Fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis

Abstract

The invention relates to a fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis, and the system comprises a fuel cell system which is composed of auxiliary systems and a fuel cell stack and is used for providing fuel for power generation; the electrochemical impedance spectroscopy measurement system is used for collecting the voltage and current of the fuel cell stack and calculating the impedance spectroscopy of the fuel cell stack through online processing; and the multi-objective optimization system is connected with the fuel cell system and the electrochemical impedance spectroscopy measurement system, and is used for obtaining the optimal operation conditions of the current fuel cell stack under different current densities according to the operation condition sensitivity database, the set constraint conditions, the multi-objective parameters and the to-be-optimized parameters so as to complete multi-objective optimization. By adopting the system provided by the invention, the parameter calibration of the system can be realized more quickly, the development period can be greatly shortened, and a large amount of test calibration time and cost can be saved; and a good foundation is laid for impedance-based adaptive parameter optimization application of the vehicle-mounted FCU.

Description

Technical Field

[0001] The present invention relates to the field of fuel cells, and in particular to a fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis. Background Art

[0002] Proton exchange membrane fuel cells have attracted much attention as a clean, efficient, environmentally adaptable and energy-dense energy conversion technology, and are widely used in vehicle engine systems. Vehicle fuel cell systems generally include cathode subsystems, anode subsystems, thermal management subsystems, electrical and electronic control subsystems, and other BOPs and fuel cell stacks. The development of vehicle fuel cell systems requires a lot of calibration work, such as the hydrogen discharge frequency related to economy, the system net output power related to power, the cell voltage and cell consistency related to durability and life, and the low-temperature cold start related to environmental adaptability tests. Each indicator requires a long period of development and calibration, and there is a coupling relationship between different goals.

[0003] CN114970192A discloses a multi-objective intelligent control method for a fuel cell system, including setting the operating parameters of the fuel cell, the optimization target quantity and decision variables of the fuel cell, setting the range of variation of the operating variables, setting the rated load power of the fuel cell, setting the priority of the optimization target quantity of the fuel cell, to establish a multi-objective optimization model for the fuel cell, and outputting the decision variable value corresponding to the optimal performance. However, this scheme belongs to mathematical modeling based on simulation, and only optimizes the output performance.

[0004] CN112289385A discloses a method for obtaining the optimal working point of a fuel cell. The method calculates the minimum value of the power coordination allocation optimization function in real time through the health status, discharge characteristics and charge state of the fuel cell and the auxiliary power source, obtains the optimal power allocation of the fuel cell and the auxiliary power source that meets the vehicle power requirements and has the best input-output ratio, realizes the control of the optimal working point of the fuel cell, and maximizes the input-output ratio of the fuel cell. However, the scheme uses the fuel cell output power, start-stop times and output current to calculate the fuel cell profile status index, and optimizes the power allocation algorithm of the vehicle engine system, but fails to characterize the internal state of the fuel cell stack.

[0005] At present, the evaluation of multi-objective intelligent control methods for fuel cells is mainly based on model prediction, algorithm optimization and other methods. Most of them are based on pure algorithms to achieve optimal control of the single goal of output power, and have limitations such as inability to evaluate the internal state of the stack (polarization resistance) or incomplete information. In this regard, when the system multi-objective optimization calibrates the operating parameters, how to adjust the operating parameters more efficiently, the changes in the polarization resistances after adjusting the parameters, and the internal state of the stack; at this stage, it is impossible to judge and identify based only on the single cell voltage of the stack.

[0006] Therefore, it is necessary to propose an effective solution to overcome the shortcomings of the current existing technology. Summary of the invention

[0007] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis.

[0008] In order to achieve the above objectives, the multi-objective optimization system of fuel cells based on electrochemical impedance spectroscopy and quantitative analysis of the present invention is as follows:

[0009] The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis has the following main features:

[0010] The fuel cell system consists of various auxiliary systems and a fuel cell stack, which is used to provide fuel for power generation;

[0011] an electrochemical impedance spectroscopy measurement system, connected to the fuel cell system, for collecting the voltage and current of the fuel cell stack and calculating the impedance spectrum of the fuel cell stack through online processing; and

[0012] The multi-objective optimization system is connected to the fuel cell system and the electrochemical impedance spectroscopy measurement system, and is used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and obtain the optimal operating conditions of the current fuel cell stack under different electrical densities, thereby completing the multi-objective optimization.

[0013] Preferably, the fuel cell system specifically comprises:

[0014] A cathode subsystem for controlling operating parameters including humidity, flow, pressure, and temperature of the air;

[0015] an anode subsystem for controlling operating parameters including flow, pressure, and temperature of hydrogen;

[0016] Thermal management subsystem, used to control operating parameters including flow, pressure, and temperature of the coolant;

[0017] A fuel cell control unit, which is used to collect sensor feedback signals from the gas, fuel, and cooling supply units, and drive the actuators to achieve the set target parameters; and

[0018] The fuel cell stack is used to generate electrical energy and water through internal electrochemical reactions.

[0019] Preferably, the electrochemical impedance spectroscopy measurement system specifically comprises:

[0020] A signal acquisition unit is connected to the fuel cell stack, and includes a multi-channel voltage acquisition module, a current acquisition module and a chassis, and is used to acquire and measure the voltage, current and single cell voltage at the positive and negative terminals of the fuel cell;

[0021] An electronic load, connected to the fuel cell stack, for converting a small-amplitude AC voltage signal of a signal generator into an AC current disturbance, and injecting the disturbance into the fuel cell stack under test;

[0022] an excitation signal unit, connected to the electronic load, and used for generating a function generator of an arbitrary AC waveform; and

[0023] The EIS control unit is connected to the fuel cell control unit, the signal acquisition unit, and the excitation signal unit, and is an industrial computer or controller used to run the control logic, control algorithm and strategy of the current device.

[0024] Preferably, the multi-objective optimization system comprises:

[0025] An impedance quantitative analysis unit, connected to the EIS control unit, for receiving impedance measurement data from the EIS control unit and analyzing all characteristic peaks in the EIS control unit according to a DRT algorithm;

[0026] An operating condition sensitivity database, connected to the impedance quantitative analysis unit, is used to quantitatively analyze the impedance obtained by testing the operating condition change mode under different electrical densities, and establish the sensitivity and correlation of the operating conditions under each electrical density to each polarization impedance;

[0027] A multi-objective optimization unit connected to the operating condition sensitivity database, used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and to obtain optimal operating conditions under different power densities in combination with the mathematical model of the fuel cell system; and

[0028] The optimal operating condition database is connected to the multi-objective optimization unit and is used to read the optimal battery stack operating parameters of the multi-objective optimization unit to obtain the optimal operating conditions.

[0029] Preferably, the operating condition sensitivity database is established according to the following steps:

[0030] (1.1) Establish the sensitivity conditions of the fuel cell stack operating conditions;

[0031] (1.2) Select the current density;

[0032] (1.3) Setting target parameters of air, hydrogen and cooling water according to the selected current density;

[0033] (1.4) After the set parameters are stable, set the DC current of the electronic load and start loading;

[0034] (1.5) After the stack output is stable, set the electronic load AC measurement parameters and generate AC current;

[0035] (1.6) Conduct electrochemical impedance spectroscopy test;

[0036] (1.7) Determine whether the current test is completed. If so, proceed to step (1.9), otherwise proceed to step (1.8);

[0037] (1.8) Determine whether the operation under the current electrical secret condition is completed. If completed, return to step (1.2); otherwise, change the operating conditions and return to step (1.3);

[0038] (1.9) Based on the polarization resistance identification of the DRT algorithm, a database of the sensitivity of operating conditions under all current densities is obtained, so as to analyze the sensitivity and correlation of operating conditions to each polarization impedance under each current density.

[0039] Preferably, the multi-objective optimization unit includes the following processing steps:

[0040] (2.1) Setting the main operating condition parameters, constraints and target parameters to be optimized;

[0041] (2.2) Obtaining the optimal operating conditions under different power densities through the simulation model of the fuel cell system and the particle swarm algorithm;

[0042] (2.3) reading the optimal operating conditions through the fuel cell control unit, and performing actuator calibration test and EIS test in combination with the simulation model of the fuel cell system;

[0043] (2.4) Perform target determination. If the current optimal operating conditions meet the requirements, the processing is terminated directly. Otherwise, the corresponding operating parameters are adjusted according to the sensitivity and correlation of the operating conditions under each electrical density to each impedance resistor, and return to step (2.3).

[0044] Preferably, the impedance quantitative analysis unit comprises the following processing steps:

[0045] (3.1) Analyze the validity of the real and imaginary parts of the electrochemical impedance spectroscopy (EIS);

[0046] (3.2) Determine whether the current residual meets the requirements. If yes, proceed to the next step. Otherwise, do not perform DRT analysis on the current data.

[0047] (3.3) Set the parameters of the improved DRT algorithm;

[0048] (3.4) Calculate the distribution function of the radial basis function and all polarization resistances;

[0049] (3.5) Identify the polarization resistance in each polarization process to complete the DRT quantitative analysis.

[0050] The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis of the present invention can obtain the optimal operating parameters according to the multi-objective optimization method of the operating condition sensitivity database based on impedance of the target parameters: such as cathode / anode intake pressure, cathode humidity, cathode / anode gas stoichiometric ratio, coolant temperature, hydrogen discharge frequency, and ensure that the stack is always in a stable and healthy working state by constraining the polarization resistance and the minimum cell voltage. At the same time, based on the sensitivity and correlation of each polarization resistance under the operating conditions, the system parameter calibration can be achieved more quickly, which can greatly shorten the development cycle and save a lot of test calibration time and cost; this also lays a good foundation for the impedance-based adaptive parameter optimization application of the on-board FCU. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall architecture of the fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis of the present invention.

[0052] Figure 2 A schematic diagram of operating condition sensitivity testing and polarization impedance database establishment for a fuel cell stack of the present invention.

[0053] Figure 3 It is a schematic diagram of multi-objective optimization of the fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis of the present invention.

[0054] Figure 4 This is a schematic diagram of various polarization impedances obtained by DRT quantitative analysis of the fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis of the present invention. DETAILED DESCRIPTION

[0055] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0056] Before describing in detail embodiments according to the present invention, it should be noted that, hereinafter, the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, whereby a process, method, article or apparatus comprising a series of elements includes not only these elements, but also other elements not explicitly listed or inherent to such process, method, article or apparatus.

[0057] See also Figure 1 As shown, the fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis, wherein the system includes:

[0058] The fuel cell system consists of various auxiliary systems and a fuel cell stack, which is used to provide fuel for power generation;

[0059] an electrochemical impedance spectroscopy measurement system, connected to the fuel cell system, for collecting the voltage and current of the fuel cell stack and calculating the impedance spectrum of the fuel cell stack through online processing; and

[0060] The multi-objective optimization system is connected to the fuel cell system and the electrochemical impedance spectroscopy measurement system, and is used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and obtain the optimal operating conditions of the current fuel cell stack under different electrical densities, thereby completing the multi-objective optimization.

[0061] As a preferred embodiment of the present invention, the fuel cell system specifically comprises:

[0062] A cathode subsystem for controlling operating parameters including humidity, flow, pressure, and temperature of the air;

[0063] an anode subsystem for controlling operating parameters including flow, pressure, and temperature of hydrogen;

[0064] Thermal management subsystem, used to control operating parameters including flow, pressure, and temperature of the coolant;

[0065] A fuel cell control unit, which is used to collect sensor feedback signals from the gas, fuel, and cooling supply units, and drive the actuators to achieve the set target parameters; and

[0066] The fuel cell stack is used to generate electrical energy and water through internal electrochemical reactions.

[0067] As a preferred embodiment of the present invention, the electrochemical impedance spectroscopy measurement system specifically comprises:

[0068] A signal acquisition unit is connected to the fuel cell stack, and includes a multi-channel voltage acquisition module, a current acquisition module and a chassis, and is used to acquire and measure the voltage, current and single cell voltage at the positive and negative terminals of the fuel cell;

[0069] An electronic load, connected to the fuel cell stack, for converting a small-amplitude AC voltage signal of a signal generator into an AC current disturbance, and injecting the disturbance into the fuel cell stack under test;

[0070] an excitation signal unit, connected to the electronic load, and used for generating a function generator of an arbitrary AC waveform; and

[0071] The EIS control unit is connected to the fuel cell control unit, the signal acquisition unit, and the excitation signal unit, and is an industrial computer or controller used to run the control logic, control algorithm and strategy of the current device.

[0072] As a preferred embodiment of the present invention, the multi-objective optimization system comprises:

[0073] An impedance quantitative analysis unit, connected to the EIS control unit, for receiving impedance measurement data from the EIS control unit and analyzing all characteristic peaks in the EIS control unit according to a DRT algorithm;

[0074] An operating condition sensitivity database, connected to the impedance quantitative analysis unit, is used to quantitatively analyze the impedance obtained by testing the operating condition change mode under different electrical densities, and establish the sensitivity and correlation of the operating conditions under each electrical density to each polarization impedance;

[0075] A multi-objective optimization unit connected to the operating condition sensitivity database, used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and to obtain optimal operating conditions under different power densities in combination with the mathematical model of the fuel cell system; and

[0076] The optimal operating condition database is connected to the multi-objective optimization unit and is used to read the optimal battery stack operating parameters of the multi-objective optimization unit to obtain the optimal operating conditions.

[0077] As a preferred embodiment of the present invention, the operating condition sensitivity database is established according to the following steps:

[0078] (1.1) Establish the sensitivity conditions of the fuel cell stack operating conditions;

[0079] (1.2) Select the current density;

[0080] (1.3) Setting target parameters of air, hydrogen and cooling water according to the selected current density;

[0081] (1.4) After the set parameters are stable, set the DC current of the electronic load and start loading;

[0082] (1.5) After the stack output is stable, set the electronic load AC measurement parameters and generate AC current;

[0083] (1.6) Conduct electrochemical impedance spectroscopy test;

[0084] (1.7) Determine whether the current test is completed. If so, proceed to step (1.9), otherwise proceed to step (1.8);

[0085] (1.8) Determine whether the operation under the current electrical secret condition is completed. If completed, return to step (1.2); otherwise, change the operating conditions and return to step (1.3);

[0086] (1.9) Based on the polarization resistance identification of the DRT algorithm, a database of the sensitivity of operating conditions under all current densities is obtained, so as to analyze the sensitivity and correlation of operating conditions to each polarization impedance under each current density.

[0087] As a preferred embodiment of the present invention, the multi-objective optimization unit includes the following processing steps:

[0088] (2.1) Setting the main operating condition parameters, constraints and target parameters to be optimized;

[0089] (2.2) Obtaining the optimal operating conditions under different power densities through the simulation model of the fuel cell system and the particle swarm algorithm;

[0090] (2.3) reading the optimal operating conditions through the fuel cell control unit, and performing actuator calibration test and EIS test in combination with the simulation model of the fuel cell system;

[0091] (2.4) Perform target determination. If the current optimal operating conditions meet the requirements, the processing is terminated directly. Otherwise, the corresponding operating parameters are adjusted according to the sensitivity and correlation of the operating conditions under each electrical density to each impedance resistor, and return to step (2.3).

[0092] As a preferred embodiment of the present invention, the impedance quantitative analysis unit includes the following processing steps:

[0093] (3.1) Analyze the validity of the real and imaginary parts of the electrochemical impedance spectroscopy (EIS);

[0094] (3.2) Determine whether the current residual meets the requirements. If yes, proceed to the next step. Otherwise, do not perform DRT analysis on the current data.

[0095] (3.3) Set the parameters of the improved DRT algorithm;

[0096] (3.4) Calculate the distribution function of the radial basis function and all polarization resistances;

[0097] (3.5) Identify the polarization resistance in each polarization process to complete the DRT quantitative analysis.

[0098] The various components of the technical solution will be further described in detail below. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis includes a fuel cell system, an electrochemical impedance spectroscopy measurement system, and a multi-objective optimization system; wherein the fuel cell system includes a cathode subsystem, an anode subsystem, a thermal management subsystem, and a fuel cell control unit; the electrochemical measurement system includes a signal acquisition unit, an excitation signal unit, an EIS control unit, and an electronic load; the multi-objective optimization system includes an impedance quantitative analysis unit, an impedance-based operating condition sensitivity database, a multi-objective optimization unit, and an optimal operating condition database.

[0099] Fuel cell system: A power generation system consisting of an auxiliary system (BOP, balance of plant) and a fuel cell stack;

[0100] Fuel cell stack: Air and hydrogen produce electrochemical reactions inside to generate electricity and water. The stack is composed of multiple monomers. Monomer voltage is a commonly used parameter to characterize monomer performance, especially the monomer with the lowest voltage is the "short board" of the stack. The lowest monomer voltage determines whether the stack can operate normally.

[0101] Cathode subsystem: controls the humidity, flow, pressure, temperature and other operating parameters of the air, generally including air compressor, humidifier, throttle and sensor;

[0102] Anode subsystem: controls the flow, pressure, temperature and other operating parameters of hydrogen, generally including proportional valve (or hydrogen injector), circulation system (circulation pump or ejector), valve body and sensor;

[0103] Thermal management subsystem: controls the flow, pressure, temperature and other operating parameters of the coolant, generally including a water pump, temperature control valve and sensor;

[0104] Fuel cell control unit: collects sensor feedback signals from gas, fuel, and cooling supply units, and drives actuators to achieve set target parameters, generally referred to as fuel cell controller (FCU);

[0105] Signal acquisition unit: including multi-channel voltage acquisition module, current acquisition module and chassis, used to collect and measure the voltage and current at both ends of the positive and negative electrodes of the fuel cell and the single cell voltage, with the characteristics of high (anti-common mode) voltage, high accuracy, high bandwidth, and expandable multi-channel;

[0106] Excitation signal unit: a function generator that generates arbitrary AC waveforms;

[0107] Electronic load: used to convert the small-amplitude AC voltage signal of the signal generator into AC current disturbance and inject it into the fuel cell stack under test (the excitation signal unit function can also be integrated into the electronic load);

[0108] EIS (Electrochemical Impedance Spectroscopy) Control Unit: An industrial computer or controller that runs the control logic, control algorithms and strategies of the device;

[0109] Impedance quantitative analysis unit: receives impedance measurement data from the EIS control unit, analyzes all characteristic peaks in the EIS according to the DRT algorithm, and then identifies them as the resistance of each polarization process, and obtains the resistance of each polarization process: ohmic resistance Rohm, cathode activation resistance, cathode mass transfer resistance, and anode activation resistance;

[0110] Impedance-based operating condition sensitivity database: quantitatively analyze the impedance obtained by testing the operating condition changes under different power densities, establish the sensitivity and correlation of the operating conditions under each power density to each polarization impedance, and prepare for the calibration of operating parameters based on impedance-based multi-objective optimization;

[0111] Multi-objective optimization unit: According to the operating condition sensitivity database, set the constraints, multi-objective parameters and parameters to be optimized, and combine the mathematical model of the fuel cell system to obtain the optimal operating conditions under different power densities;

[0112] Optimal operating condition database: reads the optimal stack operating parameters of the multi-objective optimization unit.

[0113] The working principle of this technical solution is as follows: First, according to the initial calibration operating parameters of the fuel cell stack, the operating parameter sensitivity test conditions and fault injection test conditions (mainly gas pressure, temperature, humidity, metering ratio, coolant temperature) are formulated, and the fuel cell control unit controls the air, fuel, and cooling subsystems to reach the corresponding set parameters; then, the fuel cell control unit controls the sending of signals to the EIS control unit to set the DC current of the electronic load, and the fuel cell stack begins to output power to the outside; after the stack is in a stable state (single cell voltage is stable), the EIS control unit sends instructions to the excitation signal unit to control the amplitude, frequency point and number of cycles of the AC excitation current superimposed on the DC of the electronic load, and the AC amplitude is generally 3%-15% of the stack load current, so as to realize the injection of AC current excitation of the fuel cell stack. The acquisition unit of the electrochemical impedance system synchronously collects the voltage, single cell voltage, and total current of the fuel cell stack and sends them to the control unit, which processes the collected voltage and current signals online, uses the FFT algorithm to calculate the impedance spectrum of the fuel cell stack in real time, and uploads it to the impedance quantitative analysis unit of the multi-objective optimization system.

[0114] The impedance quantitative analysis unit of the multi-objective optimization system performs quantitative analysis based on the DRT algorithm, resolves all characteristic peaks in the EIS, and then identifies them as the resistance of each polarization process: ohmic resistance Rohm, cathode activation resistance, cathode mass transfer resistance, and anode activation resistance, and establishes an impedance-based operating condition sensitivity database. The multi-objective optimization unit combines the impedance-based operating condition sensitivity database, obtains the optimal operating conditions through the fuel cell system simulation model and particle swarm algorithm according to the set target parameters, and forms an optimal operating condition database. The fuel cell control unit performs calibration tests on actuators such as air compressors, throttles, and valves based on the optimal operating conditions given by the multi-objective optimization system and the fuel cell system simulation model, and then performs EIS tests under various electrical densities. According to the sensitivity and correlation of the operating conditions under each electrical density to each impedance resistor, the operating parameters are adjusted to obtain the optimal operating conditions under the target parameters. At the same time, based on the above database, a good foundation has been laid for the application of online real-time adjustment of operating conditions for vehicle-mounted fuel cell systems.

[0115] The schematic diagram of the stack operating condition sensitivity test and polarization impedance database establishment is as follows: Figure 2 As shown, first, formulate the sensitivity test conditions of the operating conditions: cathode / anode pressure, stoichiometric ratio, relative humidity, coolant temperature, and current density from small to large. For example, first select the low current density of 100mA.cm -2 The sensitivity test of cathode stoichiometry under the condition of 100°C is carried out. The target parameters of air, hydrogen and coolant are set. After the operating parameters are stable for about 3 minutes, the DC current of the electronic load is set to I dc , start loading; after the stack current output is stable, set the electronic load AC i ac Measurement parameters: frequency range 1-10kHz, amplitude range is generally 3%-15% of DC current (adjusted according to current density conditions), load generates AC current i ac , and perform EIS test on the battery stack. At 100mA.cm -2 Under current conditions, according to the specified test conditions, the EIS test is performed under the next operating condition until all operating conditions under the current density complete the EIS test; then the EIS test of the next current density point is performed until all operating conditions under all current densities complete the EIS test, and the operation sensitivity test is completed. All the obtained EIS are subjected to impedance quantitative analysis, and all characteristic peaks in the EIS are analyzed according to the DRT algorithm, and then identified as the resistance of each polarization process: Ohmic resistance R ohm, cathode activation resistance, cathode mass transfer resistance, and anode activation resistance, and obtain the operating condition sensitivity database under all current densities. Next, analyze the sensitivity and correlation between each polarization impedance and the operating conditions to prepare for the design of fault injection conditions. The greater the correlation, the greater the impact of the operating condition on the impedance. Generally, the cathode stoichiometric ratio has the greatest impact on the mass transfer impedance; relative humidity has a greater impact on the ohmic impedance of the membrane; for example, when injecting a fault, adjusting the cathode stoichiometric ratio can easily achieve a gas shortage fault; adjusting the cathode stoichiometric ratio and relative humidity can easily achieve membrane drying and flooding.

[0116] The schematic diagram of impedance-based multi-objective optimization is shown in Figure 3 As shown. Set the operating condition parameters to be optimized, including cathode / anode inlet pressure, cathode humidity, cathode / anode gas stoichiometric ratio, coolant temperature, and hydrogen discharge frequency. Set the constraint condition: minimum cell voltage V min 、Ohmic resistance R ohm , cathode activation resistance, cathode mass transfer resistance, anode activation resistance, according to the mapping relationship between fault characteristics and polarization resistance, set the threshold of each polarization resistance and the minimum single cell voltage to keep the stack in a normal state. Set target parameters, such as generally considering durability, power, and economic indicators. Durability: The total output voltage of the stack V S (Under the same current density, the higher the stack output voltage, the better the performance); Power performance: system net output power (P F , stack output power minus BOP parasitic power), economic hydrogen consumption rate (m H2 , g / s); The optimal operating conditions are obtained through the fuel cell system simulation model and particle swarm algorithm. The fuel cell control unit reads the optimal operating conditions and performs calibration tests on the air compressor, throttle, valve body and other actuators in combination with the fuel cell system simulation model. The target value obtained by the actual calibration is compared with the set target value, and the operating conditions are adjusted according to the sensitivity and correlation of the impedance under each electrical density to the operating conditions.

[0117] The process of using DRT algorithm to obtain polarization resistance of EIS obtained by test is shown in Figure 4 . First, the validity of the real and imaginary parts of the EIS obtained by the test is analyzed. The KK (Kramers-Kronig) calibration can be used to obtain the residual of the real part and the residual of the imaginary part respectively, and the data quality of the EIS is judged according to the residuals; the EIS data that meets the residual requirements are processed by DRT; then the DRT algorithm parameters are set, such as regularization parameter, frequency resolution, etc.; the distribution function of the radial basis function is calculated, and the area of ​​each peak and its corresponding characteristic frequency are calculated according to the distribution function of the radial basis function; finally, the polarization resistance (ohmic resistance, cathode activation resistance, cathode mass transfer resistance, anode activation resistance, etc.) of each polarization process is identified.

[0118] Compared with the prior art, in practical application, this technical solution has made the following technical improvements:

[0119] 1. The multi-objective optimization system provided by the present invention can obtain the optimal operating parameters according to the multi-objective optimization method of the operating condition sensitivity database based on impedance of the target parameters: such as cathode / anode intake pressure, cathode humidity, cathode / anode gas stoichiometric ratio, coolant temperature, and hydrogen discharge frequency. By constraining the polarization resistance and the minimum cell voltage, the stack can be ensured to be in a stable and healthy working state. At the same time, based on the sensitivity and correlation of each polarization resistance under the operating conditions, the system parameter calibration can be realized more quickly, which can greatly shorten the development cycle and save a lot of test calibration time and cost. This also lays a good foundation for the impedance-based adaptive parameter optimization application of the on-board FCU.

[0120] 2. The multi-objective optimization system provided by the present invention can perform all-round sensitivity tests on the stack operating parameters under different current densities, and obtain the polarization resistance (ohmic resistance R ohm、 The sensitivity and correlation of cathode activation resistance, cathode mass transfer resistance, and anode activation resistance to operating parameters are analyzed to establish a comprehensive operating parameter sensitivity database to prepare for operating parameter adjustment and calibration.

[0121] 3. The multi-objective optimization system provided by the present invention utilizes the impedance-based operating condition sensitivity database to more reasonably establish the constraint boundary conditions for multi-objective optimization calculations, including the minimum cell voltage V min 、Ohmic resistance R ohm , cathode activation resistance, cathode mass transfer resistance, anode activation resistance, to avoid membrane dryness, flooding, lack of gas and other faults in advance, so that the stack is in a healthy state, set the target parameters (such as stack output voltage V S 、Net output power of the system P F , Hydrogen consumption rate m h2 ), and obtain the optimal operating conditions through the fuel cell system simulation model and particle swarm algorithm. Based on the above database, the fuel cell control unit can optimize the operating parameters in real time and quickly.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0123] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device.

[0124] A person of ordinary skill in the art may understand that all or part of the steps of the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0125] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0126] In the description of this specification, the description with reference to the terms "an embodiment", "some embodiments", "example", "specific example", or "embodiment" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0127] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0128] The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis of the present invention can obtain the optimal operating parameters according to the multi-objective optimization method of the operating condition sensitivity database based on impedance of the target parameters: such as cathode / anode intake pressure, cathode humidity, cathode / anode gas stoichiometric ratio, coolant temperature, hydrogen discharge frequency, and ensure that the stack is always in a stable and healthy working state by constraining the polarization resistance and the minimum cell voltage. At the same time, based on the sensitivity and correlation of each polarization resistance under the operating conditions, the system parameter calibration can be achieved more quickly, which can greatly shorten the development cycle and save a lot of test calibration time and cost; this also lays a good foundation for the impedance-based adaptive parameter optimization application of the on-board FCU.

[0129] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis, characterized in that: The system comprises: The fuel cell system consists of various auxiliary systems and a fuel cell stack, which is used to provide fuel for power generation; an electrochemical impedance spectroscopy measurement system, connected to the fuel cell system, for collecting the voltage and current of the fuel cell stack and calculating the impedance spectrum of the fuel cell stack through online processing; and The multi-objective optimization system is connected to the fuel cell system and the electrochemical impedance spectroscopy measurement system, and is used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and obtain the optimal operating conditions of the current fuel cell stack under different electrical densities, thereby completing the multi-objective optimization.

2. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 1, characterized in that: The fuel cell system specifically comprises: A cathode subsystem for controlling operating parameters including humidity, flow, pressure, and temperature of the air; an anode subsystem for controlling operating parameters including flow, pressure, and temperature of hydrogen; Thermal management subsystem, used to control operating parameters including flow, pressure, and temperature of the coolant; A fuel cell control unit, which is used to collect sensor feedback signals from the gas, fuel, and cooling supply units, and drive the actuators to achieve the set target parameters; and The fuel cell stack is used to generate electrical energy and water through internal electrochemical reactions.

3. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 2 is characterized in that: The electrochemical impedance spectroscopy measurement system specifically comprises: A signal acquisition unit is connected to the fuel cell stack, and includes a multi-channel voltage acquisition module, a current acquisition module and a chassis, and is used to acquire and measure the voltage, current and single cell voltage at the positive and negative terminals of the fuel cell; An electronic load, connected to the fuel cell stack, for converting a small-amplitude AC voltage signal of a signal generator into an AC current disturbance, and injecting the disturbance into the fuel cell stack under test; an excitation signal unit, connected to the electronic load, and used for generating a function generator of an arbitrary AC waveform; and The EIS control unit is connected to the fuel cell control unit, the signal acquisition unit, and the excitation signal unit, and is an industrial computer or controller used to run the control logic, control algorithm, and strategy of the current device.

4. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 3 is characterized in that: The multi-objective optimization system comprises: An impedance quantitative analysis unit, connected to the EIS control unit, for receiving impedance measurement data from the EIS control unit and analyzing all characteristic peaks in the EIS control unit according to a DRT algorithm; An operating condition sensitivity database, connected to the impedance quantitative analysis unit, is used to quantitatively analyze the impedance obtained by testing the operating condition change mode under different electrical densities, and establish the sensitivity and correlation of the operating conditions under each electrical density to each polarization impedance; A multi-objective optimization unit connected to the operating condition sensitivity database, used to set constraints, multi-objective parameters and parameters to be optimized according to the operating condition sensitivity database, and to obtain optimal operating conditions under different power densities in combination with the mathematical model of the fuel cell system; and The optimal operating condition database is connected to the multi-objective optimization unit and is used to read the optimal battery stack operating parameters of the multi-objective optimization unit to obtain the optimal operating conditions.

5. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 4, characterized in that: The operating condition sensitivity database is established according to the following steps: (1.1) Establish the sensitivity conditions of the fuel cell stack operating conditions; (1.2) Select the current density; (1.3) Setting target parameters of air, hydrogen and cooling water according to the selected current density; (1.4) After the set parameters are stable, set the DC current of the electronic load and start loading; (1.5) After the stack output is stable, set the electronic load AC measurement parameters and generate AC current; (1.6) Conduct electrochemical impedance spectroscopy test; (1.7) Determine whether the current test is completed. If so, proceed to step (1.9), otherwise proceed to step (1.8); (1.8) Determine whether the operation under the current electrical secret condition is completed. If completed, return to step (1.2); otherwise, change the operating conditions and return to step (1.3); (1.9) Based on the polarization resistance identification of the DRT algorithm, a database of the sensitivity of operating conditions under all current densities is obtained, so as to analyze the sensitivity and correlation of operating conditions to each polarization impedance under each current density.

6. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 5, characterized in that: The multi-objective optimization unit includes the following processing steps: (2.1) Setting the main operating condition parameters, constraints and target parameters to be optimized; (2.2) Obtaining the optimal operating conditions under different power densities through the simulation model of the fuel cell system and the particle swarm algorithm; (2.3) reading the optimal operating conditions through the fuel cell control unit, and performing actuator calibration test and EIS test in combination with the simulation model of the fuel cell system; (2.4) Perform target determination. If the current optimal operating conditions meet the requirements, the processing is terminated directly. Otherwise, the corresponding operating parameters are adjusted according to the sensitivity and correlation of the operating conditions under each electrical density to each impedance resistor, and return to step (2.3).

7. The fuel cell multi-objective optimization system based on electrochemical impedance spectroscopy and quantitative analysis according to claim 4, characterized in that: The impedance quantitative analysis unit includes the following processing steps: (3.1) Analyze the validity of the real and imaginary parts of the electrochemical impedance spectroscopy (EIS); (3.2) Determine whether the current residual meets the requirements. If yes, proceed to the next step. Otherwise, do not perform DRT analysis on the current data. (3.3) Set the parameters of the improved DRT algorithm; (3.4) Calculate the distribution function of the radial basis function and all polarization resistances; (3.5) Identify the polarization resistance in each polarization process to complete the DRT quantitative analysis.

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