阅读说明：本技术 多堆燃料电池发电系统协调控制方法、系统及车辆 (Multi-stack fuel cell power generation system coordinated control method and system and vehicle ) 是由 李端凯 梁建英 徐磊 李艳昆 田庆 于 2021-07-28 设计创作，主要内容包括：本公开提供了一种多堆燃料电池发电系统协调控制方法、系统及车辆,基于单堆燃料电池功率-效率曲线,建立瞬时效率最高的多堆燃料电池数学优化模型；考虑燃料电池总需求功率范围,按照设定功率间隔,进行功率全范围优化,对所述多堆燃料电池数学优化模型进行寻优,计算最优解,根据最优解,进行瞬时效率最高的多堆协调控制；本公开在保证系统所需功率的同时,通过优化算法合理分配各电堆功率,保证多堆燃料电池系统每一时刻都具有高效特性,进而提高轨道交通车辆的整车运行经济性。(The disclosure provides a multi-stack fuel cell power generation system coordination control method, a multi-stack fuel cell power generation system coordination control system and a multi-stack fuel cell power generation vehicle, wherein a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency is established based on a single-stack fuel cell power-efficiency curve; considering the total required power range of the fuel cell, carrying out power full-range optimization according to a set power interval, optimizing the mathematical optimization model of the multi-stack fuel cell, calculating an optimal solution, and carrying out multi-stack coordination control with highest instantaneous efficiency according to the optimal solution; the method reasonably distributes the power of each electric pile through an optimization algorithm while ensuring the power required by the system, ensures that the multi-pile fuel cell system has high-efficiency characteristics at each moment, and further improves the whole vehicle running economy of the rail transit vehicle.)

1. A multi-pile fuel cell power generation system coordination control method is characterized in that: the method comprises the following steps:

establishing a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency based on a single-stack fuel cell power-efficiency curve;

and (3) considering the total required power range of the fuel cell, carrying out power full-range optimization according to a set power interval, optimizing the mathematical optimization model of the multi-stack fuel cell, calculating an optimal solution, and carrying out multi-stack coordination control with highest instantaneous efficiency according to the optimal solution.

2. The coordinated control method of a multi-stack fuel cell power generation system according to claim 1, characterized in that: the specific method for establishing the mathematical optimization model of the multi-stack fuel cell with the highest instantaneous efficiency comprises the following steps: determining the power-efficiency curve of each single-pile fuel cell, wherein the quantity of the instantaneous efficiency of the multi-pile system is the average of the real-time efficiency of each single-pile fuel cell, and constructing an objective function with the maximum quantity of the instantaneous efficiency of the multi-pile system.

3. The coordinated control method of a multi-stack fuel cell power generation system according to claim 1, characterized in that: when the mathematical optimization model of the multi-stack fuel cell with the highest instantaneous efficiency is established, the method further comprises the step of constructing constraint conditions of the optimization model, wherein the constraint conditions comprise:

(1) the total required power of the current fuel cell is equal to the sum of the output power of each electric pile;

(2) the output power of each single stack has to be within a limited range.

4. The coordinated control method of a multi-stack fuel cell power generation system according to claim 1, characterized in that: and when the mathematical optimization model of the multi-stack fuel cell is optimized, optimizing calculation is carried out by utilizing a particle swarm algorithm.

5. The coordinated control method of a multi-stack fuel cell power generation system according to claim 4, characterized in that: the optimization calculation comprises the following specific steps:

establishing a particle swarm optimization model for the efficiency of the multi-stack fuel cell, and setting the population number, the iteration number and the independent variable number;

initializing parameters of a particle swarm algorithm, initializing a swarm by using random numbers, wherein the average value is the center of the output power limit range of the single-pile fuel cell, and generating an initial speed by using the random numbers;

calculating each particle target function, and finding out the current individual best and global best, and the individual best fitness value and the global best fitness value;

iteratively updating the speed and the population;

and judging whether the maximum iteration times is reached, and if so, outputting the optimal solution.

6. The coordinated control method of a multi-stack fuel cell power generation system according to claim 1, characterized in that: the specific process of carrying out power full-range optimization according to the set power interval comprises the following steps: according to the limiting range of the total required power of the current fuel cell, performing power full-range optimization within the limiting range by taking the set power as an interval to obtain the optimized power of each electric pile;

and calculating the relation between the total required power of the current fuel cell and the difference value of the output power of the corresponding electric pile by combining the total required power of the current fuel cell.

7. The coordinated control method of a multi-stack fuel cell power generation system according to claim 6, characterized in that: and performing multi-stack coordination control with highest instantaneous efficiency according to the relation between the total required power of the current fuel cell and the output power difference value of the corresponding electric stack.

8. A multi-pile fuel cell power generation system coordination control system is characterized in that: the method comprises the following steps:

the optimization model building module is configured to build a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency based on a single-stack fuel cell power-efficiency curve;

the optimizing calculation module is configured to consider the total required power range of the fuel cells, perform power full-range optimization according to a set power interval, optimize the mathematical optimization model of the multi-stack fuel cells and calculate an optimal solution;

and the coordination control module is configured to perform multi-stack coordination control with highest instantaneous efficiency on each electric stack according to the optimal solution.

9. A computer-readable storage medium characterized by: for storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 7.

10. An electronic device, characterized by: comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, which when executed by the processor, perform the steps of the method of any one of claims 1 to 7.

11. A rail transit vehicle is characterized in that: a multi-stack fuel cell power generation system coordinated control method using any one of claims 1-7 or comprising a multi-stack fuel cell power generation system coordinated control system of claim 8 or comprising an electronic device of claim 10.

Technical Field

The disclosure belongs to the technical field of battery control, and particularly relates to a coordinated control method and system for a multi-stack fuel cell power generation system and a vehicle.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Due to the advantages of no pollution, high energy density and the like of the hydrogen fuel cell, the hydrogen fuel cell has great potential in the field of serving as active power or standby power for rail transit vehicles. Fuel cells use hydrogen and oxygen as reactants to produce electricity and water. The conversion efficiency is high, no pollution is caused completely, zero emission is achieved, the development direction of future vehicle-mounted energy is achieved, and the integration of a multi-stack fuel cell system is an important way for improving the power density and the energy density of the system.

However, in the long-term operation of the multi-stack fuel cell, the difference between the stacks is increasingly increased, or a certain stack is replaced by a new stack for some reason, the difference between the stacks in different batches is large, and the current power distribution strategy cannot adapt to the multi-stack system with a significant aging difference.

Disclosure of Invention

The invention provides a multi-stack fuel cell power generation system coordination control method, a system and a vehicle for solving the problems.

According to some embodiments, the following technical scheme is adopted in the disclosure:

a multi-stack fuel cell power generation system coordination control method comprises the following steps:

establishing a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency based on a single-stack fuel cell power-efficiency curve;

and (3) considering the total required power range of the fuel cell, carrying out power full-range optimization according to a set power interval, optimizing the mathematical optimization model of the multi-stack fuel cell, calculating an optimal solution, and carrying out multi-stack coordination control with highest instantaneous efficiency according to the optimal solution.

According to the technical scheme, the power required by the system is ensured, and meanwhile, the power of each electric pile is reasonably distributed through an optimization algorithm, so that the method is suitable for the whole range of the total required power of the fuel cell.

As an alternative embodiment, the specific method for establishing the mathematical optimization model of the multi-stack fuel cell with the highest instantaneous efficiency comprises the following steps: determining the power-efficiency curve of each single-pile fuel cell, wherein the quantity of the instantaneous efficiency of the multi-pile system is the average of the real-time efficiency of each single-pile fuel cell, and constructing an objective function with the maximum quantity of the instantaneous efficiency of the multi-pile system.

As an alternative embodiment, when establishing the mathematical optimization model of the multi-stack fuel cell with the highest instantaneous efficiency, the method further comprises the step of constructing constraints of the optimization model, wherein the constraints comprise:

(1) the total required power of the current fuel cell is equal to the sum of the output power of each electric pile;

(2) the output power of each single stack has to be within a limited range.

As an alternative embodiment, when optimizing the mathematical optimization model of the multi-stack fuel cell, the optimization calculation is performed by using a particle swarm algorithm.

As a further limitation, the specific steps of the optimization calculation include:

establishing a particle swarm optimization model for the efficiency of the multi-stack fuel cell, and setting the population number, the iteration number and the independent variable number;

initializing parameters of a particle swarm algorithm, initializing a swarm by using random numbers, wherein the average value is the center of the output power limit range of the single-pile fuel cell, and generating an initial speed by using the random numbers;

calculating each particle target function, and finding out the current individual best and global best, and the individual best fitness value and the global best fitness value;

iteratively updating the speed and the population;

and judging whether the maximum iteration times is reached, and if so, outputting the optimal solution.

As an alternative embodiment, the specific process of performing power full-range optimization according to the set power interval includes: according to the limiting range of the total required power of the current fuel cell, performing power full-range optimization within the limiting range by taking the set power as an interval to obtain the optimized power of each electric pile;

and calculating the relation between the total required power of the current fuel cell and the difference value of the output power of the corresponding electric pile by combining the total required power of the current fuel cell.

And as a further limitation, performing multi-stack coordination control with highest instantaneous efficiency according to the relation between the total required power of the current fuel cell and the difference value of the output power of the corresponding electric stack.

A coordinated control system for a multi-stack fuel cell power generation system, comprising:

the optimization model building module is configured to build a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency based on a single-stack fuel cell power-efficiency curve;

the optimizing calculation module is configured to consider the total required power range of the fuel cells, perform power full-range optimization according to a set power interval, optimize the mathematical optimization model of the multi-stack fuel cells and calculate an optimal solution;

and the coordination control module is configured to perform multi-stack coordination control with highest instantaneous efficiency on each electric stack according to the optimal solution.

A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.

An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the above method.

A rail transit vehicle adopts the multi-stack fuel cell power generation system coordination control method or comprises the multi-stack fuel cell power generation system coordination control system or comprises the electronic equipment.

Compared with the prior art, the beneficial effect of this disclosure is:

the method reasonably distributes the power of each electric pile through an optimization algorithm while ensuring the power required by the system, ensures that the multi-pile fuel cell system has high-efficiency characteristics at each moment, and further improves the whole vehicle running economy of the rail transit vehicle.

The optimization calculation is carried out by adopting the particle swarm algorithm, the calculation speed is high, and the calculation result is accurate.

In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.

FIG. 1 is a schematic view of a control structure of a multi-stack fuel cell of the present embodiment;

FIG. 2 is a flow chart of the particle swarm optimization algorithm of the present embodiment;

fig. 3 is an overall flow chart of the multi-stack fuel cell efficient coordination control according to the present embodiment.

The specific implementation mode is as follows:

the present disclosure is further described with reference to the following drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

In the present disclosure, terms such as "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "side", "bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only relational terms determined for convenience in describing structural relationships of the parts or elements of the present disclosure, and do not refer to any parts or elements of the present disclosure, and are not to be construed as limiting the present disclosure.

In the present disclosure, terms such as "fixedly connected", "connected", and the like are to be understood in a broad sense, and mean either a fixed connection or an integrally connected or detachable connection; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present disclosure can be determined on a case-by-case basis by persons skilled in the relevant art or technicians, and are not to be construed as limitations of the present disclosure.

As shown in fig. 1, the multi-stack fuel cell control structure applied to this embodiment has the respective stacks connected in parallel. In the embodiment, each electric pile is connected with the ECU electronic control unit.

A method for coordinating and controlling a multi-stack fuel cell power generation system, as shown in fig. 3, comprises the following steps:

1. establishing a mathematical optimization model of a multi-stack fuel cell with the highest instantaneous efficiency:

(1-1) determining a single-stack fuel cell power-efficiency curve.

η＝aP⁵+bP⁴+cP³+dP²+eP+f

Wherein a, b, c, d, e and f represent polynomial fitting coefficients of a power-efficiency curve, eta is efficiency, and P is power.

(1-2) establishing an optimization target: defining the highest instantaneous efficiency of a multi-stack fuel cell system as eta in real-time efficiency of 1# fuel cell, 2# fuel cell and 3# fuel cell₁、η₂、η₃Output power of single fuel cell stack is P₁、P₂、P₃Then the quantity characterizing the instantaneous efficiency of the multi-stack system is H ═ η₁+η₂+η₃) And/3 is the objective function.

(1-3) construction of constraints:

(1) and (3) constraint of an equation: satisfy the total required power P of the current fuel cell_FCjEqual to the sum of the output powers of the electric piles, namely:

P₁+P₂+P₃＝P_FCj

(2) the inequality constrains: the output power of each stack must be within a limited range [ P_min, P_max]I.e. by

P_min≤P₁≤P_max

P_min≤P₂≤P_max

P_min≤P₃≤P_max

(1-4) determining variables to be optimized: output power of each stack, i.e. P₁、P₂、P₃。

2. Model solution

In this embodiment, a particle swarm algorithm is adopted to perform optimization calculation. Of course, in other embodiments, genetic algorithms, gray prediction algorithms, simulated annealing algorithms, neural network algorithms, and the like may be used instead.

In this embodiment, the solving step of the model is as follows:

and establishing a particle swarm optimization model for the efficiency of the multi-stack fuel cell, setting the number of the population of the model as m, iterating for n times, and setting the number of independent variables as D, and establishing basic data of the model.

(2-1) initializing parameters of the particle swarm algorithm, initializing a population by using random numbers, wherein the average value is the center of the output power limit range of the single-pile fuel cell, and generating an initial speed by using the random numbers.

Population of particles i: xi ═ e (xi1, xi 2.. xiD)

Velocity of particle i: vi ═ i ≦ m (Vi1, Vi 2.. ViD),1 ≦ D ≦ 3;

and (2-2) finding current individual best and global best, and an individual best fitness value and a global best fitness value by calculating each particle objective function.

Optimal population of individual particles i: pi ═ (pi1, pi 2.. piD)

Global optimal population of particles: pg ═ pg (pg1, pg 2.. pgD);

and (2-3) iteratively updating the speed and the population.

Updating speed:

updating the population:

wherein: c. C₁、c₂Which is a learning factor or acceleration factor, typically a positive constant, is set here at 1.49445. r is₁、r₂The value range is [0,1 ]]The pseudo random numbers are uniformly distributed in the interval.

(2-4) judging whether the maximum iteration times is reached, and if so, outputting an optimal solution; if not, go back to (2-3). The whole algorithm flow is shown in fig. 2.

3. Full range optimization and online power distribution for multi-stack fuel cell system

(3-1) Power full Range optimization of Multi-Stack Fuel cells

Current total fuel cell power demand P_FCIn the range of [ P_FCmin,P_FCmax]Within this range, 10kW of power is used as the interval or step, however, in other embodiments, the power can be optimized in the full range at other set power intervals, i.e. P_FCj(j is 1,2,3, …, n), where n is the total number of existing intervals, and the optimized P of each corresponding fuel cell stack can be obtained by substituting the mathematical model and performing model solution_1j、P_2j、P_3j(j＝1,2,3,…,n)。

P_FC＝[P_FC1,P_FC2,P_FC3,…,P_FCn]In which P is_FCj+1-P_FCj＝10kW(j＝1,2,3,…,n-1)， P_FC1Infinite approximation P_FCmin，P_FCnInfinite approximation P_FCmaxThen P is₁＝[P₁₁,P₁₂,P₁₃,…,P_1n]， P₂＝[P₂₁,P₂₂,P₂₃,…,P_2n]，P₃＝[P₃₁,P₃₂,P₃₃,…,P_3n]Finally, P can be obtained_FC-P₁、 P_FC-P₂、P_FC-P₃The relationship between them.

(3-2) high-efficiency coordinated control of quasi-online multi-stack fuel cells

When the whole vehicle is used, the vehicle can be driven according to P_FC-P₁、P_FC-P₂、P_FC-P₃The relation between the two is used for carrying out multi-stack coordination control with highest instantaneous efficiency.

When the control is carried out, the required power of the whole vehicle is also considered, the total output power of the fuel cell is determined by utilizing a hybrid energy control strategy, and P is combined_FC-P₁、P_FC-P₂、P_FC-P₃The current power P of each fuel cell stack is determined_1now，P_2now，P_3nAnd w, performing power distribution of the multi-stack fuel cell.

Of course, in the present embodiment, P may be_FC-P₁、P_FC-P₂、P_FC-P₃The relationship between them is represented by curve, then each curve is fitted into a table, the current power P of each fuel cell stack is determined by table look-up_1now，P_2now，P_3now。

The invention also provides the following product examples:

a coordinated control system for a multi-stack fuel cell power generation system, comprising:

the optimization model building module is configured to build a multi-stack fuel cell mathematical optimization model with the highest instantaneous efficiency based on a single-stack fuel cell power-efficiency curve;

the optimizing calculation module is configured to consider the total required power range of the fuel cells, perform power full-range optimization according to a set power interval, optimize the mathematical optimization model of the multi-stack fuel cells and calculate an optimal solution;

and the coordination control module is configured to perform multi-stack coordination control with highest instantaneous efficiency on each electric stack according to the optimal solution.

A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.

An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the above method.

A rail transit vehicle adopts the multi-stack fuel cell power generation system coordination control method or comprises the multi-stack fuel cell power generation system coordination control system or comprises the electronic equipment.

The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.