阅读说明：本技术 一种质子交换膜燃料电池一致性的热电水协同控制方法 (Thermoelectric water cooperative control method for consistency of proton exchange membrane fuel cell ) 是由 吴小娟 阳大楠 于 2019-11-21 设计创作，主要内容包括：本发明公开了一种质子交换膜燃料电池一致性的热电水协同控制方法,先以单池波动率最小作为优化目标,基于单目标动态优化算法得出外部需求负载功率下的温度、相对湿度、进出口压强差等最优操作变量,然后设计设计具有自适应滑模鲁棒迭代学习功能的控制器,并将最优操作变量作为控制器的参考轨迹输入进行更新,最后通过控制器控制电机运行,进而调节燃料流量、空气流量、散热器风扇速度和压缩机电压,从实现质子交换膜燃料电池一致性的热电水协同控制。(The invention discloses a thermoelectric water cooperative control method for proton exchange membrane fuel cell consistency, which comprises the steps of firstly using the minimum fluctuation rate of a single pool as an optimization target, obtaining optimal operation variables such as temperature, relative humidity, inlet and outlet pressure difference and the like under external demand load power based on a single-target dynamic optimization algorithm, then designing and designing a controller with an adaptive sliding mode robust iterative learning function, inputting the optimal operation variables as a reference track of the controller for updating, and finally controlling a motor to operate through the controller, so that the fuel flow, the air flow, the radiator fan speed and the compressor voltage are adjusted, and the thermoelectric water cooperative control of the proton exchange membrane fuel cell consistency is realized.)

1. A thermoelectricity water cooperative control method for the consistency of a proton exchange membrane fuel cell is characterized by comprising the following steps:

(1) integrating the non-uniform control vector parameterization algorithm ndCVP into a hybrid gradient particle swarm optimization algorithm HGPSO to obtain a non-uniform control vector parameterization hybrid gradient particle swarm optimization algorithm ndCVP-HGPSO;

(2) optimizing the cell stack by utilizing an ndCVP-HGPSO algorithm under the condition of considering the consistency of the single cells, and acquiring the optimal operating variable of the cell stack under the load demand power;

(2.1) setting an optimized objective function;

V(t)＝f(t,U(t),α(t))

wherein, [ t ]₀,t_f]Representing a stack operating time interval; s_r(t) is the single cell fluctuation rate; v_j(t) represents the voltage of the jth cell;represents the average cell voltage of the stack, f (-) is a non-linear function, U (t) is an operating variable, α (t) represents other parameters of the stack;

wherein, the operation variables u (t) ([ t), (t), re (t), Δ p (t) ], t (t) is the operation temperature of the cell stack, re (t) is the relative humidity of the cell stack, and Δ p (t) is the inlet-outlet pressure difference of the cell stack;

(2.2) determining constraint conditions;

(2.3) under the constraint condition, optimizing the objective function by utilizing the ndCVP-HGPSO algorithm to obtain the optimal operating variable of the cell stack under the load demand power;

(2.3.1) setting a fractional time parameter R ═ R_i]，r_i∈[0,1]I is 1,2, …, N is the number of time segments;

(2.3.2) setting the stack operation time interval T to [ T ═ T₀,t_f]Divided into N sub-intervals [ t ]_k,t_k+1]N-1, i.e. t, k ═ 0,1,2₀≤t₁≤…≤t_N-1≤t_N＝t_f；

Wherein the content of the first and second substances,

(2.3.3) parameterizing the operation variables u (t) ([ t (t), re (t), Δ p (t)) in the N subintervals to obtain:

wherein the content of the first and second substances,the parameterization of U (t) is shown,

(2.3.4) approximating the parameterized manipulated variable components by basis functions

Wherein, c_m,k(t) a control variable component representing the mth manipulated variable at the kth time interval;

(2.3.5) introducing a fractional time parameter R, and vectorizing the operation variables in the continuous operation time interval to obtain:

(2.3.6), carrying out global optimization by utilizing an HGPSO optimization algorithm;

1) setting the maximum iteration number g_max(ii) a Setting individual optimal positions pbest and global optimal positions gbest, and then performing iterative storage updating through minimum comparison, namely storing the optimal positions of each particle individual from the initial to the current g-th iterative search in pbest, and storing the current optimal position of the population in gbest;

2) initializing a population: randomly assigning a value to each component in the vector P to obtain a value containing M particles Q_jJ ═ 1,2,..., M;

wherein any one of the initialization particles Q_jThe position of (d) is represented as:

the corresponding initial speed values are:

solving the initial objective function values corresponding to all the particles through a solver

3) and calculating the optimized objective function value of the battery consistency corresponding to all the particles after the g-th iterationThen comparing the objective function value of each particle with the objective function value of each particle after the g-1 iteration of pbest, if

4) judging whether the current iteration time g reaches a preset maximum iteration time or not, wherein the optimization objective function value of the battery consistency after the g-th iteration meets the following requirements:k₁is a constant number, k₁Is taken as value of [1, g_max]，k₁If the number is less than g, stopping iteration, and substituting the gbest obtained after the g iteration into the step (2.3.7); otherwise, entering step 4);

5) updating the position and the speed of the particles;

6) adding 1 to the current iteration number g, and returning to the step 2);

(2.3.7) local optimization is carried out by gradient optimization algorithm

Reading global optimum operation variable in gbest, and recording as

From the initial point

(3) designing a controller with a self-adaptive sliding mode robust iterative learning function;

(3.1) setting iteration times k₃Initialization of k₃＝0；

(3.2) drawing a reference track of the controller according to the optimal operation variable;

(3.3) designing an equation of the controller;

wherein the content of the first and second substances,

(3.4) taking the difference value between the reference track and the output of the proton exchange membrane fuel cell as the input of the controller, and carrying out iterative update on the controller until the controller converges to obtain a converged adaptive sliding mode robust iterative learning controller;

(4) and the converged controller is used for a proton exchange membrane fuel cell system, and the fuel flow, the air flow, the speed of a radiator fan and the voltage of a compressor are adjusted by controlling the operation of a motor, so that the thermoelectric water cooperative control of the consistency of the proton exchange membrane fuel cell is realized.

2. The method of claim 1, wherein the temperature uncertainty is determined by a method of co-operating the proton exchange membrane fuel cell with the thermoelectric waterQuantification of

wherein the content of the first and second substances,

Technical Field

The invention belongs to the technical field of fuel cells, and particularly relates to a thermoelectric water cooperative control method for proton exchange membrane fuel cell consistency.

Background

Proton Exchange Membrane Fuel Cell (PEMFC) is a device that directly converts chemical energy stored in hydrogen Fuel and oxidant into electric energy and reactant through electrochemical reaction, and its energy conversion efficiency is not limited by "carnot cycle", and it uses hydrogen as main Fuel, and has the advantages of high practical use efficiency, clean exhaust gas and less pollution, and it is one of the most potential new energy sources in the 21 st century. In the last two decades, researchers in various countries have successfully developed PEMFCs of various types, and great progress has been made in material, design, management and control. However, the high cost and short lifetime of PEMFC systems remain obstacles to large-scale commercialization.

Unreasonable temperature and relative humidity of the cell stack can cause different degrees of aging of the internal structure of the cell stack, and even membrane drying, flooding and damage to the internal structure. An improper inlet-outlet pressure difference affects the uniform distribution of reactants inside the stack and the discharge of products. In recent years, considerable research has been conducted by related researchers on PEMFC systems in terms of system design, optimization and control, resulting in a series of controllers having different structures, and certain research results have been achieved in terms of load tracking, temperature management and water management. This contributes to PEMFC system management and optimization.

However, it should be considered that the PEMFC stack is formed by stacking a plurality of unit cells in series, and there is inconsistency in the voltage of each unit cell of the fuel cell stack due to spatial distribution unevenness of fluid, heat and humidity, differences in the level of MEA manufacture and assembly of each unit cell, and the like. Greater voltage non-uniformity can result in reduced overall performance and reduced service life of the stack. Therefore, a complete control system needs to be designed in a targeted manner by considering various constraints, so that the PEMFC can be maintained in a stable and optimal working environment, and the voltage among the unit cells can be distributed as uniformly as possible while the power required by the external load is met. No optimization control studies considering the cell inconsistency of PEMFC stacks have been found so far that this remains a challenge for PEMFC applications.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide a thermoelectric water cooperative control method for the consistency of a proton exchange membrane fuel cell, which takes the minimum fluctuation rate of a single cell as an optimization target, seeks an optimal operating variable as an optimization reference of a controller, and realizes thermoelectric water cooperative control through the controller, thereby providing a stable and optimal working atmosphere for a cell stack, improving the consistency of the single cell and ensuring the performance and the service life of the cell stack.

In order to achieve the above object, the present invention provides a method for cooperatively controlling the consistency of thermoelectric water in a proton exchange membrane fuel cell, comprising the steps of:

(1) integrating the non-uniform control vector parameterization algorithm ndCVP into a hybrid gradient particle swarm optimization algorithm HGPSO to obtain a non-uniform control vector parameterization hybrid gradient particle swarm optimization algorithm ndCVP-HGPSO;

(2) optimizing the cell stack by utilizing an ndCVP-HGPSO algorithm under the condition of considering the consistency of the single cells, and acquiring the optimal operating variable of the cell stack under the load demand power;

(2.1) setting an optimized objective function;

V(t)＝f(t,U(t),α(t))

wherein N is_sIndicates the number of single cells of the stack, [ t₀,t_f]Representing a stack operating time interval; s_r(t) is the single cell fluctuation rate; v_j(t) represents the voltage of the jth cell;

represents the average cell voltage of the stack, f (-) is a non-linear function, U (t) is an operating variable, α (t) represents other parameters of the stack;

wherein, the operation variables u (t) ([ t), (t), re (t), Δ p (t) ], t (t) is the operation temperature of the cell stack, re (t) is the relative humidity of the cell stack, and Δ p (t) is the inlet-outlet pressure difference of the cell stack;

(2.2) determining constraint conditions;

(2.3) under the constraint condition, optimizing the objective function by utilizing the ndCVP-HGPSO algorithm to obtain the optimal operating variable of the cell stack under the load demand power;

(2.3.1) setting a fractional time parameter R ═ R_i]，r_i∈[0,1]I is 1,2, …, N is the number of time segments;

(2.3.2) setting the stack operation time interval T to [ T ═ T₀,t_f]Divided into N sub-intervals [ t ]_k,t_k+1]N-1, i.e. t, k ═ 0,1,2₀≤t₁≤···≤t_N-1≤t_N＝t_f；

Wherein the content of the first and second substances,

(2.3.3) parameterizing the operation variables u (t) ([ t (t), re (t), Δ p (t)) in the N subintervals to obtain:

wherein the content of the first and second substances,

the parameterization of U (t) is shown,

parameterization of T (t), RE (t), Δ p (t) is shown;

(2.3.4) approximating the parameterized manipulated variable components by basis functions

m＝1,2,3；

Wherein, c_m,k(t) a control variable component representing the mth manipulated variable at the kth time interval;

(2.3.5) introducing a fractional time parameter R, and vectorizing the operation variables in the continuous operation time interval to obtain:

(2.3.6), carrying out global optimization by utilizing an HGPSO optimization algorithm;

1) setting the maximum iteration number g_max(ii) a Setting individual optimal positions pbest and global optimal positions gbest, and then performing iterative storage updating through minimum comparison, namely storing the optimal positions of each particle individual from the initial to the current g-th iterative search in pbest, and storing the current optimal position of the population in gbest;

2) initializing a population: randomly assigning a value to each component in the vector P to obtain a value containing M particles Q_jJ ═ 1,2,..., M;

wherein any one of the initialization particles Q_jThe position of (d) is represented as:

the corresponding initial speed values are:

solving the initial objective function values corresponding to all the particles through a solverThen storing the initial objective function value and the corresponding operation variable of each particle in pbest, and J⁰Element with minimum initial objective function value

And the corresponding operation variable is used as a global optimal position and is stored in the gbest;

3) and calculating the optimized objective function value of the battery consistency corresponding to all the particles after the g-th iteration

Then comparing the objective function value of each particle with the objective function value of each particle after the g-1 iteration of pbest, if

Is greater thanThen use

And corresponding operation variable replacement

And corresponding operating variables, otherwise, holding

And corresponding operating variables; at the same time, selecting J^gElement with the smallest value of the objective functionBy usingAnd in gbest

Compare if, if

Is greater than

Then use

And corresponding operation variable replacement

And corresponding operating variables, otherwise, holding

And corresponding operating variables;

4) judging whether the current iteration time g reaches a preset maximum iteration time or not, wherein the optimization objective function value of the battery consistency after the g-th iteration meets the following requirements:

k₁is a constant number, k₁Is taken as value of [1, g_max]，k₁If the number is less than g, stopping iteration, and substituting the gbest obtained after the g iteration into the step (2.3.7); otherwise, entering step 4);

5) updating the position and the speed of the particles;

6) adding 1 to the current iteration number g, and returning to the step 2);

(2.3.7) local optimization is carried out by gradient optimization algorithm

Reading global optimum operation variable in gbest, and recording as

Then will be

As an initial point of a gradient optimization algorithm

From the initial point

Initially, a pair of gradient optimization algorithms is utilized

Performing local optimization, if the difference value of the two iterative optimization is less than the preset threshold value, that is

Is less than the preset threshold, the kth₂Secondary optimization results

As the final optimal operation variable, otherwise, let the iteration number k₂Adding 1, and performing the next round of optimization searching operation;

(3) designing a controller with a self-adaptive sliding mode robust iterative learning function;

(3.1) setting iteration times k₃Initialization of k₃＝0；

(3.2) drawing a reference track of the controller according to the optimal operation variable;

(3.3) designing an equation of the controller;

wherein the content of the first and second substances,

andrespectively an iterative learning control law and a sliding mode control law;is the operating temperature of the cell stack,

represents a temperature uncertainty;

error between the output of the PEM fuel cell and the desired trajectory; f. of₆(. cndot.) is a PID learning law operator; f. of₇(. and f)₈(. h) is a nonlinear operator; kappa₁And kappa₂Is a feedback gain constant; sgn (·) is a sign function;

(3.4) taking the difference value between the reference track and the output of the proton exchange membrane fuel cell as the input of the controller, and carrying out iterative update on the controller until the controller converges to obtain a converged adaptive sliding mode robust iterative learning controller;

(4) and the converged controller is used for a proton exchange membrane fuel cell system, and the fuel flow, the air flow, the speed of a radiator fan and the voltage of a compressor are adjusted by controlling the operation of a motor, so that the thermoelectric water cooperative control of the consistency of the proton exchange membrane fuel cell is realized.

The invention aims to realize the following steps:

the invention discloses a thermoelectric water cooperative control method for proton exchange membrane fuel cell consistency, which comprises the steps of firstly, taking the minimum fluctuation rate of a single pool as an optimization target, obtaining optimal operation variables such as temperature, relative humidity, inlet and outlet pressure difference and the like under external demand load power based on a single-target dynamic optimization algorithm, then designing and designing a controller with an adaptive sliding mode robust iterative learning function, updating the optimal operation variables as reference track input of the controller, and finally controlling a motor to operate through the controller, so that fuel flow, air flow, radiator fan speed and compressor voltage are adjusted, and thermoelectric water cooperative control of proton exchange membrane fuel cell consistency is realized.

Meanwhile, the thermoelectric water cooperative control method for the consistency of the proton exchange membrane fuel cell also has the following beneficial effects:

(1) after the proton exchange membrane fuel cell is subjected to thermoelectric water cooperative control, a stable and optimal working atmosphere can be provided for the cell stack, and the performance and the service life of the cell stack are ensured;

(2) the invention is based on the control strategy of combining the non-uniform control vector parameterized mixed gradient particle swarm dynamic optimization algorithm and the adaptive sliding mode robust iterative learning control, can effectively control the optimal operating variable on the tracking of the proton exchange membrane fuel cell system, reduces the voltage inconsistency among cells, and lays a solid foundation for the high performance and the long service life of the proton exchange membrane fuel cell system.

Drawings

FIG. 1 is a schematic diagram of a proton exchange membrane fuel cell system according to the present invention;

FIG. 2 is a flow chart of a method for controlling the consistency of a proton exchange membrane fuel cell in cooperation with thermoelectric water according to the present invention;

FIG. 3 is a flow chart for finding optimal operating variables of a stack using the ndCVP-HGPSO algorithm;

fig. 4 is a control schematic of the iterative learning controller.

Detailed Description

The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.