Temperature control method and system for high-temperature fuel cell HT-PEMFC based on fuzzy ADRC

Abstract

The invention discloses a high temperature fuel cell HT-PEMFC temperature control method and system based on fuzzy ADRC. The method comprises the following steps: constructing an HT-PEMFC system comprising an electric pile, a hydrogen supply pipeline, an air supply pipeline, a hot oil loop, a data acquisition device, a direct current electronic load and a host; according to the operation data of each device in the HT-PEMFC system, a temperature dynamic model of the HT-PEMFC system is established based on a multi-physics field Modelica language platform Dymola; a fuzzy bandwidth adjustment strategy (Fuzzy-ADRC) is designed; the fuzzy bandwidth adjustment strategy Fuzzy-ADRC is used for stable control of the stack temperature under load current disturbance and circulating pump flow disturbance. According to the method, the problem of over-compensation energy consumption of a traditional ADRC under strong disturbance and the problem of high regulation energy consumption of PID are solved, and the method is one of optimal controllers for thermal management of the HT-PEMFC system.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of fuel cell temperature control, and particularly relates to a high-temperature fuel cell HT-PEMFC temperature control method and system based on fuzzy ADRC. BACKGROUND

[0002] Under the current global energy transformation and carbon neutralization target, hydrogen energy as a clean energy carrier has become one of the key paths to cope with climate change and energy security challenges. Proton exchange membrane fuel cell (PEMFC) is one of the power generation equipment using hydrogen energy, which has the advantages of zero pollution, high efficiency, continuous power supply, etc. It has been widely used in vehicles, aircraft and combined heat and power systems as the core power supply unit. Generally, according to the different working temperatures, PEMFC is divided into two categories. Low temperature (LT) PEMFC operates below 80℃, while high temperature (HT) PEMFC operates between 120℃ and 200℃. In order to maintain the high performance output of HT-PEMFC stack, it is necessary to determine the optimal operating parameters such as temperature, load demand, and flow rate of hydrogen and air, especially under transient conditions. HT-PEMFC with phosphoric acid doped polybenzimidazole (PBI) membrane almost does not need water management during normal operation due to the avoidance of "flooding". Therefore, temperature has a decisive influence on the performance, life and efficiency of HT-PEMFC. High temperature operation improves the reaction kinetics and the tolerance of hydrogen purity, but temperature fluctuations can cause accelerated degradation of the polybenzimidazole (PBI) membrane, resulting in loss of phosphoric acid electrolyte and catalyst activity, thereby shortening the life of the fuel cell. Therefore, the cooling system needs to take out the excess heat of the stack to achieve fine thermal management, which helps to stabilize the output performance of the stack and prolong the life of the stack. In order to operate HT-PEMFC under reliable conditions, it is necessary to establish an accurate temperature control model of the cooling system. HT-PEMFC has nonlinear, time-varying, strong coupling and dynamic characteristics, and a large number of random disturbances will occur during operation, such as load current, cooling liquid thermal properties, environmental conditions, etc. A large number of studies have been conducted by technicians to control the temperature of PEMFC, including proportional-integral-derivative (PID) controller in classical control, to fuzzy logic controller (FLC) and active disturbance rejection control (ADRC) in modern control theory. However, the existing technology cannot achieve high-precision modeling of the cooling system of the stack, and under various internal and external disturbances, the temperature control precision of the stack is not high, and it is difficult for the PID controller and the traditional ADRC controller to achieve a balance between high control precision and minimum actuator power consumption. SUMMARY

[0003] Invention purposes: The purpose of the present application is to provide a high-temperature fuel cell HT-PEMFC temperature control method and system based on fuzzy ADRC, to improve the modeling accuracy of HT-PEMFC and enhance the temperature control effect. Compared with traditional PID, Fuzzy-PID and ADRC, the temperature control accuracy is optimal, the average overshoot is minimal, and the regulation speed is the fastest under current disturbance and circulating pump flow disturbance.

[0004] Technical scheme: The HT-PEMFC temperature control method based on fuzzy ADRC of the present application comprises the following steps:

[0005] Step 1, constructing a HT-PEMFC system including a stack, a hydrogen supply pipeline, an air supply pipeline, a hot oil circuit, a data acquisition device, a direct current electronic load and a host computer;

[0006] Step 2, establishing a temperature dynamic model of the HT-PEMFC system based on the operation data of each device in the HT-PEMFC system through Dymola based on the multi-physical field Modelica language platform;

[0007] Step 3, designing a fuzzy bandwidth adjustment strategy Fuzzy-ADRC based on the temperature dynamic model of the HT-PEMFC system;

[0008] Step 4, using the fuzzy bandwidth adjustment strategy Fuzzy-ADRC for stable control of the stack temperature under load current disturbance and circulating pump flow disturbance.

[0009] Further, step 1 is specifically: the HT-PEMFC system includes a stack, a hydrogen supply pipeline, an air supply pipeline, a hot oil circuit, a data acquisition device, a direct current electronic load and a host computer; the hydrogen supply pipeline includes a secondary pressure reducing valve, a flow control valve, a plate heat exchanger and an exhaust electromagnetic valve; the air supply pipeline includes an air compressor, a pressure regulator, a flow control valve, the same plate heat exchanger and a back pressure valve; the hot oil circuit uses triethylene glycol as a heat conducting medium, and the hot oil circuit includes a variable frequency circulating pump, a variable frequency fan coil radiator, a positive temperature coefficient PTC heater, a hot oil flowmeter, a hot oil expansion tank and a three-way bypass valve; the measurement signals in the HT-PEMFC system are integrated and transmitted through I / O devices, and transmitted to the host computer through a local area network, and the wattmeter, the hydrogen leakage detector and the circuit breaker jointly constitute auxiliary measuring instruments and safety protection mechanisms.

[0010] Further, step 2 specifically comprises the following steps:

[0011] Step 2.1, modeling the dynamic temperature of the stack based on the reaction heat of hydrogen and oxygen, the output power of the stack, the enthalpy carried by hydrogen and air, and the heat exchange between the TEG and the fuel cell stack;

[0012] Step 2.2: Model the temperature of the two plate heat exchangers;

[0013] Step 2.3: Model the temperatures of the positive temperature coefficient PTC heater, the variable frequency circulating pump, and the variable frequency fan coil radiator.

[0014] Furthermore, step 2.1 specifically involves: the temperature change of the fuel cell stack being related to the heat of reaction of hydrogen and oxygen, the output power of the fuel cell stack, the enthalpy carried by hydrogen and air, and the heat exchange between the triethylene glycol (TEG) and the fuel cell stack; system variables include the circulation pump duty cycle D. p Duty cycle D of fan coil radiator f PTC heater power P P Power and stack load current I st The dynamic temperature characteristics of the fuel cell stack are as follows:

[0015]

[0016] in, It is the reaction heat power W of the fuel cell stack. and The excess gas at the anode and cathode carries away the heat power W and P. st It is the electric power W of the fuel cell stack. The heat output W;m from the TEG st It is the mass of the fuel cell stack (kg), C p,st It is the average specific heat capacity of the fuel cell stack, J / kg / K. It is the rate of temperature change of the fuel cell stack, in K / s;

[0017] Since the operating temperature is sufficient to completely evaporate the water produced in the reaction, the power of the heat generated by the hydrogen-oxygen reaction is expressed as:

[0018]

[0019] Where ΔH is the enthalpy change (J / kg) when gaseous water is produced by the reaction. It is the hydrogen consumption rate (kg / s) of the hydrogen-oxygen electrochemical reaction inside the fuel cell stack;

[0020] The average temperature of the gases is used to estimate the heat carried by hydrogen and air at the fuel cell stack outlet, assuming that the average specific heat capacity and convective heat transfer coefficient of the reactants are constant. In the anode channel, heat is transferred through hydrogen and outlet steam, while cathode heat is carried out by water vapor, consumed oxygen, and unreacted nitrogen. The convective heat transfer power between the TEG and the internal flow channels of the fuel cell is expressed as:

[0021]

[0022] Among them, T T,st,i and T T,st,oThe TEG inlet and outlet temperatures (°C) of the HT-PEMFC reactor are as follows: and The inlet and outlet temperatures of H2 are in °C and T, respectively. Air,st,i and T Air,st,o These are the air inlet and outlet temperatures of the fuel cell stack, in °C. p，H2 The specific heat capacity of hydrogen is J / kg / K, C. p,vap Average specific heat capacity (J / kg / K) and It is the mass flow rate of water vapor at the cathode inlet and anode outlet (kg / s). It is the cathode oxygen consumption in kg / s. It is the specific heat capacity of oxygen (J / kg / K). and C p,T The flow rate (kg / s) and specific heat capacity (J / kg / K, m³) of TEG are given. N20 C p，N2 It is the specific heat capacity of nitrogen (J / kg / K).

[0023] Further, step 2.2 specifically involves using two plate heat exchangers to preheat the H2 and air entering the fuel cell stack to reduce the thermal shock of the PBI membrane; in the H2 heat exchanger, the cold fluid and the hot fluid are H2 and TEG, respectively; in the air heat exchanger, the cold side receives compressed air from the compressor, while the hot side utilizes exhaust gas from the fuel cell stack, and the two heat exchangers have the same geometric parameters.

[0024] Assuming the convective heat transfer coefficient of the plate heat exchanger is constant, for the H2 heat exchanger, the outlet temperatures of H2 and TEG are expressed as:

[0025]

[0026] Among them, T T,ex,i and T T,ex,o These are the TEG inlet and outlet temperatures (°C) of the hydrogen heat exchanger. The convective heat transfer rate W and ρ between hydrogen and TEG. T and The densities of TEG and hydrogen are kg / m³ 3 V ex The heat exchange region volume is m 3 , and These are the hydrogen inlet and outlet temperatures (°C) of the hydrogen heat exchanger. The specific heat capacity of hydrogen is J / kg / K, S. ex It is the heat exchange area in m. 2 h1 is the convective heat transfer coefficient (W / m). 2 / K, It is the flow rate of TEG in kg / s; This refers to the hydrogen consumption rate (kg / s) of the hydrogen-oxygen electrochemical reaction inside the fuel cell stack; C p,T Specific heat capacity of TEG (J / kg / K) and Represents the rate of temperature change in °C / s;

[0027] In an air heat exchanger, the dynamic process of the fuel cell stack inlet temperature and the heat exchanger outlet temperature during air self-heating is expressed as follows:

[0028]

[0029] Among them, T amb and T Air,st,i These are the inlet and outlet temperatures (°C) of the cold fluid in the air heat exchanger. The convective heat transfer rate W, ρ during the air self-heating process Air It is the density of air (kg / m³) 3 V ex The heat exchange region volume is m 3 T Air,ex,o and T Air,st,o These are the inlet and outlet temperatures (°C) of the air heat exchanger's heat flow. p,Air It is the specific heat capacity of air (J / kg / K, S). ex It is the heat exchange area in m. 2 h2 is the convective heat transfer coefficient (W / m). 2 / K;m Air，st，i and m Air，st，o It is the air inlet and outlet mass flow rate of the fuel cell stack (kg / s); and It is the rate of change of air temperature in °C / s;

[0030] The heat transfer coefficients of the four convective heat transfer processes are estimated using the following equations:

[0031]

[0032] Where Nu is the Nusselt number, Re and Pr represent the Reynolds number and Prandtl number respectively when the fluid is heated, n = 0.3, otherwise n = 0.4 according to the Dittus-Boelter formula, λ is the thermal conductivity of the fluid, d is the hydraulic diameter of the plate heat exchanger, and L w φ is the effective plate width, b is the average channel clearance, and φ is the amplification factor.

[0033] Furthermore, step 2.3 specifically states that the heating power of the positive temperature coefficient PTC heater varies with the input voltage and its own temperature.

[0034]

[0035] Among them, P PIt is the heating power in W and N. P It refers to the number of heating elements, U P It is the heater voltage V, η P It is electrical efficiency, R P It is the heater resistance in Ω, ρ P The resistivity of the heating plate, Ω·m, L, is affected by temperature. P and A P Let m be the length of the heating element and m be the contact area. 2 The power of a PTC heater increases with increasing temperature, reaching its maximum at the Curie point;

[0036] During the dynamic process, the TEG outlet temperature of the PTC heater is expressed as:

[0037]

[0038] in, It is the flow rate of TEG (kg / s); T T,f,o The TGE output temperature of the fan coil unit radiator is °C, V. P The volume (m) of the pipe to which the heater is attached. 3 T T,st,i and The TEG inlet temperature (°C) and rate of change (°C / s) of the HT-PEMFC reactor are shown.

[0039] The flow rate and speed of the variable frequency circulating pump are related, and the speed depends on the duty cycle of the pulse width modulation (PWM) signal. The frequency of the circulating pump is adjusted using a PWM wave, and frequency conversion control is performed using the V / F method, meaning the operating voltage V of the equipment has an approximately linear relationship with the operating frequency F (V∝F). Experimental data are fitted using a nonlinear least squares method.

[0040]

[0041] in, and Indicates head and maximum head, ξ p It is the pipe resistance coefficient, ρ T It is the density of TEG, D p It is the duty cycle of the circulating pump PWM signal, ranging from [24%, 100%], β 01 ,β 02 and β 03 These are the fitting coefficients related to the circulating pump;

[0042] The dynamic temperature of the TEG outlet of the variable frequency fan coil radiator is expressed as follows:

[0043]

[0044] Among them, h fConvection heat transfer coefficient W / m 2 / K,S f It is the heat exchange area in m. 2 V f The volume of the coil is m3; T T,ex,o The TEG outlet temperature of the hydrogen heat exchanger is °C, T T,f,o and The TGE output temperature (°C) and temperature change rate (°C / s) of the fan coil unit radiator;

[0045] A fan fixed to the coil is used to cool the TEG inside the tube. The PWM duty cycle of the fan and the circulating pump affects the convective heat transfer process between the cold air and the TEG. When the TEG inlet temperature is constant, the total heat dissipation increases with the increase of the fan speed; while the heat dissipation performance is improved by the increase of the TEG flow velocity inside the coil when the circulating pump speed increases. The convective heat transfer coefficient of the heat exchange process is affected by D. f and D p The coupling effect is considered; a semi-empirical formula based on data fitting is used to estimate the overall convective heat transfer coefficient of the fan coil unit.

[0046]

[0047] Among them, P f,ra η is the rated power of the fan. f η is the electrical efficiency of the wind turbine. me For mechanical efficiency, a, b, c are fitting coefficients, and γ is the coefficient of performance. i It is a set of fitting coefficients based on experimental data, and the mass flow rate of cooling air. and D f A nonlinear relationship exists between them, which is obtained by identifying steady-state experimental data using nonlinear least squares; the fan does not work at low duty cycles, therefore D f It changed from 28% to 100%.

[0048] Furthermore, step 3 specifically involves the following: The state-space description of the HT-PEMFC temperature control system is as follows:

[0049]

[0050] Where f(T) st Let w(t),t) represent the combined effect of internal and external disturbances on the stack temperature, b be the gain of the control input, and u(t) be the control input of the system. For a continuous system, a second-order ESO is constructed to track the target temperature and estimate the uncertainty disturbance. The ESO takes the unknown model state and internal and external disturbances in the system as the total disturbance signal and extends it into a new state. By collecting and analyzing the system output, the total disturbance of the system is estimated in real time and dynamically compensated.

[0051]

[0052] Where e is the deviation between the estimated state z1 and the measured state y of the ESO, z2 is the estimate of the total disturbance, and β1 and β2 are the parameters of the ESO, which are tuned using the bandwidth method, with β1 = 2ω. o , ω o It is the observer bandwidth;

[0053] By using the estimated state z1 of the ESO and the reference state T of the system ref The control quantity is generated by feedback from a nonlinear function;

[0054]

[0055] in It is the estimation error, u is the control law based on u0 with disturbance compensation, ω c For controller bandwidth;

[0056] There are three key parameters in the ADRC controller [b, ω] o ,ω c The parameter b is selected based on experimental experience; fuzzy logic control is used to adjust the parameter ω. o and ω c It includes three processes: input fuzzification, fuzzy inference, and defuzzification.

[0057] Furthermore, the three processes of input fuzzification, fuzzy inference, and defuzzification are as follows: The input-output fuzzification process involves dividing fuzzy variables based on the error and its changes. Three levels of fuzzy linguistic values, "N", "Z", and "P", are defined as negative, zero, and positive, respectively; the output quantity ω... o and k p,1 The fuzzy linguistic values ​​are divided into three levels: "S", "M", and "L" representing small, medium, and large, respectively; the fundamental universe of discourse for the temperature deviation of the fuel cell stack is [-30, 30], and the fundamental universe of discourse for the variation of the temperature deviation is [-10, 10]; the observer bandwidth ω o The fundamental universe of discourse is [1,10], and the controller bandwidth ω c The fundamental universe of discourse is [10, 100], and the ω of ADRC o and ω c The fuzzy rules are obtained based on the behavioral analysis of the PEMFC system under different temperature errors and change inerrors; the fuzzy rule base adopts language rules defined by IF-THEN conditions.

[0058] Furthermore, the fuzzy rule base uses IF-THEN condition-defined language rules specifically as follows: when the temperature error is "N" and the error change is "N", the ADRC observer bandwidth and controller bandwidth are set to "L" and "L", respectively, representing an increase of ω when the error and error change are negative. o and ω c It rapidly changes the temperature trend, pulling the current temperature closer to the reference state.

[0059] Table 1. ESO Parameter Fuzzy Inference Rules Table.

[0060]

[0061] Subsequently, fuzzy synthesis is performed according to the set rules. The input and output are weighted using triangular membership functions and rectangular membership functions respectively. The final step of FLC is defuzzification, which converts the fuzzy inference result into a clear numerical output. Since the inference process involves multiple rules, the result is often a fuzzy set. Therefore, a method is needed to "interpret" this fuzzy result into a specific control quantity. The goal of defuzzification is to extract the most representative value from the fuzzy control action for use in the actual control system. In practical applications, common defuzzification methods include the maximum membership value averaging method, the center position of the maximum membership value, the geometric center of the fuzzy graph area, and the centroid of the entire fuzzy region. This invention uses the centroid method of the entire fuzzy region for defuzzification.

[0062] Leveraging the advantages of Modelica in multi-domain modeling, an HT-PEMFC system was built based on the aforementioned content, and the FMU (Fuel Module Unit) was exported to Simulink using FMI. This FMU serves as the controlled object, and a temperature controller was designed in Simulink to maintain the temperature of the HT-PEMFC. The controller provides adaptive ADRC bandwidth parameters and fan duty cycle output based on the current temperature state and reference values. External disturbance signals, including the fuel cell loading current and TEG flow rate, were constructed in Simulink to test the stability of the controller and FMU model. The duty cycle signal and the external disturbance signal were jointly input into the FMU file, and dynamic simulation was performed using the solver built into the Dymola platform, providing the system's feedback state and completing the entire co-simulation process.

[0063] This invention also discloses a high-temperature fuel cell (HT-PEMFC) temperature control system based on fuzzy ADRC, comprising a fuel cell stack, a hydrogen supply pipeline, an air supply pipeline, a hot oil circuit, a data acquisition device, a DC electronic load, and a main unit. The hydrogen supply pipeline includes a secondary pressure reducing valve, a flow control valve, a plate heat exchanger, and an exhaust solenoid valve. The air supply pipeline includes an air compressor, a pressure regulator, a flow control valve, a plate heat exchanger, and a back pressure valve. The hot oil circuit uses triethylene glycol as the heat transfer medium and includes a variable frequency circulating pump, a variable frequency fan coil radiator, a positive temperature coefficient (PTC) heater, a hot oil flow meter, a hot oil expansion tank, and a three-way bypass valve. Measurement signals in the HT-PEMFC system are integrated and transmitted through I / O devices, and transmitted to the main unit via a local area network. A wattmeter, a hydrogen leak detector, and a circuit breaker together constitute auxiliary measuring instruments and a safety protection mechanism.

[0064] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0065] (1) Compared with traditional PID, fuzzy PID, and ADRC controllers, Fuzzy-ADRC temperature control has higher control accuracy, faster adjustment speed, and smaller overshoot, which can better meet control requirements. Compared with traditional PID controllers, Fuzzy-ADRC improved overshoot suppression by 66.95% and adjustment speed by 87.44% in the fuel cell setpoint temperature tracking experiment. In the current disturbance experiment, the control performance of Fuzzy-ADRC completely surpassed that of PID and Fuzzy-PID, and compared with ADRC, its overshoot suppression effect was improved by 5.23% and adjustment speed by 2.84%. Finally, in the TEG flow disturbance experiment, the control performance of both ADRC controllers remained superior. Compared with ADRC controllers, Fuzzy-ADRC improved overshoot suppression by 16.93% and adjustment speed by 32.62%;

[0066] (2) Considering the large thermal inertia of the HT-PEMFC system, the average power consumption of the fan using the Fuzzy-ADRC controller is lower than that of the traditional PID and Fuzzy-PID controllers in various disturbance scenarios. The fuzzy bandwidth strategy does not increase the control cost of ADRC and effectively reduces the high regulation power consumption problem of PID controllers. Attached Figure Description

[0067] Figure 1 This is the HT-PEMFC system designed in the embodiments of the present invention.

[0068] Figure 2 This is a control principle diagram of the ADRC controller designed in this invention.

[0069] Figure 3 This refers to the membership relationship of the fuzzy logic bandwidth design method used in this invention.

[0070] Figure 4 This is a schematic diagram of the joint simulation control experiment in this invention.

[0071] Figure 5 This invention demonstrates the temperature maintenance effect of Fuzzy-ADRC in temperature tracking experiments.

[0072] Figure 6 This invention relates to the temperature maintenance effect of Fuzzy-ADRC under current disturbance.

[0073] Figure 7 The Fuzzy-ADRC in this invention is in D p Temperature maintenance effect under disturbance.

[0074] Figure 8 This is a comparison of the power consumption of the actuators under the four control strategies in this invention. Detailed Implementation

[0075] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0076] To highlight the control performance of the Fuzzy-ADRC controller proposed in this invention, a 1kW HT-PEMFC fuel cell stack was selected, and the hydrogen flow rate was set to 5×10⁻⁶. -5 The hydrogen-to-air ratio was 1.25:2, and the reactant pressure was 1.05 atm. To verify the effectiveness of the proposed simulation model and control strategy, experimental simulations were conducted based on the configuration of an actual HT-PEMFC system. The system structure is shown below. Figure 1 The system model was built using Modelica in Dymola 2023, and the fuzzy active disturbance rejection controller was implemented in MATLAB / Simulink R2024b. A co-simulation framework was established using Function Mock-up Units (FMUs) to achieve dynamic interaction between the control and plant models. Figure 2 and Figure 4The 1kW HT-PEMFC battery pack is manufactured by SENOHERS. The PHE contains 20 copper heating plates. The circulation pump operates at 220VAC, 50Hz, with a rated power of 180W. Its maximum head is 22m, rated flow rate is 30L / min, and rated speed is 2850r / min. The fan coil unit operates at 220VAC, 50Hz, with a rated power of 100W and a rated speed of 3000r / min. It is equipped with a copper coil for efficient heat exchange. The PTC heater consists of six aluminum heating plates, each with a rated power of 200W.

[0077] Mathematical modeling is performed for each component:

[0078] The temperature variation of the HT-PEMFC stack is mainly related to the heat of reaction of hydrogen and oxygen, the output power of the stack, the enthalpy carried by hydrogen and air, and the heat exchange between the TEG and the fuel cell stack. System variables include the circulation pump duty cycle (D). p ) and the duty cycle (D) of fan coil radiators f PTC heater power (P) P The power and stack load current (I) st The dynamic temperature characteristics of a fuel cell stack can be expressed as follows:

[0079]

[0080] in, It is the reaction heat power (W) of the fuel cell stack. and The excess gas at the anode and cathode carries away the heat power (W), P. st It is the electric power (W) of the fuel cell stack. It is the heat output (W) carried out by the TEG.

[0081] Considering that the operating temperature is sufficient to completely evaporate the water produced in the reaction, the latent heat of vaporization of the produced water cannot be ignored. The power of the hydrogen-oxygen reaction in generating heat is expressed as:

[0082]

[0083] Where ΔH is the enthalpy change (J / kg) when gaseous water is produced by the reaction.

[0084] The stoichiometric ratio of the reactants results in the removal of excess heat. Since the composition and temperature variations of hydrogen and air are not significant, the average temperature of the gases is used to estimate the heat carried by the hydrogen and air at the fuel cell stack outlet. Furthermore, it is assumed that the average specific heat capacity and convective heat transfer coefficient of the reactants are constant.

[0085] In the anode conduit, heat is transferred via hydrogen and outlet steam. Cathode heat is transferred via water vapor, consumed oxygen, and unreacted nitrogen. The convective heat transfer power between the TEG and the internal flow channels of the fuel cell can be expressed as:

[0086]

[0087] Where T T,st,i and T T,st,o These are the TEG inlet and outlet temperatures (°C) of the HT-PEMFC reactor. and T represents the temperature (°C) at the H2 inlet and outlet. Air,st,i and T Air,st,o These are the air inlet and outlet temperatures (°C) of the fuel cell stack.

[0088] The plate heat exchanger described uses two plate heat exchangers to preheat the H2 and air entering the fuel cell stack to minimize thermal shock to the PBI membrane. In the H2 heat exchanger, the cold fluid is H2 and the hot fluid is TEG, respectively. In the air heat exchanger, compressed air from the compressor is supplied to the cold side, while the hot side utilizes exhaust gas from the fuel cell stack. The geometric parameters of the two heat exchangers are identical.

[0089] In this study, it is assumed that the convective heat transfer coefficient of the plate heat exchanger is constant, and the temperature rise and heat loss of the plate heat exchanger are neglected. For the H2 heat exchanger, the outlet temperatures of H2 and TEG are expressed as:

[0090]

[0091] In an air heat exchanger, the dynamic process of the fuel cell stack inlet temperature and the heat exchanger outlet temperature during air self-heating can be expressed as:

[0092]

[0093] The heat transfer coefficients of the above four convective heat transfer processes can be estimated using the following equations:

[0094]

[0095] Where Nu is the Nusselt number, Re and Pr represent the Reynolds number and Prandtl number when the fluid is heated, respectively, n = 0.3, otherwise n = 0.4 according to the Dittus-Boelter formula, λ is the thermal conductivity of the fluid, d is the hydraulic diameter of the plate heat exchanger (mm), Lw is the effective plate width (mm), b is the average channel clearance (mm), and φ is the amplification factor.

[0096] The PTC heater described in this invention has an aluminum PTC heating element attached to the outer wall of the pipe, so only a portion of the heat is absorbed by the internal TEG, and its heating power varies with the input voltage and its own temperature.

[0097]

[0098] Where ρ P This refers to the resistivity (Ω·m) of the heating plate, which is affected by temperature. For example... Figure 3 As shown, the power of the PTC heater increases with increasing temperature, reaching its maximum at the Curie point.

[0099] During the dynamic process, the TEG outlet temperature of the heater can be expressed as:

[0100]

[0101] Where T T,f,o It is the TGE output temperature (°C) of the fan coil unit radiator.

[0102] In the HT-PEMFC system, the circulating pump is a crucial component, controlling the flow rate of heat transfer oil in the main circuit. The flow rate of the circulating pump is primarily related to its rotational speed, which in turn depends on the duty cycle of the pulse width modulation (PWM) signal. This invention employs a PWM wave to adjust the frequency of the circulating pump, using the V / F method for frequency conversion control, where the operating voltage V of the equipment has an approximately linear relationship with the operating frequency F: V∝F.

[0103] At 1 atm pressure, increasing the temperature from 298 K to 450 K causes a gradual decrease in the viscosity and density of TEG. When D p When the TEG temperature is 1, the circulating pump operates at full speed. As the TEG temperature increases, the TEG flow rate... Gradually increasing, leading to and D p There is a non-linear relationship between them. When the TEG temperature exceeds 119.1℃, the TEG mass flow rate almost no longer changes with temperature. Therefore, it can be considered that the TEG mass flow rate at high temperatures only changes with D. p Changes. Subsequent experiments will set 120℃ as the lower limit of the fuel cell stack's temperature to reduce the impact of TEG flow fluctuations on the controller's performance. Different D p Below, the outlet velocity and head variation curves of the circulating pump are calculated using a nonlinear least squares method. The TEG mass flow rate at the circulating pump outlet is related to the duty cycle of the input PWM signal; experimental data are fitted using a nonlinear least squares method.

[0105]

[0106] in and Indicates head and maximum head, ξ p It is the pipe resistance coefficient, ρ T It is the density of TEG, D p The range is between [24%, 100%].

[0107] The TEG temperature at the outlet of the aforementioned fan coil unit radiator is related to the forced convection heat transfer on the coil. The TEG outlet temperature at the fan coil unit radiator can be dynamically expressed as:

[0108]

[0109] A fan mounted on the coil is used to cool the TEG inside the tube. The PWM duty cycle of the fan and the circulating pump affects the convective heat transfer process between the cool air and the TEG. When the TEG inlet temperature is constant, the total heat dissipation increases as the fan speed increases. Conversely, as the circulating pump speed increases, the increased flow velocity of the TEG within the coil also contributes to heat dissipation. Therefore, the convective heat transfer coefficient of this heat exchange process is affected by D... f and D p The coupling effect. This invention uses data fitting and semi-empirical formulas from existing research to estimate the overall convective heat transfer coefficient of fan coil units:

[0110]

[0111] Where P f,ra η is the rated power of the fan. f η is the electrical efficiency of the wind turbine. me For mechanical efficiency, a, b, and c are fitting coefficients. The mass flow rate of the cooling air is... and D f A nonlinear relationship exists between them, which was obtained by nonlinear least squares identification of steady-state experimental data. The fan does not operate at low duty cycles, therefore D... f It changed from 28% to 100%.

[0112] The control parameters of the proposed Fuzzy-ADRC controller are shown in Table 2. PID, ADRC, and fuzzy PID controllers were selected as comparison control strategies. The parameters of the PID and ADRC controllers are shown in Table 2. For the ADRC controller, the control gain was selected based on empirical values. Furthermore, the fuzzy logic control design process, by combining the fuzzy tuning mechanism of PID parameters kp, ki, and kd, developed a fuzzy PID controller.

[0113] Table 2 Controller Parameter Configuration

[0114]

[0115] To further quantify the effectiveness of the fuzzy active disturbance rejection control strategy, this invention employs the integral of squared error (ISE) and the time-weighted integral of absolute error (ITAE), defined as follows:

[0116]

[0117] The ISE index represents the cumulative squared error of the system state, emphasizing overshoot during the control process. ITAE represents the time-weighted cumulative absolute error, highlighting the impact of controller response speed. Smaller values ​​of ISE and ITAE correspond to reduced overshoot and faster settling time, respectively, indicating better overall control performance.

[0118] The present invention uses three operating conditions to test the performance of the proposed Fuzzy-ADRC controller.

[0119] First, the initial test involved tracking experiments at different temperature settings. The tracking performance of the four controllers was compared at different temperature setpoints. When the HT-PEMFC system reached its steady-state operating point, the reactor temperature was 173.17°C, the reactor current was 60A, the circulating pump duty cycle was 1, and the blower system was off. At t = 5000 seconds, the reactor temperature reference value was set to 160°C, and then decreased by 20°C every 500 seconds. At t = 6500 seconds, the setpoint increased to 140°C. Considering the relatively slow heating process when the PTC heater was off, the temperature setpoint was reset to 160°C at t = 7500 seconds. Figure 5 The control performance of four controllers during the five temperature setpoint steps is shown. During the first setpoint temperature step, the PID controller exhibited a temperature overshoot of approximately 1°C, followed by a steady-state error of approximately 0.5°C. The Fuzzy-PID controller did not exhibit overshoot, and its steady-state error was approximately 0.4°C. In contrast, the ADRC and Fuzzy-ADRC controllers were able to maintain the fuel cell stack temperature around 160°C with a steady-state error of approximately 0.3°C, but the steady-state error and overshoot of the Fuzzy-ADRC controller were lower than those of the ADRC controller for most of the time. During the two subsequent temperature decrease steps, both the PID and Fuzzy-PID controllers exhibited unavoidable steady-state errors. During the two subsequent temperature increase steps, the PID and Fuzzy-PID controllers failed to adjust the fuel cell stack temperature promptly when the temperature returned to the setpoint. During the two subsequent temperature increase steps, the control performance of the ADRC and Fuzzy-ADRC controllers was comparable and both outperformed the PID and Fuzzy-PID controllers.

[0120] For the second test condition, the applied current is one of the key factors affecting the fuel cell temperature. This section discusses the response of four controllers under different current steps. The initial value of the applied current is 60A. When the stack temperature reaches a steady state of 160℃, the current is changed every 1000s to compare the temperature control performance of the four different controllers to current disturbances. Figure 6 The effect of time-interval current steps on the fuel cell stack temperature is described. It can be observed that the PID controller and the Fuzzy-PID controller exhibit sawtooth-shaped temperature changes and significant steady-state errors. In contrast, the steady-state errors of the ADRC controller and the Fuzzy-ADRC controller are controlled within ±0.2℃. At t=9000s, when the current suddenly decreases from 100A to 60A, the heat generation of the fuel cell stack drops abruptly, and the stack temperature decreases rapidly. For the PID controller, the response is relatively slow, resulting in a maximum overshoot of 0.4℃, after which the stack temperature gradually recovers to 159.8℃. The Fuzzy-PID controller has a faster response but exhibits a steady-state error of approximately 0.03℃. The Fuzzy-ADRC controller demonstrates superior steady-state error and maximum overshoot compared to the other controllers.

[0121] For the third test condition, to evaluate the impact of different TEG flow rates on stack heat dissipation, the control effects of each controller were tested under varying duty cycle disturbances of the circulating pump. At t = 5000s, the set temperature reference value was 160℃. Subsequently, the duty cycle of the circulating pump was changed every 1000s, i.e., under different TEG flow rates, to observe the anti-interference effect of each controller. (See...) Figure 7 .

[0122] When D p Under undisturbed conditions, all controllers stabilized the temperature between 160°C and 161°C. The PID and Fuzzy-PID controllers maintained slightly higher temperatures (approximately 160.5°C to 161°C), exhibiting minor steady-state errors. The ADRC and Fuzzy-ADRC controllers maintained temperatures closer to 160°C, with smaller steady-state errors, smoother curves, and stronger noise immunity. For the first disturbance, the duty cycle decreased from 1 to 0.9, and the reduced TEG flow caused a brief temperature rise, which was subsequently suppressed by the controllers. The PID controller exhibited a significantly larger overshoot in the Fuzzy-PID controller, with a very slow recovery speed. The ADRC controller showed smaller fluctuations, approximately ±0.1°C. The Fuzzy-ADRC controller showed almost no significant fluctuations. For the second disturbance... p Despite disturbances, the Fuzzy-ADRC controller still performs better. For the third, largest D... p The ADRC controller exhibited significant fluctuations due to disturbances, approximately ±0.2℃. In contrast, the Fuzzy-ADRC controller showed almost no fluctuations, achieving temperature stability around 8500 seconds. When D...p When the temperature rose back to 1, the PID and Fuzzy-PID controllers experienced a second temperature fluctuation and overshoot rebound, after which their steady-state error was approximately 0.1℃. The ADRC and Fuzzy-ADRC controllers achieved a smooth temperature recovery to 160℃, with accurate disturbance compensation and strong steady-state recovery capability.

[0123] Finally, a comparative test was conducted on another performance indicator of the different controllers. The controller outputs control signals to the fan coil unit actuator, and its power consumption reflects the parasitic power within the HT-PEMFC system. Based on the experiments conducted above, Figure 8 This paper compares the power consumption of fan coil units under different controllers in various disturbance scenarios. In Case 1, under temperature tracking, both ADRC control strategies reduce energy consumption by minimizing unnecessary adjustments, achieving approximately 30% energy savings compared to the PID controller. In Case 2, the PID and Fuzzy-PID controllers exhibit moderate adjustment intensity under load current disturbances due to large temperature fluctuations, resulting in lower fan energy consumption. Under strong load current disturbances, ADRC, in its attempt to quickly suppress temperature fluctuations, outputs an excessively large control signal amplitude, leading to a surge in energy consumption due to overcompensation. The fuzzy bandwidth method alleviates the overcompensation of ADRC, achieving higher energy consumption than PID but significantly lower than ADRC. In Case 3, fuzzy logic optimizes the control intensity of ADRC in real time, avoiding the undercompensation phenomenon of PID or the overcompensation phenomenon of ADRC, thus achieving on-demand adjustment.

[0124] Fuzzy-ADRC optimizes the observer and controller bandwidth through fuzzy adaptive optimization, achieving the lowest average energy consumption and the most stable control effect in three disturbance scenarios. It solves the "overcompensation energy consumption problem" of traditional ADRC under strong disturbances and the "high regulation energy consumption problem" of PID, making it one of the optimal controller choices for thermal management of HT-PEMFC systems.

Claims

1. A temperature control method for high-temperature fuel cells (HT-PEMFC) based on fuzzy ADRC, characterized in that, Includes the following steps: Step 1: Construct an HT-PEMFC system including a fuel cell stack, hydrogen supply pipeline, air supply pipeline, hot oil circuit, data acquisition device, DC electronic load and host; Step 2: Based on the operating data of each device in the HT-PEMFC system, establish a dynamic temperature model of the HT-PEMFC system using the Dymola multiphysics Modelica language platform; Step 3: Based on the temperature dynamic model of the HT-PEMFC system, design the fuzzy bandwidth adjustment strategy Fuzzy-ADRC; Step 4: Apply the fuzzy bandwidth adjustment strategy (Fuzzy-ADRC) to stabilize the stack temperature under load current disturbances and circulating pump flow disturbances.

2. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 1, characterized in that, Step 1 specifically includes: The HT-PEMFC system includes a fuel cell stack, hydrogen supply pipeline, air supply pipeline, hot oil circuit, data acquisition device, DC electronic load and main unit; the hydrogen supply pipeline includes a secondary pressure reducing valve, flow control valve, plate heat exchanger and exhaust solenoid valve; The air supply line includes an air compressor, pressure regulator, flow control valve, identical plate heat exchanger, and back pressure valve; the hot oil circuit uses triethylene glycol as the heat transfer medium and includes a variable frequency circulating pump, variable frequency fan coil radiator, positive temperature coefficient PTC heater, hot oil flow meter, hot oil expansion tank, and three-way bypass valve; the measurement signals in the HT-PEMFC system are integrated and transmitted through I / O devices, and transmitted to the host via a local area network. A wattmeter, hydrogen leak detector, and circuit breaker together constitute auxiliary measuring instruments and safety protection mechanisms.

3. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 2, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Model the dynamic temperature of the fuel cell stack based on the heat of reaction of hydrogen and oxygen, the output power of the fuel cell stack, the enthalpy carried by hydrogen and air, and the heat exchange between the TEG and the fuel cell stack. Step 2.2: Model the temperature of the two plate heat exchangers; Step 2.3: Model the temperatures of the positive temperature coefficient PTC heater, the variable frequency circulating pump, and the variable frequency fan coil radiator.

4. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 3, characterized in that, Step 2.1 specifically refers to the following: the temperature change of the fuel cell stack is related to the heat of reaction of hydrogen and oxygen, the output power of the fuel cell stack, the enthalpy carried by hydrogen and air, and the heat exchange between triethylene glycol (TEG) and the fuel cell stack. System variables include the circulating pump duty cycle D. p Duty cycle D of fan coil radiator f PTC heater power P P Power and stack load current I st The dynamic temperature characteristics of the fuel cell stack are as follows: in, It is the reaction heat power W of the fuel cell stack. and The excess gas at the anode and cathode carries away the heat power W and P. st It is the electric power W of the fuel cell stack. The heat output W;m from the TEG st It is the mass of the fuel cell stack (kg), C p,st It is the average specific heat capacity of the fuel cell stack, J / kg / K. It is the rate of temperature change of the fuel cell stack, in K / s; Since the operating temperature is sufficient to completely evaporate the water produced in the reaction, the power of the heat generated by the hydrogen-oxygen reaction is expressed as: Where ΔH is the enthalpy change (J / kg) when gaseous water is produced by the reaction. It is the hydrogen consumption rate (kg / s) of the hydrogen-oxygen electrochemical reaction inside the fuel cell stack; The average temperature of the gases is used to estimate the heat carried by hydrogen and air at the fuel cell stack outlet, assuming that the average specific heat capacity and convective heat transfer coefficient of the reactants are constant. In the anode channel, heat is transferred through hydrogen and outlet steam, while cathode heat is carried out by water vapor, consumed oxygen, and unreacted nitrogen. The convective heat transfer power between the TEG and the internal flow channels of the fuel cell is expressed as: Among them, T T,st,i and T T,st,o The TEG inlet and outlet temperatures (°C) of the HT-PEMFC reactor are as follows: and The inlet and outlet temperatures of H2 are in °C and T, respectively. Air,st,i and T Air,st,o These are the air inlet and outlet temperatures of the fuel cell stack, in °C. p，H2 The specific heat capacity of hydrogen is J / kg / K, C. p,vap Average specific heat capacity (J / kg / K) and It is the mass flow rate of water vapor at the cathode inlet and anode outlet (kg / s). It is the cathode oxygen consumption in kg / s. It is the specific heat capacity of oxygen (J / kg / K). and C p,T These are the flow rate (kg / s) and specific heat capacity (J / kg / K) of TEG. It is the mass flow rate of cathode nitrogen (kg / s), C p，N2 It is the specific heat capacity of nitrogen (J / kg / K).

5. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 3, characterized in that, Step 2.2 specifically involves using two plate heat exchangers to preheat the H2 and air entering the fuel cell stack to reduce the thermal shock of the PBI membrane. In the H2 heat exchanger, the cold fluid and the hot fluid are H2 and TEG, respectively. In the air heat exchanger, the cold side receives compressed air from the compressor, while the hot side utilizes exhaust gas from the fuel cell stack. The two heat exchangers have the same geometric parameters. Assuming the convective heat transfer coefficient of the plate heat exchanger is constant, for the H2 heat exchanger, the outlet temperatures of H2 and TEG are expressed as: Among them, T T,ex,i and T T,ex,o These are the TEG inlet and outlet temperatures (°C) of the hydrogen heat exchanger. The convective heat transfer rate W and ρ between hydrogen and TEG. T and The densities of TEG and hydrogen are kg / m³ 3 V ex The heat exchange region volume is m 3 , and These are the hydrogen inlet and outlet temperatures (°C) of the hydrogen heat exchanger. The specific heat capacity of hydrogen is J / kg / K, S. ex It is the heat exchange area in m. 2 h1 is the convective heat transfer coefficient (W / m). 2 / K, It is the flow rate of TEG in kg / s; This refers to the hydrogen consumption rate (kg / s) of the hydrogen-oxygen electrochemical reaction inside the fuel cell stack; C p, T Specific heat capacity of TEG (J / kg / K); In an air heat exchanger, the dynamic process of the fuel cell stack inlet temperature and the heat exchanger outlet temperature during air self-heating is expressed as follows: Among them, T amb and T Air,st,i These are the inlet and outlet temperatures (°C) of the cold fluid in the air heat exchanger. The convective heat transfer rate W, ρ during the air self-heating process Air It is the density of air (kg / m³). 3 V ex The heat exchange region volume is m 3 T Air,ex,o and T Air,st,o These are the inlet and outlet temperatures (°C) of the air heat exchanger's heat flow. p,Air It is the specific heat capacity of air (J / kg / K, S). ex It is the heat exchange area in m. 2 h2 is the convective heat transfer coefficient (W / m). 2 / K;m Air，st，i and m Air，st，o It is the air inlet and outlet mass flow rate of the fuel cell stack (kg / s); and It is the rate of change of air temperature in °C / s; The heat transfer coefficients of the four convective heat transfer processes are estimated using the following equations: Where Nu is the Nusselt number, Re and Pr represent the Reynolds number and Prandtl number respectively when the fluid is heated, n = 0.3, otherwise n = 0.4 according to the Dittus-Boelter formula, λ is the thermal conductivity of the fluid, d is the hydraulic diameter of the plate heat exchanger, and L w φ is the effective plate width, b is the average channel clearance, and φ is the amplification factor.

6. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 3, characterized in that, Step 2.3 specifically states that the heating power of a positive temperature coefficient PTC heater varies with the input voltage and its own temperature. Among them, P P It is the heating power in W and N. P It refers to the number of heating elements, U P It is the heater voltage V, η P It is electrical efficiency, R P It is the heater resistance in Ω, ρ P The resistivity of the heating plate, Ω·m, L, is affected by temperature. P and A P Let m be the length of the heating element and m be the contact area. 2 The power of a PTC heater increases with increasing temperature, reaching its maximum at the Curie point; During the dynamic process, the TEG outlet temperature of the PTC heater is expressed as: in, It is the flow rate of TEG in kg / s, T T,f,o The TGE output temperature of the fan coil unit radiator is °C, V. P The volume (m) of the pipe to which the heater is attached. 3 T T,st,i The TEG inlet temperature (°C) of the HT-PEMFC reactor is ρ. T The density of TEG is kg / m³ 3 ; The flow rate and speed of the variable frequency circulating pump are related, and the speed depends on the duty cycle of the pulse width modulation (PWM) signal. The frequency of the circulating pump is adjusted using a PWM wave, and frequency conversion control is performed using the V / F method, meaning the operating voltage V of the equipment has an approximately linear relationship with the operating frequency F (V∝F). Experimental data are fitted using a nonlinear least squares method. in, and Indicates head and maximum head, ξ p It is the pipe resistance coefficient, ρ T It is the density of TEG, D p It is the duty cycle of the circulating pump PWM signal, ranging from [24%, 100%], β 01 ,β 02 and β 03 These are the fitting coefficients related to the circulating pump; The dynamic temperature of the TEG outlet of the variable frequency fan coil radiator is expressed as follows: Among them, h f Convection heat transfer coefficient W / m 2 / K,S f It is the heat exchange area in m. 2 V f The volume of the coil is m3; T T,ex,o The TEG outlet temperature of the hydrogen heat exchanger is °C, T T,f,o It is the TGE output temperature of the fan coil unit radiator in °C; A fan fixed to the coil is used to cool the TEG inside the tube. The PWM duty cycle of the fan and the circulating pump affects the convective heat transfer process between the cold air and the TEG. When the TEG inlet temperature is constant, the total heat dissipation increases with the increase of the fan speed; while the heat dissipation performance is improved by the increase of the TEG flow velocity inside the coil when the circulating pump speed increases. The convective heat transfer coefficient of the heat exchange process is affected by D. f and D p The coupling effect is considered; a semi-empirical formula based on data fitting is used to estimate the overall convective heat transfer coefficient of the fan coil unit. Among them, P f,ra η is the rated power of the fan. f η is the electrical efficiency of the wind turbine. me For mechanical efficiency, a, b, c are fitting coefficients, and γ is the coefficient of performance. i It is a set of fitting coefficients based on experimental data, and the mass flow rate of cooling air. and D f A nonlinear relationship exists between them, which is obtained by identifying steady-state experimental data using nonlinear least squares; the fan does not work at low duty cycles, therefore D f It changed from 28% to 100%.

7. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 1, characterized in that, Step 3 specifically involves the state-space description of the HT-PEMFC temperature control system as follows: Where f(T) st Let w(t),t) represent the combined effect of internal and external disturbances on the stack temperature, b be the gain of the control input, and u(t) be the control input of the system. For a continuous system, a second-order ESO is constructed to track the target temperature and estimate the uncertainty disturbance. The ESO takes the unknown model state and internal and external disturbances in the system as the total disturbance signal and extends it into a new state. By collecting and analyzing the system output, the total disturbance of the system is estimated in real time and dynamically compensated. Where e is the deviation between the estimated state z1 and the measured state y of the ESO, z2 is the estimate of the total disturbance, and β1 and β2 are the parameters of the ESO, which are tuned using the bandwidth method, with β1 = 2ω. o , ω o It is the observer bandwidth; By using the estimated state z1 of the ESO and the reference state T of the system ref The control quantity is generated by feedback from a nonlinear function; in It is the estimation error, u is the control law based on u0 with disturbance compensation, ω c For controller bandwidth; There are three key parameters in the ADRC controller [b, ω] o ,ω c The parameter b is selected based on experimental experience; fuzzy logic control is used to adjust the parameter ω. o and ω c It includes three processes: input fuzzification, fuzzy inference, and defuzzification.

8. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 7, characterized in that, The three processes of input fuzzification, fuzzy inference, and defuzzification are as follows: The input-output fuzzification process involves dividing fuzzy variables based on the error and its variation. Three levels of fuzzy linguistic values, "N", "Z", and "P", are defined as negative, zero, and positive, respectively; the output quantity ω... o and k p,1 The fuzzy linguistic values ​​are divided into three levels: "S", "M", and "L" representing small, medium, and large, respectively; the fundamental domain of discourse for the temperature deviation of the fuel cell stack is [-30, 30], and the fundamental domain of discourse for the variation of the temperature deviation is [-10, 10]; the observer bandwidth ω o The fundamental universe of discourse is [1,10], and the controller bandwidth ω c The fundamental universe of discourse is [10, 100], and the ω of ADRC o and ω c The fuzzy rules are obtained based on the behavioral analysis of the PEMFC system under different temperature errors and change-in-error conditions; the fuzzy rule base adopts language rules defined by IF-THEN conditions.

9. The temperature control method for a high-temperature fuel cell HT-PEMFC based on fuzzy ADRC according to claim 8, characterized in that, The fuzzy rule base uses IF-THEN conditions to define language rules, specifically: when the temperature error is "N" and the error change is "N", the ADRC observer bandwidth and controller bandwidth are "L" and "L", respectively, representing an increase of ω when the error and error change are negative. o and ω c It rapidly changes the temperature trend, pulling the current temperature closer to the reference state.

10. A temperature control system for a high-temperature fuel cell (HT-PEMFC) based on fuzzy ADRC, used to implement the method as described in claim 1, characterized in that, It includes an electric fuel cell stack, hydrogen supply pipeline, air supply pipeline, hot oil circuit, data acquisition device, DC electronic load and main unit; the hydrogen supply pipeline includes a secondary pressure reducing valve, flow control valve, plate heat exchanger and exhaust solenoid valve; The air supply line includes an air compressor, pressure regulator, flow control valve, identical plate heat exchanger, and back pressure valve; the hot oil circuit uses triethylene glycol as the heat transfer medium and includes a variable frequency circulating pump, variable frequency fan coil radiator, positive temperature coefficient PTC heater, hot oil flow meter, hot oil expansion tank, and three-way bypass valve; the measurement signals in the HT-PEMFC system are integrated and transmitted through I / O devices, and transmitted to the host via a local area network. A wattmeter, hydrogen leak detector, and circuit breaker together constitute auxiliary measuring instruments and safety protection mechanisms.

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