Proton exchange membrane fuel cell waste heat power generation control method, system, equipment and medium

Abstract

The invention discloses a proton exchange membrane fuel cell waste heat power generation control method, system and equipment and a medium. The method comprises the following steps: collecting the temperature of a proton exchange membrane fuel cell stack, the temperature of an exhaust pipe, the temperature of a cooling fin, the output voltage of a thermoelectric generator and the output current of the thermoelectric generator; calculating the output power of the thermoelectric generator according to the output voltage of the thermoelectric generator and the output current of the thermoelectric generator, and adjusting the load resistance according to the ratio of the variable quantity of the output power of the thermoelectric generator to the variable quantity of the output voltage of the thermoelectric generator; and when the temperature difference is greater than or equal to the preset temperature difference threshold value, natural convection heat dissipation is switched. A closed-loop control relation among heat source temperature control, thermoelectric conversion power optimization and cold end heat dissipation management is established by collecting the temperature of a fuel cell stack, the temperature of an exhaust pipe, the temperature of a cooling fin and output voltage and current of a thermoelectric generator.

Description

Technical Field

[0001] This invention relates to the field of new energy and energy-saving technology, and in particular to a method, system, equipment and medium for controlling waste heat power generation from a proton exchange membrane fuel cell. Background Technology

[0002] Proton exchange membrane fuel cells (PEMFCs) are widely used in clean energy fields such as electric vehicles, backup power supplies, and distributed energy resources due to their high energy conversion efficiency, low-temperature operation, and fast start-up speed. However, during operation, approximately 40% to 50% of the chemical energy in a PEMFC is converted into waste heat. Traditional cooling methods directly release this heat into the environment through air or liquid cooling, resulting in significant energy waste. Unrecovered waste heat can reduce the overall system efficiency. Thermoelectric generators, as solid-state thermoelectric conversion devices, can directly convert temperature differences into electrical energy, providing a feasible approach for waste heat recovery from PEMFCs.

[0003] When existing thermoelectric generator waste heat recovery technologies are applied to proton exchange membrane fuel cell systems, traditional thermoelectric generator systems are mostly independent modules, lacking deep integration with the fuel cell fluid system. The layout of the thermoelectric generator does not fully consider the impact of exhaust pipe flow channel design on heat transfer, resulting in high thermal contact resistance and limited thermoelectric conversion efficiency. During fuel cell operation, parameters such as cathode air flow and hydrogen pressure are in a state of flux. Existing technologies have failed to provide a real-time control scheme for thermoelectric generator load matching, resulting in insufficient maximum power point tracking efficiency and unstable thermoelectric generator output power. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, device, and medium for controlling waste heat power generation from a proton exchange membrane fuel cell to solve the problems of low integration between existing thermoelectric generator systems and fuel cells, limited thermoelectric conversion efficiency, and insufficient maximum power point tracking efficiency and unstable output power due to the lack of real-time load matching control.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for controlling waste heat power generation in a proton exchange membrane fuel cell, comprising the following steps: collecting the temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator; determining the cathode air flow adjustment amount through fuzzy control rules based on the deviation between the temperature of the proton exchange membrane fuel cell stack and a preset temperature and the rate of change of the deviation, and adjusting the cathode air flow according to the cathode air flow adjustment amount; calculating the output power of the thermoelectric generator based on the output voltage and the output current of the thermoelectric generator, and adjusting the load resistance according to the ratio of the change in the output power of the thermoelectric generator to the change in the output voltage of the thermoelectric generator, until the thermoelectric generator operates at its maximum power point; switching the heat dissipation mode according to the temperature difference between the exhaust pipe temperature and the heat sink temperature, wherein when the temperature difference is less than a preset temperature difference threshold, a fan is activated for forced air cooling, and when the temperature difference is greater than or equal to the preset temperature difference threshold, natural convection heat dissipation is switched.

[0007] In a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells according to the present invention, the step of determining the cathode air flow rate adjustment amount through fuzzy control rules includes: calculating the deviation between the temperature of the proton exchange membrane fuel cell stack and a preset temperature, and calculating the rate of change of the deviation; fuzzifying the deviation and the rate of change of the deviation, wherein the universe of discourse of the deviation is... The universe of discourse for the rate of change of the deviation is Each is divided into 7 fuzzy subsets; the corresponding fuzzy output is queried according to the preset fuzzy rule table; the fuzzy output is defuzzified using the centroid method to obtain the cathode air flow regulation amount.

[0008] The beneficial effects of this preferred technical solution are as follows: by dividing the deviation and the rate of change of deviation into 7 fuzzy subsets and combining them with a fuzzy rule table for querying, fine control of fuel cell stack temperature is achieved, improving the accuracy and response speed of temperature regulation.

[0009] In a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells according to the present invention, the fuzzy subset includes negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; after adjusting the cathode air flow according to the cathode air flow adjustment amount, the method further includes: adjusting the proportional coefficient, integral coefficient, and derivative coefficient of the proportional-integral-derivative controller according to the fuzzy output amount; calculating the proportional-integral-derivative control output amount according to the adjusted proportional coefficient, integral coefficient, and derivative coefficient, and adjusting the cathode air flow according to the proportional-integral-derivative control output amount.

[0010] The beneficial effects of this preferred technical solution are: by combining fuzzy control with proportional-integral-derivative (PID) control, the parameters of the PID controller are adjusted by adjusting the fuzzy output, thus balancing the robustness of fuzzy control and the accuracy of PID control.

[0011] In a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells according to the present invention, the step of adjusting the load resistance includes: calculating the ratio of the output current of the thermoelectric generator to the output voltage of the thermoelectric generator to obtain a conductance value, and calculating the ratio of the change in the output current of the thermoelectric generator to the change in the output voltage of the thermoelectric generator to obtain a conductance change; when the sum of the conductance change and the conductance value is equal to zero, keeping the load resistance unchanged; when the sum of the conductance change and the conductance value is greater than zero, increasing the load resistance; and when the sum of the conductance change and the conductance value is less than zero, decreasing the load resistance.

[0012] The beneficial effects of this preferred technical solution are as follows: by using the incremental conductivity method to adjust the load resistance by judging the positive or negative relationship between the change in conductivity and the sum of the conductivity values, the position of the current operating point relative to the maximum power point can be determined, thereby improving the accuracy and convergence speed of maximum power point tracking.

[0013] In a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells described in this invention, the step size for each adjustment when increasing or decreasing the load resistance is 0.1 ohms; when the change in the output power of the thermoelectric generator is less than the power error threshold, it is determined that the thermoelectric generator has been operating at the maximum power point, and the power error threshold is 0.1 milliwatts.

[0014] In a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells according to the present invention, the fan speed is negatively correlated with the temperature difference when the temperature difference is less than the preset temperature difference threshold. After switching the heat dissipation mode according to the temperature difference between the exhaust pipe temperature and the heat sink temperature, the method further includes: evaluating the system performance based on the output power of the thermoelectric generator, the temperature of the proton exchange membrane fuel cell stack, and the waste heat recovery efficiency, wherein the waste heat recovery efficiency is the ratio of the output power of the thermoelectric generator to the waste heat power of the proton exchange membrane fuel cell; and adjusting the fuzzy rule table and the adjustment step size of the load resistor based on the evaluation results of the system performance.

[0015] The beneficial effects of this preferred technical solution are: establishing a negative correlation between fan speed and temperature difference, and adjusting the fuzzy rule table and load resistance adjustment step size according to the system performance evaluation results, thereby achieving coordinated optimization of the heat dissipation system and control parameters.

[0016] As a preferred embodiment of the waste heat power generation control method for proton exchange membrane fuel cells according to the present invention, the following is included: collecting the temperature of the proton exchange membrane fuel cell stack, the exhaust pipe temperature, the heat sink temperature, the thermoelectric generator output voltage, and the thermoelectric generator output current at a fixed period; and filtering the collected proton exchange membrane fuel cell stack temperature, the exhaust pipe temperature, the heat sink temperature, the thermoelectric generator output voltage, and the thermoelectric generator output current.

[0017] In a second aspect, the present invention provides a waste heat power generation control system for a proton exchange membrane fuel cell, comprising: a high-temperature proton exchange membrane fuel cell stack for converting chemical energy into electrical energy through a hydrogen-oxygen electrochemical reaction, wherein the high-temperature proton exchange membrane fuel cell stack uses a polybenzimidazole membrane electrolyte and a temperature sensor is provided on the bipolar plate. A quadrilateral exhaust pipe is connected to the high-temperature proton exchange membrane fuel cell stack and is used to exhaust the high-temperature exhaust gas generated by the high-temperature proton exchange membrane fuel cell stack. A thermoelectric generator module, installed at the quadrilateral exhaust pipe outlet, includes two sets of thermoelectric generator units connected in series. The heat dissipation system includes a heat pipe radiator and a DC fan. The heat pipe radiator is connected to the cold side of the thermoelectric generator module, and a digital temperature sensor is provided on the surface of the heat pipe radiator. The intelligent control unit is used to collect the temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator, and to perform fuzzy proportional-integral-derivative control and maximum power point tracking control.

[0018] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the proton exchange membrane fuel cell waste heat power generation control method.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the proton exchange membrane fuel cell waste heat power generation control method.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting the temperature of the fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, and the output voltage and current of the thermoelectric generator, a closed-loop control relationship is established among the three elements of heat source temperature control, thermoelectric conversion power optimization, and cold end heat dissipation management, which solves the technical defects of the prior art in which each control link is independent and cannot work together.

[0021] The cathode airflow adjustment is determined by fuzzy control rules to maintain the stability of the fuel cell stack temperature. The load resistance is adjusted by judging the sign of the sum of the conductivity change and the conductivity value to achieve maximum power point tracking. The heat dissipation mode is switched by judging the temperature difference between the exhaust pipe temperature and the heat sink temperature to maintain the temperature difference between the hot and cold ends of the thermoelectric generator. The three control links form a linkage mechanism based on unified data acquisition. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the overall process of the waste heat power generation control method for proton exchange membrane fuel cells according to an embodiment of the present invention. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0025] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for controlling waste heat power generation from a proton exchange membrane fuel cell is provided, comprising the following steps: S100 collects the temperature of the proton exchange membrane fuel cell stack, the exhaust pipe temperature, the heat sink temperature, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator.

[0026] S200. Based on the deviation between the proton exchange membrane fuel cell stack temperature and the preset temperature and the rate of change of the deviation, the cathode air flow rate adjustment amount is determined by fuzzy control rules, and the cathode air flow rate is adjusted according to the cathode air flow rate adjustment amount.

[0027] S300. Calculate the output power of the thermoelectric generator based on the output voltage and output current of the thermoelectric generator, and adjust the load resistance according to the ratio of the change in the output power of the thermoelectric generator to the change in the output voltage of the thermoelectric generator, until the thermoelectric generator operates at its maximum power point.

[0028] S400: Switch the heat dissipation mode according to the temperature difference between the exhaust pipe temperature and the heat sink temperature. When the temperature difference is less than a preset temperature difference threshold, start the fan for forced air cooling. When the temperature difference is greater than or equal to the preset temperature difference threshold, switch to natural convection heat dissipation.

[0029] It should be noted that during the operation of a proton exchange membrane fuel cell, approximately 40% to 50% of the chemical energy is converted into waste heat. Traditional cooling methods directly release this heat into the environment, resulting in energy waste. Unrecovered waste heat can reduce the overall system efficiency by 10% to 15%. Thermoelectric generators can directly convert temperature differences into electrical energy, providing a feasible way to recover waste heat from fuel cells. However, the surface temperature distribution of the fuel cell exhaust pipe is uneven, with temperature gradients reaching 50 to 100 Kelvin, and the cold source is affected by ambient temperature fluctuations, leading to unstable output power of the thermoelectric generator. Simultaneously, parameters such as cathode airflow and hydrogen pressure are constantly changing during fuel cell operation. If the load resistance of the thermoelectric generator cannot match its internal resistance, maximum power output cannot be achieved. Furthermore, when the temperature difference between the hot and cold ends of the thermoelectric generator is insufficient, the thermoelectric conversion efficiency will decrease. Therefore, the coordinated control of fuel cell stack temperature, thermoelectric generator power, and the cooling system is crucial.

[0030] Therefore, to address the aforementioned issues of low waste heat recovery efficiency and poor control coordination, the following steps (S100-S400) are employed: First, the fuel cell stack temperature, exhaust pipe temperature, heat sink temperature, and thermoelectric generator output voltage and current are collected to provide a data foundation for subsequent control. Then, fuzzy control rules are used to adjust the cathode airflow to maintain the fuel cell stack temperature stable near the preset temperature, ensuring the stability of the heat source temperature. Next, the load resistance is adjusted using the incremental conductivity method to ensure that the thermoelectric generator always operates at its maximum power point. Finally, the heat dissipation mode is switched based on the temperature difference between the exhaust pipe temperature and the heat sink temperature. When the temperature difference is insufficient, the fan is activated for forced air cooling to increase the temperature difference; when the temperature difference is sufficient, natural convection cooling is switched to reduce energy consumption. This achieves coordinated operation of heat source control, power optimization, and heat dissipation management, improving the overall energy utilization efficiency of the proton exchange membrane fuel cell system.

[0031] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a method for controlling waste heat power generation of a proton exchange membrane fuel cell is provided.

[0032] In this embodiment of the application, step S100 collects the temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator.

[0033] Step S100 specifically includes A1~A2: A1. The temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator are collected at fixed intervals.

[0034] Specifically, the intelligent control unit uses an STM32F407 microcontroller equipped with a 16-bit analog-to-digital converter for data acquisition. The data sampling frequency is set to 10 Hz, meaning data is acquired every 50 milliseconds. The temperature of the proton exchange membrane fuel cell stack is measured by a PT100 temperature sensor mounted on the bipolar plate, with a measurement accuracy of ±0.5℃; the exhaust pipe temperature is measured by two sets of T-type thermocouples arranged along the exhaust pipe axis, with a measurement resolution of 0.1℃; the heat sink temperature is measured by a digital temperature sensor attached to the surface of the heat pipe radiator; the output voltage of the thermoelectric generator is measured by a multimeter, with a measurement accuracy of ±0.1 millivolts; and the output current of the thermoelectric generator is measured by a Hall sensor, with a measurement accuracy of ±0.1 milliamperes.

[0035] A2. The collected temperatures of the proton exchange membrane fuel cell stack, the exhaust pipe, the heat sink, the thermoelectric generator output voltage, and the thermoelectric generator output current are filtered.

[0036] Specifically, a moving average filtering algorithm is used to filter the acquired raw data. The filter window length is set to 5 sampling points, and the arithmetic mean of 5 consecutive sample values ​​is calculated as the valid data output for the current moment. This filtering process can effectively suppress high-frequency noise and random interference during sensor measurement, while ensuring the real-time performance of the data, providing an accurate and reliable data foundation for subsequent temperature control calculations, maximum power point tracking, and heat dissipation mode switching.

[0037] In one optional implementation, data can be collected at a fixed period in step A1, or an event-triggered acquisition method can be used. When the rate of change of the proton exchange membrane fuel cell stack temperature exceeds a preset threshold, or when a sudden change occurs in the output power of the thermoelectric generator, the intelligent control unit automatically increases the sampling frequency to 20 Hz to respond more quickly to changes in system state; once the system stabilizes, it automatically returns to the normal sampling frequency. This approach can reduce the computational load of the microcontroller and the system power consumption while ensuring control accuracy.

[0038] In another optional implementation, the collected data is filtered in step A2. The Kalman filter algorithm can be used instead of the moving average filter algorithm. The Kalman filter algorithm establishes system state equations and observation equations, and uses the state estimate from the previous moment and the observation from the current moment to perform optimal state estimation. This allows for better tracking of data trends while filtering out noise. This approach is particularly suitable for fuel cell systems with rapidly changing loads, reducing data latency and improving the response speed of the control system while ensuring filtering effectiveness.

[0039] In this embodiment of the application, step S200 determines the cathode air flow rate adjustment amount by using fuzzy control rules based on the deviation between the proton exchange membrane fuel cell stack temperature and the preset temperature and the rate of change of the deviation, and adjusts the cathode air flow rate according to the cathode air flow rate adjustment amount.

[0040] In step S200, the steps of determining the cathode air flow rate adjustment amount through fuzzy control rules include B1~B4: B1. Calculate the deviation between the temperature of the proton exchange membrane fuel cell stack and the preset temperature, and calculate the rate of change of the deviation.

[0041] Specifically, the preset temperature is set to 160℃, which is the optimal operating temperature of the polybenzimidazole membrane electrolyte. The deviation is calculated by subtracting the preset temperature from the currently collected proton exchange membrane fuel cell stack temperature. The rate of change of the deviation is calculated by dividing the difference between the deviation at the current moment and the deviation at the previous moment by the sampling period. For example, if the fuel cell stack temperature collected at the current moment is 165℃, then the deviation is 5℃; if the deviation at the previous moment was 3℃ and the sampling period is 50 milliseconds, then the rate of change of the deviation is 40℃ / s. The deviation and its rate of change reflect the degree and speed of deviation of the fuel cell stack temperature from the target temperature, providing a decision basis for subsequent fuzzy control.

[0042] B2. The deviation and the rate of change of the deviation are fuzzified. The universe of discourse of the deviation is (-X℃, Y℃), and the universe of discourse of the rate of change of the deviation is (−A℃ / s, B℃ / s). Both are divided into 7 fuzzy subsets, including negative large, negative medium, negative small, zero, positive small, positive medium, and positive large.

[0043] Specifically, the domain of the aforementioned deviation The membership functions corresponding to the seven fuzzy subsets are triangular membership functions, with the center values ​​of each subset being -20℃, -10℃, -5℃, 0℃, 5℃, 10℃, and 20℃, respectively. The universe of discourse for the rate of change of the deviation (−5℃ / s, 5℃ / s) per second also uses a triangular membership function, with the center values ​​of each subset being -5℃ / s, -2.5℃ / s, -1℃ / s, 0℃ / s, 1℃ / s, 2.5℃ / s, and 5℃ / s, respectively. Through fuzzification, the numerical values ​​are transformed into fuzzy linguistic variables, facilitating the establishment of fuzzy control rules using human experience and knowledge.

[0044] B3. Query the corresponding fuzzy output quantity according to the preset fuzzy rule table.

[0045] Specifically, the fuzzy rule table is a 7x7 matrix containing 49 fuzzy control rules. The rule format is "when the deviation is a certain fuzzy subset and the rate of change of the deviation is a certain fuzzy subset, then the cathode airflow adjustment amount is a certain fuzzy subset." For example, when the deviation is large positive and the rate of change of the deviation is medium, it indicates that the fuel cell stack temperature is much higher than the preset temperature and is continuously rising. In this case, the cathode airflow adjustment amount should be large positive, i.e., significantly increasing the cathode airflow to enhance cooling. When the deviation is small negative and the rate of change of the deviation is small positive, it indicates that the fuel cell stack temperature is slightly lower than the preset temperature but is recovering. In this case, the cathode airflow adjustment amount should be small negative, i.e., appropriately reducing the cathode airflow to slow down the temperature drop. By querying the fuzzy rule table, the fuzzified deviation and the rate of change of the deviation are mapped to the corresponding fuzzy output amount.

[0046] B4. The fuzzy output quantity is defuzzified using the centroid method to obtain the cathode air flow rate adjustment quantity.

[0047] The centroid method obtains the precise output value by calculating the center of area of ​​all membership functions of the fuzzy output. The universe of discourse for the cathode airflow adjustment is set to -20 standard liters per minute to +20 standard liters per minute, corresponding to the decrease and increase in cathode airflow. The defuzzification calculation formula is: the cathode airflow adjustment equals the sum of the products of the universe of discourse corresponding to each fuzzy output and its membership degree, divided by the sum of the membership degrees. Through defuzzification processing, the fuzzy output is converted into a precise value that can be directly used to control the cathode airflow. The intelligent control unit controls the air compressor speed through pulse width modulation signals based on this adjustment value, thereby achieving precise regulation of the cathode airflow.

[0048] After adjusting the cathode air flow rate according to the cathode air flow rate adjustment amount, the method further includes: B5. Adjust the proportional coefficient, integral coefficient, and derivative coefficient of the proportional-integral-derivative controller according to the fuzzy output.

[0049] Specifically, the parameter adjustment rules of the proportional-integral-derivative (PID) controller are set based on the magnitude of the fuzzy output. When the absolute value of the fuzzy output is large, it indicates a large temperature deviation in the fuel cell stack. In this case, the proportional coefficient is increased to accelerate the system response speed, while the integral and derivative coefficients are decreased to avoid system overshoot and oscillation. When the absolute value of the fuzzy output is small, it indicates that the fuel cell stack temperature is close to the preset temperature. In this case, the proportional coefficient is decreased to improve control accuracy, the integral coefficient is appropriately increased to eliminate steady-state error, and the derivative coefficient is increased to suppress temperature fluctuations. For example, when the fuzzy output is positive, the proportional coefficient is set to 0.8, the integral coefficient to 0.01, and the derivative coefficient to 0.05; when the fuzzy output is zero, the proportional coefficient is set to 0.4, the integral coefficient to 0.05, and the derivative coefficient to 0.15. By adjusting the PID controller parameters according to the fuzzy output, an organic combination of fuzzy control and PID control is achieved, balancing fast response and control accuracy.

[0050] B6. Calculate the proportional-integral-derivative control output based on the adjusted proportional coefficient, integral coefficient, and derivative coefficient, and adjust the cathode air flow rate based on the proportional-integral-derivative control output.

[0051] Specifically, the formula for calculating the proportional-integral-derivative (PID) control output is: the PID control output equals the cumulative sum of the proportional coefficient multiplied by the deviation and the integral coefficient multiplied by the deviation, plus the derivative coefficient multiplied by the rate of change of the deviation. The PID control output is superimposed with the cathode air flow adjustment obtained in step B4 to form the final cathode air flow control command. This command is output to the air compressor drive circuit, achieving precise control of the cathode air flow by adjusting the air compressor speed. Coarse adjustment is achieved through fuzzy control, and fine adjustment is achieved through PID control. The two work together to ensure stable operation of the fuel cell stack temperature within a preset temperature range of ±3℃, avoiding problems such as membrane electrolyte degradation due to excessively high temperatures or the impact of excessively low temperatures on the electrochemical reaction rate.

[0052] In an optional implementation, step B3 involves querying the corresponding fuzzy output quantity according to a preset fuzzy rule table. Alternatively, a fuzzy neural network method can be used instead of the traditional fuzzy rule table query. The fuzzy neural network combines fuzzy logic reasoning with neural network learning capabilities. By collecting operating data of the fuel cell system under different operating conditions, it trains the neural network to automatically learn the optimal fuzzy control rules, continuously optimizing the control effect based on actual operating conditions. This approach is particularly suitable for applications with complex and variable operating conditions in fuel cell systems, improving the adaptability and robustness of the control system.

[0053] In another optional implementation, the adjustment of the proportional-integral-derivative (PID) controller parameters based on the fuzzy output in step B5 can also employ a gain scheduling method. This method pre-identifies multiple sets of optimal PID controller parameters for different operating ranges of the fuel cell system through offline identification and establishes a correspondence table between parameters and operating ranges. During actual control, the intelligent control unit determines the system's operating range based on current fuel cell stack temperature, load power, and other state variables, directly calling the corresponding PID controller parameter set without requiring parameter adjustment through fuzzy inference. This approach reduces computational load, improves the real-time performance of the control system, and is suitable for applications requiring high control response speed.

[0054] In this embodiment of the application, step S300 calculates the output power of the thermoelectric generator based on the output voltage and output current of the thermoelectric generator, and adjusts the load resistance according to the ratio of the change in the output power of the thermoelectric generator to the change in the output voltage of the thermoelectric generator, until the thermoelectric generator operates at its maximum power point.

[0055] In step S300, the steps for adjusting the load resistance include C1~C3: C1. Calculate the ratio of the output current of the thermoelectric generator to the output voltage of the thermoelectric generator to obtain the conductance value, and calculate the ratio of the change in the output current of the thermoelectric generator to the change in the output voltage of the thermoelectric generator to obtain the change in conductance.

[0056] Specifically, the formula for calculating the conductivity value is as follows: ; Where G is the conductivity, I is the output current of the thermoelectric generator, and V is the output voltage of the thermoelectric generator.

[0057] The formula for calculating the change in conductivity is: ; Where ΔG is the change in conductivity, This represents the output current of the thermoelectric generator at the current moment. This represents the output current of the thermoelectric generator at the previous moment. This represents the current output voltage of the thermoelectric generator. This represents the output voltage of the thermoelectric generator at the previous moment.

[0058] For example, if the thermoelectric generator's output voltage is currently 3.2 volts and its output current is 30 mA, then the conductance is 9.375 millisiemens. If the previous output voltage was 3.0 volts and the previous output current was 28 mA, then the change in conductance was 10 millisiemens. This conductance value reflects the load characteristics of the thermoelectric generator at its current operating point, and the change in conductance reflects the slope of the power-voltage curve at the current operating point. The relationship between these two factors determines the position of the current operating point relative to the maximum power point.

[0059] C2. When the sum of the change in conductance and the conductance value is zero, keep the load resistance constant; when the sum of the change in conductance and the conductance value is greater than zero, increase the load resistance; when the sum of the change in conductance and the conductance value is less than zero, decrease the load resistance. The adjustment step for each increase or decrease in the load resistance is 0.1 ohms.

[0060] Specifically, according to the power-voltage characteristic curve of a thermoelectric generator, when the generator operates at its maximum power point, the derivative of power with respect to voltage is zero, meaning the sum of the change in conductance and the conductance value is zero. When the sum of the change in conductance and the conductance value is greater than zero, it indicates that the current operating point is to the left of the maximum power point. In this case, the load resistance is less than the optimal load resistance, and the load resistance needs to be increased to move the operating point towards the maximum power point. When the sum of the change in conductance and the conductance value is less than zero, it indicates that the current operating point is to the right of the maximum power point. In this case, the load resistance is greater than the optimal load resistance, and the load resistance needs to be decreased to move the operating point towards the maximum power point. The intelligent control unit achieves equivalent adjustment of the load resistance by controlling the duty cycle of the DC-DC converter; an increase in the duty cycle corresponds to an increase in load resistance, and a decrease in the duty cycle corresponds to a decrease in load resistance.

[0061] Specifically, the setting of the load resistance adjustment step size needs to balance maximum power point tracking speed and system stability. An excessively large step size will cause the operating point to oscillate around the maximum power point, reducing the stability of the thermoelectric generator's output power; an excessively small step size will result in slow tracking speed, making it impossible to adjust to the maximum power point in a timely manner when the fuel cell's operating conditions change. For example, if the current load resistance is 10 ohms and the sum of the conductance change and the conductance value is greater than zero, the intelligent control unit will adjust the load resistance to 10.1 ohms; through multiple iterative adjustments, the load resistance gradually approaches the optimal load resistance value, allowing the thermoelectric generator to operate near its maximum power point.

[0062] C3. When the change in the output power of the thermoelectric generator is less than the power error threshold, it is determined that the thermoelectric generator has been operating at the maximum power point, and the power error threshold is 0.1 milliwatts.

[0063] Specifically, the output power of the thermoelectric generator is calculated by multiplying the output voltage and output current of the thermoelectric generator. The change in the output power is the difference between the current output power and the previous output power. After multiple adjustments to the load resistance, the change in the output power gradually decreases. When the absolute value of this change is less than 0.1 milliwatts, it indicates that the thermoelectric generator has reached its maximum power point. At this point, the intelligent control unit stops adjusting the load resistance and maintains the current load resistance value unchanged. The setting of the power error threshold comprehensively considers the measurement accuracy of the thermoelectric generator's output power and the acceptable power loss range in practical applications. This judgment mechanism avoids frequent adjustments of the control system near the maximum power point, reduces power oscillations, and improves the stability of the thermoelectric generator's output power. Under a 900-watt fuel cell load, the thermoelectric generator's output power can reach 113.96 milliwatts using this maximum power point tracking control method.

[0064] In one optional implementation, step C2, which adjusts the load resistance using a fixed step size, can also employ a variable step size adjustment method. This method dynamically adjusts the load resistance adjustment step size based on the absolute value of the sum of the conductance change and the conductance value. A larger step size is used when the absolute value is large to accelerate tracking, and a smaller step size is used when the absolute value is small to improve tracking accuracy. For example, when the absolute value is greater than 5 millisiemens, the adjustment step size is set to 0.5 ohms; when the absolute value is between 1 and 5 millisiemens, the adjustment step size is set to 0.1 ohms; and when the absolute value is less than 1 millisiemens, the adjustment step size is set to 0.05 ohms. This method enables rapid response to sudden load changes in the fuel cell and maintains high-precision control during steady-state operation.

[0065] In another optional implementation, step C3, determining whether the thermoelectric generator is operating at its maximum power point, can also employ a power observation time window method. This method sets a time window, for example, 5 seconds, and continuously monitors the change in the thermoelectric generator's output power within this window. Only when the absolute value of the power change at all sampling points within the time window is less than a power error threshold is it determined that the thermoelectric generator has stably operated at its maximum power point. This approach avoids misjudgments caused by instantaneous measurement errors or accidental disturbances, improving the reliability of maximum power point determination, and is particularly suitable for applications where the operating conditions of fuel cell systems fluctuate frequently.

[0066] In this embodiment of the application, step S400 switches the heat dissipation mode according to the temperature difference between the exhaust pipe temperature and the heat sink temperature. When the temperature difference is less than a preset temperature difference threshold, the fan is started for forced air cooling, and when the temperature difference is greater than or equal to the preset temperature difference threshold, it switches to natural convection heat dissipation.

[0067] When the temperature difference is less than the preset temperature difference threshold, the fan speed is negatively correlated with the temperature difference; In step S400, the steps for switching the heat dissipation mode include D1~D2: D1. Calculate the temperature difference between the exhaust pipe temperature and the heat sink temperature, and switch the heat dissipation mode according to the relationship between the temperature difference and the preset temperature difference threshold.

[0068] Specifically, the temperature difference is calculated by subtracting the heat sink temperature from the exhaust pipe temperature, and the preset temperature difference threshold is set to 3°C. This threshold is based on the Seebeck effect principle of thermoelectric generators. The output voltage of a thermoelectric generator is proportional to the temperature difference between the hot and cold ends. When the temperature difference is less than 3°C, the thermoelectric conversion efficiency of the thermoelectric generator will be less than 2%, and the output power will decrease significantly, failing to meet the economic requirements of waste heat recovery. When the temperature difference is greater than or equal to 3°C, it indicates that there is a sufficient temperature gradient between the exhaust pipe and the heat sink, allowing the thermoelectric generator to operate efficiently. In this case, the intelligent control unit shuts off the DC fan, and the system adopts a natural convection cooling mode. The heat sink fins exchange heat with the ambient air through natural convection. This mode requires no additional energy consumption, reducing the system's auxiliary power consumption. When the temperature difference is less than 3°C, it indicates that the heat sink temperature is too high or the exhaust pipe temperature is too low, resulting in insufficient temperature difference between the hot and cold ends. In this case, the intelligent control unit starts the DC fan, switching to a forced air cooling mode. By increasing the airflow velocity on the surface of the heat sink, heat dissipation is accelerated, reducing the heat sink temperature, thereby increasing the temperature difference between the hot and cold ends and improving the output power of the thermoelectric generator.

[0069] D2. When the temperature difference is less than the preset temperature difference threshold, calculate the fan speed based on the temperature difference and control the fan to run.

[0070] Specifically, the fan speed is negatively correlated with the temperature difference; that is, the smaller the temperature difference, the higher the fan speed and the stronger the heat dissipation capacity. The formula for calculating the fan speed is: fan speed equals 3000 minus 1000 multiplied by the temperature difference and then divided by 3. For example, when the temperature difference is 2℃, the fan speed is calculated as 3000 minus 1000 multiplied by 2 and then divided by 3, which equals 2333 revolutions per minute; when the temperature difference is 1℃, the fan speed is calculated as 3000 minus 1000 multiplied by 1 and then divided by 3, which equals 2667 revolutions per minute; when the temperature difference is close to 0℃, the fan speed is close to 3000 revolutions per minute, reaching the maximum heat dissipation capacity. This calculation formula ensures that the fan speed changes linearly with the temperature difference, providing strong heat dissipation capacity when the temperature difference is small, and reducing the fan speed to save energy when the temperature difference is close to the threshold. The intelligent control unit controls the DC fan drive circuit through pulse width modulation signals to achieve precise adjustment of the fan speed, so that the temperature difference between the hot and cold ends is stabilized within the range of 3-5℃, ensuring the efficient operation of the thermoelectric generator.

[0071] After switching the heat dissipation mode based on the temperature difference between the exhaust pipe temperature and the heat sink temperature, it also includes D3~D4: D3. Evaluate the system performance based on the output power of the thermoelectric generator, the temperature of the proton exchange membrane fuel cell stack, and the waste heat recovery efficiency, wherein the waste heat recovery efficiency is the ratio of the output power of the thermoelectric generator to the waste heat power of the proton exchange membrane fuel cell.

[0072] Specifically, the waste heat power of the proton exchange membrane fuel cell is calculated using the fuel cell stack temperature, exhaust pipe flow rate, and exhaust pipe temperature. The calculation formula is: waste heat power of the proton exchange membrane fuel cell equals exhaust pipe flow rate multiplied by air specific heat capacity multiplied by the temperature difference between the exhaust pipe temperature and the ambient temperature. The exhaust pipe flow rate is measured by a cathode air flow sensor, the air specific heat capacity is taken as 1.005 kJ / kg Kelvin, and the ambient temperature is measured by an ambient temperature sensor. The waste heat recovery efficiency is calculated as: waste heat recovery efficiency equals the output power of the thermoelectric generator divided by the waste heat power of the proton exchange membrane fuel cell multiplied by 100%. For example, when the waste heat power of the proton exchange membrane fuel cell is 30 watts and the output power of the thermoelectric generator is 113.96 milliwatts, the waste heat recovery efficiency is 0.38%. System performance evaluation also includes monitoring indicators such as the fluctuation range of the fuel cell stack temperature, the stability of the thermoelectric generator output power, and the percentage of fan operating time. The intelligent control unit performs a system performance evaluation every 10 minutes and stores the evaluation results in non-volatile memory.

[0073] D4. Adjust the fuzzy rule table and the adjustment step size of the load resistor based on the evaluation results of the system performance.

[0074] Specifically, when the system performance evaluation results show that the fuel cell stack temperature fluctuation range exceeds ±3℃, it indicates that the fuzzy control effect is poor. The intelligent control unit fine-tunes the fuzzy rule table through a self-learning algorithm. The self-learning algorithm uses reinforcement learning, taking the fuel cell stack temperature fluctuation range as the negative value of the reward function, and adjusts the fuzzy output corresponding to each rule in the fuzzy rule table through gradient descent to maximize the reward function. For example, if the fuel cell stack temperature continues to rise beyond the expected range under the condition that the deviation is small and the rate of change of the deviation is small, the fuzzy output corresponding to that rule is increased, thereby increasing the cathode airflow adjustment and enhancing the cooling effect. When the system performance evaluation results show that the output power fluctuation of the thermoelectric generator exceeds 5 milliwatts, it indicates that there is an oscillation phenomenon in the maximum power point tracking. The intelligent control unit reduces the adjustment step size of the load resistance from 0.1 ohms to 0.05 ohms to reduce the tracking step size and improve stability. When the system performance evaluation results show that the waste heat recovery efficiency is less than 0.3%, it indicates that the heat dissipation system efficiency is insufficient. The intelligent control unit adjusts the preset temperature difference threshold from 3℃ to 4℃ and starts the forced air cooling mode in advance to increase the temperature difference between the hot and cold ends. By adjusting the control parameters based on the system performance evaluation results, adaptive optimization of the control system was achieved, enabling the proton exchange membrane fuel cell waste heat power generation control system to maintain efficient and stable operation under different operating conditions and environmental conditions.

[0075] In one optional implementation, the fan speed calculation based on the temperature difference in step D2 can also employ a segmented speed control method. This method divides the temperature difference range into multiple intervals, each corresponding to a fixed fan speed setting. For example, when the temperature difference is less than 1°C, the fan speed is set to 3000 rpm; when the temperature difference is between 1-2°C, the fan speed is set to 2500 rpm; and when the temperature difference is between 2-3°C, the fan speed is set to 2000 rpm. This approach simplifies the fan control logic, reduces the computational load on the microcontroller, and avoids noise and mechanical wear caused by frequent changes in fan speed. It is suitable for applications where high control accuracy is not required but high system reliability is.

[0076] In another optional implementation, adjusting the control parameters based on the system performance evaluation results in step D4 can also employ an expert system approach. This method pre-establishes a knowledge base containing various operating conditions and fault modes, with each condition or fault mode corresponding to a set of optimal control parameters. The intelligent control unit compares the current system performance evaluation results with the patterns in the knowledge base through rule matching, identifies the most similar pattern, and directly calls the control parameter set corresponding to that pattern. For example, when the pattern "insufficient heat dissipation leading to a small temperature difference between the hot and cold ends" is identified, the system automatically increases the preset temperature difference threshold, increases the coefficient of the fan speed calculation formula, and decreases the load resistance adjustment step size, achieving rapid optimization of the control parameters. This approach leverages the experience and knowledge of domain experts to improve the control system's ability to cope with complex operating conditions, making it suitable for applications where fuel cell systems operate in variable environments and have extremely high reliability requirements.

[0077] In summary, by collecting data on fuel cell stack temperature, exhaust pipe temperature, heat sink temperature, and thermoelectric generator output voltage and current, a closed-loop control relationship was established among heat source temperature control, thermoelectric conversion power optimization, and cold end heat dissipation management. This solves the technical defect in the existing technology where each control link is independent and cannot work in coordination.

[0078] The cathode airflow adjustment is determined by fuzzy control rules to maintain the stability of the fuel cell stack temperature. The load resistance is adjusted by judging the sign of the sum of the conductivity change and the conductivity value to achieve maximum power point tracking. The heat dissipation mode is switched by judging the temperature difference between the exhaust pipe temperature and the heat sink temperature to maintain the temperature difference between the hot and cold ends of the thermoelectric generator. The three control links form a linkage mechanism based on unified data acquisition.

[0079] Example 3 illustrates a schematic scheme for a proton exchange membrane fuel cell waste heat power generation control method. It should be noted that the technical solution of this proton exchange membrane fuel cell waste heat power generation control system belongs to the same concept as the technical solution of the proton exchange membrane fuel cell waste heat power generation control method described above. Details not described in detail in this embodiment can be found in the description of the technical solution of the proton exchange membrane fuel cell waste heat power generation control method described above.

[0080] This embodiment also provides a waste heat power generation control system for a proton exchange membrane fuel cell, including: A high-temperature proton exchange membrane fuel cell stack is used to convert chemical energy into electrical energy through a hydrogen-oxygen electrochemical reaction. The high-temperature proton exchange membrane fuel cell stack uses a polybenzimidazole membrane electrolyte and a temperature sensor is installed on the bipolar plate. A quadrilateral exhaust pipe is connected to the high-temperature proton exchange membrane fuel cell stack and is used to exhaust the high-temperature exhaust gas generated by the high-temperature proton exchange membrane fuel cell stack. A thermoelectric generator module, installed at the quadrilateral exhaust pipe outlet, includes two sets of thermoelectric generator units connected in series. The heat dissipation system includes a heat pipe radiator and a DC fan. The heat pipe radiator is connected to the cold side of the thermoelectric generator module, and a digital temperature sensor is provided on the surface of the heat pipe radiator. The intelligent control unit is used to collect the temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator, and to perform fuzzy proportional-integral-derivative control and maximum power point tracking control.

[0081] This embodiment also provides an electronic device suitable for controlling waste heat power generation from a proton exchange membrane fuel cell, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for controlling waste heat power generation from a proton exchange membrane fuel cell as proposed in the above embodiment.

[0082] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the waste heat power generation control method for proton exchange membrane fuel cells as proposed in the above embodiments.

[0083] The storage medium proposed in this embodiment and the method for controlling waste heat power generation of proton exchange membrane fuel cells proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0084] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method of controlling waste heat power generation of a proton exchange membrane fuel cell, characterized by, Includes the following steps: The temperature of the proton exchange membrane fuel cell stack, the exhaust pipe temperature, the heat sink temperature, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator are collected. Based on the deviation between the proton exchange membrane fuel cell stack temperature and the preset temperature, and the rate of change of the deviation, the cathode air flow rate adjustment amount is determined by fuzzy control rules, and the cathode air flow rate is adjusted according to the cathode air flow rate adjustment amount. The output power of the thermoelectric generator is calculated based on the output voltage and output current of the thermoelectric generator, and the load resistance is adjusted according to the ratio of the change in the output power of the thermoelectric generator to the change in the output voltage of the thermoelectric generator until the thermoelectric generator operates at its maximum power point. The cooling mode is switched according to the temperature difference between the exhaust pipe temperature and the heat sink temperature. When the temperature difference is less than a preset temperature difference threshold, the fan is activated for forced air cooling, and when the temperature difference is greater than or equal to the preset temperature difference threshold, natural convection cooling is switched.

2. The method of claim 1, wherein the temperature of the fuel cell is controlled by adjusting the amount of heat supplied to the fuel cell. The steps for determining the cathode airflow regulation amount using fuzzy control rules include: Calculate the deviation between the temperature of the proton exchange membrane fuel cell stack and the preset temperature, and calculate the rate of change of the deviation; The deviation and the change rate of the deviation are fuzzified, the domain of the deviation is , and the domain of the change rate of the deviation is , both of which are divided into 7 fuzzy subsets. The corresponding fuzzy output quantity is queried according to the preset fuzzy rule table; The fuzzy output quantity is defuzzified using the centroid method to obtain the cathode air flow rate adjustment quantity.

3. The method of claim 2, wherein the temperature of the fuel cell is controlled by adjusting the amount of power generated by the fuel cell. The fuzzy subset includes negative large, negative medium, negative small, zero, positive small, positive medium, and positive large; After adjusting the cathode air flow rate according to the cathode air flow rate adjustment amount, the method further includes: Adjust the proportional coefficient, integral coefficient, and derivative coefficient of the proportional-integral-derivative controller according to the fuzzy output; The proportional-integral-derivative (PID) control output is calculated based on the adjusted proportional coefficient, integral coefficient, and derivative coefficient, and the cathode air flow is adjusted based on the PID control output.

4. The method of claim 3, wherein the temperature of the fuel cell is controlled by adjusting the amount of heat supplied to the fuel cell. The steps for adjusting the load resistance include: The conductance value is obtained by calculating the ratio of the output current of the thermoelectric generator to the output voltage of the thermoelectric generator, and the conductance change is obtained by calculating the ratio of the change in the output current of the thermoelectric generator to the change in the output voltage of the thermoelectric generator. When the sum of the change in conductivity and the conductivity value is zero, the load resistance remains unchanged; When the sum of the change in conductivity and the conductivity value is greater than zero, the load resistance is increased; When the sum of the change in conductivity and the conductivity value is less than zero, the load resistance is reduced.

5. The method of claim 4, wherein the temperature of the fuel cell is controlled by adjusting the amount of power generated by the fuel cell. When increasing or decreasing the load resistance, the adjustment step is 0.1 ohms each time; When the change in the output power of the thermoelectric generator is less than the power error threshold, it is determined that the thermoelectric generator has been operating at the maximum power point, and the power error threshold is 0.1 milliwatts.

6. The method of claim 5, wherein the temperature of the fuel cell is controlled by adjusting the amount of power generated by the fuel cell. When the temperature difference is less than the preset temperature difference threshold, the fan speed is negatively correlated with the temperature difference; After switching the heat dissipation mode based on the temperature difference between the exhaust pipe temperature and the heat sink temperature, the method further includes: The system performance is evaluated based on the output power of the thermoelectric generator, the temperature of the proton exchange membrane fuel cell stack, and the waste heat recovery efficiency, wherein the waste heat recovery efficiency is the ratio of the output power of the thermoelectric generator to the waste heat power of the proton exchange membrane fuel cell. The fuzzy rule table and the adjustment step size of the load resistance are adjusted based on the evaluation results of the system performance.

7. The waste heat power generation control method for proton exchange membrane fuel cells as described in claim 1, characterized in that, The system collects data on the proton exchange membrane fuel cell stack temperature, exhaust pipe temperature, heat sink temperature, thermoelectric generator output voltage, and thermoelectric generator output current, specifically including: The temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator are collected at fixed intervals. The collected temperatures of the proton exchange membrane fuel cell stack, the exhaust pipe, the heat sink, the thermoelectric generator output voltage, and the thermoelectric generator output current are filtered.

8. A waste heat power generation control system for a proton exchange membrane fuel cell, using the method described in any one of claims 1-7, characterized in that, include: A high-temperature proton exchange membrane fuel cell stack is used to convert chemical energy into electrical energy through a hydrogen-oxygen electrochemical reaction. The high-temperature proton exchange membrane fuel cell stack uses a polybenzimidazole membrane electrolyte and a temperature sensor is installed on the bipolar plate. A quadrilateral exhaust pipe is connected to the high-temperature proton exchange membrane fuel cell stack and is used to exhaust the high-temperature exhaust gas generated by the high-temperature proton exchange membrane fuel cell stack. A thermoelectric generator module, installed at the quadrilateral exhaust pipe outlet, includes two sets of thermoelectric generator units connected in series. The heat dissipation system includes a heat pipe radiator and a DC fan. The heat pipe radiator is connected to the cold side of the thermoelectric generator module, and a digital temperature sensor is provided on the surface of the heat pipe radiator. The intelligent control unit is used to collect the temperature of the proton exchange membrane fuel cell stack, the temperature of the exhaust pipe, the temperature of the heat sink, the output voltage of the thermoelectric generator, and the output current of the thermoelectric generator, and to perform fuzzy proportional-integral-derivative control and maximum power point tracking control.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the proton exchange membrane fuel cell waste heat power generation control method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the proton exchange membrane fuel cell waste heat power generation control method according to any one of claims 1 to 7.

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