Gas-heat-electricity balance management and control method and system for kilowatt-level SOFC (Solid Oxide Fuel Cell) combined heat and power system

Abstract

The invention discloses a gas-heat-electricity balance management and control method and system for a kilowatt-level SOFC (solid oxide fuel cell) cogeneration system, and belongs to the field of solid oxide fuel cells. The method comprises the following steps: setting initial operation parameters according to structure parameters and design working conditions of an SOFC electric pile, a heat exchanger, a reformer and a catalytic burner; a module start-stop coordination instruction is generated through the integrated controller, and temperature and pressure changes of all the modules in the start-stop process are controlled; system operation data are monitored in real time through a sensor, real-time diagnosis is conducted through a multi-dimensional data fusion algorithm, and abnormal states such as over-burning, over-air and temperature runaway are recognized; and according to the diagnosis result, the fuel flow, the air flow, the heat exchange power and the like are adjusted through a multivariable dynamic regulation and control strategy, so that reactor temperature fluctuation is controlled, and a temperature runaway early warning signal is generated. According to the method, the overall stability and efficiency of the SOFC combined heat and power system are improved by optimizing gas-heat-power balance management and control.

Description

Technical Field

[0001] The present application relates to the technical field of solid oxide fuel cells (SOFCs), and in particular to a method and system for controlling the gas-heat-electricity balance of a kilowatt-class SOFC combined heat and power system. Background Art

[0002] As a highly efficient and clean energy conversion device, SOFC is widely used in combined heat and power systems due to its high efficiency and environmentally friendly features. However, in actual operation, SOFC systems often face problems such as uneven temperature distribution, imbalance in gas, heat, and power, over-combustion, over-air, and temperature runaway. These problems not only affect cell efficiency but also lead to performance degradation and shorten the system's service life.

[0003] In order to meet the application requirements of SOFC for high efficiency, long life, and multiple start-up and shutdown, the five modules, including the fuel cell stack, heat exchange, reforming, catalytic combustion, and gas-heat-power balance, must be optimized to ensure their coordinated operation and avoid the impact of overload or imbalance of a single module on the performance of the entire system. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and system for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, in a first aspect, a method for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system is provided. The kilowatt-class SOFC combined heat and power system comprises: a SOFC stack, a heat exchanger, a reformer, a catalytic burner, and an integrated controller. The method is executed by the integrated controller and comprises the following steps:

[0006] Initial operating parameters are set based on the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic burner, and module start-stop coordination instructions are generated through an integrated controller; the module includes the SOFC stack, heat exchanger, reformer, and catalytic burner;

[0007] Acquiring real-time monitoring data of the combined heat and power system from a plurality of sensors disposed in the SOFC stack, heat exchanger, reformer, catalytic burner, and gas heat flow path;

[0008] Based on the real-time monitoring data, a multi-dimensional data fusion algorithm is used to perform real-time diagnosis on the operating efficiency indicators of the cogeneration system to generate a system status identification result, wherein the system status identification result is used to identify overfire, over-air, local temperature runaway and abnormal heat exchange conditions;

[0009] According to the system state identification result and the initial operating parameters, the fuel flow, air flow, heat exchange power and fuel reforming ratio of the cogeneration system are adjusted in real time through a multivariable dynamic control strategy. Based on the module start-stop coordination instructions, control instructions are generated to control the stack temperature fluctuation amplitude, and a temperature runaway warning signal is generated based on the temperature gradient threshold.

[0010] In a second aspect, a gas-heat-electricity balance control system for a kilowatt-class SOFC combined heat and power system is provided, the gas-heat-electricity balance control system comprising:

[0011] Multiple sensors are installed in the SOFC stack, heat exchanger, reformer, catalytic burner and gas heat flow path to collect real-time monitoring data of the cogeneration system;

[0012] An integrated controller is connected to the sensor signal, and the integrated controller includes:

[0013] a parameter setting module for setting initial operating parameters based on the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic combustor; specifically, the parameter setting module is used to set the fuel flow rate, air flow rate, startup temperature, pressure, and gas ratio among the initial operating parameters based on the anode effective area of ​​the SOFC stack, the heat transfer coefficient of the heat exchanger, the catalyst loading of the reformer, and the volume parameters of the catalytic combustor;

[0014] A start-stop control module, connected to the parameter setting module for generating module start-stop coordination instructions;

[0015] Specifically, the module start-stop coordination instructions include the sequential start-up logic of the SOFC stack, heat exchanger, reformer and catalytic burner, and control the temperature gradient rate and pressure gradient change rate of each module;

[0016] A data fusion algorithm module is used to calculate the operation efficiency index based on the real-time monitoring data using a multi-dimensional data fusion algorithm to generate a system status identification result;

[0017] Specifically, the data fusion algorithm module is specifically used to calculate the gas heat utilization rate, stack temperature uniformity, oxygen utilization rate and fuel utilization rate based on the real-time monitoring data of the fuel cell stack inlet and outlet gas temperature, fuel flow rate, air flow rate, stack temperature distribution and residual fuel concentration, using a multi-dimensional data fusion algorithm to generate a system status recognition result including an overfire risk probability value, an over-air risk probability value, a local runaway temperature coordinate and a heat exchange abnormality status indicator;

[0018] A control instruction generation module is used to adjust the fuel flow, air flow, heat exchange power and fuel reforming ratio of the kilowatt-class SOFC cogeneration system in real time through a multivariable dynamic control strategy based on the system state identification result and the initial operating parameters, generate control instructions based on the module start and stop coordination instructions to control the stack temperature fluctuation amplitude, and generate a temperature runaway warning signal based on the temperature gradient threshold.

[0019] Specifically, the control instruction generation module is used to generate fuel flow correction, air flow compensation, heat exchange power adjustment and fuel reforming ratio dynamic adjustment coefficient through a multivariable dynamic control strategy, and generate a temperature runaway warning signal based on the temperature gradient threshold.

[0020] Furthermore, the gas-heat-power balance management and control system may also include a comprehensive control strategy module, which is data-connected to the control instruction generation module, and is used to establish and execute a comprehensive control strategy for the kilowatt-level SOFC cogeneration system based on the system state identification results, the real-time monitoring data and the initial operating parameters.

[0021] The short-time scale control strategy maintains gas heat balance by adjusting the opening of the anode tail gas circulation valve in seconds and the cathode inlet temperature in minutes. The long-time scale control strategy dynamically optimizes the reformer operating temperature setting value and the fuel ratio compensation coefficient based on the stack polarization curve offset and the residual fuel concentration change trend.

[0022] The above technical solution has the following beneficial technical effects:

[0023] Gradient temperature and pressure control based on module start-stop coordination instructions effectively reduces thermomechanical stress during system startup and shutdown, improving equipment operational reliability. The combination of a distributed sensor network and a multidimensional data fusion algorithm establishes high-precision system state recognition capabilities, enabling real-time diagnosis of abnormal operating conditions and early warnings to avoid runaway temperatures and overfire risks. A multivariable dynamic control strategy improves gas-heat utilization efficiency and stack temperature uniformity through the coordinated adjustment of the fuel reforming ratio and heat load. This solution deeply integrates gas-heat balance control with electrochemical property optimization, resolving the technical issues of traditional SOFC systems, such as the difficulty of multi-parameter coupled control and the severe degradation of startup and shutdown performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are provided for a better understanding of the present invention and are not intended to limit the present invention.

[0025] Figure 1 This is a flow chart of a method for controlling gas-heat-electricity balance in a kilowatt-class SOFC combined heat and power system according to an embodiment of the present invention;

[0026] Figure 2This is a functional block diagram of a gas-heat-power balance control system for a kilowatt-class SOFC combined heat and power system according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of the structure of a computer system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following description of exemplary embodiments of the present invention is made in conjunction with the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding. These details should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0029] Embodiments of the present invention relate to solid oxide fuel cell (SOFC) technology, and in particular to a method for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system, to optimize the performance of multiple modules in the SOFC system, including the stack, heat exchange, reforming, catalytic combustion, and gas-heat-power balance, thereby improving its operating efficiency, extending the system life, and ensuring the feasibility of multiple starts and stops.

[0030] One of the objectives of the embodiments of the present invention is to propose a gas-heat-power balance control method for a kilowatt-class SOFC cogeneration system, focusing on studying the performance influencing factors and their mutual relationships among the five modules, and clarifying the dominant factors affecting over-combustion, over-air, fuel cell runaway temperature, performance degradation, etc., and then proposing an effective control strategy.

[0031] The kilowatt-class SOFC combined heat and power system consists of the following main components, which work together to achieve efficient electrical and thermal energy output. Its specific structure includes:

[0032] A SOFC stack (Solid Oxide Fuel Cell Stack) is a key component of a combined heat and power system, responsible for reacting fuel (hydrogen or natural gas) and oxygen to generate electricity and heat. The stack consists of multiple single cells connected by electrolyte and electrode layers to carry out electrochemical reactions.

[0033] The integrated controller is responsible for real-time monitoring and regulation of the operating status of each component of the SOFC system. It collects data from sensors and executes control strategies to ensure that all modules work together to optimize the gas, heat, and power balance.

[0034] Heat exchangers are used to recover and utilize waste heat generated in the system, transferring it to other components requiring heat energy or for other purposes such as heating. Heat exchangers work in conjunction with the fuel cell stack to channel waste heat generated by the stack into the water or air circulation system, improving overall energy efficiency.

[0035] The reformer converts the hydrocarbons in the raw fuel (e.g., natural gas, methane, etc.) into hydrogen or synthesis gas for the reactor. This process, called reforming, is essential for fuel systems using hydrocarbons like natural gas.

[0036] Catalytic burners are used to efficiently and effectively burn incompletely reacted fuel or volatiles in the fuel with minimal pollution. Catalytic reactions accelerate the combustion process, ensuring complete combustion and minimizing polluting emissions. Their purpose is to improve the overall energy efficiency of the system.

[0037] The fuel supply system is used to provide fuel (such as hydrogen, natural gas, etc.) to the reformer or fuel cell stack to ensure a stable gas supply. The fuel supply system includes fuel storage, delivery pipelines, regulating valves, and other components.

[0038] The air supply system is used to provide oxygen or air to the SOFC stack. This air supply system includes an air compressor, filters, air flow control valves, etc. to ensure that the stack receives the required oxygen or air supply.

[0039] The power output system, which converts the direct current (DC) generated by the SOFC stack into alternating current (AC) and outputs it to the grid or other power-consuming devices via an inverter, includes components such as an inverter and a transformer.

[0040] The heat output system is used to transfer waste heat generated by the fuel cell stack through heat exchange equipment or heat utilization systems to provide applications such as building heating, hot water, and process heating. It includes hot water pumps, heat exchangers, and heat pipes.

[0041] The exhaust gas treatment system is used to treat exhaust gases emitted by the SOFC system, including desulfurization and denitrification processes, to ensure that emissions meet environmental standards. The exhaust gas treatment system includes catalysts, filters, etc.

[0042] Cooling system. Since some components in the SOFC system (such as the fuel cell stack) generate a lot of heat during operation, the cooling system is used to ensure that the temperature of each component in the system remains within a safe range through water cooling or air cooling to prevent overheating.

[0043] Gas sensors and monitoring systems are used to monitor the flow, temperature, pressure, oxygen concentration, etc. of fuel and air, collect data in real time and feed it back to the integrated controller to help adjust operating parameters and ensure the safe operation of the SOFC system.

[0044] Example 1

[0045] like Figure 1 As shown, a method for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system is provided. The kilowatt-class SOFC combined heat and power system includes: a SOFC stack, a heat exchanger, a reformer, a catalytic burner, and an integrated controller. The method includes the following steps performed by the integrated controller:

[0046] Step S10: Initial operating parameters are set according to the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic burner, and module start-stop coordination instructions are generated through the integrated controller.

[0047] Specifically, the above modules include a SOFC stack, a heat exchanger, a reformer and a catalytic burner; the initial operating parameters include fuel flow, air flow, starting temperature, pressure and gas ratio; the module start-stop coordination instructions are used to control the temperature gradient rate and pressure gradient change rate of each module during the start-stop process.

[0048] Specifically, in this embodiment, the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic combustor are set based on actual application requirements and system design conditions. For example, the designed operating voltage range of the SOFC stack, the heat exchange efficiency of the heat exchanger, and the conversion efficiency of the reformer are all used as initial input conditions. The integrated controller collects these design operating condition data and combines them with preset system parameters (such as fuel flow rate, air flow rate, startup temperature, pressure, and gas ratio) to set the initial operating parameters.

[0049] Through the control module of the integrated controller, module start and stop coordination instructions are generated to ensure that when the system starts, each module can be started and stopped one by one in a preset order. In the start and stop process, the temperature gradient rate and pressure gradient change rate of each module are controlled to avoid a single module starting too fast or too slow, and to prevent equipment damage or system instability due to drastic changes in temperature and pressure.

[0050] Step S20: Acquire real-time monitoring data of the kilowatt-class SOFC cogeneration system from multiple sensors installed in the SOFC stack, heat exchanger, reformer, catalytic burner, and gas-heat flow path. The real-time monitoring data includes the stack inlet and outlet gas temperatures, fuel flow rate, air flow rate, stack temperature distribution, and residual fuel concentration.

[0051] Specifically, in this embodiment, a plurality of sensors are arranged at key locations of the SOFC stack, heat exchanger, reformer, catalytic burner and gas heat flow path. These sensors are used to collect real-time monitoring data. The temperature sensor (for example, arranged at the anode inlet, cathode outlet, etc. of the SOFC stack) is used to monitor the temperature distribution of the gas at the inlet and outlet of the stack; the pressure sensor (for example, arranged at the inlet and outlet of the stack) monitors the gas pressure change in real time; the flow sensor is used to detect the flow rate of fuel and air respectively to ensure that the supply of both meets the design requirements; the core temperature sensor is used to collect the temperature distribution data inside the stack. The gas composition sensor is used to detect the residual fuel concentration at the outlet of the catalytic burner in order to determine whether the combustion is complete. These real-time monitoring data are transmitted to the data acquisition module of the integrated controller through a shielded signal line to form a distributed sensor network. The integrated controller performs dynamic control and adjustment based on these real-time data.

[0052] Specifically, the gas-heat flow path refers to the channel or path for gas and heat exchange in the SOFC cogeneration system. These gas-heat flow paths connect multiple components in the system, such as the fuel cell stack, heat exchanger, reformer and catalytic burner, to ensure that gas and heat flow and exchange in the system in a predetermined manner. The gas-heat flow path is not only responsible for the transportation of gas, but also involves the management and regulation of heat. For example, the fuel gas in the fuel cell stack flows in through the gas-heat flow path and releases energy during the chemical reaction with the solid oxide in the fuel cell stack. At the same time, the gas-heat flow path is also connected to the heat exchanger to transfer the generated heat to other parts of the system, such as the reformer or air preheater.

[0053] Step S30: Based on the real-time monitoring data, a multi-dimensional data fusion algorithm is used to perform real-time diagnosis of the operating efficiency indicators of the kilowatt-level SOFC cogeneration system to generate system status identification results. The system status identification results are used to identify overfire, over-air, local temperature runaway and abnormal heat exchange conditions; the operating efficiency indicators include gas heat utilization rate, stack temperature uniformity, oxygen utilization rate and fuel utilization rate.

[0054] Specifically, overcombustion occurs when the fuel supply in a solid oxide fuel cell (SOFC) combined heat and power (CHP) system exceeds the required combustion or electrochemical reaction. In this situation, the excess fuel fails to react completely, resulting in reduced system efficiency and unnecessary heat or exhaust emissions. Overcombustion can affect the temperature distribution and gas flow within the stack, causing localized excessive or uneven temperatures, ultimately impacting the performance and life of the cells.

[0055] Specifically, an over-oxygen state occurs when the oxygen supply in the system is too high, exceeding the fuel cell's requirements. Excess oxygen can lead to incomplete reactions within the battery or partial damage to the electrolyte. This condition results in insufficient oxygen utilization, reducing the system's energy conversion efficiency. Furthermore, the excess oxygen can cause corrosion or other material problems within the system. Over-oxygen also leads to excessively high stack temperatures, further compromising system stability.

[0056] Specifically, hotspots occur when certain areas of a SOFC system experience significantly higher temperatures than others, creating localized hotspots. This phenomenon is caused by uneven airflow or incomplete heat exchange. Hotspots can severely impact cell components, leading to thermal stress and material damage, which in turn affects the system's long-term stability and efficiency. In some cases, hotspots can also cause chemical reaction inhomogeneities, further impairing fuel cell performance.

[0057] Specifically, heat exchange anomalies refer to problems with the heat exchange process in the SOFC system, resulting in an inefficient transfer of heat from high-temperature areas to low-temperature areas, or a significant reduction in heat exchange efficiency. These anomalies can be caused by heat exchanger failure, poor gas flow, damaged thermal conductive materials, or improper design. These anomalies can lead to uneven temperature distribution in the system, causing localized overheating or hypothermia, thereby impacting the overall performance and stability of the combined heat and power system.

[0058] Specifically, using real-time monitoring data, the integrated controller employs a multi-dimensional data fusion algorithm to perform real-time diagnosis of key indicators of the kilowatt-class SOFC combined heat and power system, including gas and heat utilization, stack temperature uniformity, oxygen utilization, and fuel utilization. This data fusion algorithm comprehensively analyzes data from various sensors to accurately identify the system's current operating status. Through real-time analysis of data such as stack temperature, fuel flow, air flow, and stack temperature distribution, the integrated controller generates system status identification results. These status identification results help the integrated controller identify problems such as overfire, over-air, localized temperature runaway, and heat exchange anomalies, promptly detecting abnormal conditions in the system and providing a basis for subsequent control strategy adjustments.

[0059] Step S40: Based on the system state identification results and initial operating parameters, the fuel flow, air flow, heat exchange power and fuel reforming ratio of the kilowatt-level SOFC cogeneration system are adjusted in real time through a multivariable dynamic control strategy. Based on the module start-stop coordination instructions, control instructions are generated to control the stack temperature fluctuation amplitude, and a temperature runaway warning signal is generated based on the temperature gradient threshold.

[0060] Specifically, based on the system state identification result obtained in step S30, the integrated controller will adjust parameters such as fuel flow, air flow, heat exchange power and fuel reforming ratio in real time through a multivariable dynamic control strategy to ensure the stable operation of the fuel cell and each module. The control instructions will be issued through the integrated controller to adjust the working state of each module to avoid local overheating or excessive pressure changes. In addition, the integrated controller will also generate a temperature runaway warning signal based on the set temperature gradient threshold. When the system detects that the local temperature gradient exceeds the set threshold, the warning signal will be triggered, and the system will automatically adjust the fuel supply or increase the heat exchange power to prevent the rapid temperature change from damaging the fuel cell and other modules.

[0061] Specifically, the fuel reforming ratio refers to the ratio of the amount of fuel used for the fuel reforming process to the total amount of fuel supplied in the solid oxide fuel cell cogeneration system. Fuel reforming is the process of converting raw fuel (such as methane or natural gas) into gas components (such as hydrogen and carbon monoxide) that are more suitable for fuel cell reactions. In the SOFC system, the adjustment of the fuel reforming ratio directly affects the supply ratio of hydrogen and carbon monoxide, which in turn affects the performance and efficiency of the fuel cell. If the reforming ratio is too high, it will lead to an incomplete reforming process and reduced thermal efficiency; if the reforming ratio is too low, it will not provide enough available hydrogen, affecting the reaction efficiency of the battery.

[0062] Specifically, the multivariable dynamic control strategy is a control method based on the interdependencies between multiple operating variables in a system. This strategy dynamically adjusts the system's operating state based on the changing trends and interactions of multiple parameters (such as fuel flow, air flow, heat exchange power, and fuel reforming ratio) in real time, ensuring the stability and efficiency of the SOFC cogeneration system under various operating conditions. Unlike traditional single-variable control, multivariable dynamic control considers the coupling effects between various variables, enabling more precise control of various system components and preventing overload or failure of any one component from impacting the operation of the entire system.

[0063] Specifically, the goal of the multivariable dynamic control strategy is to maintain the system at its optimal operating point by comprehensively adjusting multiple control variables in the system (such as fuel flow, air flow, heat exchange power, and fuel reforming ratio), thereby ensuring system stability and efficiency. This strategy not only considers the real-time status of each module, but also the interactions between modules, achieving overall performance optimization through control of each module. For example, when the fuel supply is insufficient, the air flow needs to be increased; when the heat exchanger temperature is too high, the fuel reforming ratio needs to be adjusted to reduce heat input.

[0064] Specifically, through a multivariable dynamic control strategy, the integrated controller can adjust multiple operating parameters in real time to avoid problems such as local overheating or excessive pressure fluctuations. For example, based on monitoring data, the integrated controller can identify areas in the system where excessive temperature gradients or rapid pressure fluctuations may occur. When these abnormal trends are detected, the controller will immediately issue adjustment instructions, such as reducing fuel flow, adjusting air flow, or optimizing heat exchange power. These adjustments help balance the operating conditions of each module, prevent the abnormal state of a module from causing overall system instability, and avoid equipment damage or reduced efficiency.

[0065] Specifically, a key feature of the multivariable dynamic control strategy is its ability to generate a temperature runaway warning signal based on a temperature gradient threshold. This warning is triggered when the system detects that the temperature gradient in a local area exceeds a preset threshold. This indicates that the rate of temperature change has exceeded a safe range and that the system could be damaged by the rapid temperature change. In this case, the integrated controller automatically initiates control measures, such as reducing fuel supply or increasing heat exchange power, to prevent damage to the system caused by the sudden temperature change.

[0066] Specifically, the multivariable dynamic control strategy can adopt reinforcement learning control algorithm, particle swarm optimization algorithm, optimal control algorithm, etc.

[0067] In a further embodiment, the method may further include step S50: establishing and executing a comprehensive control strategy for a kilowatt-class SOFC cogeneration system based on the system state identification results, real-time monitoring data and initial operating parameters, wherein the comprehensive control strategy realizes the maintenance of the dynamic operating stability of the system and the suppression of long-term performance degradation through the coordinated implementation of the short-time scale control strategy and the long-time scale control strategy; wherein the short-time scale control strategy suppresses the fluctuation amplitude of the stack temperature and maintains the gas-heat balance state by adjusting the airflow and thermal power at the second to minute level; the long-time scale control strategy dynamically optimizes the operating parameters of each module based on the performance degradation trend, for example, by periodically optimizing the reformer operating temperature, the fuel ratio compensation coefficient and the heat exchange power setting value, the system efficiency degradation is delayed.

[0068] Specifically, in this embodiment, the goal of the short-time scale control strategy is to cope with rapid changes in the system, such as airflow fluctuations, thermal power changes, and load fluctuations. The integrated controller calculates and adjusts the supply of fuel and air in real time based on real-time monitoring data (such as fuel flow, air flow, oxygen concentration, etc.). Specifically, when there are large fluctuations in load demand, the integrated controller adjusts the air flow and fuel flow to ensure the stable operation of the stack. For example, in the event of a sudden increase in load, the system will quickly adjust the supply of air and fuel to ensure that the electrical energy and thermal energy output of the stack will not be negatively affected. At the same time, the integrated controller will adjust the thermal power in real time based on the stack temperature distribution data (such as stack temperature distribution, core temperature, etc.) to ensure that the temperature of each area of ​​the stack is balanced. If the local temperature is too high, the integrated controller will increase the cooling power or adjust the airflow direction to avoid overheating.

[0069] Specifically, in this embodiment, the long-term control strategy is mainly used to cope with the gradual changes of the system during long-term operation, such as performance degradation, long-term load changes, and equipment aging. The integrated controller will analyze the overall performance degradation trend of the system based on historical operating data and real-time monitoring data. For example, as the system usage time increases, the efficiency of the battery will gradually decrease. The integrated controller will predict the decline in system performance based on the output power and temperature changes of the battery, and make adjustments in advance to maintain optimal performance. The integrated controller adapts to these long-term changes by dynamically optimizing operating parameters. For example, when the battery efficiency decreases, the integrated controller will adjust parameters such as fuel flow, heat exchange efficiency, and air flow to ensure that the system continues to operate efficiently. In addition, the integrated controller can also issue early warnings based on equipment aging data to remind maintenance or replacement of battery stack components to reduce the risk of failure in long-term operation.

[0070] Specifically, in this embodiment, the integrated controller coordinates and optimizes short-timescale control strategies and long-timescale control strategies. The short-timescale control strategy addresses rapidly changing operating conditions within the system and provides a quick response, while the long-timescale control strategy focuses on optimizing equipment operation and predicting system performance degradation. The combination of the two enables the system to respond to rapid changes such as load changes and airflow adjustments in the short term, while also ensuring that the system maintains an efficient and stable operating state during long-term operation. Through this coordinated operation, the integrated controller can maintain system stability and efficiency under various operating conditions, ensuring long-term stable operation and extending the service life of the SOFC system.

[0071] In this embodiment, the integrated controller achieves efficient operation and long-term stability in a kilowatt-class SOFC cogeneration system by combining short- and long-timescale control strategies. The short-timescale control strategy rapidly responds to load fluctuations and temperature changes, while the long-timescale control strategy optimizes device operation and slows performance degradation. Ultimately, the system operates efficiently and stably, extending device life and improving overall energy efficiency.

[0072] The advantages of the above technical solution are:

[0073] The gas-heat-electricity balance control method for the kilowatt-class SOFC cogeneration system of the embodiment of the present invention can effectively improve the operating efficiency and stability of the system by accurately coordinating and optimizing the various modules of the system through an integrated controller. Through real-time monitoring and multi-dimensional data fusion, the system can dynamically adjust key parameters such as fuel flow, air flow, and heat exchange power to ensure that the stack maintains stable changes in temperature and pressure during the start-up and shutdown process, thereby preventing system failures caused by overheating or overcooling. In addition, by combining short-time scale and long-time scale control strategies, it can not only quickly respond to instantaneous changes in the system, but also optimize and adjust the performance degradation during long-term operation, thereby extending the service life of the SOFC system and improving its overall energy utilization efficiency. This method makes full use of data feedback and intelligent regulation, has strong adaptability and reliability, and is suitable for SOFC cogeneration systems that are efficient, long-life, and can be started and stopped multiple times.

[0074] The method of the embodiment of the present invention can improve the overall operating efficiency and stability of the SOFC system, optimize the gas-heat-electricity balance, thereby extending the system life and reducing performance degradation caused by problems such as over-combustion, over-air, and temperature runaway. At the same time, the embodiment of the present invention provides a control strategy that adapts to multiple starts and stops to ensure the reliability of the system, and through a management and control method based on multiple time scales, ensures that the system can operate stably under various operating conditions, meeting the application requirements of high efficiency, long life, and multiple starts and stops. The gas-heat-electricity balance management and control method of the kilowatt-level SOFC cogeneration system provided by the embodiment of the present invention can effectively solve the problems of over-combustion, over-air, temperature runaway, and life degradation in the current SOFC system, improve system performance, extend service life, and ensure the feasibility of multiple starts and stops.

[0075] Example 2

[0076] Step S10 includes the following sub-steps:

[0077] Step S11: According to the effective area of ​​the anode of the SOFC stack, the catalyst loading of the reformer, the heat transfer coefficient of the heat exchanger and the volume parameters of the catalytic burner, the fuel flow rate, air flow rate and starting temperature in the initial operating parameters are set, wherein the air flow rate and the fuel flow rate are in a preset proportional relationship.

[0078] In this embodiment, the initial operating parameters are set based on key parameters such as the effective area of ​​the anode of the SOFC stack, the catalyst loading of the reformer, the heat transfer coefficient of the heat exchanger, and the volume of the catalytic burner. The integrated controller sets the fuel flow rate, air flow rate, and start-up temperature based on these parameters. The air flow rate and the fuel flow rate are set according to a preset proportional relationship. For example, the system sets the ratio between the air flow rate and the fuel flow rate based on the operating characteristics and combustion requirements of the SOFC stack to ensure that the stack can obtain sufficient oxygen and achieve efficient reaction. In addition, the start-up temperature will be set to the optimal temperature value to ensure the normal startup of each module of the system based on the requirements of the stack, heat exchanger, and catalytic burner.

[0079] Step S12: Based on the thermal expansion coefficient of the SOFC stack and the pressure limit of the catalytic combustor, the allowable temperature gradient rate and the allowable pressure gradient rate of each module are calculated by the integrated controller.

[0080] In this embodiment, the integrated controller calculates the allowable temperature gradient rate and allowable pressure gradient rate of each module based on the thermal expansion coefficient of the SOFC stack and the pressure resistance limit of the catalytic burner. First, by analyzing the thermal expansion coefficient of the SOFC stack, the integrated controller can determine the temperature variation range that the stack can withstand during operation, ensuring that the temperature gradient does not exceed the tolerance of the stack. Next, the pressure resistance limit of the catalytic burner is used to calculate the maximum pressure variation range of the burner during startup or operation to prevent system damage caused by excessive pressure changes. Based on these calculation results, the integrated controller generates a control range for the temperature gradient and pressure gradient to ensure safe operation of the system.

[0081] The calculation formula for the allowable temperature gradient rate of the SOFC stack is as follows:

[0082] Among them, the temperature gradient rate Indicates the temperature change rate per unit time, with the unit of °С / s. This value is calculated by the maximum thermal stress (σ max , unit: Pa) and the thermal expansion coefficient of the SOFC stack (β SOFC , unit: 1 / ℃) is calculated, where σ max is the maximum thermal stress that the SOFC stack can withstand during operation, β SOFC This coefficient represents the change in length of the SOFC stack material per unit temperature change. The temperature gradient rate calculation formula also involves unit time (Δt, unit: s), which is used to determine the maximum temperature change within a certain period of time.

[0083] The calculation formula for the allowable pressure gradient rate of the catalytic burner is as follows:

[0084] Among them, the pressure gradient rate Indicates the rate of change of pressure per unit time, in Pa / s. This value is equal to the pressure limit of the catalytic burner (P max , unit: Pa) and pressure regulation coefficient (κ burner ) is calculated. max It is the maximum pressure that the catalytic burner can withstand, which depends on the design and material of the burner. burner This is a coefficient used to adjust the rate of pressure change allowed during burner operation, ensuring that pressure changes do not exceed a safe range. Additionally, Δt (in seconds) represents the unit time and determines the maximum pressure change the burner can withstand during this time period.

[0085] Step S13: Based on the initial operating parameters, as well as the allowable temperature gradient rate and allowable pressure gradient rate of each module, a module start-stop coordination instruction including a sequential start-up logic is generated, wherein the start-up timing of the reformer precedes the SOFC stack and maintains a preset delay, and the start-up timing of the catalytic burner is triggered after the heat exchanger reaches the set temperature.

[0086] In this embodiment, the integrated controller generates module start and stop coordination instructions based on the required startup sequence to ensure that the system runs smoothly in sequence during startup. Specifically, the start-up sequence of the reformer will precede the start-up of the SOFC stack and maintain a preset delay to ensure that sufficient hydrogen or synthesis gas is supplied to the stack. The delay setting for the start-up of the reformer is based on the conversion efficiency of the fuel and the response time of the stack. The start-up sequence of the catalytic burner is triggered after the heat exchanger reaches the set temperature to ensure that the catalytic burner can burn efficiently at the optimal operating temperature. This sequential startup logic is based on detailed calculations of the thermodynamic and kinetic characteristics of each module, which can avoid drastic fluctuations in temperature or pressure during module startup.

[0087] Specifically, in step S13, the integrated controller generates module start / stop coordination instructions, including sequential startup logic, based on the initial operating parameters and the permissible temperature and pressure gradients for each module. This process aims to ensure stable startup of the entire SOFC cogeneration system through a reasonable module start / stop sequence and time delays, while also preventing system instability caused by significant temperature and pressure differences between modules.

[0088] The working process and specific implementation of step S13 are as follows:

[0089] S131: Determine the startup sequence based on the initial operating parameters. These initial operating parameters, including fuel flow rate, air flow rate, and startup temperature, are obtained by analyzing the design conditions and structural parameters of each module, including the SOFC stack, heat exchanger, reformer, and catalytic burner. Based on these parameters, the integrated controller further calculates the temperature and pressure gradient rates required for each module during startup. Because different modules face different thermal expansion and pressure change requirements during startup, a reasonable startup sequence is crucial.

[0090] S132: Analyze the impact of each module's physical characteristics on the startup process. Each module has different physical characteristics during startup, particularly regarding the rate of change of temperature and pressure. For example, the thermal expansion coefficient of the SOFC stack and the pressure limit of the catalytic combustor are key factors in determining whether these modules can withstand the pressure and temperature changes during startup. Therefore, when generating start-stop coordination commands, the integrated controller needs to comprehensively consider the allowable temperature gradient rate and pressure gradient rate of each module and use these constraints as a prerequisite for generating commands.

[0091] S133: Generate sequential startup logic. After analyzing the initial operating parameters and the physical characteristics of each module, the integrated controller will generate sequential startup logic. This logic specifies the startup sequence of each module and ensures that the temperature and pressure changes of each module at startup are within the allowable range. Specifically, the startup timing of the reformer should be before the SOFC stack and maintain the preset delay. This is because the reformer takes a certain amount of time to reach the appropriate working conditions, and starting the SOFC stack too early may cause instability. The startup timing of the catalytic burner should be triggered after the heat exchanger reaches the set temperature to ensure that the catalytic burner is started in a suitable working environment to avoid system instability caused by starting too early or too late.

[0092] S134: Develop phased control instructions. To ensure that the temperature and pressure fluctuations during startup of each module do not exceed the allowable range, the integrated controller needs to send phased control instructions to each module's actuator based on the module's allowable temperature and pressure gradient rates. In this way, the system can adjust the flow rate of the fuel supply line at different time stages, thereby controlling temperature and pressure fluctuations and ensuring a smooth system startup. The control instructions should take into account the startup time and energy required for each module and be dynamically adjusted based on the system's real-time operating status.

[0093] As an example of module start-stop coordination instructions, in a kilowatt-class SOFC combined heat and power system, the startup sequence of the reformer, SOFC stack, heat exchanger, and catalytic burner is as follows:

[0094] First, the reformer is started. The start-up sequence of the reformer should precede the start-up of the SOFC stack, and there should be a certain delay. For example, wait 10 seconds after starting to ensure that the catalyst in the reformer reaches the temperature required for the reaction. Then, the SOFC stack is started. The start-up of the SOFC stack should be carried out after the reformer is operating stably, and the temperature rise of the SOFC stack should be controlled within the maximum temperature gradient limit. For example, the temperature rise rate during startup must not exceed 2°C / second. Then, the heat exchanger is heated. The heating process of the heat exchanger needs to be carried out after the SOFC stack is started to ensure that the heat exchanger can reach the set operating temperature. The temperature rise rate at this time should be less than 1.5°C / second. Finally, the catalytic burner is started. When the heat exchanger reaches the set temperature, the catalytic burner starts to ensure a smooth combustion process and avoid excessive temperature fluctuations due to incomplete combustion.

[0095] Based on the above sequence and temperature and pressure limits, the integrated controller generates the following module start-stop coordination instructions: First, instruction 1 requires the reformer to start, with a 10-second delay; second, instruction 2 requires the SOFC stack to start, and the temperature rise rate must be strictly controlled within 2°C / second; then, instruction 3 requires the heat exchanger to be heated, and its temperature rise rate should be controlled within 1.5°C / second; finally, instruction 4 requires the catalytic burner to start after waiting for the heat exchanger to reach the set temperature.

[0096] Step S13 generates module start-up and shutdown coordination instructions, incorporating sequential startup logic, by rationally arranging the startup sequence and taking into account the physical characteristics of each module and the temperature and pressure constraints. These instructions ensure a smooth startup of the SOFC cogeneration system, avoiding system instability caused by excessive temperature and pressure fluctuations. Furthermore, by regulating these instructions in stages, the system can dynamically adjust parameters such as fuel flow and air flow to meet the needs of different operating stages.

[0097] Step S14: According to the allowable temperature gradient rate and the allowable pressure gradient rate, the integrated controller sends a staged control instruction to the actuator of each module to control the staged flow regulation and pressure monitoring feedback of the fuel supply pipeline.

[0098] In this embodiment, the integrated controller achieves a smooth startup by sending phased control instructions to the actuators of each module based on the calculated allowable temperature gradient rate and pressure gradient rate. Specifically, the integrated controller first controls the phased flow regulation of the fuel supply pipeline to ensure that the fuel is gradually increased according to the set flow rate at the initial startup to avoid overheating or overpressure caused by excessive instantaneous flow. At the same time, the system will start the pressure monitoring feedback mechanism to monitor the pressure changes in the module in real time to ensure that the system pressure is within a safe range. As the fuel supply gradually increases, the integrated controller continuously adjusts the ratio of fuel and air flow to ensure that the stack can operate stably within the preset temperature and pressure range.

[0099] In this embodiment, through the specific implementation of steps S11 to S14, the integrated controller realizes the initial operating parameter setting, module start and stop coordination, temperature and pressure gradient calculation, and generation of precise control instructions for the kilowatt-level SOFC cogeneration system.

[0100] Example 3

[0101] In step S20, the sensor includes:

[0102] The temperature sensor and pressure sensor installed at the anode inlet of the SOFC stack and the temperature sensor and pressure sensor at the cathode outlet are used to monitor the temperature and pressure distribution of the gas inlet of the stack;

[0103] Multiple core temperature sensors are evenly arranged around the circumference of the stack to obtain real-time stack temperature distribution data;

[0104] The flow sensors installed in the fuel supply pipeline and the air supply pipeline are used to detect the real-time flow of fuel and air respectively;

[0105] The temperature sensor at the reformer inlet and the flow sensor at the catalytic burner inlet are used to monitor the reforming reaction temperature and the fuel supply to the combustion chamber;

[0106] A gas component sensor is provided in the outlet pipe of the catalytic burner to detect the concentration of residual fuel that has not been completely reacted;

[0107] The temperature sensor, pressure sensor, flow sensor and gas component sensor are electrically connected to the data acquisition module of the integrated controller through shielded signal lines to form a distributed sensor network;

[0108] The temperature sensors are embedded in the temperature measuring holes of the fuel cell stack casing in a circular array. The distance between adjacent temperature sensors is a preset ratio of the fuel cell stack diameter, which is used to build a three-dimensional temperature field monitoring system.

[0109] Furthermore, step S20 includes the following sub-steps:

[0110] Step S21: configuring a distributed sensor network, which includes a plurality of sensors arranged in the SOFC stack, heat exchanger, reformer, catalytic burner and gas heat flow path.

[0111] Specifically, multiple sensors include temperature sensors and pressure sensors set at the anode inlet and cathode outlet of the SOFC stack, multiple core temperature sensors are evenly arranged along the circumference of the stack, and flow sensors and gas composition sensors are set in the fuel supply line, air supply line and catalytic burner outlet line.

[0112] In this embodiment, in order to accurately obtain the operating status of the kilowatt-level SOFC cogeneration system, a distributed sensor network is configured. Specifically, temperature sensors and pressure sensors are installed at the anode inlet and cathode outlet of the SOFC stack, respectively, to monitor the temperature and pressure distribution of the gas at the inlet and outlet of the stack. In addition, in order to obtain the temperature distribution inside the stack, multiple core temperature sensors are evenly arranged along the circumference of the stack, and these temperature sensors obtain the temperature data inside the stack in real time. In order to monitor the airflow status and combustion efficiency, flow sensors and gas composition sensors are also provided in the fuel supply pipeline, air supply pipeline and catalytic burner outlet pipeline, respectively, to monitor the fuel flow, air flow and residual fuel concentration at the catalytic burner outlet. Through the arrangement of these sensors, the integrated controller can collect key operating data of the system in real time.

[0113] Step S22: triggering the sensor data acquisition task of the corresponding module according to the startup phase identifier of the module start-stop coordination instruction, wherein the flow data of the fuel supply pipeline and the anode inlet temperature data are preferentially collected during the reformer startup phase.

[0114] In this embodiment, the integrated controller triggers sensor data collection tasks for the corresponding modules based on the startup phase identifier in the module start / stop coordination instructions. For example, during the reformer startup phase, the system prioritizes collecting flow data from the fuel supply line and anode inlet temperature data. This is because during reformer startup, the fuel supply and temperature are critical factors in ensuring stable stack operation. This phased task triggering ensures that relevant monitoring data is collected and processed promptly and accurately during module startup.

[0115] Step S23: The integrated controller preprocesses the collected raw sensor data to obtain preprocessed monitoring data. This preprocessing includes performing spatial interpolation calculations on the core temperature sensor's measured values ​​to generate three-dimensional temperature field distribution data, and performing noise filtering on the gas composition sensor's output signals.

[0116] Specifically, spatial interpolation calculations to generate three-dimensional temperature field distribution data can use interpolation algorithms to process the measurements of the core temperature sensor. For data interpolation in three-dimensional space, the methods used include linear interpolation, polynomial interpolation, or spline interpolation.

[0117] Specifically, in this embodiment, the integrated controller preprocesses the collected raw sensor data to improve its accuracy and usability. For example, the integrated controller uses spatial interpolation to generate three-dimensional temperature field distribution data for the core temperature sensor's measurements. This allows the uniformity of temperature distribution within the fuel cell stack to be determined, accurately reflecting temperature differences across different regions. The integrated controller also performs noise filtering on the output signals of the gas composition sensors to remove noise caused by the environment or the sensors themselves, ensuring the accuracy and reliability of the measured data.

[0118] Furthermore, the method may also include step S24: performing validity verification on the pre-processed monitoring data according to the operating mode of the SOFC stack, which includes comparing whether the pressure difference between the anode inlet and the cathode outlet is within a preset pressure difference range, and whether the real-time ratio of the fuel flow rate to the air flow rate conforms to the gas ratio in the initial operating parameters.

[0119] Specifically, in this embodiment, the integrated controller verifies the validity of the pre-processed monitoring data according to the operating mode of the SOFC stack. First, by comparing the pressure difference between the anode inlet and the cathode outlet, ensure that it is within the preset pressure difference range. If the pressure difference exceeds the set range, it means that there is an abnormality in the system such as airflow or pressure imbalance. Secondly, the integrated controller will also check the real-time ratio of the fuel flow rate to the air flow rate to ensure that it meets the gas ratio initially set. If the flow ratio is abnormal, the integrated controller will adjust the relevant parameters in time to ensure that the mixing ratio of fuel and air meets the operating requirements of the stack. Through this verification process, potential problems in the operation of the system can be detected in real time to prevent phenomena such as overburning, over-emptiness or incomplete combustion.

[0120] Furthermore, the method may also include step S25: classifying and storing the verified real-time monitoring data according to the stack temperature field data, gas flow data and residual fuel concentration data, and further transmitting the data to subsequent diagnosis steps through the data bus of the integrated controller.

[0121] Specifically, in this embodiment, the integrated controller categorizes and stores verified real-time monitoring data. Stack temperature field data, gas flow data, and residual fuel concentration data are stored in separate databases for subsequent analysis and processing. This categorized data is transmitted via the integrated controller's data bus to subsequent diagnostic steps, where it is used for further performance evaluation, system diagnosis, and optimization. Through efficient data storage and transmission mechanisms, the system enables real-time monitoring and dynamic control, improving the operational stability and safety of the SOFC cogeneration system.

[0122] This embodiment facilitates precise control of a SOFC cogeneration system by configuring a distributed sensor network, collecting key monitoring data, and preprocessing and verifying the data's validity. By categorizing, storing, and transmitting data, an integrated controller monitors system status in real time and makes dynamic adjustments, ensuring efficient and stable system operation.

[0123] Example 4

[0124] Step S30 includes the following sub-steps:

[0125] Step S31: Receive real-time monitoring data and initial operating parameters, perform time series alignment processing on the stack inlet and outlet gas temperature, fuel flow rate, and air flow rate data to generate a normalized diagnostic input data set.

[0126] Specifically, in this embodiment, the integrated controller receives real-time monitoring data and initial operating parameters from sensors, including data such as the inlet and outlet gas temperature of the fuel stack, fuel flow rate, and air flow rate. First, these data are time-series aligned. Since different sensors collect data at different time points, the integrated controller uses an interpolation method to unify all data points to the same time base. By normalizing the inlet and outlet gas temperature and fuel flow rate / air flow rate data, the scale of the data set is ensured to be consistent. After time series alignment and normalization, a normalized diagnostic input data set is generated to ensure that all input data can be used for subsequent algorithm analysis.

[0127] Step S32: using the fuzzy logic model in the multidimensional data fusion algorithm, the gas heat utilization rate deviation value is calculated based on the normalized diagnosis input data set, and the stack temperature uniformity index is generated in combination with the stack temperature distribution data.

[0128] In this embodiment, the integrated controller uses the fuzzy logic model in the multidimensional data fusion algorithm to calculate the gas-heat utilization rate deviation value based on the normalized diagnostic input data set. The gas-heat utilization rate deviation value reflects the efficiency of the system's gas-heat conversion. A larger deviation value indicates an imbalance in the gas-heat balance. At the same time, the integrated controller also calculates the stack temperature uniformity index in combination with the stack temperature distribution data. The stack temperature uniformity index is an indicator that measures the uniformity of the temperature distribution of the fuel cell stack. The smaller the index value, the more uniform the stack temperature. Through these calculations, the integrated controller can evaluate the system's gas-heat utilization and the temperature distribution of the fuel cell stack in real time, thereby providing data support for subsequent diagnosis.

[0129] Specifically, the stack temperature uniformity index can be calculated using the stack temperature uniformity index formula based on standard deviation or the stack temperature uniformity index formula based on temperature difference. In the stack temperature uniformity index formula based on standard deviation, the average temperature value of all measurement points in the stack (i.e., the average temperature of the stack) is first calculated. Then, the difference between the temperature value of each measurement point and the average temperature is calculated, and the standard deviation is finally obtained. The stack temperature uniformity index is obtained by dividing the standard deviation by the average temperature of the stack. The smaller the index value, the more uniform the temperature distribution in the stack and the better the thermal equilibrium state; if the index value is larger, it means that there is a large temperature difference in the stack, the temperature distribution is uneven, and the performance is reduced. In the stack temperature uniformity index formula based on temperature difference, the temperature values ​​of each point in the stack are first measured, and the maximum and minimum values ​​of these temperature values ​​are found. Then, the difference between the maximum temperature and the minimum temperature (i.e., the temperature difference) is calculated. The stack temperature uniformity index is obtained by dividing this temperature difference by the average temperature of the stack. The smaller the temperature difference, the more uniform the temperature distribution of the fuel cell stack, the better the temperature uniformity, and the smoother the system operation; conversely, the larger the temperature difference, the more uneven the temperature distribution of the fuel cell stack, which will affect the stability and efficiency of the system.

[0130] Step S33: Through the neural network model in the multidimensional data fusion algorithm, the oxygen utilization rate, the fuel utilization rate and the gas ratio in the initial operating parameters are dynamically correlated and analyzed to output the overfire risk probability value and the over-air risk probability value.

[0131] Specifically, the integrated controller uses a neural network model within a multidimensional data fusion algorithm to dynamically correlate oxygen utilization, fuel utilization, and the gas ratio in the initial operating parameters. Through this neural network model, the integrated controller can identify the relationship between oxygen and fuel utilization and the gas ratio, and based on this relationship, predict and output overfire and over-fuel risk probability values. Specifically, by training and learning from historical data, the neural network model automatically adjusts weights and analyzes changes in oxygen and fuel utilization in real time, outputting overfire and over-fuel risk probability values. If the risk probability value exceeds a preset threshold, the integrated controller will issue an alarm and adjust relevant parameters.

[0132] Step S34: Determine a local temperature runaway state indicator based on the stack temperature uniformity index and the gas heat utilization rate deviation value.

[0133] Specifically, a three-dimensional temperature field gradient matrix is ​​constructed based on the stack temperature uniformity index and the gas heat utilization rate deviation value, and the coordinates of the region exceeding the preset gradient threshold in the three-dimensional temperature field gradient matrix are identified as the local temperature runaway state mark.

[0134] Specifically, in this embodiment, the integrated controller constructs a three-dimensional temperature field gradient matrix based on the stack temperature uniformity index and the gas heat utilization rate deviation value. This matrix represents the temperature gradient conditions in different areas of the fuel cell stack. By analyzing the temperature field gradient matrix, the integrated controller identifies the coordinates of the areas where the preset temperature gradient threshold is exceeded, and these areas are marked as local temperature runaway states. The local temperature runaway state refers to the rapid temperature change in certain areas of the fuel cell stack. If it is not controlled in time, it will cause damage to the fuel cell stack or other modules. Through real-time analysis of the three-dimensional temperature field gradient, the system can identify the risk of temperature runaway in advance and take timely control measures.

[0135] Step S35: comparing the overfire risk probability value, the over-air risk probability value and the local runaway temperature state flag with a preset risk threshold, and generating a system state identification result including the abnormal state type and risk level.

[0136] Specifically, the integrated controller compares the overfire risk probability values, over-air risk probability values, and localized runaway temperature status indicator obtained in steps S33 and S34 with preset risk thresholds to generate a system status identification result, including the abnormality type and risk level. If the overfire risk or over-air risk probability values ​​exceed the preset thresholds, or if a localized runaway temperature status is triggered, the system identifies the abnormality as occurring. The integrated controller outputs different risk levels based on the abnormality type and provides corresponding response measures or adjustment strategies.

[0137] Furthermore, the method further includes step S36: cross-validating the system state identification result, which includes performing a heat balance inversion calculation on the abnormal heat exchange state and the gas component data of the catalytic burner outlet pipeline to verify the validity of the abnormal state;

[0138] Specifically, in this embodiment, the integrated controller cross-validates the system status identification results. The integrated controller performs a heat balance inversion calculation based on the abnormal heat exchange status and the gas composition data from the catalytic burner outlet pipeline to verify the validity of the abnormal status. Through this inversion calculation, the integrated controller can analyze the relationship between gas composition and heat exchange efficiency and confirm whether the heat exchange anomaly is real. For example, if the residual fuel concentration at the catalytic burner outlet is too high and the heat exchange efficiency is reduced, it indicates an abnormality in the heat exchange system. Cross-validation can reduce false positives and improve the accuracy of system diagnosis.

[0139] Furthermore, the method further includes step S37: classifying and storing the verified system status identification results by abnormality type, and transmitting them to subsequent control steps through the diagnostic data interface of the integrated controller.

[0140] In this embodiment, the integrated controller categorizes and stores the verified system status identification results by anomaly type. This categorized data includes specific information on anomaly types such as overfire, over-air, and overheating, and is ranked according to the risk level of each anomaly. The verified system status identification results are transmitted to subsequent control steps via the integrated controller's data bus.

[0141] The technical solution of this embodiment achieves accurate diagnosis and dynamic control of kilowatt-class SOFC cogeneration systems through an integrated controller and multi-dimensional data fusion algorithm, combined with real-time monitoring data and initial operating parameters. Through the application of time series alignment, data preprocessing, fuzzy logic models and neural network models, the system can effectively evaluate key indicators such as gas heat utilization rate, stack temperature uniformity, oxygen utilization rate and fuel utilization rate, and identify potential risks such as overfire, over-air and local temperature runaway in real time. By constructing a three-dimensional temperature field gradient matrix and performing cross-validation, the system can accurately locate and respond to local temperature runaway conditions, reducing equipment damage caused by temperature anomalies. In addition, the system can verify the validity of all monitoring data and optimize and adjust according to the diagnostic results, ensuring the efficient operation and long-term stability of the system, and improving the energy utilization efficiency, safety and reliability of the SOFC system.

[0142] Example 5

[0143] Step S40 includes the following sub-steps:

[0144] Step S41: generating dynamic compensation parameters of the fuel flow rate and the air flow rate according to the overfire risk probability value and the air pressure risk probability value in the system state identification result.

[0145] In this embodiment, the integrated controller generates dynamic compensation parameters for the fuel flow rate and the air flow rate based on the overfire risk probability value and the over-air risk probability value in the system state identification result. Specifically, the integrated controller will first evaluate the current risk of the system entering the overfire state and the risk of entering the over-air state based on the real-time monitoring data (such as oxygen concentration, fuel flow rate, air flow rate, etc.) and the corresponding risk probability value output, based on the fuzzy logic control algorithm, neural network algorithm, decision tree algorithm or support vector machine algorithm. If the overfire risk is high, the integrated controller will automatically adjust the fuel flow rate and increase the air flow rate to ensure that the fuel is fully burned and reduce the overfire phenomenon. On the contrary, if the over-air risk is high, the integrated controller will reduce the air flow rate and appropriately reduce the fuel flow rate to prevent energy waste caused by excessive air. Through this dynamic compensation, the system can adjust the airflow ratio in real time and optimize the combustion process.

[0146] Specifically, the dynamic compensation parameter ΔF of the fuel flow rate fuel Can be adjusted according to the value of overfire risk and over-empty risk: ΔF fuel=k1·P overfuel -k2·P underfuel ;

[0147] Among them, k1 and k2 are the adjustment coefficients of the controller, which indicate the influence of over-combustion and over-air risks on the fuel flow adjustment. overfuel Is the overfire risk probability value, the higher the value, the greater the possibility of overfire, and the integrated controller needs to increase the fuel flow. underfuel It is the probability value of over-empty risk. The higher it is, the greater the possibility of over-empty, and the integrated controller needs to reduce the fuel flow.

[0148] Specifically, the dynamic compensation parameter ΔF of the air flow air Can be adjusted according to the value of overfire risk and over-empty risk: ΔF air =k3·P overfuel -k4·P underfuel ;

[0149] Among them, k3 and k4 are the adjustment coefficients of the controller, which indicate the influence of over-combustion and over-air risks on the air flow adjustment. overfuel Is the probability value of overfire risk, the higher it is, the more air flow needs to be increased. underfuel The higher the value, the more likely it is that the air flow needs to be reduced. The adjustment coefficients k1, k2, k3, and k4 can be determined through engineering experience or experimental data.

[0150] Step S42: Calculate the heat exchange power adjustment parameters and fuel reforming ratio adjustment parameters of the corresponding area of ​​the SOFC stack based on the coordinate information of the local runaway temperature state identifier.

[0151] Specifically, in this embodiment, the integrated controller calculates the heat exchange power adjustment parameters and fuel reforming ratio adjustment parameters of the corresponding area of ​​the SOFC stack based on the coordinate information of the local temperature runaway state identifier. When the local temperature runaway state is identified, the integrated controller will determine the target area with a larger temperature gradient through the three-dimensional temperature field gradient matrix, and calculate the heat exchange power adjustment parameters required for the target area. This adjustment parameter is used to enhance the heat exchange efficiency of the target area and prevent overheating. At the same time, the integrated controller will also adjust the fuel reforming ratio according to the temperature runaway state to ensure a balanced distribution of the heat load and reduce the risk of local overheating without affecting the overall efficiency of the system. This calculation process can be precisely controlled according to the specific temperature runaway situation to avoid damage to the stack caused by local overheating.

[0152] Specifically, the calculation formula of the heat exchange power adjustment parameter is as follows:

[0153]

[0154] Q adjustIt is the heat transfer power adjustment in the target area, measured in watts (W), and represents the amount of heat required to increase or decrease in order to ensure uniform temperature distribution in the area.

[0155] ΔT target (x i ,y j ,z k ) is the temperature gradient of the target area, indicating the temperature difference in that area, in degrees Celsius. This is the temperature variation between different areas within the stack, reflecting the degree of temperature non-uniformity, and is obtained from the local runaway temperature state identification.

[0156] K heat (x i ,y j ,z k ) is the thermal conductivity of the area, with the unit of W / (m·℃), which indicates the heat exchange capacity of the material under a given temperature difference.

[0157] ∑i,j,k represents the summation of the entire target area (stack area) in three-dimensional space, and the calculation of the comprehensive effect of temperature gradient and thermal conductivity coefficient at different coordinate points.

[0158] The meaning of the formula is to convert the temperature gradient ΔT target (x i ,y j ,z k ) and thermal conductivity K heat (x i ,y j ,z k ) and then sum all points in the stack area to get the heat transfer power adjustment Q in the area. adjust .

[0159] Specifically, the calculation formula of the fuel reforming ratio adjustment parameter is as follows:

[0160]

[0161] Among them, R adjust is the adjustment coefficient of the fuel reforming ratio, λ is the control coefficient, which is used to adjust the sensitivity of the fuel reforming ratio and is set according to system characteristics and control requirements, ΔT max It is the maximum temperature difference in the stack, obtained by identifying the local runaway temperature state, and the unit is ℃.

[0162] Step S43: Perform multivariable coupling optimization on the dynamic compensation parameters, heat exchange power adjustment parameters and fuel reforming ratio adjustment parameters, generate a control instruction set including an execution sequence and send it to the corresponding execution mechanism.

[0163] In this embodiment, the integrated controller generates a control instruction set including an execution sequence by performing multivariable coupling optimization on the dynamic compensation parameters, heat exchange power adjustment parameters and fuel reforming ratio adjustment parameters. The multivariable coupling optimization process comprehensively considers multiple factors such as fuel flow, air flow, heat exchange power and fuel reforming ratio to ensure that the relationship between these parameters is optimized to achieve the optimal operating state of the system. The generated control instruction set includes a specific execution sequence and is sent to the corresponding actuator (such as fuel flow control valve, air flow control valve, heat exchanger adjustment device, etc.) through the integrated controller. These control instructions will ensure the coordinated work of each module, avoid any module overload or imbalance, and maximize the system operation efficiency.

[0164] Specifically, multivariable coupling optimization can adopt genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, multi-objective optimization algorithm, and genetic programming algorithm.

[0165] Step S44: monitor the stack temperature fluctuation amplitude and temperature gradient changes in real time, and trigger a temperature runaway warning signal when it is detected that the temperature gradient exceeds a preset threshold.

[0166] In this embodiment, the integrated controller monitors the stack temperature fluctuation amplitude and temperature gradient changes in real time. If the temperature gradient in any area of ​​the stack exceeds a preset threshold, the system triggers a temperature runaway warning signal, indicating that the area is overheating.

[0167] Furthermore, a phased fuel supply reduction and heat exchange power compensation strategy is implemented. In response to the temperature runaway warning signal, the integrated controller immediately implements a phased fuel supply reduction strategy and increases heat exchange power for compensation to lower the temperature in that area. Specifically, the integrated controller gradually reduces the heat load in that area by adjusting the fuel supply flow rate, while simultaneously enhancing the heat exchange efficiency of the heat exchanger to rapidly reduce the temperature and prevent localized overheating from damaging the stack.

[0168] In a further embodiment, step S45 may be further included: performing closed-loop verification on the regulated stack temperature uniformity and residual fuel concentration, and updating the gas ratio setting value in the initial operating parameters according to the verification result.

[0169] In this embodiment, the integrated controller performs closed-loop verification of the regulated stack temperature uniformity and residual fuel concentration. The integrated controller evaluates the temperature uniformity of the stack and analyzes the residual fuel concentration based on real-time monitoring data. If the stack temperature uniformity and fuel concentration do not meet the set targets, the integrated controller will adjust the gas ratio set values ​​in the initial operating parameters accordingly. For example, if the fuel concentration is too high and the temperature is uneven, the integrated controller will further optimize the fuel-air ratio to improve combustion efficiency and enhance the thermal balance of the stack. Through this closed-loop verification and adjustment, the system can self-correct and optimize operating parameters, thereby keeping the system operating in optimal condition.

[0170] This technical solution achieves precise gas and heat control and safety protection under abnormal operating conditions through the coordinated control of dynamic compensation parameter generation and multivariable coupling optimization. Its beneficial technical effects are: based on the dynamic compensation parameter generation of over-combustion and over-air risk probabilities, it can correct the fuel and air flow ratio deviation in real time and effectively suppress abnormal combustion conditions; combined with the heat exchange power and reforming ratio adjustment of the local runaway temperature coordinates, it can achieve precise spatial distribution optimization of the thermal load of the fuel stack and improve the uniformity of the temperature field; through the phased execution of multivariable control instructions and real-time monitoring of temperature gradients, a rapid response mechanism for runaway temperature risks is established to avoid material damage caused by local overheating; closed-loop verification and parameter update mechanisms ensure the continuous optimization of the control strategy and maintain efficient and stable operation under gas and heat balance.

[0171] Example 6

[0172] Step S50 includes the following sub-steps:

[0173] Step S51: Based on the stack temperature uniformity index and the gas heat utilization rate deviation value in the system state identification result, a short-time scale control strategy is generated, which includes a second-level adjustment instruction for the anode tail gas circulation valve opening and a minute-level adjustment instruction for the cathode inlet temperature.

[0174] In this embodiment, the integrated controller generates a short-time-scale control strategy based on the stack temperature uniformity index and the gas-heat utilization rate deviation value in the system state identification result. First, the integrated controller evaluates the temperature distribution of the stack based on the stack temperature uniformity index. If the stack temperature is uneven, the system will give priority to adjusting the opening of the anode tail gas circulation valve, and quickly respond to the situation where the local temperature of the stack is too high through second-level adjustment. At the same time, the integrated controller dynamically adjusts the cathode inlet temperature according to the gas-heat utilization rate deviation value, and ensures the overall gas-heat balance of the system through minute-level adjustment instructions. The purpose of this strategy is to quickly solve the thermal imbalance problem in the system by accurately adjusting the airflow and temperature in a short time, and prevent equipment damage or performance degradation caused by excessively high or low temperatures.

[0175] Step S52: Based on the stack polarization curve offset and residual fuel concentration change trend in the real-time monitoring data, a long-term control strategy is generated through a performance degradation prediction model. The strategy includes periodic optimization instructions for the reformer operating temperature setting value and a trend compensation coefficient for the fuel ratio.

[0176] In this embodiment, the integrated controller generates a long-term control strategy using a performance degradation prediction model based on the offset of the polarization curve of the stack and the trend of the residual fuel concentration in the real-time monitoring data. Specifically, the offset of the polarization curve of the stack reflects the change between the voltage and current of the stack during operation, and the change in the offset indicates the gradual decline of the battery performance. Combined with the trend of the residual fuel concentration, the integrated controller is able to predict the long-term performance degradation of the system. Based on the prediction result, the integrated controller generates a periodic optimization instruction for the operating temperature of the reformer and adjusts the trend compensation coefficient of the fuel ratio. The periodic optimization instruction dynamically adjusts the operating temperature of the reformer to compensate for the efficiency reduction caused by the system performance degradation; and the trend compensation coefficient adjusts the gas ratio based on the trend of the fuel concentration to maintain the long-term stability and efficient operation of the system.

[0177] Step S53: Co-optimize the short-time-scale control strategy and the long-time-scale control strategy to generate a comprehensive control instruction set including execution priorities and conflict resolution rules.

[0178] In this embodiment, the integrated controller collaboratively optimizes the short-time scale control strategy and the long-time scale control strategy. First, the integrated controller will comprehensively consider the priority of each control strategy based on the real-time monitoring data and performance degradation prediction results, and generate a comprehensive control instruction set containing execution priority and conflict resolution rules. The execution priority rule determines the adjustment order between short-time scale control and long-time scale control. For example, short-time scale control gives priority to sudden temperature fluctuations, while long-time scale control adjusts the reformer temperature and fuel ratio in time during system operation. The conflict resolution rule ensures that there are no conflicting adjustment behaviors between the two, such as avoiding over-adjustment caused by simultaneous short-time scale adjustment and long-time scale adjustment. Through this collaborative optimization, the system can maintain the best operating state in both the short and long term.

[0179] Step S54: Synchronously execute the comprehensive control instruction set through the integrated controller to monitor the system operation efficiency parameters and life decay rate in real time.

[0180] Specifically, in this embodiment, the integrated controller synchronously executes various adjustment tasks according to the generated comprehensive control instruction set. The integrated controller monitors the operating efficiency parameters and life decay rate of the system in real time.

[0181] Furthermore, the method also includes step S55: verifying whether the gas-heat-electricity balance state meets the preset target range, and dynamically updating the air flow reference value, fuel reforming ratio and heat exchange power setting value in the initial operating parameters according to the verification result to form a closed-loop parameter optimization link.

[0182] Specifically, the system continuously assesses whether the gas-heat-power balance remains within a preset target range. The system monitors parameters such as the stack's output power, temperature distribution, and the fuel-air flow ratio to identify any imbalances or efficiency degradation. If the system detects a deviation from the target range, the integrated controller immediately adjusts according to a preset regulation strategy to ensure optimal gas-heat-power balance.

[0183] Specifically, in this embodiment, based on the verification results, the integrated controller dynamically updates the initial operating parameters, including the baseline air flow rate, fuel reforming ratio, and heat exchange power setpoints. If the system detects that the gas-heat-power balance does not meet the desired target, the integrated controller optimizes and adjusts these key parameters based on the feedback data. For example, if the air flow rate is too low, resulting in incomplete combustion, the system increases the air flow rate; if the fuel reforming ratio deviates from the optimal ratio, the integrated controller automatically adjusts the reforming ratio. Through this closed-loop control, the system can optimize operating parameters in real time, ensuring sustained efficiency and stability during long-term operation.

[0184] The short-time-scale dynamic regulation of seconds to minutes in this embodiment quickly responds to gas and heat fluctuations, which is conducive to ensuring the immediate stability of the system output power; the long-time-scale optimization strategy based on performance decay prediction effectively delays the performance degradation of key components and extends the service life of the system; the application of multi-strategy collaborative optimization and conflict resolution rules resolves the execution contradictions of control targets in different time dimensions and ensures the overall operation coordination; real-time monitoring and gas, heat and power balance verification mechanism form a dynamic feedback link to achieve continuous and accurate adaptation of operating parameters; the closed-loop parameter update system directly applies the optimization effect to the initial operating parameter setting, forming an adaptive gas and heat management capability.

[0185] Example 7

[0186] The method further comprises step S60, which comprises the following steps:

[0187] S61: Obtain the operating energy efficiency parameters and life loss parameters of the cogeneration system in real time. The operating energy efficiency parameters include the comprehensive electric and thermal efficiency, fuel utilization rate and waste heat recovery rate. The life loss parameters include the stack output voltage attenuation rate and the reformer catalyst activity decrease rate.

[0188] In this embodiment, the system collects basic data through multiple sensors. These sensors are installed on key components such as the SOFC stack, reformer, and heat exchanger to collect data such as gas flow, temperature, and pressure. Through calculations based on these sensor data, the system obtains operating energy efficiency parameters and life loss parameters. Operating energy efficiency parameters include comprehensive electrothermal efficiency, fuel utilization rate, and waste heat recovery rate. These parameters respectively represent the ratio of electrical energy to thermal energy generated by the system, the ratio of fuel converted into electrical energy, and the efficiency of the system in recovering and utilizing waste heat. Life loss parameters include the stack output voltage decay rate and the reformer catalyst activity decrease rate, which reflect the decay rate of the stack performance and the aging rate of the catalyst, respectively. By calculating these parameters, the system can determine its current working status and provide data support for subsequent optimization and adjustment.

[0189] S62: Generate an operating parameter compensation instruction through an adaptive optimization algorithm based on the operating energy efficiency parameter, the life loss parameter, and the performance decay curve in the historical operation database. The operating parameter compensation instruction includes a fuel flow reference value offset, an air excess coefficient correction factor, and a reforming temperature compensation value.

[0190] In this embodiment, after obtaining operating energy efficiency parameters and life loss parameters, the system combines these data with the performance decay curves in the historical operation database and analyzes them using an adaptive optimization algorithm. This algorithm combines real-time monitoring data, historical decay curves, and the current operating status of the system to predict future performance degradation and generate operating parameter compensation instructions. These compensation instructions include: a fuel flow baseline offset, which compensates by increasing the fuel flow when the system's fuel utilization rate decreases; an air excess coefficient correction factor, which adjusts the air-to-fuel ratio based on changes in air flow to optimize combustion efficiency; and a reformer temperature compensation value, which adjusts the temperature setting when the reformer temperature changes to ensure the reformer operates in optimal operating conditions. Through these compensation instructions, the system can dynamically adjust and optimize operating parameters based on actual needs.

[0191] In this embodiment, the adaptive optimization algorithm may adopt a reinforcement learning algorithm, a genetic algorithm, or a particle swarm optimization algorithm.

[0192] S63: adjusting the gas ratio setting value and the heat exchange power setting value in the initial operating parameters according to the operating parameter compensation instruction; if the adjusted comprehensive electric thermal efficiency does not reach the preset optimization target, further adjusting the initial operating parameters until the preset optimization target is reached.

[0193] In this embodiment, according to the operating parameter compensation instruction generated in step S62, the system will adjust the initial operating parameters, mainly including the gas ratio setting value and the heat exchange power setting value. The gas ratio setting value determines the ratio of fuel to air, which directly affects the combustion efficiency and the output power of the fuel cell; the heat exchange power setting value ensures the recovery and effective utilization of waste heat by adjusting the power output of the heat exchanger. After adjusting the operating parameters, the system continues to monitor the comprehensive electrothermal efficiency and verify whether the adjusted energy efficiency reaches the preset optimization target. If the adjusted efficiency does not reach the preset optimization target, the system will further adjust the operating parameters until the system achieves the expected optimization effect. This process ensures that the system can maintain the best operating state under various working conditions and improves overall energy efficiency.

[0194] This embodiment provides a method for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system through real-time monitoring and an adaptive optimization algorithm. This method uses real-time collected operating energy efficiency and lifespan loss parameters, combined with historical data, to dynamically optimize and adjust system operating parameters, achieving optimal energy efficiency and extending the equipment's service life. This method ensures efficient and stable operation of the SOFC system under varying loads and operating conditions.

[0195] Example 8

[0196] The method further comprises step S70, which comprises the following steps:

[0197] Step S71: monitor the operating status of the kilowatt-class SOFC cogeneration system in real time, and generate an emergency shutdown command when abnormal operating condition signals such as local temperature runaway of the stack or interruption of fuel supply are detected.

[0198] In this embodiment, the integrated controller monitors the operating status of the kilowatt-class SOFC cogeneration system in real time, paying particular attention to abnormal operating signals such as localized temperature runaway of the stack and fuel supply. Localized temperature runaway manifests as a sharp rise in temperature in certain areas of the stack, leading to stack damage or reduced combustion efficiency; while a fuel supply interruption can prevent the stack from maintaining normal operating conditions. The integrated controller uses sensors to collect data such as temperature and flow in real time. Upon detecting abnormal signals such as temperature runaway or fuel supply interruption, the integrated controller immediately generates an emergency shutdown command. This command enables prompt action to prevent further malfunction and ensure safe system shutdown.

[0199] Step S72: Initiate a rapid cooling process according to the emergency shutdown instruction, simultaneously reduce the flow setting value of the fuel supply pipeline to a safety threshold and increase the power output of the heat exchanger to the maximum working mode.

[0200] In this embodiment, the integrated controller starts a rapid cooling process according to the emergency shutdown command. This process includes the following two operations: First, the integrated controller immediately reduces the flow setting value of the fuel supply pipeline to a safe threshold, reduces the load on the fuel cell stack, and prevents excessive fuel from continuing to flow into the fuel cell stack and causing overheating. Second, the integrated controller increases the power output of the heat exchanger to the maximum operating mode, increases the heat transfer efficiency, and quickly reduces the system temperature. These two operations ensure that in an emergency, the fuel cell stack and other modules can be cooled quickly to prevent equipment damage due to excessive temperatures.

[0201] Step S73: Generate module coordinated shutdown instructions to control the shutdown sequence of the SOFC stack, reformer and catalytic burner so that the temperature change rate of each module does not exceed a preset rate threshold.

[0202] In this embodiment, the integrated controller generates module-coordinated shutdown instructions to ensure that the shutdown timing of each module (including the SOFC stack, reformer, and catalytic burner) is coordinated during the system shutdown process. The integrated controller calculates the temperature change rate of each module based on the system's real-time data and ensures that these rates do not exceed the preset rate threshold. Through this control method, the system can cool down smoothly, avoiding thermal shock to the modules due to sudden temperature changes, thereby reducing the risk of equipment damage. The integrated controller adjusts the shutdown sequence and temperature change rate of each module to ensure the safe shutdown of the entire system.

[0203] Furthermore, the method may further include step S74: dynamically coordinating the residual gas exhaust ratio of the fuel flow rate and the air flow rate during the shutdown process to maintain a gas-heat balance state until the system enters a cold state.

[0204] In this embodiment, the integrated controller dynamically coordinates the residual gas discharge ratio between fuel and air flow during shutdown. By monitoring gas flow in real time, the integrated controller adjusts the fuel and air flow rates to maintain a reasonable fuel-to-residual gas discharge ratio, maintaining gas-heat balance. Specifically, the integrated controller appropriately reduces air and fuel flow rates to ensure smooth discharge of residual gas while avoiding gas stagnation or excessive combustion. This ensures a smooth transition to a cold state and prevents equipment damage caused by gas-heat imbalance.

[0205] Furthermore, step S75 may be included: updating the starting temperature setting value and the gas ratio compensation coefficient in the initial operating parameters according to the cumulative value of the historical start-stop times and the performance attenuation data, so that the system maintains a gas heat utilization rate not lower than the preset ratio of the initial performance after multiple starts and stops.

[0206] In this embodiment, the integrated controller dynamically updates the startup temperature setpoint and gas ratio compensation coefficient in the initial operating parameters based on the cumulative number of historical starts and stops and performance degradation data. Each system startup and shutdown will result in a certain degree of performance degradation. Especially in the case of multiple starts and stops, the system's gas and heat utilization rate may gradually decrease. The integrated controller updates the startup temperature and gas ratio compensation coefficient based on the performance degradation data after each start and stop, ensuring that the system can maintain a high gas and heat utilization rate after multiple starts and stops, and that it does not fall below the preset proportion of the initial performance. In this way, the system can maintain stable and efficient operation after multiple starts and stops, avoiding performance degradation during long-term operation.

[0207] This technical solution improves system safety and multiple start-stop performance through rapid response to abnormal operating conditions and dynamic optimization of start-stop parameters. Its beneficial technical effects are reflected in: real-time monitoring of abnormal operating condition signals and rapid triggering of emergency shutdown commands, effectively preventing damage to the stack caused by local overheating; a coordinated cooling strategy that simultaneously reduces fuel flow and maximizes heat exchange power output to achieve rapid and safe system shutdown; modular coordinated shutdown timing control combined with temperature change rate limitation to avoid thermal stress accumulation during the shutdown process; dynamic adjustment of residual gas ratio to ensure gas-heat balance during the shutdown phase and reduce the risk of residual fuel retention; a parameter self-optimization mechanism based on historical start-stop data to maintain the system's gas-heat utilization efficiency and operational stability after multiple starts and stops. This solution constructs a gas-heat coordinated control system under abnormal operating conditions, solving the technical problems of severe attenuation of start-stop performance and insufficient abnormal recovery capabilities of traditional SOFC systems.

[0208] Example 9

[0209] like Figure 2 As shown, this embodiment provides a gas-heat-power balance control system for a kilowatt-class SOFC combined heat and power system, which includes:

[0210] Multiple sensors are installed in the SOFC stack, heat exchanger, reformer, catalytic burner and gas heat flow path to collect real-time monitoring data of the system;

[0211] An integrated controller is connected to the sensor signal. The integrated controller includes:

[0212] The parameter setting module is used to set the initial operating parameters such as fuel flow, air flow, start-up temperature, pressure and gas ratio according to the effective area of ​​the anode of the SOFC stack, the heat transfer coefficient of the heat exchanger, the catalyst loading of the reformer and the volume parameters of the catalytic combustor;

[0213] The start-stop control module is connected to the parameter setting module to generate module start-stop coordination instructions. The module start-stop coordination instructions include the sequential startup logic of the SOFC stack, heat exchanger, reformer and catalytic burner, and control the temperature gradient rate and pressure gradient change rate of each module;

[0214] The data fusion algorithm module is used to calculate the gas heat utilization rate, stack temperature uniformity, oxygen utilization rate and fuel utilization rate based on the real-time monitoring data of the fuel cell stack inlet and outlet gas temperature, fuel / air flow rate, stack temperature distribution and residual fuel concentration using a multi-dimensional data fusion algorithm, and generate a system status recognition result including the overfire risk probability value, the over-air risk probability value, the local runaway temperature coordinates and the heat exchange abnormality status mark;

[0215] The control instruction generation module is used to generate fuel flow correction, air flow compensation, heat exchange power adjustment and fuel reforming ratio dynamic adjustment coefficient based on the system status identification results through a multi-variable dynamic control strategy, and generate a temperature runaway warning signal based on the temperature gradient threshold;

[0216] The integrated control strategy module is data-connected to the control instruction generation module and is used to implement the coordinated control of the short-time scale control strategy and the long-time scale control strategy. The short-time scale control strategy maintains the gas heat balance by adjusting the opening of the anode tail gas circulation valve in seconds and the cathode inlet temperature in minutes. The long-time scale control strategy dynamically optimizes the reformer operating temperature setting value and the fuel ratio compensation coefficient based on the offset of the stack polarization curve and the trend of the residual fuel concentration.

[0217] The parameter setting module forms a closed-loop connection with the integrated control strategy unit, and is used to update the gas ratio setting value in the initial operating parameters according to the optimized fuel ratio compensation coefficient.

[0218] Specifically, the SOFC stack is used to react fuel with oxygen to generate electrical energy and thermal energy; the heat exchanger is used to recover and transfer the waste heat generated by the system; the reformer is used to convert hydrocarbons in the fuel into hydrogen or synthesis gas; and the catalytic burner is used to burn incompletely reacted fuel.

[0219] This technical solution achieves intelligent balance management of gas, heat and power parameters of kilowatt-level SOFC systems through the integrated application of modular collaborative control and multi-dimensional dynamic optimization technology.

[0220] Its beneficial technical effects are specifically as follows: the integrated controller effectively reduces the thermal stress impact of the start-stop process through modular start-stop coordination instructions, thereby improving the system operation stability; the combination of multi-source sensor network and data fusion diagnosis technology builds a high-precision real-time identification capability for abnormal states, which can provide early warning of local temperature runaway and combustion abnormality risks; the multivariable dynamic control strategy improves the comprehensive utilization efficiency of gas and heat and the uniformity of the temperature field through the linkage optimization of the fuel reforming ratio and the heat load; the rapid response control on a short time scale complements the performance attenuation compensation strategy on a long time scale, which ensures the stability of the output power while delaying the degradation of the system performance; the synergistic effect of the catalytic burner and the reformer improves the recycling efficiency of the unreacted fuel, thereby achieving overall energy efficiency optimization of the system.

[0221] Example 10

[0222] refer to Figure 3 , which shows a schematic diagram of the structure of a computer system 800 suitable for implementing an electronic device according to an embodiment of the present invention. Figure 3 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803. Various programs and data required for the operation of the computer system 800 are also stored in the RAM 803. The CPU 801, ROM 802 and RAM 803 are connected to each other via a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804. The CPU 801 is used to implement a gas-heat-electricity balance control method for a kilowatt-class SOFC cogeneration system provided by the present invention.

[0223] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, and the like; an output section 807 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 808 including a hard disk; and a communication section 809 including a network interface card such as a LAN card or a modem. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 810 as needed, so that computer programs read therefrom can be installed in the storage section 808 as needed.

[0224] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for controlling the gas-heat-power balance of a kilowatt-class SOFC combined heat and power system, characterized in that: The kilowatt-class SOFC cogeneration system includes: a SOFC stack, a heat exchanger, a reformer, a catalytic burner, and an integrated controller. The method is executed by the integrated controller and includes: S10: setting initial operating parameters based on the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic burner, and generating module start-stop coordination instructions; the module includes the SOFC stack, heat exchanger, reformer, and catalytic burner; S20: acquiring real-time monitoring data of the cogeneration system from a plurality of sensors disposed in the SOFC stack, the heat exchanger, the reformer, the catalytic burner, and the gas-heat flow path; S30: Based on the real-time monitoring data, a multi-dimensional data fusion algorithm is used to perform real-time diagnosis on the operating efficiency indicators of the cogeneration system to generate a system status identification result; S40: According to the system state identification result and the initial operating parameters, the fuel flow, air flow, heat exchange power and fuel reforming ratio of the cogeneration system are adjusted in real time through a multivariable dynamic control strategy, a control instruction is generated based on the module start-stop coordination instruction to control the stack temperature fluctuation amplitude, and a temperature runaway warning signal is generated based on the temperature gradient threshold.

2. The method according to claim 1, characterized in that Step S10 includes: Step S11: setting the fuel flow rate, air flow rate and start-up temperature in the initial operating parameters according to the anode effective area of ​​the SOFC stack, the catalyst loading of the reformer, the heat transfer coefficient of the heat exchanger and the volume parameters of the catalytic combustor; Step S12: Calculating the allowable temperature gradient rate and the allowable pressure gradient rate of each module based on the thermal expansion coefficient of the SOFC stack and the pressure limit of the catalytic combustor; Step S13: Based on the initial operating parameters and the allowable temperature gradient rate and allowable pressure gradient rate of each module, a module start-stop coordination instruction including sequential startup logic is generated, wherein the start-up sequence of the reformer precedes the start-up sequence of the SOFC stack and maintains a preset delay, and the start-up sequence of the catalytic burner is triggered after the heat exchanger reaches a set temperature; Step S14: sending a staged control instruction to the actuator of each module according to the allowable temperature gradient rate and the allowable pressure gradient rate to control the staged flow regulation of the fuel supply pipeline.

3. The method according to claim 1, characterized in that Step S20 includes: S21: configuring a distributed sensor network, which includes a plurality of sensors disposed in the SOFC stack, the heat exchanger, the reformer, the catalytic burner, and the gas-heat flow path; S22: triggering a sensor data acquisition task of a corresponding module according to the startup phase identifier of the module start / stop coordination instruction, wherein flow data of the fuel supply pipeline and anode inlet temperature data are preferentially collected during the startup phase of the reformer; S23: Preprocessing the collected raw sensor data to obtain preprocessed real-time monitoring data.

4. The method according to claim 1, wherein Step S30 includes: S31: receiving the real-time monitoring data and the initial operating parameters, performing time series alignment processing on the inlet and outlet gas temperatures, fuel flow rate, and air flow rate data of the fuel cell stack to generate a normalized diagnostic input data set; S32: using a fuzzy logic model in a multidimensional data fusion algorithm, calculating a gas and heat utilization rate deviation value based on the normalized diagnostic input data set, and generating a stack temperature uniformity index in combination with the stack temperature distribution data; S33: Using a neural network model in the multidimensional data fusion algorithm, dynamically correlate the oxygen utilization rate, the fuel utilization rate, and the gas ratio in the initial operating parameters to obtain an overfire risk probability value and an over-air risk probability value; S34: determining a local temperature runaway state indicator according to the stack temperature uniformity index and the gas heat utilization rate deviation value; S35: Compare the overfire risk probability value, the overflight risk probability value, and the local runaway temperature state identifier with a preset risk threshold to generate a system state identification result.

5. The method according to claim 4, characterized in that Step S40 includes: S41: generating dynamic compensation parameters of fuel flow and air flow according to the overfire risk probability value and the air flow risk probability value in the system state identification result; S42: Calculating a heat exchange power adjustment parameter and a fuel reforming ratio adjustment parameter for a region corresponding to the SOFC stack based on the coordinate information of the local runaway temperature state identifier; S43: performing multivariable coupling optimization on the dynamic compensation parameter, the heat exchange power adjustment parameter, and the fuel reforming ratio adjustment parameter, generating a control instruction set including an execution sequence, and sending the control instruction set to a corresponding execution mechanism; S44: Monitor the stack temperature fluctuation amplitude and temperature gradient changes in real time, and trigger a temperature runaway warning signal when it is detected that the temperature gradient exceeds a preset threshold.

6. The method according to claim 1, characterized in that The method further includes step S50: establishing and executing a comprehensive control strategy for the cogeneration system based on the system state identification result, the real-time monitoring data and the initial operating parameters.

7. The method according to claim 6, characterized in that Step S50 includes: S51: generating a short-time-scale control strategy based on the stack temperature uniformity index and the gas-heat utilization rate deviation value in the system state identification result; S52: generating a long-term control strategy through a performance degradation prediction model according to the stack polarization curve offset and residual fuel concentration change trend in the real-time monitoring data; S53: Co-optimizing the short-timescale control strategy and the long-timescale control strategy to generate a comprehensive control instruction set including execution priorities and conflict resolution rules; S54: Execute the comprehensive control instruction set.

8. The method according to claim 1, characterized in that The method further comprises step S60, which comprises: S61: Acquire in real time the operating energy efficiency parameters and life loss parameters of the cogeneration system, wherein the operating energy efficiency parameters include comprehensive electric and thermal efficiency, fuel utilization rate, and waste heat recovery rate, and the life loss parameters include stack output voltage attenuation rate and reformer catalyst activity reduction rate; S62: generating an operating parameter compensation instruction using an adaptive optimization algorithm based on the operating energy efficiency parameter, the life loss parameter, and a performance decay curve in a historical operating database, the operating parameter compensation instruction including a fuel flow reference value offset, an air excess coefficient correction factor, and a reforming temperature compensation value; S63: adjusting the gas ratio setting value and the heat exchange power setting value in the initial operating parameters according to the operating parameter compensation instruction; if the adjusted comprehensive electric thermal efficiency does not reach the preset optimization target, further adjusting the initial operating parameters until the preset optimization target is reached.

9. The method according to claim 1, characterized in that The method further comprises step S70, which comprises: S71: Real-time monitoring of the operating status of the kilowatt-class SOFC cogeneration system, and generating an emergency shutdown command when abnormal operating condition signals such as local temperature runaway of the stack or interruption of fuel supply are detected; S72: Initiating a cooling process according to the emergency shutdown instruction, simultaneously reducing the flow setting value of the fuel supply pipeline to a safety threshold and increasing the power output of the heat exchanger to a maximum operating mode; S73: Generate a module coordinated shutdown instruction to control the shutdown sequence of the SOFC stack, the reformer and the catalytic burner so that the temperature change rate of each module does not exceed a preset rate threshold.

10. A gas-heat-power balance control system for a kilowatt-class SOFC combined heat and power system, characterized in that: The gas, heat and power balance management and control system includes: Multiple sensors are installed in the SOFC stack, heat exchanger, reformer, catalytic burner and gas heat flow path to collect real-time monitoring data of the cogeneration system; An integrated controller is connected to the sensor signal, and the integrated controller includes: Parameter setting module, used to set initial operating parameters according to the structural parameters and design operating conditions of the SOFC stack, heat exchanger, reformer, and catalytic combustor; A start-stop control module, connected to the parameter setting module for generating module start-stop coordination instructions; A data fusion algorithm module is used to calculate the operation efficiency index based on the real-time monitoring data using a multi-dimensional data fusion algorithm to generate a system status identification result; A control instruction generation module is used to adjust the fuel flow, air flow, heat exchange power and fuel reforming ratio of the cogeneration system in real time through a multivariable dynamic control strategy based on the system state identification result and the initial operating parameters, generate control instructions based on the module start and stop coordination instructions to control the stack temperature fluctuation amplitude, and generate a temperature runaway warning signal based on the temperature gradient threshold.

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