2.2 for sequential execution; wherein (1)>Representing the optimal output power of the jth electric pile;
FIG. 6 is a flow chart of power distribution based on a consistency algorithm, wherein in each iteration process, a dominant node only needs to exchange incremental hydrogen consumption values with adjacent nodes, and then the incremental hydrogen consumption values are iteratively updated according to the consistency algorithm, so that the corresponding output power is ensured to be within a limit range; the autonomous node needs to receive the total dispatching power instruction of the energy management center, and besides the information exchange and the consistency iteration with the adjacent nodes, the autonomous node also needs to calculate the difference value between the total output power of each system of the stacks and the upper dispatching instruction power and substitutes the difference value into the consistency iteration. When the consistency variable iterations of all the nodes reach consistency and the power difference value is smaller than the set deviation value, the power distribution of the optimal system efficiency is realized by meeting the scheduling power instruction requirement of the energy center, and the output power instructions corresponding to each single stack are issued to the control unit, so that the fuel cells output corresponding power, and the aim of optimal operation of the multi-stack fuel cell system efficiency is fulfilled.
Step 3: single stack fuel cell system control:
when power change or load fluctuation is scheduled, the high-pressure hydrogen storage tank and the air compressor are required to provide sufficient hydrogen and oxygen for the electric pile, so that safe and reliable chemical reaction is ensured. The cathode air inlet system is an important point of air inlet control, air is fed into the supply pipeline at a certain flow rate and pressure by adjusting the voltage of the compressor to change the rotating speed of the impeller, and the air compressor and the pipeline enable the cathode air inlet system to have obvious time lag. The dynamic response of the stack system as a whole is mainly affected by the cathode air intake system. The power of the air compressor accounts for about 80% of the power of the auxiliary system, and reasonable control of the air compressor is beneficial to reducing the power consumption of the auxiliary system and improving the efficiency of the electric pile. Therefore, the invention mainly adjusts the air inflow through controlling the air compressor.
Fig. 3 is a block diagram of air supply control of an air compressor, according to the principle of electrochemical reaction of a flow field in a pile, the air quantity required by the fuel cell reaction is obtained from the output current of the fuel cell, and in order to avoid adverse effect on the pile performance caused by the phenomenon of oxygen starvation caused by abrupt current change, a certain excess air is provided to the pile by introducing a peroxy ratio lambda, so as to obtain a reference value of the air quantity, specifically as shown in the formula (19):
in the formula (19), N is the number of galvanic pile monomers, I fc For outputting current to the pile, mo 2 Is the molar mass of oxygen, χ O2 Is the oxygen ratio in the air, F isFaraday constant.
After the air flow reference value is obtained, closed-loop control of the air flow is carried out, so that the voltage of the air compressor is controlled, the rotating speed of the air compressor is changed, and enough oxygen is provided for the electric pile.
Since the dynamic response time of the stack is in the order of seconds and the DC/DC converter is in the order of milliseconds, when the scheduled power command increases, the regulation of the stack air intake system has a significant time lag, a rapid increase in DC/DC output power may result in a rapid increase in the stack output current, a significant drop in voltage, and the air compressor may not be able to supply enough oxygen, which may cause "oxygen starvation" and damage the stack. Therefore, reasonable cooperation is needed between the pile air supply system and the DC/DC converter to ensure safe and stable operation of the system.
When the instruction power is reduced, the gas in the electric pile is sufficient, the DC/DC can immediately output upper-layer required power, when the instruction power is increased, the hydrogen and the oxygen in the electric pile do not reach the gas flow corresponding to the required power, and if the DC/DC directly outputs the required power, oxygen starvation phenomenon can occur, so that the electric pile is seriously damaged. According to two change trends of the command power, a matching strategy of the fuel cell and DC/DC control is provided.
The j-th single-stack fuel cell system receives the optimal output powerAfter that, the optimal output power from last received +.>Comparing if->Then the command power P transmitted to the jth stack controller is obtained by using equation (20) fc And command power P of the jth DC/DC controller dc The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the command power P transmitted to the jth stack controller is obtained by using the formula (21) fc And command power P of jth DC/DC controller at t time dc (t)；
In equation (20), τ is the FC dynamic time constant.
The present invention provides an electronic device including a memory for storing a program for supporting the processor to execute the above-described distributed control method, and a processor configured to execute the program stored in the memory.
The present invention is a computer-readable storage medium, on which a computer program is stored, which when being executed by a processor, performs the steps of the above-described distributed control method.
In order to verify the effectiveness of the distributed control strategy, a simulation model is built in a simulation platform Matlab/Simulink, four fuel cell stacks are connected with a direct current bus in parallel through DC/DC, each single fuel cell is regarded as an intelligent agent node, and communication lines among the nodes adopt ring topology as shown in figure 7 and are used for information transmission of the nodes in distributed control. To verify the effectiveness of the proposed consistency algorithm for power distribution and efficiency optimization of a multi-stack system, four stacks with different output characteristics were selected for verification, and the efficiency/power curves of the individual stacks of fuel cells are shown in fig. 8.
Simulation 1: efficiency comparison of different power allocation modes:
in order to verify the effectiveness and superiority of the method provided by the invention, the method is compared and analyzed with the traditional average power distribution method and the chained power distribution method. The average distribution is equal distribution, and the target total power is divided by the total number of the stacks of the multi-stack system, so that the output of each single stack can be obtained; the chained distribution pile is started step by step, and when the output of the previous pile reaches the maximum value, the next pile can be started until the total power instruction is reached.
Four 150kw fuel cells are selected for analysis, the initial power values of each single stack cell are 100kw, 90kw, 100kw and 90kw respectively, the electric stack 1 is taken as a leading node, a total scheduling power instruction of a multi-stack system of a control center is received, and the total power instruction is 520kw.
As shown in fig. 9a and 9b, the initial total power does not reach the target value and the consistency variable is not equal, after a certain number of iterations, the consistency variable tends to be consistent, and meanwhile, the total power of the multi-stack system also reaches the scheduling command value, and each single-stack force is shown in fig. 9 c.
Table 1 shows that when the total instruction power is 520kw, the different power allocation modes are compared with the system efficiency corresponding to average allocation, chained allocation and consistency allocation, and the consistency allocation has obvious efficiency improvement compared with the traditional two allocation modes.
TABLE 1 comparison of different Power Allocation modes for 520kw Total instruction Power
As can be seen from the comparison of the data, the power optimization distribution strategy based on the consistency algorithm has obvious advantages in system efficiency compared with the traditional average distribution and chained distribution, and the efficiency of the multi-stack fuel cell system is respectively improved by 1.02% and 13.43%. Meanwhile, the power distribution improves the operation efficiency of the multi-stack fuel cell system, reduces the hydrogen consumption of the system, and realizes the efficient and economical operation of the multi-stack system.
As can be seen from the efficiency curves of the four stacks in fig. 8, the corresponding efficiencies are different for different power intervals due to different stack performances. Compared with the traditional two power distribution modes, the power distribution mode provided by the invention considers the performance difference among the electric stacks, and in the same power interval, the electric stacks with high efficiency have more output, so that the operating pressure of the fuel cells with insufficient output capacity or poorer performance is reduced, the service life of the fuel cells and the overall performance of the multi-stack system are prolonged, and the performance consistency of the electric stacks and the long-term operation of the multi-stack system are maintained.
Simulation 2: comparison of different communication topologies:
the communication topology has an effect on the iteration speed of the consistency algorithm, three different communication topologies of four nodes are selected for comparison in this section, and the three communication topologies are respectively ring type, star type and chain type, as shown in fig. 10. The initial power values of the single-pile cells are respectively 60kw, 90kw, 70kw and 80kw, the electric pile 1 is taken as a leading node, and the total power instruction of the receiving dispatching center is 360kw.
Fig. 11a is a power distribution consistency iteration process of a ring topology stack fuel cell system, fig. 11b is a power distribution consistency iteration process of a star topology multi-stack fuel cell system, and fig. 11c is a power distribution consistency iteration process of a chain topology multi-stack fuel cell system, wherein the three topologies can be consistent through a certain number of iterations, the number of iterations of the ring topology is minimum, and the number of iterations of the chain topology is maximum. As can be seen from comparison of the three groups of diagrams, the (a) type topology has higher iteration convergence speed, and the (c) type topology has certain fault tolerance capability, when a certain communication line fails, the communication connection of the whole node is not affected, the system can complete the goal of iteration consistency of the node, and the iteration speed is only affected. Simulation 3: DC/DC power given mode contrast:
when the dispatching power changes, the electric pile controller and the DC/DC controller of a single pile layer are required to update respective reference power in time, and the electric pile controller can directly control the air compressor to provide corresponding air flow according to new instruction power; the DC/DC dynamic response speed is high, and the power setting mode needs to consider the cooperation with the pile side.
As shown in fig. 12, mode 1 is a given mode of the conventional DC/DC reference power, and mode 2 is a given mode of the DC/DC reference power proposed by the present invention. As can be seen from the output voltage characteristic curve of the pile of fig. 13, when the command power is suddenly changed, the conventional power setting mode of the DC/DC can generate obvious voltage drop, and the performance and the operation durability of the pile can be damaged.
Claims (3)
1. The distributed control method for optimizing the efficiency of the multi-stack fuel cell system is applied to the multi-stack fuel cell system, wherein the multi-stack fuel cell system comprises n single-stack fuel cell systems, and any jth single-stack fuel cell system comprises: a jth electric pile, a jth DC/DC converter, a jth DC/DC controller and a jth electric pile controller; the distributed control method is characterized by comprising the following steps of:
step 1: the jth electric pile controller collects the voltage v of the electric pile fc,j Current i fc,j Auxiliary system power Paux, j, thereby obtaining single-stack fuel cell efficiency eta corresponding to the current jth electric stack output power by using the formula (1) fcs,j ：
In the formula (1), P fc,j Represents the output power of the jth electric pile, N is the number of electric pile monomers, eta s,j Indicating the thermal efficiency, eta of the jth stack e,j Represents the electrical efficiency, eta of the jth stack f,j Indicating the fuel utilization rate of the jth electric pile; e, a coefficient related to the reaction heat of the PEMFC; j=1, 2,3, …, n; n is the total number of stacks; p (P) aux,j Representing the power consumption of auxiliary equipment of a jth electric pile in the PEMFC system;
the fitting relation between the output power and the efficiency of each electric pile in the multi-pile fuel cells is identified through a least square method, so that a fitting function of the characteristic of the jth electric pile characterization curve is obtained through the formula (2):
η fcs,j ＝a 0 +a 1 P fc,j +a 2 (P fc,j ) 2 +a 3 (P fc,j ) 3 +a 4 (P fc,j ) 4 +a 5 (P fc,j ) 5 +a 6 (P fc,j ) 6 (2)
in the formula (2), a 0 ，a 1 ，a 2 ，a 3 ，a 4 ，a 5 ，a 6 Is the coefficient to be identified;
step 2: selecting one electric pile from the multiple stacks of fuel cells as a leading node, and marking the electric pile as a main electric pile; the n-1 electric piles except the leading node are all autonomous nodes;
step 2.1: defining the current update times as k, and initializing k=1; the output power P of the dominant node fc,main Initial output power P as the dominant node for the k-1 th update k-1,main The method comprises the steps of carrying out a first treatment on the surface of the Output power P of any v-th autonomous node fc,v Initial output power P as the dominant node for the k-1 th update k-1,v ；
Step 2.2: the master node receives the current total instruction power P from the energy management center ref After that, the dominant node updates the output power P according to the k-1 th time of the dominant node k-1,main Obtaining the increment hydrogen consumption value h updated by the dominant node at the kth-1 time by using the formula (3) main (k-1)；
In the formula (3), eta fcs,main Representing dominant node efficiency;
step 2.3: the n-1 autonomous nodes also obtain the self-updated incremental hydrogen consumption value at the k-1 time according to the formula (3)Wherein h is v (k-1) represents an incremental hydrogen consumption value updated by the v-th autonomous node at the k-1 th time;
step 2.4: the v-th autonomous node obtains the k-th updated incremental hydrogen consumption value h by using the formula (4) v (k)；
In the formula (4), d v,u Is the element of the v th row and the u th column in the state transition matrix, and represents the v th autonomous node and the u th self-bodyThe connection condition between the nodes is treated;
step 2.5: the dominant node obtains the increment hydrogen consumption value h updated for the kth time by using the formula (5) main (k)；
In the formula (5), d main,j Representing the connection status between the main leading node main and the jth electric pile, h j (k-1) represents the increment hydrogen consumption value of the jth cell stack of k-1 iterations, ΔP (k-1) represents the difference between the total output power and the command power of the k-1 iterations, and P fc,j (k-1) represents the output power of the jth cell stack of k-1 iterations, P ref Representing total instruction power, wherein mu is a convergence coefficient;
step 2.6: obtaining the output power P of the jth electric pile updated at the kth time by using the method (6) fc,j (k)；
In formula (6), P min Representing the minimum output power of the galvanic pile, P max Represents the maximum output power of the electric pile, h j (k) Represents the increment hydrogen consumption value, h of the jth galvanic pile in the kth iteration j -1 [·]The expression (2) represents an inverse function of the function;
step 2.7: obtaining a difference value delta P (k) between the total output power of the electric pile and the instruction power in the kth iteration by using the formula (7);
step 2.8: judging increment hydrogen consumption values of n electric stacksEqual or not, and |ΔP (k) |<Epsilon is true, if so, the efficiency of the multi-stack fuel cell is expressedOptimal and will present the total instruction power P ref The output power of the corresponding individual galvanic pile +.>To the respective single stack fuel cell system; otherwise, after assigning k to k-1, returning to the step 2.2 for sequential execution; wherein (1)>Representing the optimal output power of the jth electric pile;
step 3: the j-th single-stack fuel cell system receives the optimal output powerAfter that, the optimal output power from last received +.>Comparing if->Then the command power P transmitted to the jth stack controller is obtained by using equation (8) fc And command power P of the jth DC/DC controller dc The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the command power P transmitted to the jth cell stack controller is obtained by using the formula (9) fc And command power P of jth DC/DC controller at t time dc (t)；
In the formula (9), τ is an FC dynamic time constant.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to perform the distributed control method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the distributed control method of claim 1.
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