Stack humidity state observation method, state observer, fuel cell system, equipment and medium

Abstract

The invention relates to a stack humidity state observation method, a state observer, a fuel cell system, equipment and a medium. The method comprises the following steps: constructing a similarity criterion model, and introducing a dimensionless coefficient to describe a humidity dynamic process in the fuel cell system; collecting the stack voltage of the fuel cell, calculating the difference value between the stack voltage and the stack voltage state observation estimation value to obtain a voltage deviation e, and constructing a sliding film surface s according to the voltage difference value e; according to the sliding mode surface s and a dimensionless coefficient estimated value obtained through online system identification, a quantitative process control algorithm is used for conducting sliding mode correction and feeding back the correction amount, the similarity criterion model is updated, and the estimation process of humidity and dimensionless parameters is identified through the online system. Compared with the prior art, the method has the advantages of being high in humidity estimation precision, high in environment adaptive capacity, small in calculation amount and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fuel cell systems, in particular to a method for observing the state of the humidity of an electric pile, a state observer, a fuel cell system, equipment and a medium. BACKGROUND

[0002] Hydrogen fuel cells are considered to be promising new energy devices, which have the advantages of high mass energy density, strong recyclability and short hydrogen refueling time compared with lithium batteries. The humidity of the fuel cell affects the efficiency and service life of the fuel cell system. If the humidity is too high, water flooding will cause dynamic oxygen deficiency, and if the humidity is too low, the proton exchange membrane will be too dry, causing a large ohmic polarization, so the humidity needs to be maintained within a reasonable range. However, the humidity sensor has the problems of long response time, low precision and high cost, so a state observation technology is needed to estimate the humidity to provide accurate feedback for active humidity control.

[0003] The prior art has the following defects in the process of estimating the state of the humidity of the fuel cell electric pile: (1) The adaptive state observer is established to estimate the unknown parameters in the electric pile while observing the relative humidity in the electric pile, and the effectiveness of the algorithm is verified through simulation and experiment, but the observation error problem caused by working condition perturbation is not considered, and the effectiveness in the whole working condition range is not effectively explained.

[0004] (2) The state of the humidity is observed by defining a dimensionless coefficient to simplify the model of the fuel cell system and by using a working condition adaptive sliding mode state observation technology, but the dimensionless parameter changes with time through a feedforward method, and cannot be corrected according to real-time feedback. As the use time of the electric pile increases, the accuracy of the feedforward table decreases, and the humidity estimation error will increase.

[0005] The above-mentioned solutions cannot realize online system identification of model parameters in the whole working condition range and the whole life cycle, and the robustness is difficult to meet the use of the fuel cell system in high-precision scenarios. SUMMARY

[0006] The purpose of the present application is to overcome the defects of the prior art and provide a method for observing the state of the humidity of an electric pile, a state observer, a fuel cell system, equipment and a medium, which has the advantages of high humidity estimation accuracy, strong environmental adaptability and small calculation amount.

[0007] The purpose of the present application can be achieved by the following technical solutions: According to a first aspect of the present application, a method for observing the state of the humidity of an electric pile of a fuel cell system is provided, comprising: Constructing a similarity criterion model and introducing a dimensionless coefficient Describing the dynamic process of the humidity in the fuel cell system; Collecting stack voltage of fuel cell , calculating stack voltage , and calculating the difference between the stack voltage state observation estimate value to obtain the voltage deviation e, and constructing the sliding surface s according to the voltage difference e; , and the dimensionless coefficient estimate value obtained by online system identification , using a quantitative process control algorithm to perform sliding mode correction and feedback correction amount, updating the similarity criterion model and the estimation process of humidity and dimensionless parameters by online system identification.

[0008] Preferably, the similarity criterion model describes the humidity dynamic process in the fuel cell system by defining dimensionless parameters, and the specific expression is: In the formula: is the cathode water vapor partial pressure, is the anode water vapor partial pressure; is the time-varying hysteresis coefficient; is the cathode water vapor partial pressure steady-state value, is the anode water vapor partial pressure steady-state value; is the dimensionless water distribution coefficient, is the dimensionless environmental coefficient, is the dimensionless anode outlet flow coefficient, is the dimensionless hydrogen discharge coefficient; is the peroxide ratio; is the cathode pressure, is the anode pressure; is the atmospheric temperature; is the saturated vapor pressure at temperature ; is the stack temperature; is the cathode relative humidity, is the anode relative humidity; is the stack voltage; is the mapping relationship between the cathode and anode humidity and the stack voltage; is the correction term, which is updated by sliding mode correction.

[0009] Preferably, the peroxide ratio , and the calculation expression is: , In the formula: is the oxygen volume fraction in the atmosphere; is the cathode flow rate; is the Faraday constant; is the molar mass of atmospheric air; is the number of fuel cell monomer pieces; is the current.

[0010] Preferably, the sliding surface s is calculated by the expression: , wherein, is an adjustable parameter.

[0011] Preferably, the online system identification obtains a dimensionless coefficient estimate , and the corresponding identification expression is: , , wherein: is a mapping relationship, which is updated by a sliding mode correction, and satisfies , is an adjustable parameter. is a correction term, which is updated by a sliding mode correction.

[0012] Preferably, the sliding surface s and the dimensionless coefficient estimate are used to perform a sliding mode correction and feedback correction amount by using a quantitative process control algorithm, and to update the similarity criterion model and the online system identification process for humidity and dimensionless parameter estimation, specifically including: solving the following matrix inequality by using a quantitative process control method: wherein: is an adjustable positive definite matrix. the correction amount and the mapping relationship are updated in real time, and the similarity criterion model and the online system identification process for humidity and dimensionless parameter estimation are iteratively updated.

[0013] According to a second aspect of the present application, there is provided a stack humidity state observer for a fuel cell system, which applies the method, including: a humidity dynamic process description module for constructing a similarity criterion model, introducing a dimensionless coefficient to describe a humidity dynamic process in the fuel cell system. a sliding surface construction module for collecting a stack voltage of the fuel cell, calculating a voltage deviation e by using a difference between the stack voltage and a stack voltage state observation estimate , and constructing a sliding surface s according to the voltage deviation e. a sliding mode correction and iterative update estimation module for updating the similarity criterion model and the online system identification process for humidity and dimensionless parameter estimation by using the sliding surface s and the dimensionless coefficient estimate The quantitative process control algorithm is used for sliding mode correction and feedback correction, the estimation process of humidity and dimensionless parameters is updated by using a similarity criterion model and online system identification.

[0014] According to a third aspect of the present application, a fuel cell system is provided, comprising the stack humidity state observer.

[0015] According to a fourth aspect of the present application, an electronic device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing any of the methods when executing the program.

[0016] According to a fifth aspect of the present application, a computer readable storage medium is provided, storing a computer program, and the program being executed by a processor to implement any of the methods.

[0017] Compared with the prior art, the present application has the following beneficial effects: (1) The present application uses a similarity criterion model, and introduces a dimensionless coefficient The model is small in calculation amount and high in precision, and is more suitable for state observation than other fuel cell system models.

[0018] (2) The fuel cell humidity parameter changes with the environment, so the humidity observation model precision is affected by the parameter adaptive ability. The existing sliding mode state observation cannot directly affect the parameters of the humidity observation model, but the present application combines the traditional sliding mode with the adaptive mechanism, uses Lyapunov stability in state observation, lets the sliding mode feedback process affect the dynamic characteristics of the model itself, and calibrates the model in real time, which is beneficial to improving the humidity state estimation precision.

[0019] (3) The present application uses online system identification technology to identify the dimensionless parameters of the system in real time, improves the precision of the humidity state observation algorithm in the whole working condition range and the whole life cycle, and has good robustness. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The flow chart of the method of the present application.

[0021] Figure 2 The data flow chart of the stack humidity state observation for the fuel cell system. DETAILED DESCRIPTION

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] Example like Figure 1 As shown in the figure, this embodiment provides a method for observing the humidity status of a fuel cell system stack. The method includes: Construct a similarity criterion model and introduce dimensionless coefficients. The dynamic process of humidity within the fuel cell system is described; Collect fuel cell stack voltage Calculate the stack voltage Compared with the observed estimated value of the stack voltage state The voltage difference is used to obtain the voltage deviation e, and the sliding surface s is constructed based on the voltage difference e; Based on the estimated dimensionless coefficient values ​​obtained from the synovial surface s and the online system. The quantitative process control algorithm is used to perform sliding mode correction and feedback correction amount, update similarity criterion model and online system identification of the estimation process of humidity and dimensionless parameters.

[0024] Next, the method of the embodiment will be described in detail.

[0025] The similarity criterion model describes the dynamic humidity process within the fuel cell system by defining dimensionless parameters, specifically expressed as follows: In the formula: The partial pressure of water vapor at the cathode. This refers to the partial pressure of water vapor at the anode. The time-varying hysteresis coefficient; This represents the steady-state value of the cathode water vapor partial pressure. This represents the steady-state value of the anode water vapor partial pressure. The dimensionless water distribution coefficient. This is a dimensionless environmental coefficient. The dimensionless anode outlet flow coefficient. This is a dimensionless hydrogen expulsion coefficient; This refers to the oxygen per unit volume; For cathode pressure, This refers to the anode pressure. Atmospheric temperature; For temperature The saturated vapor pressure at that time; This refers to the temperature of the fuel cell stack. is the relative humidity of the cathode, is the relative humidity of the anode; is the voltage of the stack; is the mapping relationship between the humidity of the cathode and the anode and the voltage of the stack, which is characterized by the polarization curve formula; is the correction term, which is updated by the sliding mode correction.

[0026] Oxygen ratio The calculation expression is: , In the formula, is the volume fraction of oxygen in the atmosphere; is the cathode flow rate; is the Faraday constant; is the molar mass of atmospheric air; is the number of fuel cell monomers; is the current.

[0027] Sliding surface s, the calculation expression is: , In the formula, is an adjustable parameter.

[0028] Non-dimensional coefficient estimate value obtained by online system identification The corresponding identification expression is: , , In the formula, is the mapping relationship, which is updated by the sliding mode correction and satisfies , is an adjustable parameter; is the correction term, which is updated by the sliding mode correction.

[0029] According to the sliding surface s and the non-dimensional coefficient estimate value obtained by online system identification , the quantitative process control algorithm is used for sliding mode correction and feedback correction, updating the similarity criterion model and the estimation process of humidity and non-dimensional parameters by online system identification, which specifically includes: Using the quantitative process control method, the following matrix inequality is solved: In the formula, is an adjustable positive definite matrix; By correcting the amount and the mapping relationship The real-time updating, iterative updating of the similarity criterion model, and the estimation process of the humidity and dimensionless parameters by online system identification are performed. The data stream of the stack humidity state observation is shown in FIG. 8. Figure 2

[0030] The embodiment also provides a stack humidity state observer for a fuel cell system, which applies the stack humidity state method, and comprises the following modules: a humidity dynamic process description module, which is configured to construct a similarity criterion model and introduce dimensionless coefficients to describe the humidity dynamic process in the fuel cell system; a sliding surface construction module, which is configured to collect the stack voltage of the fuel cell, calculate the difference between the stack voltage and the estimated value of the stack voltage state observation to obtain the voltage deviation e, and construct the sliding surface s according to the voltage deviation e; a sliding correction and iterative updating estimation module, which is configured to use the sliding surface s and the estimated value of the dimensionless coefficient obtained by online system identification to perform sliding correction and feedback correction by using a quantitative process control algorithm, update the similarity criterion model, and update the estimation process of the humidity and dimensionless parameters by online system identification.

[0031] The embodiment also provides a fuel cell system, which comprises the stack humidity state observer for the fuel cell system.

[0032] In the embodiment, the fuel cell system comprises an air supply system, a hydrogen supply system, a water and heat management system, a fuel cell stack, and a fuel cell system control unit. The air supply system comprises an air filter, an air compressor, an intercooler, a back pressure valve, a cathode inlet temperature and pressure integrated sensor, an air flow meter, and other components; the hydrogen supply system comprises an anode on-off valve, an anode pressure regulating valve, a hydrogen circulating pump, a hydrogen exhaust valve, a water exhaust valve, a gas-water separator, an anode inlet temperature and pressure integrated sensor, and other components; and the water and heat management system comprises a water pump, a radiator, a thermostat, a deionizer, an expansion water kettle, a stack cooling liquid inlet temperature and pressure integrated sensor, a stack cooling liquid outlet temperature and pressure integrated sensor, and other components.

[0033] The control process in the fuel cell system further comprises the following steps: power tracking control: according to the current load demand of the fuel cell system, the target values of the cathode flow rate, the cathode pressure, the anode pressure, and the stack inlet and outlet cooling liquid temperature are determined.

[0034] cathode flow rate and pressure decoupling control: according to the current target values of the cathode flow rate and the cathode pressure, the actual cathode flow rate and the actual cathode pressure feedback signals are received by using a quantitative process control method, and the speed of the air compressor and the opening degree of the back pressure valve are controlled, so that the actual cathode flow rate and the actual cathode pressure track the target values.​

[0035] Anode pressure control: according to an anode pressure target value, receiving an actual anode pressure feedback signal, controlling an anode pressure regulating valve opening degree by using a quantitative process control method, so that the actual anode pressure tracks the target value.

[0036] Stack temperature control: according to a stack temperature target value, receiving actual stack inlet and outlet coolant temperature values, controlling a thermostat opening degree, a radiator fan rotating speed and a water pump rotating speed, so that the actual stack coolant inlet and outlet temperatures track the target value.

[0037] The electronic device of the present application includes a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). Various programs and data required for device operation can also be stored in the RAM. The CPU, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0038] A plurality of components in the device are connected to the I / O interface, including: an input unit such as a keyboard, mouse, etc.; an output unit such as various types of displays, speakers, etc.; a storage unit such as a magnetic disk, optical disk, etc.; and a communication unit such as a network card, modem, wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0039] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on the device via the ROM and / or the communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the methods by any other appropriate means, such as by means of firmware.

[0040] The functions described above in the detailed description can be implemented in at least partially by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0041] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / operations specified in the flowchart diagrams and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, or entirely on a remote machine or server.

[0042] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable storage medium can include, but are not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

[0043] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of observing a state of humidity of an electric pile for a fuel cell system, characterized by, Comprising: A similarity criterion model is constructed, and a dimensionless coefficient is introduced The dynamic process of humidity in fuel cell system is described; Collecting a stack voltage of a fuel cell , calculating a stack voltage and a difference between the stack voltage state observation estimate value a voltage deviation e, and constructing a sliding surface s according to the voltage deviation e According to the sliding surface s and the dimensionless coefficient estimation value obtained by online system identification The sliding mode correction is performed by using a quantitative process control algorithm, and the correction amount is fed back to update the similarity criterion model and the estimation process of the humidity and the dimensionless parameter by online system identification.

2. A method of observing a state of humidity of a stack for a fuel cell system according to claim 1, characterized by, The similarity criterion model describes the humidity dynamic process in the fuel cell system by defining a dimensionless parameter, and the specific expression is: wherein: is the cathode water vapor partial pressure, is the anode water vapor partial pressure; is the time-varying hysteresis coefficient; is the cathode water vapor partial pressure steady-state value, is the anode water vapor partial pressure steady-state value; is the dimensionless water distribution coefficient, is the dimensionless environmental coefficient, is the dimensionless anode outlet flow coefficient, is the dimensionless hydrogen venting coefficient; is the peroxide ratio; is the cathode pressure, is the anode pressure; is the atmospheric temperature; is the temperature of the saturated vapor pressure at time t; is the stack temperature; is the cathode relative humidity, is the anode relative humidity; is the stack voltage; is the mapping relationship of the cathode and anode humidity and the stack voltage; is the correction term, which is updated by the sliding mode correction.

3. A method of observing a state of humidity of an electric pile for a fuel cell system according to claim 2, characterized by, The peroxide ratio The calculation expression is: ， wherein: is the volume fraction of oxygen in the atmosphere; is the cathode flow rate; is the Faraday constant; is the molar mass of atmospheric air; is the number of fuel cell monopiles; is the current.

4. A method of observing a state of humidity of a stack for a fuel cell system according to claim 2, characterized by, The slip film surface s, and the calculation expression is: ， In the formulae, are adjustable parameters.

5. A method of observing a state of humidity of a stack for a fuel cell system according to claim 2, characterized by, The online system recognizes the dimensionless coefficient estimate value The corresponding identification expression is: ， ， In the formula: is a mapping relationship, and is updated through a sliding mode correction. , is an adjustable parameter; is a correction term, and is updated through a sliding mode correction.

6. A method of observing a state of humidity of a stack for a fuel cell system according to claim 5, characterized by, The estimation value of the dimensionless coefficient obtained according to the synovial membrane surface s and online system identification The estimation value of the dimensionless coefficient obtained according to the synovial membrane surface s and online system identification Solving the following matrix inequality by using the quantitative process control method: wherein: is a positive definite matrix; by updating the correction amount and the mapping relationship in real time, iteratively updating the similarity criterion model, and online system identification of the estimation process for humidity and dimensionless parameters.

7. A stack humidity state observer for a fuel cell system, characterized by, Applying the method of any one of claims 1-6, comprising: A humidity dynamic process description module is used to construct a similarity criterion model and introduce a dimensionless coefficient A humidity dynamic process in a fuel cell system is described; a sliding surface construction module for collecting the stack voltage of the fuel cell , calculating the stack voltage and the difference between the stack voltage state observation estimate value to obtain the voltage deviation e, and constructing the sliding surface s according to the voltage deviation e; a sliding mode correction and iterative update estimation module for updating the estimation of the dimensionless coefficients based on the sliding surface s and the online system identification a sliding mode correction using a quantitative process control algorithm and feedback correction, updating the similarity criterion model and the estimation process of the humidity and dimensionless parameters by online system identification.

8. A fuel cell system characterized by comprising: The fuel cell system stack humidity state observer of claim 7.

9. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, The processor implements the method of any one of claims 1-6 when executing the program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-6.

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