Optimized control method for hydrogen fuel cell

Abstract

The invention discloses an optimization control method for a hydrogen fuel cell, and relates to the technical field of cell optimization control. The method comprises the following steps: designing a control algorithm: designing an algorithm according to characteristics and environmental parameters in a starting stage to control gas supply and electric pile preheating so as to realize rapid and stable starting, and monitoring, adjusting and maintaining the optimal working state of the electric pile through a self-adaptive algorithm in steady-state operation, power adjustment achieves accurate power response based on a prediction algorithm in combination with related information planning, a flow is formulated in a closing stage to reduce power, and cooling and purging are started to protect the battery. Rapid starting and efficient energy conversion are achieved through the control algorithm, energy loss is reduced, the operation efficiency is improved, and the energy consumption is reduced. A comprehensive monitoring and fault detection system is established, energy flow is optimized through an intelligent energy management mechanism, reasonable energy distribution under different driving working conditions is achieved, and the overall efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery optimization control, and in particular to an optimization control method for a hydrogen fuel cell. Background Art

[0002] As a novel energy conversion device, hydrogen fuel cells have attracted significant attention worldwide due to their high efficiency and environmental friendliness. Their high energy conversion efficiency and zero emissions make them promising for diverse applications, including transportation and distributed power generation. However, despite their numerous advantages, hydrogen fuel cell technology still faces several technical and operational challenges in practical applications, which have limited its further promotion and application.

[0003] Currently, the main challenges encountered in practical applications of hydrogen fuel cells include long startup times, low energy conversion efficiency, poor system stability, limited fault detection and handling capabilities, and inadequate energy management. These issues not only affect hydrogen fuel cell performance but also increase their operation and maintenance costs. To overcome these challenges, it is necessary to develop an optimized control method that can improve hydrogen fuel cells' startup speed, operating efficiency, system stability, enhance their fault detection and handling capabilities, and achieve more sophisticated energy management.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems existing in the existing hydrogen fuel cell control technology and to propose an optimized control method for hydrogen fuel cells.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An optimization control method for a hydrogen fuel cell comprises the following steps: S1. Control algorithm design: During the startup phase, an algorithm is designed based on the characteristics and environmental parameters to control the gas supply and stack preheating to achieve a fast and smooth startup. During steady-state operation, an adaptive algorithm is used to monitor and adjust the stack to maintain optimal working conditions. Power adjustment is based on a predictive algorithm combined with relevant information planning to achieve precise power response. During the shutdown phase, a process is developed to reduce power and initiate cooling and purge to protect the battery. S2. Fault Detection and Handling: Build a multi-dimensional system to monitor key parameters and identify potential hazards. This includes selecting sensors for each parameter and determining their location, setting frequency and accuracy, processing data based on physical models, selecting and training models, and taking appropriate measures to ensure system safety or maintain functionality. S3. Energy Management: Develop strategies to optimize energy flow based on the operating condition design mechanism, including classifying operating conditions, extracting features, and building models to describe battery characteristics. Calculate and adjust the power of hydrogen fuel cells and power batteries during high power demands such as acceleration and climbing. Calculate recovered energy and adjust charging power during deceleration and braking. Adjust hydrogen fuel cell power based on battery charge under low-power conditions, relying on the power battery to maintain operation.

[0007] Furthermore, the specific process of S1 is as follows: Startup phase: Design a startup control algorithm to control the hydrogen and oxygen supply rate and pressure, as well as the stack preheating process, based on the characteristics of the hydrogen fuel cell and environmental parameters. By gradually increasing the reactant flow rate and optimizing the stack heating curve, a fast and smooth startup is achieved, reducing energy loss and impact on the stack during startup. Steady-state operation: During steady-state operation, an adaptive control algorithm is used to monitor the output voltage, current, and temperature parameters of the fuel cell stack in real time. The reactant supply and the operating status of the cooling system are dynamically adjusted according to the parameters to maintain the fuel cell stack at the optimal operating point and improve energy conversion efficiency. Power adjustment: When power output needs to be adjusted, the model predictive control algorithm combines load demand forecasts and battery internal state information to plan reactant supply and current output strategies in advance, achieving fast and accurate power response to meet power requirements under different operating conditions. Shutdown phase: Develop a shutdown control process to gradually reduce power output before stopping the hydrogen supply. At the same time, start the cooling and purging procedures of the fuel cell stack to remove residual reactants inside the stack, prevent electrolyte corrosion and electrode aging, and extend the battery life.

[0008] Furthermore, the specific steps for building a multi-dimensional system in S2 to monitor key parameters and identify hidden dangers are as follows: S201. For voltage monitoring, select the corresponding voltage sensor. For single cell voltage monitoring of the battery stack, install the voltage sensor directly at the electrode connection point of each single cell. For the total output voltage, install a voltage sensor with a matching range at the corresponding node of the output line to ensure accurate measurement of voltage changes under different operating conditions. S202. Current monitoring: Select the corresponding current sensor, select a sensor with a matching range based on the rated current of the stack and the maximum current peak, and install it in the main current loop; S203. Temperature sensor layout: Install temperature sensors at various levels of the stack, including the anode and cathode sides, membrane electrode, cooling system inlet and outlet, and hydrogen and oxygen inlet pipes. Thermocouple sensors are used for monitoring high-temperature areas, and thermistor sensors are used for monitoring low- and normal-temperature areas. S204. Pressure sensors are installed at the nodes of the hydrogen and oxygen supply pipelines, the reaction chamber inside the stack, and the gas exhaust port. Pressure sensors with corresponding ranges and accuracy are selected according to the pressure ranges of different parts. S205. Gas concentration sensors are installed at the air inlet and outlet, as well as at detection points within the fuel cell stack. For hydrogen concentration sensors, use sensors based on electrochemical or thermal conductivity principles to ensure rapid and accurate detection of changes in hydrogen concentration. Oxygen concentration sensors are selected and installed based on their specific detection principles to enable real-time monitoring of oxygen concentration. S206. Set the data acquisition frequency based on the system's requirements for timely fault detection. For voltage and current parameters, specifically: According to Shannon's sampling theorem, for voltage and current signals, the sampling frequency satisfy ,in It is the highest frequency component of the signal. The highest frequency component is estimated by performing fast Fourier transform on the signal. Specifically: for the current signal , after fast Fourier transform, the frequency domain signal is obtained , find the frequency corresponding to its highest amplitude, then the sampling frequency At least ; For temperature and pressure signals, the sampling frequency is calculated based on the dynamic characteristics of the signal and the system's requirements for response speed. The first-order inertia link is used to perform the dynamic response of the temperature or pressure sensor. The transfer function is: ,in is the time constant. By calculating the response time of the sensor to the step input and combining it with the safety factor, the sampling frequency is determined. If the response time of the sensor to the step input is , then the sampling frequency Set to ,in is the safety factor; S207. Improve data acquisition accuracy through calibration and error compensation. Regularly calibrate sensors and use standard signal sources or standard physical quantities for comparison and calibration. Use software algorithms to perform error compensation on the collected data.

[0009] Furthermore, the specific operation steps of S207 for temperature compensation of the pressure sensor are as follows: For the temperature compensation of the pressure sensor, the polynomial fitting method is used to establish the compensation formula, and the output value of the pressure sensor is recorded as , the temperature is , after experiments, we get a set of data , , is the number of experimental data points, and the relationship between pressure and temperature is obtained by least square fitting: , where is the fitting coefficient, is the order of the polynomial, and the pressure measurement values ​​at different temperatures are compensated by this formula to improve the accuracy.

[0010] Furthermore, the S2 further includes: Fault diagnosis model establishment: Based on the operating principle and internal structure of hydrogen fuel cells, a physical model covering the electrochemical reaction, gas diffusion, and heat transfer processes is established. By modeling and analyzing the electrochemical, mass conservation, and energy conservation equations, the inherent relationships and laws of system parameters under normal working conditions are determined; Using physical models, combined with stack design performance parameters and chemical reaction kinetics, the theoretical normal ranges of voltage, current, temperature, and pressure parameters under different operating conditions are calculated, and safety margins are set as thresholds for preliminary abnormality assessments. Data analysis algorithm integration and optimization: Use threshold judgment, trend analysis, and correlation analysis algorithms to process collected data, determine whether the data exceeds the threshold, monitor parameter change trends, and analyze parameter correlations as fault warning signals; Adopting an adaptive threshold algorithm to automatically adjust the threshold according to working conditions and environmental changes, a multi-parameter fusion analysis algorithm comprehensively analyzes multiple parameters to reduce false positives and false negatives, and using data mining technology to extract potential fault feature patterns to optimize the model; Application of artificial intelligence technology: Collect normal and fault data under various working conditions to build a training data set, classify and label faults to ensure data accuracy and completeness, and preprocess the data, including cleaning and normalization, removing outliers and noise, and unifying measurement ranges and units; Select the corresponding artificial intelligence model based on system complexity and data characteristics, determine the network structure for the neural network, select the kernel function and parameters for the support vector machine, use the data set to train the model, optimize the parameters through iteration, use cross-validation to evaluate the performance, and continuously adjust the structure and parameters to improve generalization ability and prediction accuracy; Continuously collect new data during system operation. When new fault types or operating conditions change, add new data to retrain and optimize the model to improve fault detection and identification capabilities. When a system anomaly is detected, the fault handling mechanism is immediately activated and appropriate measures are taken based on the type and severity of the fault, including adjusting system operating parameters, switching to backup components, and starting safety protection programs to ensure that the system can safely shut down in a faulty state or continue to maintain limited functions while waiting for maintenance.

[0011] Furthermore, the specific operation steps of S3 are as follows: Design an intelligent energy management mechanism to optimize the energy flow between the hydrogen fuel cell and the power battery, and formulate an energy distribution strategy based on the vehicle's driving conditions, the remaining battery power, and the output capacity of the hydrogen fuel cell; Under high-power demand conditions such as vehicle acceleration and climbing, hydrogen fuel cells are used to provide the main power, while the fast response characteristics of power batteries are used to assist power output; During vehicle deceleration and braking, the energy recovery system converts kinetic energy into electrical energy and stores it in the power battery. Under low-power demand conditions, the output power of the hydrogen fuel cell is reduced, and the power battery is used to maintain vehicle operation, thereby improving the overall efficiency of the system.

[0012] Furthermore, the driving condition classification and feature extraction process in S3 is as follows: Driving conditions are divided into different types according to vehicle speed, acceleration, and road slope parameters, including acceleration conditions, climbing conditions, constant speed conditions, and deceleration conditions; Characteristic parameters are extracted for each operating condition; the slope value and continuous climbing distance under climbing conditions provide a basis for formulating targeted energy allocation strategies. A driving condition database is established through actual road testing and simulation experiments, recording the vehicle's energy consumption, power requirements, and the performance of hydrogen fuel cells and power batteries under different operating conditions. Data analysis tools are used to explore the relationship between different operating conditions and energy requirements. Establish an equivalent circuit model for the power battery. Based on the battery's charge and discharge characteristic curve, internal resistance variation, and the impact of temperature on performance, determine the battery's output capacity and charge and discharge efficiency under different remaining power and operating conditions. Through experiments, we obtain the charge and discharge data of the battery at different temperatures and current rates, fit the model parameters, and achieve accurate modeling of battery performance; The performance of hydrogen fuel cells is modeled, and the polarization curve of the stack, the impact of hydrogen supply pressure and flow on output power, and the impact of temperature and humidity on efficiency are analyzed. A mathematical model is established to determine the output characteristics of the fuel cell under different working conditions, and the model is verified and corrected through experiments.

[0013] Furthermore, the energy allocation strategy formulation process in S3 is as follows: High-power demand operating condition strategy formulation: When the vehicle is in high-power demand conditions such as acceleration or climbing, the energy management system calculates the power share that the hydrogen fuel cell should provide based on the real-time power demand and the current maximum output capacity of the hydrogen fuel cell. When the hydrogen fuel cell output can meet more than 70% of the power demand, the hydrogen supply flow rate and air compressor speed control parameters are adjusted according to this target to ensure that the fuel cell outputs the corresponding power. At the same time, the rapid response characteristics of the power battery are used to assist power output. According to the remaining power and the maximum discharge rate limit of the battery, the auxiliary power that the battery can provide is calculated. The specific formula is as follows: P bat =U bat C max Q (SOC) △t, where U bat is the battery terminal voltage, △t is the time interval, SOC is the remaining capacity of the battery, C max is the maximum discharge rate, Q is the rated capacity of the battery; The energy management system controls the battery's discharge current and voltage, releasing additional power in a short period of time. This system works in conjunction with the hydrogen fuel cell to meet the vehicle's high power requirements and reduce instantaneous impact on the fuel cell. Deceleration braking energy recovery strategy formulation: During vehicle deceleration or braking, the energy management system activates the energy recovery system and calculates the amount of energy that can be recovered based on the vehicle's deceleration and current speed. Specifically: Get vehicle kinetic energy , where m and v are the vehicle mass and speed respectively, and the energy power that can be recovered during deceleration is , where The deceleration is calculated by the formula to calculate the power that the energy recovery system can recover during the deceleration braking process; By controlling the motor's reverse rotation to work as a generator, the vehicle's kinetic energy is converted into electrical energy. The charging power of the energy recovery system is adjusted according to the remaining power and charging capacity of the power battery. When the remaining battery power is lower than the preset threshold, the battery is charged at the preset power. When the battery is about to reach more than 80%, the charging power is reduced or a braking resistor is used to dissipate energy to avoid overcharging the battery while ensuring braking safety and energy recovery efficiency. Low power demand operating condition strategy formulation: Under low-power demand conditions, the energy management system formulates a strategy based on the remaining battery charge and the hydrogen fuel cell's minimum stable operating power factor. If the remaining battery charge exceeds 50%, the hydrogen fuel cell's output power is reduced to the minimum maintenance level or the fuel cell is temporarily shut down, and the vehicle relies on the power battery to power the vehicle. Monitor battery charge changes in real time. When the battery charge drops to 30% or below, gradually increase the output power of the hydrogen fuel cell to charge the battery and provide part of the vehicle's power needs at the same time. By optimizing the start and stop timing of the hydrogen fuel cell and the output power adjustment strategy, energy loss is reduced and the overall efficiency of the system is improved.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention achieves rapid and smooth startup and efficient energy conversion of hydrogen fuel cells through a carefully designed control algorithm. During the startup phase, the energy loss and impact on the fuel cell stack are reduced by controlling the supply rate and pressure of hydrogen and oxygen and the preheating process of the fuel cell stack. During steady-state operation, an adaptive control algorithm is used to monitor and adjust the working state of the fuel cell stack in real time to maintain it operating at the optimal working point, thereby improving energy conversion efficiency. When power output needs to be adjusted, a model predictive control algorithm is used, combined with load demand prediction and battery internal state information, to achieve a rapid and accurate power response, meeting the power requirements under different working conditions. (2) The present invention has established a multi-dimensional system monitoring and fault detection mechanism. By real-time monitoring of key parameters such as voltage, current, temperature, pressure, and gas concentration, potential fault hazards can be quickly identified. This mechanism includes the selection and installation of sensors, the setting of data acquisition frequency and accuracy, and the establishment of a fault diagnosis model based on physical models and artificial intelligence technology. The integrated application of these technologies improves the accuracy and response speed of fault detection, ensuring the safe operation of the system and the continued maintenance of its functions. (3) The present invention designs an intelligent energy management mechanism to optimize the energy flow between the hydrogen fuel cell and the power battery. According to the vehicle's driving conditions, the remaining power of the battery and the output capacity of the hydrogen fuel cell, a reasonable energy distribution strategy is formulated. Under high power demand conditions, the hydrogen fuel cell provides the main power, while the power battery assists the output; during deceleration or braking, kinetic energy is converted into electrical energy through the energy recovery system; under low power demand conditions, the output power of the hydrogen fuel cell is reduced and the power battery is relied upon to maintain operation. This energy management strategy not only improves the overall efficiency of the system, but also extends the service life of the battery, providing strong technical support for the practical application of hydrogen fuel cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a diagram of the boost DCDC topology electronic and electrical architecture in the present invention; Figure 3 This is a diagram of the buck DCDC topology electronic appliance architecture in the present invention; Figure 4 This is a diagram of the buck-boost DCDC topology electronic appliance architecture in the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0018] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should further be understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0019] like Figure 1 As shown, the optimization control method for a hydrogen fuel cell comprises the following steps: Control algorithm design: During the startup phase, an algorithm is designed based on characteristics and environmental parameters to control gas supply and stack preheating to achieve a fast and smooth startup. Steady-state operation is monitored and adjusted through an adaptive algorithm to maintain the optimal working state of the stack. Power adjustment is based on a predictive algorithm combined with relevant information planning to achieve precise power response. During the shutdown phase, a process is formulated to reduce power and initiate cooling and purge to protect the battery. Startup phase: Design a startup control algorithm to control the hydrogen and oxygen supply rate and pressure, as well as the stack preheating process, based on the characteristics of the hydrogen fuel cell and environmental parameters. By gradually increasing the reactant flow rate and optimizing the stack heating curve, a fast and smooth startup is achieved, reducing energy loss and impact on the stack during startup. Steady-state operation: During steady-state operation, an adaptive control algorithm is used to monitor the stack's output voltage, current, temperature and other parameters in real time. The reactant supply and cooling system's operating status are dynamically adjusted based on these parameters to maintain the stack operating at the optimal operating point and improve energy conversion efficiency. Power adjustment: When power output needs to be adjusted, the model predictive control algorithm combines load demand forecasts and battery internal state information to plan reactant supply and current output strategies in advance, achieving fast and accurate power response to meet power requirements under different operating conditions. Shutdown phase: Develop a shutdown control process to gradually reduce power output before stopping the hydrogen supply. At the same time, start the cooling and purging procedures of the fuel cell stack to remove residual reactants inside the stack, prevent electrolyte corrosion and electrode aging, and extend the battery life.

[0020] Fault detection and handling: Build a multi-dimensional system to monitor key parameters and identify potential hazards, including selecting appropriate sensors for each parameter and determining their location, setting frequency and accuracy, processing data based on physical models, selecting model training and updating, and taking measures to ensure system safety or maintain functionality based on the fault situation. Build a multi-dimensional fault detection system to monitor the hydrogen fuel cell system's key parameters, such as voltage, current, temperature, pressure, and gas concentration, in real time. By establishing a fault diagnosis model and applying data analysis algorithms and artificial intelligence technology, potential fault hazards can be quickly identified. The specific process is as follows: Sensor selection and installation location determination: For voltage monitoring, high-precision, high-sampling-frequency voltage sensors are selected. For single-cell voltage monitoring of the fuel cell stack, miniaturized voltage sensors are installed directly at the electrode connection points of each single cell. For total output voltage, voltage sensors with appropriate ranges are installed at key nodes of the output circuit to ensure accurate measurement of voltage changes under different operating conditions. For current monitoring, low-resistance, high-sensitivity current sensors are used. Sensors with appropriate ranges are selected based on the rated current of the fuel cell stack and the maximum possible current peak, and are installed in the main current loop. The sensor must be installed in a manner that does not affect the normal flow of current and can effectively shield electromagnetic interference. In terms of temperature sensor layout, temperature sensors are installed at different levels of the fuel cell stack (such as the anode side, cathode side, and membrane electrode), at the inlet and outlet of the cooling system, and at the hydrogen and oxygen inlet pipes. Thermocouple sensors are selected for monitoring high-temperature areas, and thermistor sensors are selected for monitoring low-temperature and normal-temperature areas to ensure that temperature information of all key parts of the system is fully obtained; pressure sensors are installed at key nodes of the hydrogen and oxygen supply pipes, the reaction chamber inside the fuel cell stack, and the gas exhaust port. Pressure sensors with appropriate range and accuracy are selected according to the pressure range of different parts. For example, a pressure sensor with a larger range can be selected at the front end of the supply pipe, while a high-precision small pressure sensor is required inside the fuel cell stack; gas concentration sensors are installed at the air inlet, outlet, and specific detection points inside the fuel cell stack; for hydrogen concentration sensors, sensors based on electrochemical principles or thermal conductivity principles are used to ensure that changes in hydrogen concentration can be detected quickly and accurately; oxygen concentration sensors are selected and installed according to the specific detection principle (such as the zirconia principle) to achieve real-time monitoring of oxygen concentration.

[0021] Data acquisition frequency and accuracy setting: Set the data acquisition frequency according to the system's requirements for timely fault detection. For voltage and current parameters, set a higher acquisition frequency, such as 100 to 1000 acquisitions per second, so as to capture rapidly changing electrical signal anomalies in a timely manner; for temperature and pressure parameters, set the acquisition frequency to 10 to 50 times per second according to their change rate; for gas concentration parameters, set the acquisition frequency to 1 to 5 times per second due to their relatively slow changes; specifically: According to Shannon's sampling theorem, for signals that change rapidly, such as voltage and current, the sampling frequency Need to meet ,in It is the highest frequency component of the signal. In practical applications, the highest frequency component is estimated by performing Fast Fourier Transform (FFT) on the signal. For example, for the current signal , after FFT, we get the frequency domain signal , find the frequency corresponding to its highest amplitude, then the sampling frequency is at least. Accurately calculating the sampling frequency helps to more accurately capture signal changes and avoid aliasing. For temperature and pressure signals, their change frequency is relatively low. The sampling frequency is calculated based on the dynamic characteristics of the signal and the system's requirements for response speed. For example, using a first-order inertia link to approximate the dynamic response of a temperature or pressure sensor, the transfer function is (in is the time constant), the sampling frequency is determined by calculating the response time of the sensor to the step input and combining a certain safety factor. For example, if the response time of the sensor to the step input is , then the sampling frequency Set to ( is the safety factor, which should be between 5 and 10).

[0022] At the same time, the accuracy of data acquisition is improved through calibration and error compensation. Sensors are calibrated regularly and compared with standard signal sources or standard physical quantities. Software algorithms are used to compensate for errors in collected data, such as correcting the measured value of the pressure sensor according to the temperature compensation algorithm, to ensure that the collected data is accurate and reliable. Specifically: For the temperature compensation of the pressure sensor, the polynomial fitting method is used to establish the compensation formula. Assume that the output value of the pressure sensor is , the temperature is , after experiments, we get a set of data ( , is the number of experimental data points), the relationship between pressure and temperature is obtained by least square fitting: (in is the fitting coefficient, is the order of the polynomial), the pressure measurement values ​​at different temperatures are compensated by this formula to improve the accuracy.

[0023] Fault diagnosis model establishment: Based on the physical model, the fault diagnosis foundation is constructed. A physical model is established according to the working principle and internal structure of the hydrogen fuel cell. The model covers the electrochemical reaction process, gas diffusion process, and heat transfer process. By modeling and analyzing the electrochemical equations, mass conservation equations, and energy conservation equations within the fuel cell stack, the inherent relationship and interaction laws between the various system parameters under normal working conditions are analyzed. The physical model is used to determine the normal operating range and threshold of each parameter. For example, based on the design performance parameters of the fuel cell stack and the principles of chemical reaction kinetics, the theoretical normal range of parameters such as voltage, current, temperature, and pressure under different operating conditions is calculated, and a certain safety margin is set as the threshold to preliminarily determine whether there is any abnormality in the system. Integration and optimization of data analysis algorithms: Use data analysis algorithms to process collected data, and use threshold judgment algorithms to compare real-time collected data with preset thresholds. When the threshold range is exceeded, it is marked as an abnormal data point. Trend analysis algorithms are used to monitor parameter change trends, such as the identification of abnormal trends such as a continuous drop in voltage or a rapid rise in temperature. Correlation analysis algorithms analyze the relationship between different parameters, such as the correlation between hydrogen flow and stack voltage. When the correlation changes abnormally, it can be used as a fault warning signal. Optimize data analysis algorithms to improve the accuracy and reliability of fault detection. Use an adaptive threshold algorithm to automatically adjust the threshold according to the system's operating conditions and environmental changes. Multi-parameter fusion analysis algorithms conduct a comprehensive analysis of multiple parameters such as voltage, current, and temperature to reduce false alarms and missed reports. At the same time, use data mining technology to extract potential fault feature patterns from a large amount of historical operating data for optimizing diagnostic models. Application of artificial intelligence technology: Collect normal operation data and fault data of hydrogen fuel cell systems under various operating conditions, construct a training data set, and conduct detailed classification and labeling of different types of faults to ensure the accuracy and completeness of the training data; preprocess the data, including data cleaning and normalization, to remove outliers and noise data, and normalize data of different ranges and units to make them comparable and facilitate learning and analysis by artificial intelligence algorithms; select appropriate artificial intelligence algorithm models, such as neural network models, support vector machine models, or decision tree models. For neural network models, determine the network structure based on the complexity of the system and data characteristics, including parameters such as the number of layers and the number of neurons; for support vector machine models, select appropriate kernel functions and parameters for optimization, use the training data set to train the artificial intelligence model, and optimize the model parameters through repeated iterations to enable it to accurately identify fault characteristic patterns. During the training process, use cross-validation and other techniques to evaluate the model performance, continuously adjust the model structure and parameters, and improve the model's generalization ability and prediction accuracy; during the actual operation of the system, continuously collect new data and update the artificial intelligence model online. When a new fault type or operating condition change is detected, the new data is added to the training dataset and the model is retrained and optimized so that it can adapt to the dynamic changes of the system and continuously improve the ability to detect and identify faults.

[0024] Once a system anomaly is detected, the fault handling mechanism is immediately activated. Depending on the type and severity of the fault, appropriate measures are taken, such as adjusting system operating parameters, switching to backup components, and starting safety protection programs, to ensure that the system can safely shut down in a faulty state or continue to maintain limited functions while waiting for maintenance.

[0025] Energy management: Develop strategies to optimize energy flow based on the operating condition design mechanism. This includes classifying operating conditions, extracting features, and establishing models to describe battery characteristics. The system calculates and adjusts the power of the hydrogen fuel cell and power battery during high-power demand during acceleration and climbing. It calculates recovered energy and adjusts charging power during deceleration and braking. Under low-power conditions, the power of the hydrogen fuel cell is adjusted based on the battery charge, allowing operation to be maintained by the power battery. An intelligent energy management mechanism is designed to optimize the energy flow between the hydrogen fuel cell and the power battery. A reasonable energy allocation strategy is formulated based on the vehicle's driving conditions, the remaining battery charge, and the output capacity of the hydrogen fuel cell. The driving condition classification and feature extraction process is as follows: Driving conditions are categorized into different types based on vehicle speed, acceleration, and road slope parameters, including acceleration (acceleration greater than a preset threshold), climbing (road slope greater than a set angle), constant speed (speed fluctuations within a small range and acceleration approximately zero), and deceleration (negative acceleration). Characteristic parameters are extracted for each condition, such as maximum acceleration and acceleration time in acceleration conditions, and slope and continuous climbing distance in climbing conditions, to provide a basis for developing targeted energy allocation strategies. A driving condition database is established through actual road testing and simulation experiments, recording data such as vehicle energy consumption, power requirements, and the performance of hydrogen fuel cells and power batteries under different conditions. Data analysis tools are then used to explore the relationship between different conditions and energy requirements, enabling more precise strategy development. Establish an equivalent circuit model for the power battery. Based on the battery's charge and discharge characteristic curve, internal resistance variation, and the impact of temperature on performance, describe the battery's output capacity and charge and discharge efficiency under different remaining charges and operating conditions. Experimentally obtain battery charge and discharge data at different temperatures and current rates, fit model parameters, and achieve accurate modeling of battery performance. Perform performance modeling on hydrogen fuel cells, considering the stack's polarization curve, the effects of hydrogen supply pressure and flow on output power, and the effects of temperature and humidity on efficiency. Establish a mathematical model to describe the fuel cell's output characteristics under different operating conditions. Verify and modify the model through experiments to accurately evaluate its output capacity. The energy allocation strategy formulation process is as follows: Formulate a strategy for high-power demand conditions: When the vehicle is in a high-power demand condition such as acceleration or climbing, the energy management system first calculates the power share that the hydrogen fuel cell should provide based on the real-time power demand and the current maximum output capacity of the hydrogen fuel cell. When the output of the hydrogen fuel cell can meet most of the power demand (for example, 70%-80%), the control parameters such as the hydrogen supply flow rate and the air compressor speed are adjusted according to this target to make the fuel cell output the corresponding power. At the same time, the fast response characteristics of the power battery are used to assist the power output. Based on the remaining power and the maximum discharge rate limit of the battery, the auxiliary power that the battery can provide is calculated. The specific formula is as follows: P bat =U bat C max Q (SOC) △t, where U bat is the battery terminal voltage, △t is the time interval, SOC is the remaining capacity of the battery, C max is the maximum discharge rate, and Q is the rated capacity of the battery. The energy management system controls the discharge current and voltage of the battery, allowing it to release additional power in a short period of time. This system works in conjunction with the hydrogen fuel cell to meet the vehicle's high power requirements, reduce instantaneous impact on the fuel cell, and extend its service life. Deceleration and braking energy recovery strategy formulation: During vehicle deceleration or braking, the energy management system activates the energy recovery system and calculates the amount of recoverable energy based on the vehicle's deceleration and current speed. The specific formula is: The vehicle kinetic energy Ek is obtained, where m and v are the vehicle mass and speed respectively, and the energy power that can be recovered during deceleration is , where For deceleration, this formula is used to calculate the amount of power that the energy recovery system can recover during deceleration braking, so that parameters such as charging power can be properly adjusted. The vehicle's kinetic energy is converted into electrical energy by controlling the motor's reverse rotation to work as a generator. The charging power of the energy recovery system is adjusted based on the remaining power and charging acceptance of the power battery. If the remaining power of the battery is low and the charging acceptance is strong, the battery is charged at a higher power. If the battery is close to full charge, the charging power is appropriately reduced or a braking resistor is used to dissipate some energy to prevent battery overcharging while ensuring braking safety and energy recovery efficiency. Low-power demand operating condition strategy formulation: Under low-power demand conditions, such as when the vehicle is driving at low speed or idling in congested urban roads, the energy management system formulates a strategy based on factors such as the remaining battery power and the minimum stable operating power of the hydrogen fuel cell. If the remaining battery power is sufficient (for example, above 50%), the output power of the hydrogen fuel cell is reduced to the minimum maintenance level or the fuel cell is temporarily shut down, relying on the power battery to power the vehicle. The battery power level changes are monitored in real time. When the battery power level drops to a certain level (for example, below 30%), the output power of the hydrogen fuel cell is gradually increased to charge the battery and provide part of the vehicle's power needs at the same time. By optimizing the start and stop timing of the hydrogen fuel cell and the output power adjustment strategy, unnecessary energy loss is reduced and the overall efficiency of the system is improved. Under high-power demand conditions such as vehicle acceleration and climbing, hydrogen fuel cells are preferentially used to provide the main power, while the rapid response characteristics of the power battery are used to assist power output; during vehicle deceleration and braking, kinetic energy is converted into electrical energy and stored in the power battery through the energy recovery system; under low-power demand conditions, the output power of the hydrogen fuel cell is appropriately reduced, and the power battery is relied upon to maintain vehicle operation, thereby improving the overall efficiency of the system.

[0026] In energy management, it is applied to both industrial and civil power supply scenarios, adjusting the energy flow between hydrogen fuel cells and power batteries according to specific load requirements, such as the constant power requirements of industrial equipment and the intermittent load requirements of civil power supply; Energy management also includes the capacity management of the hydrogen fuel cell output end, that is, the DCDC converter function. The functional principle includes three topological principles and the overall electronic and electrical architecture of the product. Figure 2-Figure 4 As shown, Figure 2-Figure 4 The letters of are as follows: MCU is the motor controller, FCS is the hydrogen fuel cell stack, PU is the protection unit, DCDC is the DC-DC converter, BOP is the hydrogen fuel cell system accessory, EMS is the energy management system, BAT is the battery, FECU is the fuel cell electronic control unit, CHARGE indicates the battery charging status, and CAN is the controller area network; The three topology principles specifically include: Boost DCDC topology (BOOST topology): Reference Figure 2 As shown in the figure, the input voltage (PU related to the hydrogen fuel cell) is connected to the series circuit of the inductor and the switch tube. When the switch tube is turned on, the current flows through the inductor to store energy, and the diode is cut off at this time; when the switch tube is turned off, the back electromotive force generated by the inductor is superimposed on the input voltage and charges the output capacitor and the load (such as BAT) through the diode, thereby achieving a boost function. For example, when the output voltage of the hydrogen fuel cell is low and cannot meet the needs of certain loads with high voltage requirements, the BOOST topology can increase the voltage to a suitable value. Buck topology: Reference Figure 3 As shown in the figure, in the BUCK topology, the input voltage (PU related to the hydrogen fuel cell) is first connected to the series circuit of the switch tube and the inductor, and then connected to the output capacitor and the load (such as BAT) through the diode. When the switch tube is turned on, the input voltage charges the inductor and supplies power to the load. When the switch tube is turned off, the inductor continues to supply power to the load through the diode. At this time, the output voltage is lower than the input voltage, realizing the step-down function. For example, if the output voltage of the hydrogen fuel cell is too high, the BUCK topology is used to reduce the voltage to an appropriate value for some low-voltage electrical devices (such as certain sensors or low-voltage electronic components). Buck-boost topology: Reference Figure 4 As shown, this topology combines the characteristics of both the buck and boost topologies. When the switch is on, current flows through the inductor to store energy, while a portion of this energy can be supplied to the load via the capacitor and diode (part of the working principle of the buck topology). When the switch is off, the back electromotive force generated by the inductor is superimposed on the input voltage (boost topology), charging the output capacitor and the load through the diode. This topology flexibly implements boost or buck functions as needed, adapting to the mismatch between the output voltage and load voltage requirements of hydrogen fuel cells under different operating conditions, thereby achieving more effective energy management and utilization. For example, when the load voltage fluctuates above and below the hydrogen fuel cell output voltage, the buck-boost topology can dynamically adjust the voltage to meet the load demand.

[0027] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An optimization control method for a hydrogen fuel cell, characterized in that: The following steps are involved: S1. Control algorithm design: In the startup phase, the algorithm is designed according to the characteristics and environmental parameters to control the gas supply and stack preheating to achieve fast and smooth startup. Steady-state operation is monitored and adjusted by an adaptive algorithm to maintain the optimal working state of the stack. Power adjustment is based on a prediction algorithm combined with relevant information planning to achieve accurate power response. In the shutdown phase, a process is formulated to reduce power and start cooling and purge to protect the battery. S2. Fault detection and handling: Build a multi-dimensional system to monitor key parameters and identify hidden dangers, including selecting sensors for parameters and determining their locations, setting frequencies and accuracy, processing data based on physical models, selecting model training and updating, and taking measures to ensure system safety or maintain functionality based on the fault situation; S3. Energy management: Develop strategies to optimize energy flow based on the operating condition design mechanism, including classifying operating conditions to extract features and establish models to describe battery characteristics; calculating and adjusting the power of hydrogen fuel cells and power batteries when high power is required during acceleration and climbing; calculating recovered energy and adjusting charging power during deceleration and braking; and adjusting the power of hydrogen fuel cells according to the battery power under low-power conditions and relying on power batteries to maintain operation.

2. The optimization control method for a hydrogen fuel cell according to claim 1, characterized in that: The specific process of S1 is as follows: Startup phase: Design a startup control algorithm to control the supply rate and pressure of hydrogen and oxygen and the preheating process of the fuel cell according to the characteristics of the hydrogen fuel cell and environmental parameters. By gradually increasing the reactant flow rate and optimizing the fuel cell heating curve, a fast and smooth startup is achieved, reducing energy loss and impact on the fuel cell during the startup process. Steady-state operation: During steady-state operation, an adaptive control algorithm is used to monitor the output voltage, current, and temperature parameters of the fuel cell stack in real time, and the supply of reactants and the working state of the cooling system are dynamically adjusted according to the parameters to maintain the fuel cell stack at the optimal operating point and improve energy conversion efficiency; Power adjustment: When the power output needs to be adjusted, based on the model predictive control algorithm, combined with the load demand forecast and the battery internal state information, the reactant supply and current output strategy are planned in advance to achieve fast and accurate power response and meet the power requirements under different working conditions; Shutdown phase: Develop a shutdown control process to gradually reduce power output before stopping the hydrogen supply. At the same time, start the cooling and purging procedures of the fuel cell stack to remove residual reactants inside the fuel cell stack, prevent electrolyte corrosion and electrode aging, and extend the battery life.

3. The optimization control method for a hydrogen fuel cell according to claim 1, characterized in that: The specific steps for building a multi-dimensional system to monitor key parameters and identify hidden dangers in S2 are as follows: S201. For voltage monitoring, select the corresponding voltage sensor. For the voltage monitoring of the single cell of the battery stack, install the voltage sensor directly at the electrode connection point of each single cell; for the total output voltage, install a voltage sensor with a matching range at the corresponding node of the output line to ensure that the voltage changes under different working conditions can be accurately measured; S202. Current monitoring: Select the corresponding current sensor, select a sensor with a matching range according to the rated current of the battery stack and the maximum current peak, and install it in the main current loop; S203. Temperature sensor layout: Temperature sensors are installed at different levels of the stack, including the anode side, cathode side, membrane electrode, cooling system inlet and outlet, and hydrogen and oxygen intake pipes. Thermocouple sensors are used for high temperature area monitoring, and thermistor sensors are used for low temperature and normal temperature area monitoring; S204. The pressure sensors are installed at the nodes of the hydrogen and oxygen supply pipelines, the reaction chamber inside the fuel cell stack, and the gas discharge port. The pressure sensors with corresponding range and accuracy are selected according to the pressure range of different parts; S205. Gas concentration sensors are installed at the air inlet, air outlet and detection points inside the fuel cell stack; for hydrogen concentration sensors, sensors based on electrochemical principles or thermal conductivity principles are used to ensure that changes in hydrogen concentration can be detected quickly and accurately; oxygen concentration sensors are selected and installed according to specific detection principles to achieve real-time monitoring of oxygen concentration; S206. Set the data acquisition frequency according to the system's requirements for timely fault detection. For voltage and current parameters, specifically: According to Shannon sampling theorem, for voltage and current signals, the sampling frequency satisfy ,in It is the highest frequency component of the signal. The highest frequency component is estimated by fast Fourier transform of the signal. Specifically: for the current signal , after fast Fourier transform, the frequency domain signal is obtained , find the frequency corresponding to its highest amplitude, then the sampling frequency At least ; For temperature and pressure signals, the sampling frequency is calculated based on the dynamic characteristics of the signal and the system's requirements for response speed. The first-order inertia link is used to perform the dynamic response of the temperature or pressure sensor. The transfer function is: ,in is the time constant. By calculating the response time of the sensor to the step input and combining it with the safety factor, the sampling frequency is determined. If the response time of the sensor to the step input is , then the sampling frequency Set to ,in is the safety factor; S207. Improve data acquisition accuracy through calibration and error compensation, perform regular calibration on sensors, use standard signal sources or standard physical quantities for comparison and calibration; use software algorithms to perform error compensation on acquired data.

4. The optimization control method for a hydrogen fuel cell according to claim 3, characterized in that: The specific operation steps of S207 for temperature compensation of pressure sensor are as follows: For the temperature compensation of the pressure sensor, the compensation formula is established by using the polynomial fitting method, and the output value of the pressure sensor is recorded as , the temperature is , after experiments, we get a set of data , , is the number of experimental data points. The relationship between pressure and temperature obtained by least squares fitting is: , where is the fitting coefficient, is the order of the polynomial, and the pressure measurement values ​​at different temperatures are compensated by this formula to improve the accuracy.

5. The optimization control method for a hydrogen fuel cell according to claim 1, characterized in that: The S2 further includes: Establishment of fault diagnosis model: Based on the working principle and internal structure of hydrogen fuel cells, a physical model covering electrochemical reaction, gas diffusion, and heat transfer process is established. By modeling and analyzing electrochemical, mass conservation, and energy conservation equations, the internal relationship and law of system parameters under normal working conditions are determined; Using physical models, combined with the stack design performance parameters and chemical reaction kinetics, the theoretical normal range of voltage, current, temperature, and pressure parameters under different operating conditions is calculated, and a safety margin is set as a threshold for preliminary judgment of abnormalities; Data analysis algorithm integration and optimization: Use threshold judgment, trend analysis, and correlation analysis algorithms to process collected data, determine whether the data exceeds the threshold, monitor parameter change trends, and analyze parameter correlations as fault warning signals; Adaptive threshold algorithm is used to automatically adjust the threshold according to working conditions and environmental changes. Multi-parameter fusion analysis algorithm comprehensively analyzes multiple parameters to reduce false positives and false negatives. Data mining technology is used to extract potential fault feature patterns to optimize the model. Application of artificial intelligence technology: collect normal and fault data under various working conditions to build training data sets, classify and label faults to ensure data accuracy and completeness, pre-process data, including cleaning and normalization, remove outliers and noise, and unify ranges and units; Select the corresponding artificial intelligence model according to the system complexity and data characteristics, determine the network structure for the neural network, select the kernel function and parameters for the support vector machine, use the data set to train the model, optimize the parameters through iteration, use cross-validation to evaluate the performance, and continuously adjust the structure and parameters to improve the generalization ability and prediction accuracy; Continuously collect new data during system operation. When new fault types or operating conditions change, add new data to retrain and optimize the model to improve fault detection and identification capabilities. When a system abnormality is detected, the fault handling mechanism is immediately activated, and appropriate measures are taken according to the type and severity of the fault, including adjusting system operating parameters, switching backup components, and starting safety protection programs to ensure that the system can safely shut down in a faulty state or continue to maintain limited functions while waiting for maintenance.

6. The optimization control method for a hydrogen fuel cell according to claim 1, characterized in that: The specific operation steps of S3 are as follows: Design intelligent energy management mechanisms to optimize the energy flow between hydrogen fuel cells and power batteries, and formulate energy allocation strategies based on the vehicle's driving conditions, the remaining battery power, and the output capacity of hydrogen fuel cells; Under high power demand conditions such as vehicle acceleration and climbing, hydrogen fuel cells are used to provide the main power, while the rapid response characteristics of power batteries are used to assist power output; During vehicle deceleration and braking, the energy recovery system converts kinetic energy into electrical energy and stores it in the power battery. Under low power demand conditions, the output power of the hydrogen fuel cell is reduced, and the vehicle relies on the power battery to maintain operation, thereby improving the overall efficiency of the system.

7. The optimization control method for a hydrogen fuel cell according to claim 6, characterized in that: The driving condition classification and feature extraction process in S3 is as follows: The driving conditions are divided into different types according to the vehicle speed, acceleration and road slope parameters, including acceleration condition, climbing condition, constant speed condition and deceleration condition; Extract characteristic parameters for each working condition; the slope value and continuous climbing distance under climbing conditions provide a basis for formulating targeted energy allocation strategies; establish a driving condition database through actual road tests and simulation experiments, record the vehicle's energy consumption, power requirements, and performance data of hydrogen fuel cells and power batteries under different working conditions, and use data analysis tools to explore the relationship between different working conditions and energy requirements; Establish an equivalent circuit model of the power battery, and determine the output capacity and charge and discharge efficiency of the battery under different remaining power and working conditions based on the battery's charge and discharge characteristic curve, internal resistance variation law, and the impact of temperature on performance; Through experiments, the charging and discharging data of the battery at different temperatures and current rates are obtained, and the model parameters are fitted to achieve accurate modeling of battery performance; The performance of hydrogen fuel cells is modeled, and the polarization curve of the stack, the impact of hydrogen supply pressure and flow on output power, and the impact of temperature and humidity on efficiency are analyzed. A mathematical model is established to determine the output characteristics of the fuel cell under different working conditions, and the model is verified and corrected through experiments.

8. The optimization control method for a hydrogen fuel cell according to claim 6, characterized in that: The energy allocation strategy formulation process in S3 is as follows: High-power demand condition strategy formulation: When the vehicle is in a high-power demand condition such as acceleration or climbing, the energy management system calculates the power share that the hydrogen fuel cell should provide based on the real-time power demand and the current maximum output capacity of the hydrogen fuel cell. When the output of the hydrogen fuel cell can meet more than 70% of the power demand, the hydrogen supply flow and air compressor speed control parameters are adjusted according to this target to make the fuel cell output the corresponding power; At the same time, the quick response characteristics of the power battery are used to assist power output. According to the remaining power and the maximum discharge rate limit of the battery, the auxiliary power that the battery can provide is calculated. The specific formula is as follows: P bat =U bat C max Q (SOC) △t, where U bat is the battery terminal voltage, △t is the time interval, SOC is the remaining battery capacity, C max is the maximum discharge rate, Q is the rated capacity of the battery; The energy management system controls the discharge current and voltage of the battery, releasing additional power in a short period of time, working in conjunction with the hydrogen fuel cell to meet the vehicle's high power requirements and reduce instantaneous impact on the fuel cell; Deceleration braking energy recovery strategy formulation: During vehicle deceleration or braking, the energy management system activates the energy recovery system and calculates the amount of recoverable energy based on the vehicle's deceleration and current speed. Specifically: Get vehicle kinetic energy , where m and v are the vehicle mass and speed respectively, and the energy power that can be recovered during deceleration is , where is the deceleration, and the formula is used to calculate the power that the energy recovery system can recover during deceleration braking; The vehicle's kinetic energy is converted into electrical energy by controlling the motor to reverse and work as a generator; the charging power of the energy recovery system is adjusted according to the remaining power and charging acceptance capacity of the power battery; When the remaining battery power is lower than the preset threshold, the battery is charged at the preset power; when the battery is about to reach a state of more than 80%, the charging power is reduced or a braking resistor is used to consume energy to avoid overcharging the battery while ensuring braking safety and energy recovery efficiency; Low power demand condition strategy formulation: Under low power demand conditions, the energy management system formulates strategies based on the remaining battery power and the minimum stable operating power factor of the hydrogen fuel cell; If the remaining battery power is higher than 50%, the output power of the hydrogen fuel cell is reduced to the minimum maintenance level or the fuel cell is temporarily shut down, and the vehicle is powered by the power battery; Monitor battery charge changes in real time. When the battery charge drops to 30% or below, gradually increase the output power of the hydrogen fuel cell to charge the battery and provide part of the vehicle's power needs at the same time. Reduce energy loss and improve the overall efficiency of the system by optimizing the start and stop timing of the hydrogen fuel cell and the output power adjustment strategy.

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