Hydrogen storage tank pressure and temperature control method and device based on model predictive control

Abstract

The invention relates to the technical field of hydrogen energy storage and control, and discloses a hydrogen storage tank pressure and temperature control method and device based on model predictive control, and the method comprises the steps: determining a hydrogen state equation of a hydrogen storage tank, and constructing a hydrogen mass conservation equation and a hydrogen energy conservation equation; based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, a state prediction model of the hydrogen storage tank state related to the net hydrogen flow change is established, and the hydrogen storage tank state comprises hydrogen pressure and hydrogen temperature; a target function is constructed with the hydrogen storage tank state and the net hydrogen flow as control targets, and constraint conditions are configured; on the basis of the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence output by prediction, the target function is subjected to minimum solving, and the optimal net hydrogen flow sequence is obtained through iteration; and adjusting an inlet / outlet valve of the hydrogen storage tank based on the optimal net hydrogen flow sequence. The operation efficiency of the hydrogen storage system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen energy storage and control, and in particular to a method and device for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control. Background Art

[0002] With the growing global demand for clean energy, hydrogen energy, as an efficient and environmentally friendly energy carrier, and its storage technology occupy an important position in the energy system. Hydrogen has the advantages of high energy density and zero emissions, and is widely used in transportation, industrial production, and power storage. However, the low density and high diffusivity of hydrogen make its storage a technical challenge. In order to effectively store hydrogen, high-pressure gas storage, liquid storage, or solid storage are usually adopted. Among them, high-pressure gas storage is widely used in commercial and industrial hydrogen storage tanks due to its relative maturity and economy.

[0003] Existing hydrogen storage tank control systems primarily utilize traditional control methods, such as proportional-integral-derivative (PID) controllers. These controllers regulate the hydrogen filling and discharging flows to maintain the pressure and temperature within the hydrogen storage tank, ensuring the proper operation of the system. Due to their simple structure, ease of implementation, and low cost, PID controllers are the most commonly used control method in current hydrogen storage systems. However, with the expansion of hydrogen storage systems and the diversification of their application scenarios, traditional PID control methods are struggling to meet the demands for efficient, safe, and intelligent control in practical applications. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control to improve the operating efficiency and safety of the hydrogen storage system.

[0005] In a first aspect, the present invention provides a hydrogen storage tank pressure and temperature control method based on model predictive control, which includes: determining the hydrogen state equation of the hydrogen storage tank, constructing a hydrogen mass conservation equation and a hydrogen energy conservation equation; based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, establishing a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate, the hydrogen storage tank state including hydrogen pressure and hydrogen temperature; constructing an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configuring constraint conditions; based on the input net hydrogen flow rate sequence of the state prediction model and the predicted hydrogen storage tank state sequence of the predicted output, minimizing the objective function, iteratively obtaining the optimal net hydrogen flow rate sequence; adjusting the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow rate sequence.

[0006] In this implementation, the present application applies model predictive control technology to the pressure and temperature control of hydrogen storage tanks. By combining the hydrogen mass conservation equation and the hydrogen energy conservation equation to establish a state prediction model, the future state of the hydrogen storage system is predicted based on current information, which can accurately predict the pressure and temperature change trends in advance, improve the subsequent response speed and control accuracy, and achieve safe operation of the hydrogen storage system. Furthermore, the objective function is constructed with the hydrogen storage tank state and the net hydrogen flow rate as the control target, and the constraint conditions are configured. It is possible to simultaneously consider the influence of hydrogen pressure and hydrogen temperature on the hydrogen storage system, realize the joint control of the two variables of pressure and temperature, realize the coordinated optimization of the hydrogen storage system, and improve the hydrogen storage efficiency. Effectively constraining the variables improves the safety of the operation of the hydrogen storage system. At the same time, constraining the net hydrogen flow rate can achieve the optimal distribution and utilization of energy, reduce energy loss, improve energy efficiency, and increase the overall economy of the hydrogen storage system.

[0007] In an optional embodiment, determining the hydrogen state equation of the hydrogen storage tank and constructing the hydrogen mass conservation equation and the hydrogen energy conservation equation include: determining the hydrogen state equation of the hydrogen storage tank as: PV = nRT, where P is the hydrogen pressure, V is the volume of the hydrogen storage tank, n is the number of moles of the hydrogen storage tank, R is the gas constant, and T is the hydrogen temperature; the rate of change of the number of moles of the hydrogen storage tank is determined by the net hydrogen flow rate, and taking the time derivative of the hydrogen state equation to obtain the hydrogen mass conservation equation as: Where, is the net hydrogen flow.

[0008] In this implementation, the effect of the net hydrogen flow rate on the hydrogen pressure is considered in combination with the hydrogen state equation.

[0009] In an optional embodiment, constructing the hydrogen mass conservation equation and the hydrogen energy conservation equation includes: constructing the hydrogen flow heat effect equation as follows: Where α is the thermal expansion coefficient; the heat exchange equation for the hydrogen environment is: Q exchange =hA(T0-T), where h is the convective heat transfer coefficient, T0 is the ambient temperature, and A is the surface area of ​​the hydrogen storage tank; the hydrogen energy conservation equation is constructed as: Where C p is the specific heat capacity at constant pressure; combining the hydrogen flow thermal effect equation, the hydrogen environment heat exchange equation and the hydrogen state equation, the hydrogen energy conservation equation is updated to obtain:

[0010] In this implementation, the effect of the net hydrogen flow rate on the hydrogen temperature is considered in combination with the hydrogen state equation.

[0011] In an optional embodiment, based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, a state prediction model of the hydrogen storage tank state with respect to the net hydrogen flow rate change is established, including: discretizing the hydrogen mass conservation equation and the hydrogen energy conservation equation to obtain a state prediction model of the net hydrogen flow rate change: x(k+i+1)=f(x(k+i),u(k+i)),i=0,1,...,N-1, where x(k) is the hydrogen storage tank state at time k, and x(k)=[P(k),T(k)] T , P(k) is the hydrogen pressure at time k, T(k) is the hydrogen temperature at time k, u(k) is the net hydrogen flow at time k,

[0012] In this implementation, the two variables are jointly controlled based on the influence of net hydrogen flow on hydrogen temperature and pressure, and a prediction model is set up to foresee future changes in the state of the hydrogen storage system. Combined with subsequent feedback control methods, the response speed and control accuracy can be improved when the formation is subjected to load waves and external interference, thereby achieving optimal operation of the hydrogen storage system.

[0013] In an optional embodiment, constructing an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets includes: constructing an objective function with maintaining the hydrogen storage tank state stable and the net hydrogen flow rate low as control targets, and obtaining: Where, P ref is the reference value of hydrogen pressure, T ref is the hydrogen temperature reference value, and λ is the weight coefficient; the constraints for setting the hydrogen storage tank state and net hydrogen flow rate are: Where, P max is the maximum hydrogen pressure, P min is the minimum hydrogen pressure, T max is the maximum temperature of hydrogen, T min is the minimum temperature of hydrogen, is the maximum net hydrogen flow rate, is the minimum net hydrogen flow rate.

[0014] In this implementation, by jointly optimizing and controlling hydrogen temperature, hydrogen pressure, and net hydrogen flow, and setting corresponding constraints, strict physical and safety constraints can be achieved, optimal energy distribution and utilization can be realized, and system operating efficiency and overall economy can be improved.

[0015] In an optional embodiment, based on the input net hydrogen flow sequence of the state prediction model and the predicted pressure state sequence and predicted temperature state sequence of the predicted output, the objective function is minimized and solved, and the optimal net hydrogen flow sequence is iteratively obtained, including: converting the nonlinear state prediction model into a linear state prediction model; using the linear state prediction model to convert the objective function and constraints into a quadratic programming form; solving the quadratic programming problem to obtain the optimal net hydrogen flow sequence.

[0016] In this implementation, the above-mentioned prediction model is combined to perform the optimal solution, which can reduce the response lag problem. When facing rapidly changing loads or external disturbances, the hydrogen flow strategy can be adjusted in time to ensure the safe operation of the system.

[0017] In a second aspect, the present invention provides a hydrogen storage tank pressure and temperature control device based on model predictive control, which includes: a first construction module for determining the hydrogen state equation of the hydrogen storage tank and constructing the hydrogen mass conservation equation and the hydrogen energy conservation equation; an establishment module for establishing a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, wherein the hydrogen storage tank state includes hydrogen pressure and hydrogen temperature; a second construction module for constructing an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configuring constraint conditions; a solution module for minimizing the objective function based on the input net hydrogen flow rate sequence of the state prediction model and the predicted output predicted hydrogen storage tank state sequence, and iteratively obtaining the optimal net hydrogen flow rate sequence; an adjustment module for adjusting the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow rate sequence.

[0018] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the hydrogen storage tank pressure and temperature control method based on model predictive control of the above-mentioned first aspect or any corresponding embodiment thereof.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the hydrogen storage tank pressure and temperature control method based on model predictive control of the above-mentioned first aspect or any corresponding embodiment thereof.

[0020] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, which are used to enable a computer to execute the hydrogen storage tank pressure and temperature control method based on model predictive control according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 is a flow chart of a method for controlling pressure and temperature of a hydrogen storage tank based on model predictive control according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of another method for controlling pressure and temperature of a hydrogen storage tank based on model predictive control according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of a hydrogen storage tank pressure and temperature control system based on model predictive control according to an embodiment of the present invention;

[0025] Figure 4 is a structural block diagram of a hydrogen storage tank pressure and temperature control device based on model predictive control according to an embodiment of the present invention;

[0026] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0028] In recent years, with the improvement of control theory and computing power, advanced control methods such as fuzzy control, adaptive control, and intelligent control have gradually been applied to hydrogen storage systems. The PID control method has a certain application basis in simple systems, but in hydrogen energy storage systems, there are the following problems:

[0029] (1) Insufficient multivariable control capability: PID controllers typically adjust a single variable, such as controlling only pressure or only temperature, making it difficult to optimize multiple key parameters simultaneously. In practical applications, pressure and temperature often affect each other, making it difficult to achieve coordinated optimization of the system with single-variable control, resulting in limited hydrogen storage efficiency and safety.

[0030] (2) Weak constraint handling capabilities: The operation of hydrogen storage tanks is subject to strict physical and safety constraints, such as upper and lower limits on pressure and temperature, and restrictions on hydrogen charging and discharging flows. Traditional PID controllers lack effective mechanisms for handling these constraints, which can easily lead to system overshoot or oscillation, and even safety accidents. For example, when the system load changes rapidly, the PID controller may not be able to adjust the hydrogen charging and discharging flows in a timely manner, causing the pressure or temperature to exceed the safe range.

[0031] (3) Lack of prediction and optimization capabilities: The PID controller makes adjustments based on current and historical errors and cannot foresee future changes in system status. This feedback control method has limited response speed and control accuracy when facing load fluctuations or external interference, making it difficult to achieve optimal system operation. For example, during the hydrogen filling process, the PID controller cannot predict the pressure change trend in advance, resulting in significant energy loss during the hydrogen filling process.

[0032] (4) Low energy efficiency: During the hydrogen charging and discharging process, traditional control methods have difficulty achieving optimal energy distribution and utilization, resulting in significant energy losses. Due to the lack of system-level optimization strategies, the adjustment of hydrogen charging and discharging flow rates is often based on maintaining pressure and temperature stability, ignoring the improvement of energy efficiency, which affects the overall economic efficiency of the hydrogen storage system.

[0033] (5) System dynamic response lag: Traditional PID controllers may have response lag problems when dealing with dynamic systems. When the hydrogen storage system faces rapidly changing loads or external disturbances, the PID controller cannot adjust the control input in time, resulting in a delayed response of the system state, affecting the real-time control performance of the system.

[0034] Based on this, this paper proposes a method and system for controlling hydrogen storage tank pressure and temperature based on Model Predictive Control (MPC). This technical solution establishes a dynamic mathematical model of the hydrogen storage tank and leverages the prediction and optimization capabilities of MPC to achieve multivariable coordinated control of the pressure and temperature within the tank, ensuring efficient and safe operation of the system within strict physical and safety constraints.

[0035] According to an embodiment of the present invention, an embodiment of a method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0036] In this embodiment, a method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control is provided. Figure 1is a flow chart of a method for controlling pressure and temperature of a hydrogen storage tank based on model predictive control according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not based on Figure 1 The process sequence shown is limited. Figure 1 As shown, the process includes the following steps:

[0037] Step S101: determine the hydrogen state equation of the hydrogen storage tank, and construct the hydrogen mass conservation equation and the hydrogen energy conservation equation.

[0038] Among them, the hydrogen state equation is the functional relationship between its state parameters when hydrogen reaches equilibrium state. The state parameters generally include hydrogen pressure, hydrogen temperature, hydrogen mole number, and hydrogen volume.

[0039] Among them, the hydrogen mass conservation equation refers to the relationship equation between the hydrogen pressure change rate and the net hydrogen flow rate, which is obtained by changing the number of hydrogen moles under the influence of the inlet and outlet hydrogen flow rates in the hydrogen storage tank.

[0040] Among them, the hydrogen energy conservation equation refers to the balance between the thermal effect brought by the flow of inflow and outflow hydrogen and the heat exchange effect with the environment and the total thermal energy of the hydrogen in the hydrogen storage tank, and the relationship equation between the hydrogen temperature change rate and the net hydrogen flow rate is obtained.

[0041] In this implementation, combined with the hydrogen state equation of the hydrogen storage tank, the relationship equations of the net hydrogen flow rate on the hydrogen pressure and the net hydrogen flow rate on the hydrogen temperature are obtained respectively.

[0042] Step S102: Based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate is established.

[0043] Among them, the hydrogen storage tank status includes hydrogen pressure and hydrogen temperature.

[0044] Combining the hydrogen mass conservation equation and the hydrogen energy conservation equation, that is, combining the relationship equations of the net hydrogen flow rate on hydrogen pressure and the net hydrogen flow rate on hydrogen temperature, considering the comprehensive influence of net hydrogen flow rate on hydrogen pressure and hydrogen temperature, a state prediction model of hydrogen pressure and hydrogen temperature with respect to the change of net hydrogen flow rate is obtained.

[0045] Understandably, if the current hydrogen pressure and temperature are known, the hydrogen flow rate entering or exiting the hydrogen storage tank, combined with the state prediction model, and considering the impact of the net hydrogen flow rate on the hydrogen pressure and temperature, can predict the hydrogen pressure and temperature at the next moment. If the net hydrogen flow rate at multiple future moments is known, the hydrogen pressure and temperature at the initial moment, along with the state prediction model, can be used to predict the hydrogen pressure and temperature at multiple future moments.

[0046] Step S103: construct an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configure constraint conditions.

[0047] Control coefficients are set for the hydrogen storage tank state changes and net hydrogen flow at multiple moments in the future, an objective function is constructed, and the ranges of hydrogen pressure, hydrogen temperature, and net hydrogen flow are constrained.

[0048] Step S104 , based on the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence output by the prediction, the objective function is minimized and solved, and the optimal net hydrogen flow sequence is obtained by iteration.

[0049] Based on the hydrogen pressure and hydrogen temperature at the initial moment and the net hydrogen flow sequence at multiple moments in the future as the input of the state prediction model, the hydrogen pressure sequence and hydrogen temperature sequence at multiple moments in the future are predicted and output. On the basis that the net hydrogen flow sequence, hydrogen pressure sequence and hydrogen temperature sequence meet the constraints, the objective function is calculated by combining the net hydrogen flow sequence, hydrogen pressure sequence and hydrogen temperature sequence.

[0050] Furthermore, by continuously adjusting the net hydrogen flow sequence and minimizing the objective function, the optimal net hydrogen flow sequence is obtained.

[0051] Step S105: adjusting the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow sequence.

[0052] The hydrogen tank's inlet and outlet valves are adjusted based on the optimal net hydrogen flow sequence. It can be understood that when the net hydrogen flow is positive, hydrogen enters, opening the inlet valve; when the net hydrogen flow is negative, hydrogen flows out, opening the outlet valve. Furthermore, the hydrogen flow rate can be adjusted by adjusting the opening of the inlet and outlet valves.

[0053] The present embodiment provides a method for controlling the pressure and temperature of a hydrogen storage tank based on model prediction. This application applies model predictive control technology to the pressure and temperature control of a hydrogen storage tank. By combining the hydrogen mass conservation equation and the hydrogen energy conservation equation to establish a state prediction model, the future state of the hydrogen storage system is predicted based on current information. This allows for accurate prediction of pressure and temperature change trends in advance, improves subsequent response speed and control accuracy, and enables safe operation of the hydrogen storage system. Furthermore, by constructing an objective function with the hydrogen storage tank state and net hydrogen flow rate as control targets and configuring constraint conditions, it is possible to simultaneously consider the impact of hydrogen pressure and hydrogen temperature on the hydrogen storage system, achieve joint control of the two variables of pressure and temperature, achieve coordinated optimization of the hydrogen storage system, and improve hydrogen storage efficiency. By effectively constraining the variables and improving the safety of the hydrogen storage system operation, and at the same time, by constraining the net hydrogen flow rate, it is possible to achieve optimal energy distribution and utilization, reduce energy loss, improve energy efficiency, and increase the overall economy of the hydrogen storage system.

[0054] In this embodiment, a method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control is provided. Figure 2 is a flow chart of another method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control according to an embodiment of the present invention. It should be noted that if there are substantially the same results, this embodiment is not based on Figure 2 The process sequence shown is limited. Figure 2 As shown, the process includes the following steps:

[0055] Step S201: determine the hydrogen state equation of the hydrogen storage tank, and construct the hydrogen mass conservation equation and the hydrogen energy conservation equation.

[0056] This step establishes a dynamic mathematical model of the hydrogen storage tank, describing the dynamic relationship between the hydrogen pressure and temperature inside the tank, the net hydrogen flow rate, and the ambient temperature. The focus is on the impact of adjusting the hydrogen inlet and outlet flow rates on the hydrogen pressure and temperature inside the tank.

[0057] Specifically, the above step S201 includes:

[0058] Step S2011, determining the hydrogen state equation of the hydrogen storage tank.

[0059] The ideal gas state equation is used to determine the hydrogen state equation of the hydrogen storage tank:

[0060] PV=nRT

[0061] Where P is the hydrogen pressure in bar, V is the volume of the hydrogen storage tank in cubic meters (m3). 3 ), n is the number of moles of hydrogen in the storage tank, in moles (mol), R is the gas constant, which is 8.314 J / (mol·K), and T is the hydrogen temperature, in degrees Celsius (℃).

[0062] By taking the time derivative of the hydrogen state equation, we can obtain the relationship between hydrogen pressure and hydrogen temperature change:

[0063]

[0064] Among them, since the volume V of the hydrogen storage tank is constant, and the gas constant R is assumed to be constant, we have:

[0065]

[0066] Step S2012: constructing a hydrogen mass conservation equation based on the hydrogen state equation.

[0067] The rate of change of the number of moles of hydrogen is determined by the flow rate of hydrogen in and out, which is:

[0068]

[0069] Where, is the net hydrogen flow rate, measured in moles per second (mol / s). A high-precision hydrogen flowmeter can be used to measure the inflow and outflow of hydrogen in the hydrogen storage tank in real time. As can be understood, a positive value indicates a net inflow of hydrogen, while a negative value indicates a net outflow of hydrogen. Combined with the hydrogen state equation, we obtain:

[0070]

[0071] The simplified hydrogen mass conservation equation is:

[0072]

[0073] Among them, the hydrogen mass conservation equation is an equation that characterizes the relationship between the hydrogen pressure change rate and the net hydrogen flow rate.

[0074] Further processing of the hydrogen mass conservation equation shows that It indicates the change in hydrogen pressure caused directly by the change in hydrogen temperature. When the hydrogen storage tank is kept at a constant temperature, When the hydrogen tank temperature changes very slowly, or and Compared to very small, but:

[0075]

[0076] Since the net hydrogen flow rate is the net rate of change in the number of gas moles, the corresponding pressure change rate can be expressed as the sum of the contributions of the pressure increase rate of the gas inflow and the pressure decrease rate of the gas outflow, so:

[0077]

[0078] Will Considered as inflow rate Model the outflow or loss term as but:

[0079]

[0080] Furthermore,

[0081]

[0082] Where P(t) is the hydrogen pressure in the hydrogen storage tank at time t. The pressure in the hydrogen storage tank can be measured in real time by a high-precision pressure sensor. p is the pressure time constant in seconds (s), which can be obtained by Calculated, T0 is the ambient temperature, and the ambient temperature is collected in real time by the temperature sensor installed in the surrounding environment of the hydrogen storage tank, C pis the specific heat capacity at constant pressure, with the unit of J / (mol·℃). The specific heat capacity of hydrogen at constant pressure is known to be 28.836 J / mol / K.

[0083] Step S2013: constructing a hydrogen energy conservation equation based on the hydrogen state equation.

[0084] The temperature change of hydrogen in the hydrogen storage tank is determined by the thermal effect brought by the flow of hydrogen and the heat exchange with the environment.

[0085] The flow of hydrogen in and out will take away or bring in a certain amount of heat. The heat effect equation of hydrogen flow is constructed as follows:

[0086]

[0087] Where α is the thermal expansion coefficient, expressed in J / (mol·°C), which characterizes the thermal effect during hydrogen flow. The thermal expansion coefficient of hydrogen under specific conditions can be obtained by consulting relevant literature or gas thermodynamic data.

[0088] The hydrogen exchanges heat with the ambient temperature through the tank surface, and the heat exchange equation of hydrogen and environment is constructed as follows:

[0089] Q exchane =hA(T0-T)

[0090] Where h is the convective heat transfer coefficient, unit is W / (m 2 ·℃), the heat transfer theory can be used to calculate the heat exchange rate through dimensionless numbers such as the Nusselt number, Reynolds number and Prandtl number. T0 is the ambient temperature, A is the surface area of ​​the hydrogen storage tank, and its surface area can be calculated based on the geometric shape of the hydrogen storage tank.

[0091] The hydrogen energy conservation equation is constructed as:

[0092]

[0093] Where C p is the specific heat capacity at constant pressure, C p n represents the total heat capacity of hydrogen in the hydrogen storage tank.

[0094] Substituting the hydrogen flow heat effect equation and the hydrogen environment heat exchange equation into the equation, we can obtain:

[0095]

[0096] After finishing, we can get:

[0097]

[0098] Where T(t) is the hydrogen temperature at time t, and the temperature inside the hydrogen storage tank can be measured in real time by a high-precision temperature sensor.

[0099] Furthermore, the hydrogen mass conservation equation and the hydrogen energy conservation equation are expressed as a set of nonlinear differential equations:

[0100]

[0101] Step S202: Based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate is established.

[0102] The hydrogen mass conservation equation and hydrogen energy conservation equation are discretized to obtain the state prediction model of net hydrogen flow change:

[0103] x(k+i+1)=f(x(k+i),u(k+i)),i=0,1,...,N-1

[0104] Where x(k) is the state of the hydrogen storage tank at time k, x(k) = [P(k), T(k)] T , P(k) is the hydrogen pressure at time k, T(k) is the hydrogen temperature at time k, u(k) is the net hydrogen flow at time k,

[0105] Parameters such as hydrogen pressure, hydrogen temperature and net hydrogen flow in the hydrogen storage tank are collected in real time, and the current state of the hydrogen storage tank is estimated using sensor data and filtering algorithms (such as extended Kalman filtering). Assuming that the current moment is k, the state prediction model is used to predict the hydrogen pressure and hydrogen temperature states of the hydrogen storage tank at future moments k+2, k+2,…, k+N. It can be understood that the ambient temperature is assumed to be known or changes slowly.

[0106] In this implementation, the two variables are jointly controlled based on the influence of net hydrogen flow on hydrogen temperature and pressure, and a prediction model is set up to foresee future changes in the state of the hydrogen storage system. Combined with subsequent feedback control methods, the response speed and control accuracy can be improved when the formation is subjected to load waves and external interference, thereby achieving optimal operation of the hydrogen storage system.

[0107] Step S203: construct an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configure constraint conditions.

[0108] The objective function is constructed with the control objectives of maintaining the stable state of the hydrogen storage tank and low net hydrogen flow, and the following is obtained:

[0109]

[0110] Where, P ref is the reference value of hydrogen pressure, T refis the reference value of hydrogen temperature, λ is the weight coefficient, which is used to balance the state error and control the energy consumption, and i is a short time index, which is used to represent the variables predicted at different moments in the future.

[0111] The constraints for setting the hydrogen storage tank status and net hydrogen flow rate are:

[0112]

[0113] Where, P max is the maximum hydrogen pressure, P min is the minimum hydrogen pressure, T max is the maximum temperature of hydrogen, T min is the minimum temperature of hydrogen, is the maximum net hydrogen flow rate, is the minimum net hydrogen flow rate.

[0114] In this implementation, by jointly optimizing and controlling hydrogen temperature, hydrogen pressure, and net hydrogen flow, and setting corresponding constraints, strict physical and safety constraints can be achieved, optimal energy distribution and utilization can be realized, and system operating efficiency and overall economy can be improved.

[0115] Step S204 , based on the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence outputted by the prediction, the objective function is minimized and solved, and the optimal net hydrogen flow sequence is obtained by iteration.

[0116] Based on the hydrogen pressure and hydrogen temperature at the initial moment and the net hydrogen flow sequence at multiple moments in the future as the input of the state prediction model, the hydrogen pressure sequence and hydrogen temperature sequence at multiple moments in the future are predicted and output. On the basis that the net hydrogen flow sequence, hydrogen pressure sequence and hydrogen temperature sequence meet the constraints, the objective function is calculated by combining the net hydrogen flow sequence, hydrogen pressure sequence and hydrogen temperature sequence.

[0117] Specifically, at each control time k, based on the current hydrogen storage tank state x(k) = [P(k), T(k)] T By solving the optimization problem of step S203, the optimal input net hydrogen flow sequence is determined to minimize the predetermined performance index, wherein the input net hydrogen flow sequence is:

[0118]

[0119] Specifically, the above step S204 includes:

[0120] Step S2041: converting the nonlinear state prediction model into a linear state prediction model.

[0121] Since the state prediction model is nonlinear and the optimization problem is a nonlinear programming problem, in order to improve the computational efficiency and real-time performance, the state prediction model is linearized and the nonlinear model is converted into a linear model as follows:

[0122] x(k+i+1)≈Xx(k+i)+Bu(k+i)+c

[0123] Among them, the system matrix A, control matrix B and constant term c are determined by the linearization process.

[0124] Based on the linearized model, the hydrogen pressure and hydrogen temperature at the next N moments are predicted. Specifically, for each prediction step, the following can be recursively calculated:

[0125]

[0126] …

[0127]

[0128] Step S2042: Convert the objective function and constraints into a quadratic programming form using a linear state prediction model.

[0129] Convert the objective function to minimize the quadratic objective function and convert the constraints to inequality boundary constraints to obtain:

[0130] Minimize:

[0131] Constraints: lb≤u≤ub

[0132] Where u is the decision vector for the hydrogen pressure, hydrogen temperature, and net hydrogen flow rate to be optimized, H is a symmetric semi-positive definite matrix used to define the quadratic terms in the objective function, f is the vector of linear terms in the objective function, and lb and ub are the lower and upper bound vectors of the decision variables.

[0133] Step S2043: Solve the quadratic programming problem to obtain the optimal net hydrogen flow sequence.

[0134] For the quadratic programming (QP) problem constructed above, a receding horizon optimization strategy is employed. At each control moment, only the first control input of the optimal control sequence is applied, and the optimization process is repeated at the next moment. This QP problem is solved using efficient QP solvers such as MATLAB's quadprog function, the open-source OSQP solver, or commercially available tools like Gurobi and CPLEX to obtain the optimal control input vector, i.e., the optimal net hydrogen flow sequence U(k).

[0135] In this implementation, linearization processing is used to transform the nonlinear optimization problem into a quadratic programming problem. Combined with an efficient solution algorithm, real-time online optimization control is achieved to ensure the real-time performance and effectiveness of the controller.

[0136] Step S205: Adjust the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow sequence.

[0137] By repeating the above steps, real-time dynamic control is achieved.

[0138] The hydrogen storage tank pressure and temperature control method based on model predictive control provided in this embodiment applies MPC technology to the pressure and temperature control of hydrogen storage tanks for the first time. By predicting future system behavior, it can predict the future change trend of the system in advance, optimize the control strategy, improve the dynamic response speed of the system, and reduce the fluctuation amplitude of pressure and temperature. In view of the complex nonlinear characteristics of the hydrogen storage tank, a mathematical model based on thermodynamics and fluid dynamics is established, and the prediction ability and control accuracy of the controller are improved through linearization processing, thereby reducing system deviation and ensuring the stability of the hydrogen storage process. Considering the multivariable coupling relationship between pressure and temperature in the hydrogen storage tank, a coordinated optimization control strategy is designed to achieve an improvement in the overall performance of the system. An adaptive control strategy is adopted to dynamically adjust the control parameters according to real-time collected data and environmental changes, enhance the robustness and adaptability of the system, and ensure stable operation under various working conditions. Accurate pressure and temperature control prevents safety accidents caused by excessive pressure or abnormal temperature, thereby ensuring the safe operation of the hydrogen storage system.

[0139] In this embodiment, a hydrogen storage tank pressure and temperature control system based on model predictive control is provided. Figure 3 This is a schematic diagram of a hydrogen storage tank pressure and temperature control system based on model predictive control according to an embodiment of the present invention. The hydrogen storage tank pressure and temperature control system based on model predictive control includes a sensor module, a data processing and state estimation module, a model predictive controller module, an actuator and a communication and feedback module.

[0140] The sensor module is used to collect key parameters such as pressure, temperature and hydrogen flow in the hydrogen storage tank in real time.

[0141] Specifically, the sensor module includes a pressure sensor, a temperature sensor, and a flow sensor. The pressure sensor is installed inside the hydrogen storage tank to monitor pressure changes within the tank. Temperature sensors are located at various locations within the tank to ensure uniform and accurate temperature distribution. The flow sensor, installed at the hydrogen inlet and outlet valves, measures hydrogen flow in real time.

[0142] The data processing and state estimation module is used to process the collected data and estimate the current state of the system. It estimates the current pressure, flow rate, and temperature of the hydrogen storage tank in real time.

[0143] The model predictive controller module is used to calculate the optimal hydrogen flow control instructions based on the prediction model and optimization algorithm.

[0144] Specifically, the model predictive controller module includes a prediction model unit and a control strategy unit.

[0145] The prediction model unit is used to predict the changing trend of the system status in the future based on the mathematical model of the hydrogen storage tank. The optimization algorithm unit: converts the control target into an optimization problem and uses the quadratic programming (QP) method to calculate the optimal control input sequence.

[0146] The control strategy unit is used to extract the control instructions at the current moment according to the optimization results and perform necessary adjustments and optimizations.

[0147] The specific workflow is:

[0148] Receive status information: Get the current system status from the data processing and state estimation module.

[0149] State prediction: Use the prediction model to predict the system state changes in the next NNN sampling periods based on the current state.

[0150] Optimize control input: Construct the objective function and constraints, transform the control problem into a quadratic programming problem, and use an efficient QP solver (such as the interior point method) to solve the optimal hydrogen flow control instructions.

[0151] Output control instruction: transmit the first control instruction in the optimal control input sequence to the actuator to adjust the hydrogen flow rate.

[0152] The actuator controls the hydrogen flow in the hydrogen storage tank by adjusting the opening of the valve.

[0153] Specifically, the actuator includes hydrogen inlet and outlet valves and a drive control unit.

[0154] Hydrogen inlet and outlet valves, including electric or servo-driven valves, are used to precisely control the flow of hydrogen in and out. The drive control unit receives instructions from the controller and drives the valve opening to adjust the hydrogen flow.

[0155] The specific workflow is:

[0156] Receive control instructions: Receive optimal hydrogen flow control instructions from the model predictive controller module.

[0157] Drive valve adjustment: According to the control instructions, adjust the opening of the hydrogen inlet and outlet valves to achieve precise regulation of hydrogen flow.

[0158] The communication and feedback module is used to realize data transmission and information feedback between modules to ensure the real-time and collaborative performance of the system.

[0159] This embodiment also provides a hydrogen storage tank pressure and temperature control device based on model predictive control. This device is used to implement the above-mentioned embodiments and preferred embodiments. Details that have already been described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0160] This embodiment provides a hydrogen storage tank pressure and temperature control device based on model predictive control, such as Figure 4 As shown, including:

[0161] The first construction module 401 is used to determine the hydrogen state equation of the hydrogen storage tank and construct the hydrogen mass conservation equation and the hydrogen energy conservation equation;

[0162] Establishing module 402, for establishing a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, wherein the hydrogen storage tank state includes hydrogen pressure and hydrogen temperature;

[0163] The second construction module 403 is used to construct an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configure constraint conditions;

[0164] A solution module 404 is configured to minimize an objective function based on the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence output, and iteratively obtain an optimal net hydrogen flow sequence;

[0165] The adjustment module 405 is configured to adjust the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow sequence.

[0166] In some optional implementations, the first building block 401 includes:

[0167] The first determination unit is used to determine the hydrogen state equation of the hydrogen storage tank: PV=nRT, where P is the hydrogen pressure, V is the volume of the hydrogen storage tank, n is the number of moles of the hydrogen storage tank, R is the gas constant, and T is the hydrogen temperature.

[0168] The first building block is used to perform time derivative of the hydrogen state equation to obtain the hydrogen mass conservation equation:

[0169]

[0170] Where, is the net hydrogen flow rate. The rate of change of the number of moles of hydrogen in the hydrogen storage tank is determined by the net hydrogen flow rate.

[0171] In some optional implementations, the first building module 401 further includes:

[0172] The second building block is used to construct the hydrogen flow thermal effect equation: Where α is the thermal expansion coefficient; the heat exchange equation for the hydrogen environment is: Q exchange =hA(T0-T), where h is the convective heat transfer coefficient, T0 is the ambient temperature, and A is the surface area of ​​the hydrogen storage tank; the hydrogen energy conservation equation is constructed as: Where C p is the specific heat capacity at constant pressure; combining the hydrogen flow thermal effect equation, the hydrogen environment heat exchange equation and the hydrogen state equation, the hydrogen energy conservation equation is updated to obtain:

[0173] In some optional implementations, the establishing module 402 includes:

[0174] The discretization unit is used to discretize the hydrogen mass conservation equation and the hydrogen energy conservation equation, and the state prediction model of the net hydrogen flow change is obtained as follows: x(k+i+1)=f(x(k+i),u(k+i)),i=0,1,...,N-1, where x(k) is the state of the hydrogen storage tank at time k, and x(k)=[P(k),T(k)] T , P(k) is the hydrogen pressure at time k, T(k) is the hydrogen temperature at time k, u(k) is the net hydrogen flow at time k,

[0175] In some optional implementations, the solution module 404 includes:

[0176] A unit is established to construct an objective function with the control objectives of maintaining a stable hydrogen storage tank state and a low net hydrogen flow rate, and the following is obtained: Where, P ref is the reference value of hydrogen pressure, T ref is the reference value of hydrogen temperature, λ is the weight coefficient;

[0177] The setting unit is used to set the constraints of the hydrogen storage tank status and net hydrogen flow rate as follows: Where, P max is the maximum hydrogen pressure, P min is the minimum hydrogen pressure, T max is the maximum temperature of hydrogen, T min is the minimum temperature of hydrogen, is the maximum net hydrogen flow rate, is the minimum net hydrogen flow rate.

[0178] In some optional implementations, the solution module 404 further includes:

[0179] The first conversion unit is configured to convert the nonlinear state prediction model into a linear state prediction model.

[0180] The second conversion unit is used to convert the objective function and the constraint conditions into a quadratic programming form by using a linear state prediction model.

[0181] The solving unit is used to solve the quadratic programming problem and obtain the optimal net hydrogen flow sequence.

[0182] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0183] The hydrogen storage tank pressure and temperature control device based on model predictive control in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0184] The embodiment of the present invention also provides a computer device having the above Figure 5 The hydrogen storage tank pressure and temperature control device based on model predictive control is shown.

[0185] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0186] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0187] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0188] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0189] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0190] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0191] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0192] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0193] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0194] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control, characterized in that: The method comprises: Determine the hydrogen state equation of the hydrogen storage tank and construct the hydrogen mass conservation equation and hydrogen energy conservation equation; Based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate is established, wherein the hydrogen storage tank state includes hydrogen pressure and hydrogen temperature; Constructing an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configuring constraint conditions; Based on the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence of the predicted output, the objective function is minimized and solved, and the optimal net hydrogen flow sequence is iteratively obtained; The inlet and outlet valves of the hydrogen storage tank are adjusted based on the optimal net hydrogen flow sequence.

2. The hydrogen storage tank pressure and temperature control method based on model predictive control according to claim 1 is characterized in that: The method of determining the hydrogen state equation of the hydrogen storage tank and constructing the hydrogen mass conservation equation and the hydrogen energy conservation equation includes: The hydrogen state equation of the hydrogen storage tank is determined as: PV=nRT Wherein, P is the hydrogen pressure, V is the volume of the hydrogen storage tank, n is the number of moles of the hydrogen storage tank, R is the gas constant, and T is the hydrogen temperature; The rate of change of the molar number of the hydrogen storage tank is determined by the net hydrogen flow rate. The hydrogen mass conservation equation is obtained by taking the time derivative of the hydrogen state equation: Where, is the net hydrogen flow rate.

3. The method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control according to claim 2, characterized in that: The construction of the hydrogen mass conservation equation and the hydrogen energy conservation equation includes: The thermal effect equation of hydrogen flow is constructed as follows: Where α is the coefficient of thermal expansion; The heat exchange equation for the hydrogen environment is constructed as follows: Q exchange =hA(T0-T) Where h is the convective heat transfer coefficient, T0 is the ambient temperature, and A is the surface area of ​​the hydrogen storage tank; The hydrogen energy conservation equation is constructed as: Where C p is the specific heat capacity at constant pressure; Combining the hydrogen flow heat effect equation, the hydrogen environment heat exchange equation, and the hydrogen state equation, the hydrogen energy conservation equation is updated to obtain:

4. The method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control according to claim 1, characterized in that: The state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate is established based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, including: The hydrogen mass conservation equation and the hydrogen energy conservation equation are discretized to obtain a state prediction model for the net hydrogen flow change: x(k+i+1)=f(x(k+i),u(k+i)),i=0,1,...,N-1 Where x(k) is the state of the hydrogen storage tank at time k, x(k)[P(k), T(k)] T , P(k) is the hydrogen pressure at time k, T(k) is the hydrogen temperature at time k, u(k) is the net hydrogen flow at time k, 5. The method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control according to claim 4, characterized in that: The objective function is constructed with the hydrogen storage tank state and the net hydrogen flow rate as control targets, including: The objective function is constructed with the control objectives of maintaining the stable state of the hydrogen storage tank and the low net hydrogen flow rate, and the following is obtained: Where, P ref is the reference value of hydrogen pressure, T ref is the reference value of hydrogen temperature, λ is the weight coefficient; The constraints for setting the hydrogen storage tank state and the net hydrogen flow rate are: Where, P max is the maximum hydrogen pressure, P min is the minimum hydrogen pressure, T max is the maximum temperature of hydrogen, T min is the minimum temperature of hydrogen, is the maximum value of net hydrogen flow, is the minimum net hydrogen flow rate.

6. The method for controlling the pressure and temperature of a hydrogen storage tank based on model predictive control according to claim 5, characterized in that: The objective function is minimized based on the input net hydrogen flow sequence of the state prediction model and the predicted pressure state sequence and the predicted temperature state sequence of the predicted output, and the optimal net hydrogen flow sequence is iteratively obtained, including: Converting a nonlinear state prediction model into a linear state prediction model; Converting the objective function and the constraint conditions into a quadratic programming form using the linear state prediction model; The quadratic programming problem is solved to obtain the optimal net hydrogen flow sequence.

7. A hydrogen storage tank pressure and temperature control device based on model predictive control, characterized in that: The device comprises: The first building block is used to determine the hydrogen state equation of the hydrogen storage tank and construct the hydrogen mass conservation equation and the hydrogen energy conservation equation; An establishment module is used to establish a state prediction model of the hydrogen storage tank state with respect to the change of the net hydrogen flow rate based on the hydrogen mass conservation equation and the hydrogen energy conservation equation, wherein the hydrogen storage tank state includes hydrogen pressure and hydrogen temperature; A second construction module is used to construct an objective function with the hydrogen storage tank state and the net hydrogen flow rate as control targets, and configure constraint conditions; A solution module, configured to minimize the objective function based on the input net hydrogen flow sequence of the state prediction model and the predicted hydrogen storage tank state sequence output, and iteratively obtain the optimal net hydrogen flow sequence; An adjustment module is used to adjust the inlet and outlet valves of the hydrogen storage tank based on the optimal net hydrogen flow sequence.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the hydrogen storage tank pressure and temperature control method based on model predictive control according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the hydrogen storage tank pressure and temperature control method based on model predictive control according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the hydrogen storage tank pressure and temperature control method based on model predictive control according to any one of claims 1 to 6.

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