SOEC temperature control system based on off-line identification of heat transfer mechanism model

Abstract

The invention discloses an SOEC temperature control system based on off-line identification of a heat transfer mechanism model, and the system achieves the decoupling of parameter obtaining and temperature control execution through the mode of obtaining a heat balance equivalent parameter through the off-line identification of a pre-constructed system identification target function, does not need to achieve the online parameter identification, and can achieve the real-time temperature control. The environment temperature is used when the feed-forward power is calculated, so that the capability of inhibiting disturbance generated by the change of the environment temperature can be improved, stable and high-precision temperature control can be ensured, meanwhile, heat balance equivalent parameters are adopted, the temperature dynamic characteristics can be accurately described, known disturbance can be effectively compensated in the feed-forward link, and the stability of the feed-forward power is improved. And the response speed and the robustness are obviously improved. Calculation of the feedforward control signal and the feedback control signal is based on real-time measurement data instead of depending on simulation data or data obtained through an offline experiment, and the control accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of temperature control technology, and more specifically, to an SOEC temperature control system based on offline identification of a heat transfer mechanism model. Background Technology

[0002] SOEC (Solid-Oxide Electrolysis Cell) is an advanced high-temperature electrochemical energy conversion device consisting of a stack and auxiliary systems. Its core function is to efficiently electrolyze water and / or carbon dioxide into hydrogen, carbon monoxide, and oxygen using solid oxides as electrolytes at a high temperature environment of 600°C to 1000°C.

[0003] Due to its high-temperature operating characteristics, SOEC has significant advantages in thermodynamics and kinetics, enabling it to achieve extremely high energy conversion efficiency. It can also be combined with renewable energy sources such as nuclear energy and solar energy, showing great application potential in areas such as large-scale hydrogen production, synthetic fuel production, carbon capture and utilization, and long-term energy storage. It is regarded as one of the key technologies for achieving the goal of "carbon neutrality".

[0004] Temperature fluctuations encountered during SOEC operation can significantly impact its performance and durability. Its characteristic of operating at high temperatures, such as 700 to 1000 °C, makes it sensitive to temperature changes, primarily in the following ways: (1) Temperature fluctuations can cause thermal stress problems. When there is a temperature gradient inside the battery pack, the ceramic materials in different regions will expand or contract at different rates, generating internal mechanical stress. This stress may cause the battery pack to crack, delaminate or fail to seal, ultimately leading to performance degradation of the electrolytic cell or even mechanical failure.

[0005] (2) Temperature fluctuations affect the electrochemical performance of SOEC. Rapid or large temperature fluctuations can lead to unstable electrochemical reaction conditions, affecting charge and mass transport processes, and thus reducing electrolysis efficiency. Long-term operation under fluctuating temperatures can also accelerate the degradation of electrode materials, leading to performance degradation.

[0006] (3) Temperature fluctuations accelerate the chemical and structural changes of materials inside SOEC, such as element diffusion, phase transitions, and microstructure reconstruction at the electrode and electrolyte interfaces. These changes increase the ohmic resistance and polarization resistance of the battery, leading to a permanent decline in battery performance.

[0007] Therefore, temperature control is an extremely important aspect during fuel cell stack operation. However, temperature control systems typically exhibit characteristics such as large inertia, large delay, time-varying nature, and nonlinearity.

[0008] Specifically, "large inertia" refers to the slow temperature change of existing temperature control systems due to the existence of heat capacity; "large delay" stems from the time required for heat transfer, resulting in the control effect not being immediate; "time-varying" is reflected in the fact that temperature control system parameters such as heat capacity and thermal resistance may change with ambient temperature, equipment aging, or changes in operating conditions; and "nonlinearity" refers to the fact that the relationship between heating power and temperature rise is often not a simple linear proportional relationship.

[0009] Time-varying characteristics cause the performance of temperature control systems with fixed parameters to gradually deteriorate, making it impossible to maintain long-term accuracy. Nonlinearity makes it difficult for temperature control systems designed based on a single operating point to maintain consistent and accurate control performance across the entire operating range. Both steady-state error and tracking accuracy will decrease, and large delays will prevent the temperature control system from responding quickly.

[0010] Large inertia causes temperature changes to be slow, making the temperature control system prone to "over-adjustment" and resulting in overshoot oscillations. Large delay, on the other hand, causes the control action to lag significantly, easily leading to instability and continuous oscillations in the temperature control system. The combined effect of large inertia and large delay makes it extremely challenging to suppress overshoot while pursuing rapid response.

[0011] To overcome the problems of the aforementioned temperature control systems being unable to achieve high precision, fast response, and small overshoot, existing technologies have proposed online model identification temperature control schemes and pure data-driven black-box temperature control models such as neural networks.

[0012] Online model identification-based temperature control schemes place high demands on controller computing power and require significant computational resources, making real-time control impossible. Furthermore, the online identification structure is complex, and parameter updates are susceptible to signal noise and environmental interference, which is detrimental to the stability and robustness requirements of industrial applications. Purely data-driven black-box temperature control models rely entirely on fitting experimental data, and their predictive ability is limited by the coverage of experimental conditions. When actual operating conditions differ from the training data, these models struggle to guarantee accuracy. Moreover, black-box models do not explicitly characterize physical mechanisms such as heat transfer paths and heat capacity, lacking interpretability and failing to maintain accurate temperature dynamics under environmental disturbances or changes in system parameters. Controllers designed based on such models typically require conservative parameters to ensure stability, thus limiting response speed and control accuracy.

[0013] In summary, existing temperature control solutions cannot achieve high-precision, fast-response, accurate, and stable temperature control, and they also have poor robustness and cannot perform real-time temperature control. Summary of the Invention

[0014] This invention provides a SOEC temperature control system based on offline identification of a heat transfer mechanism model, which can achieve high-precision, fast-response, highly accurate, and stable temperature control, while improving robustness and enabling real-time temperature control. The specific technical solution is as follows.

[0015] In a first aspect, the present invention provides a SOEC temperature control system based on offline identification of a heat transfer mechanism model, including a controller and a blower, a mass flow meter, a first temperature sensor, an electric heater and a second temperature sensor respectively connected to the controller. The output end of the blower is connected to the air inlet of the SOEC stack. The mass flow meter, the electric heater, the first temperature sensor and the second temperature sensor are all installed in the anode pipeline of the SOEC stack. The controller is used to input a control signal to the blower to control the operation of the blower and to input a preset excitation signal to the electric heater to drive the electric heater to operate. The blower is used to pump air into the air inlet of the anode pipeline; The electric heater is used to heat the air in the anode pipe; The first temperature sensor is used to collect the current intake air temperature of the electric heater and send it to the controller; The second temperature sensor is used to collect the current outlet temperature of the electric heater and send it to the controller; The mass flow meter is used to detect the current intake mass flow rate of air in the anode pipeline and send it to the controller; The controller is further configured to acquire the maximum power of the electric heater, ambient temperature, sampling period, air specific heat capacity, thermal balance equivalent parameters of the electric heater, and target outlet temperature; calculate the feedforward power of the electric heater at the current moment based on the ambient temperature, sampling period, thermal balance equivalent parameters, target outlet temperature, current inlet temperature, current outlet temperature, current inlet mass flow rate, a pre-established first equation for the outlet temperature change rate of the electric heater, and a pre-established energy conservation equation of the electric heater; calculate the feedforward control signal of the electric heater at the current moment based on the feedforward power and the maximum power; calculate the feedback control signal of the electric heater at the current moment based on the temperature difference between each moment from the initial moment to the current moment and a discrete proportional-integral-derivative (PID) controller; and generate the outlet temperature control signal of the electric heater based on the feedforward control signal and the feedback control signal and input it to the electric heater. The thermal balance equivalent parameters are obtained offline based on a pre-constructed system identification objective function, and the temperature difference is the difference between the target outlet temperature and the outlet temperature at each moment. The electric heater is also used to receive the outlet temperature control signal and operate with an input power corresponding to the outlet temperature control signal to regulate the temperature of the SOEC stack.

[0016] Optionally, the preset excitation signal is a signal that causes the input power of the electric heater to increase by 10% of the rated power every preset time period starting from 0, until it increases to 100% of the rated power.

[0017] Optionally, the controller is further configured to: A first equation for the rate of change of the outlet temperature of the electric heater is established based on the target outlet temperature, the current outlet temperature, and the sampling period.

[0018] Optionally, the controller is further configured to: A second equation is established based on the equivalent heat capacity and outlet temperature change rate of the electric heater to establish the net heat required to heat each component of the electric heater and the internal gas. A third process is established based on the inlet mass flow rate of the anode pipeline, the specific heat capacity of the air, the outlet temperature of the electric heater, and the inlet temperature of the electric heater to determine the heat carried away by the gas flowing through the electric heater. Based on the equivalent convective heat transfer coefficient of the electric heater, the outlet temperature of the electric heater and the ambient temperature, a fourth equation is established for the heat loss due to heat convection of the electric heater, wherein the equivalent convective heat transfer coefficient is the product of the convective heat transfer coefficient of the electric heater and the heat transfer area of ​​the electric heater. A fifth equation is established based on the equivalent radiative heat transfer coefficient of the electric heater, the outlet temperature of the electric heater, and the inlet temperature of the electric heater to determine the heat loss due to thermal radiation. The equivalent radiative heat transfer coefficient is the product of the emissivity of the electric heater, the Stefan-Boltzmann constant, and the heat transfer area of ​​the electric heater. The energy conservation equation for the electric heater is established based on the input power of the electric heater, the second equation, the third equation, the fourth equation, and the fifth equation.

[0019] Optionally, the controller is further configured to: The equivalent heat capacity, the equivalent convective heat transfer coefficient, and the equivalent radiative heat transfer coefficient are used as the equivalent heat balance parameters to be identified. Based on the equivalent heat balance parameters, the sixth equation of the outlet temperature change rate, and the energy conservation equation, the system identification objective function is established.

[0020] Optionally, the controller is further configured to: An optimization problem is established that minimizes the sum of squares of the values ​​of the system identification objective function calculated at each sampling time. The system acquires the input power of the electric heater at all sampling times within the historical sampling period, the inlet temperature of the electric heater at all sampling times within the historical sampling period collected by the first temperature sensor, the outlet temperature of the electric heater at all sampling times within the historical sampling period collected by the second temperature sensor, and the inlet mass flow rate of the air in the anode pipeline at all sampling times within the historical sampling period detected by the mass flow meter. The input power, inlet air temperature, outlet air temperature, inlet air mass flow rate, ambient temperature, specific heat capacity of air, target outlet air temperature, and historical sampling period at all sampling times within the historical sampling period are substituted into the system identification objective function. The system identification objective function is then solved based on an optimization algorithm to obtain the thermal balance equivalent parameters that satisfy the optimization problem.

[0021] Optionally, the controller is specifically used for: Calculate the quotient between the feedforward power and the maximum power, and use the quotient as the feedforward control signal.

[0022] Optionally, the controller is specifically used for: The feedback control signal is calculated based on the temperature difference between each time point from the initial time to the current time and the preset PID position algorithm discrete formula.

[0023] Optionally, the controller is specifically used for: Calculate the sum between the feedforward control signal and the feedback control signal; The above and below are used as the outlet temperature control signal.

[0024] Optionally, the controller is specifically used for: The preset time period is 30 minutes.

[0025] As can be seen from the above, the SOEC temperature control system based on offline identification of heat transfer mechanism model provided by the present invention includes a controller and a blower, a mass flow meter, a first temperature sensor, an electric heater and a second temperature sensor respectively connected to the controller. The output end of the blower is connected to the air inlet of the SOEC stack. The mass flow meter, the electric heater, the first temperature sensor and the second temperature sensor are all installed in the anode pipeline of the SOEC stack. The controller is used to input control signals to the blower to control its operation and to input preset excitation signals to the electric heater to drive its operation. The blower pumps air into the air inlet of the anode pipeline. The electric heater heats the air in the anode pipeline. A first temperature sensor collects the current inlet temperature of the electric heater and sends it to the controller. A second temperature sensor collects the current outlet temperature of the electric heater and sends it to the controller. A mass flow meter detects the current inlet mass flow rate of the air in the anode pipeline and sends it to the controller. The controller also acquires the maximum power of the electric heater, ambient temperature, sampling period, air specific heat capacity, the thermal balance equivalent parameters of the electric heater, and the target outlet temperature. Based on the ambient temperature, sampling period, thermal balance equivalent parameters, target outlet temperature, current inlet air temperature, current outlet temperature, and current inlet mass flow rate... The feedforward power of the electric heater at the current moment is calculated using a first equation based on the pre-established rate of change of the outlet temperature of the electric heater and a pre-established energy conservation equation for the electric heater. Based on the feedforward power and the maximum power, the feedforward control signal of the electric heater at the current moment is calculated. Based on the temperature difference between each moment from the initial moment to the current moment and a discrete proportional-integral-derivative (PID) controller, the feedback control signal of the electric heater at the current moment is calculated. The outlet temperature control signal of the electric heater is generated based on the feedforward control signal and the feedback control signal and input to the electric heater. The thermal balance equivalent parameters are obtained offline based on a pre-built system identification objective function, and the temperature difference is the difference between the target outlet temperature and the outlet temperature at each moment. The electric heater receives the outlet temperature control signal and operates with the input power corresponding to the outlet temperature control signal to regulate the temperature of the SOEC stack. Therefore, by obtaining the thermal balance equivalent parameters offline through a pre-built system identification objective function, parameter acquisition and temperature control are decoupled, eliminating the need for online parameter identification, significantly reducing computing power requirements, and enabling real-time temperature control. This method is suitable for resource-constrained industrial embedded controllers. Furthermore, ambient temperature is used when calculating feedforward power, which improves the ability to suppress disturbances caused by changes in ambient temperature, thereby ensuring stable and high-precision temperature control. At the same time, the use of thermal balance equivalent parameters can accurately describe the dynamic characteristics of temperature, enabling effective compensation for known disturbances in the feedforward stage, and significantly improving response speed and robustness.The calculation of both feedforward and feedback control signals is based on real-time measurement data, rather than relying on simulation data or data obtained from offline experiments, thereby improving the accuracy of control.

[0026] The innovative aspects of this invention include: 1. By obtaining thermal balance equivalent parameters offline through a pre-built system identification objective function, parameter acquisition and temperature control execution are decoupled. This eliminates the need for online parameter identification, significantly reducing computational requirements and enabling real-time temperature control, making it suitable for resource-constrained industrial embedded controllers. Furthermore, ambient temperature is used when calculating feedforward power, thus improving the ability to suppress disturbances caused by changes in ambient temperature, ensuring stable and high-precision temperature control. The use of thermal balance equivalent parameters accurately describes the dynamic characteristics of temperature, effectively compensating for known disturbances in the feedforward stage, significantly improving response speed and robustness. Both feedforward and feedback control signals are calculated based on real-time measurement data, rather than relying on simulation data or data obtained from offline experiments, improving control accuracy.

[0027] 2. The complete heat transfer process of the SOEC stack is simplified into an identifiable and deployable low-order dynamic model. This provides a physically interpretable and engineering-usable mathematical basis for feedforward control.

[0028] 3. This invention constructs a complete offline parameter identification method, covering the design of test excitation signals, the acquisition of key operational data, and parameter identification algorithms based on physical models. This effectively overcomes the problems of low efficiency and poor repeatability caused by traditional methods relying on trial and error based on experience or manual debugging. It systematizes and automates the parameter identification process, while providing a high-precision, reproducible model parameter foundation for subsequent feedforward control, significantly improving the reliability and engineering feasibility of system modeling.

[0029] 4. By obtaining the equivalent parameters of thermal balance offline through the pre-constructed system identification objective function, online parameter identification is not required. Only online feedforward compensation and feedback correction are needed. A three-layer temperature control architecture of "offline modeling and identification → online lightweight feedforward compensation → feedback correction" is established. Compared with the feedback control method that only adjusts after the fact, the embodiment of the present invention proactively cancels the disturbance before it affects the controlled temperature. At the same time, the feedback correction is superimposed, which can perform temperature control in real time, fast and accurate, and solve the problems of temperature control lag and overshoot.

[0030] 5. The identification process does not require destructive testing; it only uses a temperature sensor to collect temperature data, resulting in low cost.

[0031] Of course, implementing any product or method of the present invention does not necessarily require achieving all of the advantages described above at the same time. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0033] Figure 1 This is a schematic diagram of a SOEC temperature control system based on offline identification of a heat transfer mechanism model, provided in an embodiment of the present invention.

[0034] Figure 1 The components are: 1. Controller, 2. Blower, 3. Mass flow meter, 4. First temperature sensor, 5. Electric heater, 6. Second temperature sensor, and 7. SOEC fuel cell stack. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0036] It should be noted that the terms "comprising" and "having," and any variations thereof, in the embodiments and drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0037] This invention discloses a SOEC temperature control system based on offline identification of a heat transfer mechanism model. This system achieves high-precision, fast-response, accurate, and stable temperature control, while also improving robustness and enabling real-time temperature control. The embodiments of this invention are described in detail below.

[0038] Figure 1 This is a schematic diagram of a SOEC temperature control system based on offline identification of a heat transfer mechanism model, provided in an embodiment of the present invention.

[0039] See Figure 1The SOEC temperature control system based on offline identification of heat transfer mechanism model provided in this embodiment of the invention includes a controller 1 and a blower 2, a mass flow meter 3, a first temperature sensor 4, an electric heater 5 and a second temperature sensor 6 respectively connected to the controller 1. The output end of the blower 2 is connected to the air inlet of the SOEC (Solid-Oxide Electrolysis Cell) stack 7. The mass flow meter 3, the electric heater 5, the first temperature sensor 4 and the second temperature sensor 6 are all installed in the anode pipeline of the SOEC stack 7.

[0040] The controller 1 is used to input control signals to the blower 2 to control the operation of the blower 2 and to input preset excitation signals to the electric heater 5 to drive the electric heater 5 to operate.

[0041] Blower 2 is used to pump air into the air inlet of the anode pipeline after receiving a control signal. Electric heater 5 is used to heat the air in the anode pipeline after receiving a preset excitation signal. The heated air enters the SOEC stack 7, carrying away the oxygen produced in the reaction, such as... Figure 1 As shown, oxygen-enriched air is output from SOEC stack 7.

[0042] See also Figure 1 The first temperature sensor 4 is used to collect the current inlet air temperature of the electric heater 5 and send it to the controller 1. The second temperature sensor 6 is used to collect the current outlet air temperature of the electric heater 5 and send it to the controller 1. The mass flow meter 3 is used to detect the current inlet air mass flow rate in the anode pipeline and send it to the controller 1.

[0043] This invention proposes a temperature control system based on feedforward and feedback. Specifically, controller 1 is further used to acquire the maximum power of electric heater 5, ambient temperature, sampling period, air specific heat capacity, thermal balance equivalent parameters of electric heater 5, and target outlet temperature. Based on the ambient temperature, sampling period, thermal balance equivalent parameters, target outlet temperature, current inlet temperature, current outlet temperature, current inlet mass flow rate, a pre-established first equation for the rate of change of outlet temperature of electric heater 5, and a pre-established energy conservation equation of electric heater 5, the feedforward power of electric heater 5 at the current moment is calculated. The maximum power and air specific heat capacity of electric heater 5 are fixed; the sampling period and target outlet temperature are pre-set; the ambient temperature can be measured by a temperature measuring device; and the thermal balance equivalent parameters are obtained offline based on a pre-constructed system identification objective function. These parameters are the core equivalent coefficients constituting the energy conservation equation of electric heater 5, covering heat storage, convective heat dissipation, and radiative heat dissipation, and have a decisive influence on the temperature dynamics of electric heater 5.

[0044] Controller 1 is also used for: The first equation for the rate of change of the outlet temperature of electric heater 5 is established based on the target outlet temperature, the current outlet temperature, and the sampling period.

[0045] Specifically, to facilitate control of controller 1, let the first equation be: T k = ; In the formula, T k Let k be the rate of change of the outlet temperature at time k. Let K be the target outlet gas temperature of the electric heater at time k. Let K be the outlet air temperature. The sampling period is k, and time k is the current time.

[0046] Controller 1 is also used for: A second equation is established based on the equivalent heat capacity of electric heater 5 and the rate of change of outlet temperature. This equation represents the net heat required to heat each component of electric heater 5 and the internal gas. A third process is established based on the inlet mass flow rate of the anode pipeline, the specific heat capacity of the air, the outlet temperature of the electric heater 5, and the inlet temperature of the electric heater 5 to determine the heat carried away by the gas flowing through the electric heater 5. Based on the equivalent convective heat transfer coefficient of electric heater 5, the outlet temperature of electric heater 5 and the ambient temperature, a fourth equation is established for the heat loss due to heat convection of electric heater 5, where the equivalent convective heat transfer coefficient is the product of the convective heat transfer coefficient of electric heater 5 and the heat transfer area of ​​electric heater 5. The fifth equation for the heat loss due to thermal radiation of electric heater 5 is established based on the equivalent radiation heat transfer coefficient of electric heater 5, the outlet temperature of electric heater 5 and the inlet temperature of electric heater 5. The equivalent radiation heat transfer coefficient is the product of the emissivity of electric heater 5, the Stefan-Boltzmann constant and the heat transfer area of ​​electric heater 5. Based on the input power of electric heater 5, the second equation, the third equation, the fourth equation, and the fifth equation, the energy conservation equation of electric heater 5 is established.

[0047] Specifically, the second equation is: ; In the formula, The net heat required to heat all components of electric heater 5 and the internal gas at time k. The equivalent heat capacity of electric heater 5, Let k be the rate of change of the outlet temperature at time k. The total energy required for the electric heater 5 to produce a preset temperature change is the product of the total mass of all parts of the electric heater 5 and the internal gas, the preset temperature change, and the corresponding specific heat capacity. The preset temperature change can be 1 K.

[0048] The third equation is: ; In the formula, Let k be the heat carried away by the gas flowing through electric heater 5 at time k. Let k be the inlet mass flow rate of the anode pipeline at time k. The specific heat capacity of air, Let k be the intake air temperature. Let K be the outlet temperature at time k.

[0049] The fourth equation is: ; In the formula, Let K be the heat loss due to heat convection of electric heater 5 at time k. The equivalent convective heat transfer coefficient of electric heater 5 is given. The convective heat transfer coefficient of electric heater 5 is... The heat exchange area of ​​electric heater 5 is [area missing]. Let K be the outlet air temperature. Let k be the ambient temperature at time k.

[0050] The fifth equation is: ; In the formula, Let K be the heat loss due to thermal radiation from electric heater 5 at time k. Let be the equivalent radiative heat transfer coefficient of electric heater 5. For the emissivity of electric heater 5, For Stefan-Boltzmann constant, The heat exchange area of ​​electric heater 5 is [area missing]. The outlet air temperature, This refers to the intake air temperature.

[0051] Based on the above equations, the energy conservation equation for electric heater 5 is as follows: ; Expanded to: ; In the formula, Let be the feedforward power of electric heater 5 at time k.

[0052] The established energy conservation equation for electric heater 5 is the heat transfer mechanism model. After obtaining the pre-established first equation and the energy conservation equation for electric heater 5, substituting the first equation into the energy conservation equation for electric heater 5 and rearranging terms, we obtain: ; Then the feedforward power can be calculated. Specifically, the ambient temperature, sampling period, thermal balance equivalent parameters, target outlet temperature, current inlet temperature, current outlet temperature, and current inlet mass flow rate are substituted into the above rearranged formula to calculate the feedforward power, which is the specific value of the feedforward power.

[0053] After obtaining the feedforward power, the feedforward control signal of the electric heater 5 at the current moment is calculated based on the feedforward power and the maximum power.

[0054] Specifically, controller 1 is used for: Calculate the quotient between the feedforward power and the maximum power, and use the quotient as the feedforward control signal: ; In the formula, The feedforward control signal at time k is... This is the maximum power of electric heater 5. Let be the feedforward power of electric heater 5 at time k.

[0055] After obtaining the feedforward control signal, the feedback control signal is then calculated. Specifically, the feedback control signal of the electric heater 5 at the current moment is calculated based on the temperature difference between each moment from the initial moment to the current moment and the discrete PID (Proportional Integral Derivative) controller.

[0056] Controller 1 is specifically used for: The feedback control signal is calculated based on the temperature difference between each time point from the initial time to the current time and the preset PID position algorithm discrete formula, where the temperature difference is the difference between the target outlet temperature and the outlet temperature at each time point.

[0057] Specifically, the preset PID position algorithm discretization formula is as follows: ; = ; In the formula, Let k be the feedback control signal at time k. This is the proportionality coefficient. Let k be the temperature difference at time k. for Temperature difference over time The initial temperature difference, The integral coefficient is... To control the cycle, The differential coefficients are... Let k be the target outlet gas temperature of electric heater 5. Let K be the outlet temperature at time k.

[0058] After receiving the feedback control signal, the outlet temperature control signal of the electric heater 5 can be generated based on the feedforward control signal and the feedback control signal and input to the electric heater 5.

[0059] Controller 1 is specifically used for: Calculate the sum between the feedforward control signal and the feedback control signal, and use this sum as the outlet temperature control signal. That is: ; In the formula, Let k be the feedback control signal at time k. The feedforward control signal at time k is... The outlet temperature control signal is at time k.

[0060] The process of obtaining thermal equilibrium equivalent parameters through offline identification is described below.

[0061] During offline identification, in order to collect data such as temperature, the controller 1 still needs to input a preset excitation signal to the electric heater 5. The preset excitation signal is a signal that causes the input power of the electric heater 5 to increase by 10% of the rated power every preset time period starting from 0 until it increases to 100% of the rated power. In order to make the thermal balance equivalent parameters identified offline closer to the real situation, the preset time period needs to be long enough to give the electric heater 5 sufficient response time. For example, the preset time period is 30 minutes.

[0062] Then, the system identification target function is constructed. In this embodiment of the invention, in order to reduce the parameters that need to be identified and reduce the identification difficulty, the following are respectively: , and Since the identification is performed as a whole, only three parameters need to be identified in total: , and The reason for choosing these three parameters for identification is that they are all core equivalent coefficients constituting the energy conservation equation of the electric heater, covering heat storage, convective heat dissipation, and radiative heat dissipation, and have a decisive influence on temperature dynamics. Therefore, these three parameters were chosen for identification.

[0063] In other words, given the established energy conservation equation for the electric heater 5, the controller 1 is also used for: The equivalent heat capacity, equivalent convective heat transfer coefficient, and equivalent radiative heat transfer coefficient are used as the equivalent heat balance parameters to be identified. Based on the equivalent heat balance parameters, the sixth equation of the outlet temperature change rate, and the energy conservation equation, the system identification objective function is established.

[0064] Specifically, the sixth equation for the rate of change of outlet temperature is: ; In the formula, Let k be the temperature change at time k. The outlet temperature is at time k-1.

[0065] Then, substituting the sixth equation into the energy conservation equation, we construct the system identification objective function. Since identification requires data from all sampling times, we use t instead of k to represent sampling time t in the entire sampling time sequence, and let the vector... , so that: ; After establishing the system identification objective function, parameter identification can be performed offline. Specifically, with the system identification objective function established, controller 1 is also used for: Establish an optimization problem that minimizes the sum of squares of the values ​​of the system identification objective function calculated at each sampling time. The system acquires the input power of electric heater 5 at all sampling times within the historical sampling period, the inlet temperature of electric heater 5 at all sampling times within the historical sampling period collected by the first temperature sensor, the outlet temperature of electric heater 5 at all sampling times within the historical sampling period collected by the second temperature sensor, and the inlet mass flow rate of air in the anode pipeline detected by the mass flow meter at all sampling times within the historical sampling period. Input power, inlet temperature, outlet temperature, inlet mass flow rate, ambient temperature, air specific heat capacity, target outlet temperature, and historical sampling period at all sampling times within the historical sampling period are substituted into the system identification objective function. The system identification objective function is then solved based on the optimization algorithm to obtain the thermal balance equivalent parameters that satisfy the optimization problem.

[0066] The optimization problem is as follows: ; ; The above optimization problem can be equivalently represented as: ; ; In the formula, Sampling time, This represents the final sampling moment of the historical sampling period. b is the minimum value of X, and ub is the maximum value of X.

[0067] In this embodiment of the invention, the input power, inlet air temperature, outlet air temperature, inlet air mass flow rate, ambient temperature, air specific heat capacity, target outlet air temperature, and historical sampling period at all sampling times within the historical sampling period are substituted into the system identification objective function. Then, based on the optimization algorithm, the system identification objective function is solved to obtain the equivalent thermal balance parameters that satisfy the optimization problem, that is, the three parameters Ceq, hAs, and εσAs are solved. The optimization algorithm can be either the least squares method or a genetic algorithm.

[0068] In summary, the optimization objective is to find three parameters using optimization methods such as least squares: , and This ensures that f(X) is calculated at each sampling time. 2 The goal is to minimize the sum of the squares of f(X) at all sampling times within the historical sampling period. In other words, the optimization objective must be to minimize the sum of the squares of f(X) at all sampling times within the historical sampling period.

[0069] Therefore, the complete heat transfer process of SOEC stack 7 can be simplified into an identifiable and deployable low-order dynamic model. This provides a physically interpretable and engineering-usable mathematical basis for feedforward control.

[0070] Furthermore, this invention constructs a complete offline parameter identification method, covering the design of test excitation signals, the acquisition of key operational data, and parameter identification algorithms based on physical models. This effectively overcomes the problems of low efficiency and poor repeatability caused by traditional methods that rely on trial and error based on experience or manual debugging. It systematizes and automates the parameter identification process, while providing a high-precision, reproducible model parameter foundation for subsequent feedforward control, significantly improving the reliability and engineering feasibility of system modeling.

[0071] After the controller 1 inputs the outlet temperature control signal to the electric heater 5, the electric heater 5 receives the outlet temperature control signal and operates with the input power corresponding to the outlet temperature control signal to regulate the temperature of the SOEC stack 7.

[0072] In summary, the SOEC temperature control system based on offline identification of heat transfer mechanism model provided by the embodiments of the present invention includes a controller 1 and a blower 2, a mass flow meter 3, a first temperature sensor 4, an electric heater 5 and a second temperature sensor 6 respectively connected to the controller 1. The output end of the blower 2 is connected to the air inlet of the SOEC stack 7. The mass flow meter 3, the electric heater 5, the first temperature sensor 4 and the second temperature sensor 6 are all installed in the anode pipeline of the SOEC stack 7. Controller 1 is used to input control signals to blower 2 to control the operation of blower 2 and to input preset excitation signals to electric heater 5 to drive electric heater 5 to operate; blower 2 is used to pump air into the air inlet of the anode pipeline; electric heater 5 is used to heat the air in the anode pipeline; first temperature sensor 4 is used to collect the current inlet air temperature of electric heater 5 and send it to controller 1; second temperature sensor 6 is used to collect the current outlet air temperature of electric heater 5 and send it to controller 1; mass flow meter 3 is used to detect the current inlet air mass flow rate in the anode pipeline and send it to controller 1; controller 1 is also used to obtain the maximum power of electric heater 5, ambient temperature, sampling period, air specific heat capacity, thermal balance equivalent parameters of electric heater 5 and target outlet air temperature, based on ambient temperature, sampling period, thermal balance equivalent parameters, target outlet air temperature, current inlet air temperature, current outlet air temperature, and current outlet air temperature. The feedforward power of electric heater 5 at the current moment is calculated using the gas mass flow rate, the first equation for the pre-established outlet temperature change rate of electric heater 5, and the pre-established energy conservation equation of electric heater 5. Based on the feedforward power and the maximum power, the feedforward control signal of electric heater 5 at the current moment is calculated. Based on the temperature difference between each moment from the initial moment to the current moment and the discrete proportional-integral-derivative (PID) controller, the feedback control signal of electric heater 5 at the current moment is calculated. The outlet temperature control signal of electric heater 5 is generated based on the feedforward control signal and the feedback control signal and input to electric heater 5. Here, the thermal balance equivalent parameters are obtained offline based on a pre-built system identification objective function, and the temperature difference is the difference between the target outlet gas temperature and the outlet gas temperature at each moment. Electric heater 5 receives the outlet temperature control signal and operates with the input power corresponding to the outlet temperature control signal to regulate the temperature of SOEC stack 7. Therefore, by obtaining the thermal balance equivalent parameters offline through a pre-built system identification objective function, parameter acquisition and temperature control execution are decoupled, eliminating the need for online parameter identification, significantly reducing computing power requirements, and enabling real-time temperature control. This method is suitable for resource-constrained industrial embedded controllers.Furthermore, ambient temperature is used when calculating the feedforward power, thus improving the ability to suppress disturbances caused by changes in ambient temperature. This ensures stable and high-precision temperature control. The use of thermal balance equivalent parameters accurately describes the dynamic characteristics of the temperature, enabling effective compensation for known disturbances in the feedforward stage, significantly improving response speed and robustness. Both the feedforward and feedback control signals are calculated based on real-time measurement data, rather than relying on simulation data or data obtained from offline experiments. The data used for calculating both the feedforward and feedback control signals are real data, not experimental data, improving control accuracy.

[0073] Furthermore, by obtaining the equivalent parameters of thermal balance offline through the pre-built system identification objective function, it is possible to eliminate the need for online parameter identification and perform only online feedforward compensation and feedback correction. This establishes a three-layer temperature control architecture of "offline modeling and identification → online lightweight feedforward compensation → feedback correction". Compared with feedback control methods that only adjust after the fact, this embodiment of the invention proactively cancels out disturbances before they affect the controlled temperature, and at the same time superimposed feedback correction, it can perform real-time, fast and accurate temperature control, solving the problems of temperature control lag and overshoot.

[0074] Furthermore, the identification process does not require destructive testing; it only uses a temperature sensor to collect temperature data, resulting in low cost.

[0075] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0076] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A SOEC temperature control system based on heat transfer mechanism model offline identification, characterized in that, The device includes a controller and a blower, a mass flow meter, a first temperature sensor, an electric heater, and a second temperature sensor, all connected to the controller. The output end of the blower is connected to the air inlet of the SOEC stack. The mass flow meter, the electric heater, the first temperature sensor, and the second temperature sensor are all installed in the anode pipeline of the SOEC stack. The controller is used to input a control signal to the blower to control the operation of the blower and to input a preset excitation signal to the electric heater to drive the electric heater to operate. The blower is used to pump air into the air inlet of the anode pipeline; The electric heater is used to heat the air in the anode pipe; The first temperature sensor is used to collect the current intake air temperature of the electric heater and send it to the controller; The second temperature sensor is used to collect the current outlet temperature of the electric heater and send it to the controller; The mass flow meter is used to detect the current intake mass flow rate of air in the anode pipeline and send it to the controller; The controller is further configured to acquire the maximum power of the electric heater, ambient temperature, sampling period, air specific heat capacity, thermal balance equivalent parameters of the electric heater, and target outlet temperature; calculate the feedforward power of the electric heater at the current moment based on the ambient temperature, sampling period, thermal balance equivalent parameters, target outlet temperature, current inlet temperature, current outlet temperature, current inlet mass flow rate, a pre-established first equation for the outlet temperature change rate of the electric heater, and a pre-established energy conservation equation of the electric heater; calculate the feedforward control signal of the electric heater at the current moment based on the feedforward power and the maximum power; calculate the feedback control signal of the electric heater at the current moment based on the temperature difference between each moment from the initial moment to the current moment and a discrete proportional-integral-derivative (PID) controller; and generate the outlet temperature control signal of the electric heater based on the feedforward control signal and the feedback control signal and input it to the electric heater. The thermal balance equivalent parameters are obtained offline based on a pre-constructed system identification objective function, and the temperature difference is the difference between the target outlet temperature and the outlet temperature at each moment. The electric heater is also used to receive the outlet temperature control signal and operate with an input power corresponding to the outlet temperature control signal to regulate the temperature of the SOEC stack.

2. The system of claim 1, wherein, The preset excitation signal is a signal that causes the input power of the electric heater to increase by 10% of the rated power every preset time period, starting from 0, until it increases to 100% of the rated power.

3. The system of claim 1, wherein, The controller is also used for: A first equation for the rate of change of the outlet temperature of the electric heater is established based on the target outlet temperature, the current outlet temperature, and the sampling period.

4. The system of claim 1, wherein, The controller is also used for: A second equation is established based on the equivalent heat capacity and outlet temperature change rate of the electric heater to establish the net heat required to heat each component of the electric heater and the internal gas. A third process is established based on the inlet mass flow rate of the anode pipeline, the specific heat capacity of the air, the outlet temperature of the electric heater, and the inlet temperature of the electric heater to determine the heat carried away by the gas flowing through the electric heater. Based on the equivalent convective heat transfer coefficient of the electric heater, the outlet temperature of the electric heater and the ambient temperature, a fourth equation is established for the heat loss due to heat convection of the electric heater, wherein the equivalent convective heat transfer coefficient is the product of the convective heat transfer coefficient of the electric heater and the heat transfer area of ​​the electric heater. A fifth equation is established based on the equivalent radiative heat transfer coefficient of the electric heater, the outlet temperature of the electric heater, and the inlet temperature of the electric heater to determine the heat loss due to thermal radiation. The equivalent radiative heat transfer coefficient is the product of the emissivity of the electric heater, the Stefan-Boltzmann constant, and the heat transfer area of ​​the electric heater. The energy conservation equation for the electric heater is established based on the input power of the electric heater, the second equation, the third equation, the fourth equation, and the fifth equation.

5. The system as described in claim 4, characterized in that, The controller is also used for: The equivalent heat capacity, the equivalent convective heat transfer coefficient, and the equivalent radiative heat transfer coefficient are used as the equivalent heat balance parameters to be identified. Based on the equivalent heat balance parameters, the sixth equation of the outlet temperature change rate, and the energy conservation equation, the system identification objective function is established.

6. The system as described in claim 5, characterized in that, The controller is also used for: An optimization problem is established that minimizes the sum of squares of the values ​​of the system identification objective function calculated at each sampling time. The system acquires the input power of the electric heater at all sampling times within the historical sampling period, the inlet temperature of the electric heater at all sampling times within the historical sampling period collected by the first temperature sensor, the outlet temperature of the electric heater at all sampling times within the historical sampling period collected by the second temperature sensor, and the inlet mass flow rate of the air in the anode pipeline at all sampling times within the historical sampling period detected by the mass flow meter. The input power, inlet air temperature, outlet air temperature, inlet air mass flow rate, ambient temperature, specific heat capacity of air, target outlet air temperature, and historical sampling period at all sampling times within the historical sampling period are substituted into the system identification objective function. The system identification objective function is then solved based on an optimization algorithm to obtain the thermal balance equivalent parameters that satisfy the optimization problem.

7. The system as described in claim 1, characterized in that, The controller is specifically used for: Calculate the quotient between the feedforward power and the maximum power, and use the quotient as the feedforward control signal.

8. The system as described in claim 1, characterized in that, The controller is specifically used for: The feedback control signal is calculated based on the temperature difference between each time point from the initial time to the current time and the preset PID position algorithm discrete formula.

9. The system as described in claim 1, characterized in that, The controller is specifically used for: Calculate the sum between the feedforward control signal and the feedback control signal; The above and below are used as the outlet temperature control signal.

10. The system as described in claim 2, characterized in that, The controller is specifically used for: The preset time period is 30 minutes.

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