CN117936848B - Flow pressure self-adaptive coordination control method for hydrogen fuel cell - Google Patents

Abstract

The invention discloses a hydrogen fuel cell flow pressure self-adaptive coordination control method, which belongs to the technical field of fuel cells and comprises the following steps: establishing a multi-input multi-output air supply system dynamics model by considering the strong coupling characteristics of the gas flow and the pressure in the air supply system; around the problems of uncertain parameters of the air compressor under the variable load working condition and parameter change caused by atmospheric pressure and temperature under different environmental conditions, the adaptive law is estimated by design parameters; and according to the tracking error of the gas flow and the pressure, a nonlinear item about the opening of the back pressure valve and the gas flow are selected as intermediate controlled variables, and the back pressure valve opening and the input voltage control command of the air compressor are designed to complete the flow pressure coordination control of the hydrogen fuel cell based on the back-stepping method. The invention realizes the self-adaptive coordination control of the flow pressure of the air system under the condition of uncertain parameters and strong coupling, and has the characteristics of high control precision, strong stability and easy engineering realization.

Description

A method for adaptive coordinated control of flow and pressure of hydrogen fuel cells

Technical Field

The present invention belongs to the technical field of fuel cells, and in particular relates to a hydrogen fuel cell flow rate and pressure adaptive coordinated control method.

Background technique

Proton exchange membrane fuel cells convert chemical energy in hydrogen fuel directly into electrical energy through electrochemical reactions. They are efficient power generation devices with the characteristics of high energy conversion efficiency, no pollution, and low operating temperature. They have been applied and developed in the fields of transportation, power systems, and aerospace. The fuel cell air supply system has the characteristics of strong uncertainty, high nonlinearity, and deep coupling. Flow and pressure, as key control variables, have an important impact on the safe and efficient operation of the fuel cell system. In practical applications, on the one hand, the operating state of the air compressor under complex load conditions makes the motor parameters have certain uncertainties; the altitude will change the atmospheric pressure, temperature, and air density, which will bring uncertainty to the air system model parameters. On the other hand, affected by the air compressor speed and the back pressure valve opening, there is a coupling effect between flow and pressure. Under the influence of changes in the external environment or complex working conditions, the fluctuation of gas flow can easily lead to the "oxygen starvation" phenomenon of the fuel cell stack, and the fluctuation of pressure will cause uneven distribution of gas and excessive pressure difference between the cathode and the anode, which will damage the proton exchange membrane and reduce the service life of the fuel cell. Therefore, the flow-pressure adaptive coordinated control method for air systems with uncertain parameters is a key technology, which is of great significance to improving the performance and life of fuel cell systems.

At present, in the research on coordinated control of air supply system flow and pressure, the article "Feedforward-based decoupling control of air supply for vehicular fuel cell system: Methodology and experimental validation" designed a PID controller based on feedforward decoupling to achieve tracking control of cathode pressure and excess oxygen ratio, but there is a problem that the fitted second-order transfer function model does not match the actual system model under complex operating conditions. The article "Decoupling control of PEMFC cathode intake system based on sliding mode observer" designed a control algorithm combining sliding mode observer and state feedback controller to achieve high-precision control of intake flow and pressure, but did not consider oxygen consumption and nonlinear dynamic characteristics of air compressor. Chinese patent application CN202310992892.7 (A decoupling control method for fuel cell air system) uses an anti-disturbance control algorithm to reversely decouple the transfer function of the air system under different working conditions to achieve independent control of the gas flow and pressure of the air system. Chinese patent application CN202210348576.1 (A modeling and multi-objective control method for a fuel cell air supply system for aviation) takes into account the dynamic characteristics of the air compressor at different altitudes and designs a fuzzy decoupling controller to ensure that the air pressure and the oxygen excess ratio have good dynamic characteristics. Chinese patent application CN202310254199.X (Data-driven discrete three-step decoupling control method for fuel cell air supply system) designs a model-free adaptive controller based on the discrete three-step method to make the air mass flow and pressure track the expected value, but the dynamic linearization model has the problem of large data calculation, which is not conducive to engineering implementation.

In summary, under complex load conditions and changing environmental conditions, the existing methods lack the ability to coordinate flow and pressure tracking control for high-order nonlinear and strongly coupled air supply systems. It is urgent to overcome the multivariable coordinated control method of hydrogen fuel cell air supply systems based on adaptive technology.

Summary of the invention

In view of the parameter uncertainty and the strong coupling of gas flow and pressure in the fuel cell air supply system, and to overcome the shortcomings of the prior art, the present invention provides a hydrogen fuel cell flow and pressure adaptive coordinated control method, which realizes the adaptive adjustment of controller parameters under complex load conditions and harsh environmental conditions, and designs the air compressor input voltage and back pressure valve opening control instructions based on the backstepping method, thereby ensuring the independent tracking control of the flow and pressure of the proton exchange membrane fuel cell air system, and improving the working efficiency and system stability of the fuel cell.

In order to achieve the above object, the present invention adopts the following technical solutions:

A hydrogen fuel cell flow pressure adaptive coordinated control method comprises the following steps:

The first step is to analyze the influence of the air compressor and back pressure valve in the air supply system on the intake flow rate and cathode gas pressure, and to establish a multi-input and multi-output air supply system dynamic model based on the gas reaction mechanism of the air supply pipeline and cathode flow field.

In the second step, based on the dynamic model of the air supply system in the first step, the uncertain parameter vector to be estimated is defined, and the parameter estimation adaptive law is designed around the parameter uncertainty of the air compressor under variable load conditions and the parameter variation caused by atmospheric pressure and temperature under different environmental conditions;

The third step is to select the nonlinear function of the back pressure valve opening and the gas flow rate as the intermediate controlled variables based on the parameter estimation adaptive law in the second step and the tracking error of the gas flow rate and the gas pressure, and design the corresponding stabilization function;

In the fourth step, based on the stabilization function in the third step and the tracking error of the virtual control variable, the back pressure valve opening and the air compressor input voltage control instructions are designed to complete the adaptive coordinated control of the hydrogen fuel cell flow and pressure based on the backstepping method.

The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for adaptively coordinating flow and pressure control of a hydrogen fuel cell when executing the program.

The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned hydrogen fuel cell flow pressure adaptive coordinated control method.

The beneficial effects of the present invention compared with the prior art are:

In order to solve the problem of system parameter uncertainty caused by complex load conditions and changes in environmental conditions, the present invention uses adaptive technology to estimate the air system model parameters with initial value prior information, and designs the backpressure valve opening and air compressor input voltage control instructions based on the backstepping method, thereby realizing high-precision tracking and control of the flow pressure of high-order nonlinear and strongly coupled air systems, improving the working efficiency and stability of the system. The present invention has the characteristics of high control accuracy and easy engineering implementation, and is suitable for tracking and control of multivariable coupled systems of proton exchange membrane fuel cells under complex conditions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for adaptively coordinating flow and pressure control of a hydrogen fuel cell according to the present invention;

FIG2 is an air compressor MAP diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG3 is a load current diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG4 is a flow tracking curve diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG5 is a pressure tracking curve diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG6 is a manifold pressure curve diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG7 is a graph showing the air compressor speed of a hydrogen fuel cell flow pressure adaptive coordinated control method according to the present invention;

FIG8 is a graph showing an air compressor input voltage curve of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention;

FIG. 9 is a back pressure valve opening curve diagram of a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention.

Detailed ways

The present invention is described in detail below with reference to the accompanying drawings and embodiments.

As shown in FIG1 , a hydrogen fuel cell flow pressure adaptive coordinated control method of the present invention comprises the following steps:

The first step is to analyze the influence of the air compressor and back pressure valve in the air supply system on the intake flow rate and cathode gas pressure, and to establish a multi-input and multi-output air supply system dynamic model based on the gas reaction mechanism of the air supply pipeline and cathode flow field.

In the second step, based on the dynamic model of the air supply system in the first step, the uncertain parameter vector to be estimated is defined, and the parameter estimation adaptive law is designed around the parameter uncertainty of the air compressor under variable load conditions and the parameter variation caused by atmospheric pressure and temperature under different environmental conditions;

The third step is to select the nonlinear function of the back pressure valve opening and the gas flow rate as the intermediate controlled variables based on the parameter estimation adaptive law in the second step and the tracking error of the gas flow rate and the gas pressure, and design the corresponding stabilization function;

In the fourth step, based on the stabilization function in the third step and the tracking error of the virtual control variable, the back pressure valve opening and the air compressor input voltage control instructions are designed to complete the adaptive coordinated control of the hydrogen fuel cell flow and pressure based on the backstepping method.

Specifically, the first step is implemented as follows:

According to the gas reaction mechanism of the gas supply pipeline and the cathode flow field, the change of gas pressure can be expressed as:

,

in, and/> is the gas pressure and rate of change of the gas supply manifold,/> and/> is the cathode flow field gas pressure and change rate,/> 、/> are the ideal gas constants for air and oxygen, which are 286.9 J/kg/K and 259.8 J/kg/K respectively. is the stack temperature, which is 348.1K, and are the compressor outlet temperature and stack temperature, respectively. 、/> are the volume of the gas supply pipeline and the volume of the cathode flow field, respectively, and their values are / > and/> ,/> 、/> 、/> 、/> 、/> and/> They respectively represent the air compressor outlet temperature, air compressor outlet gas flow, manifold outlet flow, cathode field inlet flow, cathode field outlet flow and oxygen consumption in the fuel cell stack.

According to the flow characteristics of the air compressor and back pressure valve, the air compressor outlet gas flow and cathode outlet gas flow are expressed as:

,

in, is the atmospheric pressure, which is 101325Pa, and the air compressor outlet gas flow rate/> According to the MAP diagram, it can be fitted into the pressure ratio / > and compressor speed/> Polynomial function of, fitting coefficients/> , ,/> ,/> ,/> ,/> , the fitting results are shown in Figure 2; cathode outlet flow /> It can be expressed as the cathode field pressure / > and back pressure valve opening/> A nonlinear function of is the ideal gas constant, which is 8.314 J/mol/K,/> is the adiabatic index, with a value of 1.4,/> 、/> are valve parameters, and their values are 0.0248, /> .

Combining the above analysis with the dynamic characteristics of the air compressor and back pressure valve, the following multi-input and multi-output air supply system dynamic model is established:

,

in, are state quantities, respectively representing/> , where /> Initial value/> The value is 111460Pa,/> Initial value/> The value is 151990Pa,/> is the angular velocity of the air compressor, initial value/> The value is 7000rad/s,/> is the fuel cell load current, the specific form of which is shown in Figure 3, /> Input voltage control command for the air compressor. is the back pressure valve opening control instruction, /> ,/> ,/> ,/> Indicates/> The derivative of are system model parameters, .

Define gas flow and pressure tracking error vector as follows:

,

in, is the gas flow tracking error,/> is the pressure tracking error, /> is the expected value of the air compressor outlet gas flow rate, /> is the expected value of the cathode flow field gas pressure.

Specifically, the second step is implemented as follows:

Based on the dynamic model of the multi-input and multi-output air supply system established in the first step, the parameter vector to be estimated is defined around the uncertainty of air compressor parameters under variable load conditions and the changes in atmospheric pressure and temperature under different environmental conditions:

,

in, is the uncertain parameter vector to be estimated, the intermediate parameters/> , unknown parameters/> satisfy , the superscript T indicates the transpose of a vector;

According to Lyapunov stability analysis, the adaptive law of design parameter estimation is as follows:

,

in, Represents the uncertain parameter vector/> Estimated value of/> The rate of change, /> is the initial value of the parameter adaptive law, and its values are respectively/> ,/> ,/> ,/> ,/> , and/> is the designed positive definite diagonal matrix,/> Indicates/> The identity matrix of order, /> is the process variable, /> is a positive design parameter, with a value of 15,/> are respectively the gas flow and pressure tracking error vectors/> and virtual control variable tracking error vector/> The kth element of . Specifically, /> is the gas flow tracking error,/> is the pressure tracking error, /> ,/> No specific meaning.

Specifically, the third step is implemented as follows:

Based on the parameter estimation adaptive law in the second step, the tracking error vector of gas flow and pressure is , select the nonlinear term about the back pressure valve opening and the gas flow rate as the intermediate controlled variables/> , and design the corresponding stabilization function :

,

in, Represents the estimated value of the uncertain parameter vector/> The kth element of For process variables/> The kth element of , k=1,2,/> It is a positive design parameter and its value is 0.001.

Specifically, the fourth step is implemented as follows:

Based on the stabilization function in the third step , get the virtual control variable tracking error vector/> , design back pressure valve opening control instruction/> And air compressor input voltage control command/> for:

,

in, are the intermediate controlled variables/> The kth element of , the estimated value of the uncertain parameter vector/> The kth element of , process variable/> The kth element of , k=1,2,/> is the symbol of partial derivative, /> is the design parameter.

Example:

This embodiment takes a 100-kW water-cooled fuel cell as an example, and the system model parameters are shown in Table 1. Considering the influence of external environment such as altitude and atmospheric humidity, as well as fault conditions such as air leakage and friction, which may cause unknown changes in hydrogen fuel cell system parameters, it is necessary to adaptively estimate the unknown and uncertain parameters, and back-step control the air compressor input voltage and back pressure valve opening on the basis of adaptive adjustment parameters, so as to achieve tracking control of air flow and pressure.

Table 1

,

Specific steps are as follows:

In the first step, based on the working mechanism analysis of each component of the system, the following multi-input and multi-output air supply system dynamics model can be established for the fuel cell:

,

in, System model parameters, /> ,/> ,/> , ,/> ,/> ,/> ,/> ,/> ,/> ,/> , the specific parameter meanings and values are shown in Table 1,/> is the gas flow rate at the air compressor outlet, and the fitting result of the curve is shown in Figure 2. It can be seen that the flow characteristic curve under different air compressor speeds has a good fitting result with the experimental data. /> are state quantities, respectively representing , initial value selection see above. /> is the fuel cell load current, as shown in Figure 3.

In the second step, based on the multi-input and multi-output air supply system dynamics model and load current established in the first step, the reference value of the flow rate is defined as the expected value of the air compressor outlet gas flow rate. The reference value of pressure is the expected value of cathode flow field gas pressure/> :

,

,

In order to facilitate adaptive estimation, unknown intermediate parameters are introduced Satisfaction/> , considering as the intermediate parameter of control gain/> For unknown uncertain parameters, the inverse of the gain needs to be estimated, so the parameter vector to be estimated is defined as ;

To address the problem of model parameter uncertainty, we further define the parameter vector to be estimated: Facilitates subsequent controller design;

Design of Lyapunov functions , based on Lyapunov stability analysis, the adaptive law for parameter estimation is designed;

In the third step, based on the parameter estimation adaptive law in the second step, the error vector is tracked according to the gas flow and pressure. , select the nonlinear term about the back pressure valve opening and the gas flow rate as the intermediate controlled variables/> , and design the corresponding stabilization function/> .

The fourth step is based on the stabilization function in the third step. , according to the virtual control variable tracking error vector/> , design back pressure valve opening control instruction/> And air compressor input voltage control command/> .

In order to verify the effectiveness of the designed control method, the step load current shown in Figure 3 is selected. The reference values of flow and pressure can be determined according to the load current. The air compressor input voltage and back pressure valve opening can be calculated according to the designed control method. The effectiveness of the present invention is verified by observing whether the air compressor outlet flow and the cathode flow field gas pressure can track the flow reference value and the pressure reference value respectively.

In the back pressure valve opening control command And air compressor input voltage control command/> Driven by , the flow tracking curve of the system is shown in Figure 4. The flow tracking error is 0.17%, the adjustment time at the initial moment is 0.005s, and the overshoot is 0.017%. It can be seen that the actual controlled flow value and the reference value are completely overlapped, and there is a faster convergence time. The cathode pressure tracking curve is shown in Figure 5. The tracking error is 0.29%, the adjustment time at the initial moment is 0.013s, and the overshoot is 0.49%. It can be seen that the actual controlled pressure value tracks the reference value better, and has a faster convergence time and a smaller overshoot when the load changes. The manifold pressure after corresponding control is shown in Figure 6, the air compressor speed is shown in Figure 7, the air compressor input voltage is shown in Figure 8, and the back pressure valve opening is shown in Figure 9. At this point, the adaptive coordinated control of the flow pressure of the hydrogen fuel cell is completed.

The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for adaptively coordinating flow and pressure control of a hydrogen fuel cell when executing the program.

The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned hydrogen fuel cell flow pressure adaptive coordinated control method.

Those skilled in the art should understand that the present invention may be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, such as object-oriented programming language Java and interpreted scripting language JavaScript.

The present invention is described with reference to the flowchart and/or block diagram of the method and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the processes and/or boxes in the flowchart and/or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (3)

1. A hydrogen fuel cell flow pressure adaptive coordinated control method, characterized in that it includes the following steps: The first step is to analyze the influence of the air compressor and back pressure valve in the air supply system on the intake flow rate and cathode gas pressure, and to establish a multi-input and multi-output air supply system dynamic model based on the gas reaction mechanism of the air supply pipeline and cathode flow field. In the second step, based on the dynamic model of the air supply system in the first step, the uncertain parameter vector to be estimated is defined, and the parameter estimation adaptive law is designed around the parameter uncertainty of the air compressor under variable load conditions and the parameter variation caused by atmospheric pressure and temperature under different environmental conditions; In the third step, based on the parameter estimation adaptive law in the second step and the tracking errors of gas flow and gas pressure, the nonlinear function of the back pressure valve opening and the air compressor outlet gas flow are selected as intermediate controlled variables to design the corresponding stabilization function. The fourth step is to design the back pressure valve opening control command and the air compressor input voltage control command based on the stabilization function in the third step and the virtual control variable tracking error, and complete the adaptive coordinated control of the hydrogen fuel cell flow and pressure based on the backstepping method; The first step comprises: The manifold pressure is obtained based on the gas reaction mechanism of the gas supply pipeline and the cathode flow field and its rate of change/> , cathode flow field gas pressure/> and its rate of change/> as follows: ; in, and/> is the gas pressure and rate of change of the gas supply manifold,/> and/> is the cathode flow field gas pressure and change rate,/> 、/> are the ideal gas constants for air and oxygen, respectively, is the stack temperature, /> 、/> are the volume of the gas supply pipeline and the volume of the cathode flow field, respectively. 、/> 、/> 、/> 、/> and/> They represent the air compressor outlet temperature, air compressor outlet gas flow, manifold outlet flow, cathode field inlet flow, cathode field outlet flow and oxygen consumption in the stack respectively; According to the flow characteristics of the air compressor and back pressure valve, the air compressor outlet gas flow and cathode outlet gas flow are expressed as: ; in, is the atmospheric pressure, the air compressor outlet gas flow rate/> According to the MAP diagram, it is fitted to the pressure ratio/> and compressor speed/> Polynomial function of, fitting coefficients/> ,/> ,/> , ,/> ,/> ; Cathode outlet flow /> Expressed as the gas pressure of the cathode flow field / > and back pressure valve opening/> A nonlinear function of is the ideal gas constant,/> is the adiabatic index, /> is the valve flow coefficient, /> is the valve area; Combined with modeling analysis, the dynamic model of the multi-input and multi-output air supply system is established as follows: ; in, are state quantities, respectively representing/> , where /> Initial value/> The value is 111460Pa,/> Initial value/> The value is 151990Pa,/> is the angular velocity of the air compressor, initial value/> The value is 7000rad/s,/> is the fuel cell load current, /> Input voltage control command for air compressor,/> is the back pressure valve opening control instruction, /> Indicates/> The derivative of ,/> are system model parameters, /> , specifically expressed as ,/> ,/> ,/> ,/> , ,/> ,/> ,/> ,/> ,/> ; in, represents the manifold flow coefficient, /> is the number of fuel cell stacks, /> represents the molar mass of oxygen, /> is the Faraday constant, /> represents the atmospheric temperature, /> 、/> 、/> 、/> 、/> and/> They are air compressor efficiency, air compressor rotation inertia, motor resistance, motor torque parameters, motor electromagnetic parameters and motor efficiency, respectively. is the specific heat capacity, /> is the back pressure valve time constant; Define the air compressor outlet gas flow tracking error and cathode flow field gas pressure tracking error vector as follows: ; in, is the air compressor outlet gas flow tracking error, /> is the cathode flow field gas pressure tracking error,/> is the expected value of the air compressor outlet gas flow rate, /> is the expected value of cathode flow field gas pressure; ; ; The second step includes: Focusing on the parameter uncertainty problem under variable load conditions, the parameter vector to be estimated is defined as follows: ; in, is the uncertain parameter vector to be estimated, the intermediate parameters/> , unknown parameters/> satisfy , the superscript T indicates the transpose of a vector; According to the uncertain parameter vector to be estimated and based on Lyapunov stability analysis, the parameter estimation adaptive law is designed as follows: ; in, Respectively represent the estimated values of the uncertain parameter vector/> The adaptive law of is the initial value of the parameter adaptive law, and its values are respectively/> ,/> ,/> ,/> ,/> , is the designed positive definite diagonal matrix,/> Indicates/> The identity matrix of order, /> is the process variable, /> is a positive design parameter, with a value of 15,/> are respectively the air compressor outlet gas flow and cathode flow field gas pressure tracking error vector/> and virtual control variable tracking error vector/> The kth element of , k=1,2; j represents the index value; In the third step, the intermediate controlled variable is , let the stabilization function be/> , the formula is as follows: ; in, Represents the estimated value of the uncertain parameter vector/> The kth element of For process variables/> The kth element of , k=1,2,/> is a positive design parameter, and its value is 0.001; In the fourth step, based on the stabilization function in the third step , and obtain the virtual control variable tracking error vector , design back pressure valve opening control instruction/> And air compressor input voltage control command/> as follows: ; in, are the intermediate controlled variables/> , the estimated value of the uncertain parameter vector/> , process variable/> The kth element of , k=1,2,/> is the symbol of partial derivative, /> is the design parameter. 2. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of a hydrogen fuel cell flow pressure adaptive coordinated control method as described in claim 1 are implemented. 3. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a hydrogen fuel cell flow pressure adaptive coordinated control method as described in claim 1 are implemented.