Solid hydrogen storage system capacity state estimation method

Abstract

The invention discloses a solid hydrogen storage system capacity state estimation method. The method comprises the following steps of: firstly, simplifying a high-dimensional numerical calculation model into a lumped parameter model according to a chemical chemistry kinetic equation of solid hydrogen storage / desorption, and then carrying out linearization processing to obtain a state space equation and a linearization model of a solid hydrogen storage system; then designing extended Kalman filtering for monitoring the state of the solid-state hydrogen storage system, estimating the state of the solid-state hydrogen storage system at the future moment by using an extended Kalman filtering algorithm, and accordingly outputting the state of the hydrogen content SOC of the solid-state hydrogen storage system; and finally, predicting and updating the state of the solid-state hydrogen storage system through loop iteration, and adjusting the SOC estimated value of the solid-state hydrogen storage system in real time. According to the method, the phenomenon of error accumulation in hydrogen flow integration can be avoided, the SOC estimation precision is improved, meanwhile, the hydrogen content state of the system is estimated in real time under the dynamic working condition, dynamic control over system operation is guided, and errors caused by measurement noise and model uncertainty are reduced.

Description

Technical Field

[0001] The invention relates to a method for estimating the capacity state of a hydrogen storage system, in particular to a method for estimating the capacity state of an MH solid-state hydrogen storage system, and belongs to the technical field of solid-state hydrogen storage. Background Art

[0002] As a clean energy with high calorific value and no carbon dioxide produced during combustion, hydrogen energy has broad application prospects in the fields of automobiles, ships, industrial heat energy and electricity. At present, common hydrogen storage methods include gaseous hydrogen storage, liquid hydrogen storage and solid hydrogen storage. Among them, the gaseous hydrogen storage method using high-pressure gas cylinders has low volume hydrogen storage density and is not suitable for large-scale fixed hydrogen storage. It also requires specially designed expensive tanks to withstand hydrogen pressure, which is costly. Liquid hydrogen storage requires storage at extremely low temperatures, which makes transportation difficult and costly. Solid hydrogen storage mainly stores hydrogen in solid materials, which has the advantages of high volume hydrogen storage density and high safety. In addition, the use of metal hydride (MH) hydrogen storage materials can achieve fixed high-density, high-stability and fast-response solid hydrogen storage, which has greater research value and application potential.

[0003] The storage / desorption characteristics of the solid-state hydrogen storage system (MH solid-state hydrogen storage system) using metal hydride hydrogen storage materials are closely related to the Pressure-Composition-Temperature (PCT) curve of the solid-state hydrogen storage system, which can reflect the relationship between the equilibrium pressure and hydrogen content of the hydrogen storage / desorption process at different temperatures. The MH solid-state hydrogen storage system releases a large amount of heat when storing hydrogen and needs to absorb heat when desorbing hydrogen. This thermal effect directly affects the storage / desorption efficiency of hydrogen. Therefore, a reasonable thermal management system is crucial to maintain the temperature stability in the hydrogen storage tank. During the operation of the solid-state hydrogen storage system, the pressure and temperature can be directly measured in real time by sensors, but the hydrogen content is difficult to obtain in real time by direct measurement. Therefore, it is necessary to estimate the capacity state to accurately obtain the real-time hydrogen storage state of the solid-state hydrogen storage system, and then formulate an efficient thermal management strategy to achieve accurate storage / desorption rate control and extend the operating life of the solid-state hydrogen storage system.

[0004] The hydrogen content state (State of Charge, SOC) of the MH solid-state hydrogen storage system refers to the ratio of the current remaining hydrogen content of the solid-state hydrogen storage system to the total hydrogen storage capacity, usually expressed as a percentage as shown in the following formula. Accurate calculation of SOC can determine the remaining available amount of hydrogen, thereby optimizing the control of hydrogen use and improving operating efficiency.

[0005]

[0006] At present, conventional methods for estimating the SOC of MH solid-state hydrogen storage systems include flow integration method and open-circuit PCT method. The flow integration method estimates the SOC by integrating the flow of hydrogen in and out of the system. Its advantage is that the calculation is simple and it can realize the real-time calculation of SOC through continuous hydrogen flow measurement. However, the disadvantage of this method is that the sensor measurement error will accumulate over time, resulting in deviations in SOC estimation. In long-term operation, a small measurement error may lead to a large cumulative error, which seriously affects the accuracy of SOC. In addition, the flow integration method cannot effectively consider the leakage or loss of hydrogen in the system, which will further increase the deviation between the estimated SOC value and the actual state. The open-circuit PCT method combines the current pressure and temperature state of the system and uses the relationship between the equilibrium pressure and hydrogen content reflected by the PCT curve to estimate the SOC. Although this method is relatively complete in theory, its applicability is affected by the difference in hydrogen storage / discharge rate under dynamic non-equilibrium state, which may cause the estimation result to lag. In addition, the change of the PCT curve is affected by external factors such as temperature and pressure, and a slight deviation may lead to an increase in the deviation of SOC estimation. Therefore, it is urgent to develop an estimation method that can improve the SOC estimation accuracy and have good dynamic response capabilities. Summary of the invention

[0007] In view of the problems existing in the above-mentioned prior art, the present invention provides a method for estimating the capacity state of a solid-state hydrogen storage system, which can avoid the phenomenon of error accumulation in the hydrogen flow integration and improve the SOC estimation accuracy, while realizing real-time estimation of the system hydrogen content state under dynamic conditions and guiding the dynamic control of the system operation, and reducing errors caused by measurement noise and model uncertainty.

[0008] To achieve the above purpose, the capacity state estimation method of the solid-state hydrogen storage system specifically includes the following steps:

[0009] Step 1, according to the chemical reaction kinetic equation of solid-state hydrogen storage / desorption, the high-dimensional numerical calculation model is simplified into a lumped parameter model, and then linearized to obtain the state space equation and linearized model of the MH solid-state hydrogen storage system;

[0010] The linear system state space representation is as follows:

[0011]

[0012] Where: x, u, y are state vector, input vector, and output vector respectively; A, B, C, and D are state matrix, input matrix, output matrix, and feedforward matrix respectively; is the derivative of the state vector x with respect to time;

[0013] The temperature of the thermal fluid outside the solid-state hydrogen storage tank, the density of the metal hydride hydrogen storage material, and the temperature in the hydrogen storage tank are defined as state variables. The thermal power provided by the thermal management device, the pressure of the hydrogen buffer tank, and the storage / desorption flow rate are defined as control variables, measurable interference quantities, and control target quantities, respectively. The specific expression of the state space of the solid-state hydrogen storage / desorption model is described as follows:

[0014]

[0015] In the formula: In the formula: x, u, y are the state vector, input vector, and output vector respectively; x′ represents the first-order derivative vector of the state vector with respect to time; δ represents the measurable interference vector; Indicates the temperature of the hot fluid, unit K; ρ MH Indicates the density of metal hydride hydrogen storage materials, in kg / m 3 ; T MH Indicates the temperature inside the hydrogen storage tank, unit K; Indicates the hydrogen flow rate at the hydrogen channel of the hydrogen storage tank, in kg / s; P heat Indicates the thermal power provided by the thermal management device, in kW; p Bt Indicates the pressure in the hydrogen buffer tank, in Pa;

[0016] The state matrix A, input matrix B, output matrix C and feedforward matrix D in the state space of the linear system are obtained by first-order Taylor expansion under the stable working state of the system;

[0017] Step 2, use the extended Kalman filter algorithm to estimate the state of the solid-state hydrogen storage system at the future moment, and output the hydrogen content SOC state of the MH solid-state hydrogen storage system based on this; through the loop iteration execution system state prediction and update, the system SOC estimation value is adjusted in real time;

[0018] Step 2-1, initialization:

[0019] First, determine the initial state estimate When the initial SOC is estimated based on the initial conditions and prior knowledge of the system, it is set as The initial state estimate is set to a combination of guess values ​​close to the true value, that is,

[0020] Secondly, determine the initial covariance matrix, which is set according to prior knowledge and the expected error of the system and is expressed as:

[0021]

[0022] Step 2-2, prediction:

[0023] First, the state of the system is predicted, and the state vector is expanded to the following form including SOC:

[0024]

[0025] Among them, the prediction of SOC is estimated based on the system's hydrogen storage / release model and the SOC at the previous moment;

[0026] Next, the system covariance prediction is performed, and the covariance matrix is ​​expanded and updated accordingly to include the following form of SOC:

[0027]

[0028] Step 2-3, Update:

[0029] The output vector is augmented with the measured or estimated value of SOC, expressed as:

[0030]

[0031] in It is a measurement quantity related to the hydrogen storage / discharge flow rate;

[0032] The Kalman gain calculation is transformed into

[0033]

[0034] The system status is updated as:

[0035]

[0036] For the update of SOC, adjustments are made based on the Kalman gain and the difference between the measured value and the predicted value;

[0037] The system covariance is updated as:

[0038]

[0039] Step 2-4, loop iteration:

[0040] The above prediction and update steps are continuously iterated in a loop to continuously adjust and optimize the estimated value of the SOC of the hydrogen storage system, while also updating the estimate of the state variables, so as to achieve accurate monitoring of the state of the MH solid-state hydrogen storage system and real-time estimation of the SOC.

[0041] Furthermore, in Step 1, the state matrix A, input matrix B, output matrix C and feedforward matrix D in the state space of the linear system are obtained by first-order Taylor expansion under the stable working state of the system. The specific steps are as follows:

[0042] When the system is in stable working state, x0=[T 0,H2O ,ρ0,MH ,T 0,MH ]、u0=[P 0,heat ]、δ0=p 0,Bt , perform a first-order Taylor expansion on the state space to obtain the values ​​of each dynamic matrix, the expression is as follows:

[0043]

[0044] According to the obtained dynamic matrix, the state space is inversely transformed by Fourier to obtain the transfer function of the system, which is expressed as:

[0045]

[0046] Where: Y(s) and U(s) are the Laplace transforms of the input and output; s is a complex frequency domain variable.

[0047] Compared with the existing technology, this method for estimating the capacity state of a solid-state hydrogen storage system uses the model of the MH solid-state hydrogen storage system, combined with PCT characteristics and thermodynamic characteristics, to process the system's pressure, temperature, hydrogen flow and other data in real time, and expand the Kalman filter algorithm to predict and correct the SOC of the MH solid-state hydrogen storage system based on sensor data. Compared with the traditional flow integration method and open-circuit PCT method, this method for estimating the capacity state of a solid-state hydrogen storage system has the following advantages:

[0048] 1. By fusing multiple sensor data, the extended Kalman filter algorithm can filter out the uncertainty caused by flow measurement errors, temperature and pressure fluctuations, avoid the phenomenon of error accumulation in hydrogen flow integration, and improve the accuracy of SOC estimation.

[0049] 2. Since the extended Kalman filter algorithm can continuously adjust itself according to real-time data, even if the external environment (such as temperature or pressure) changes, the system can still maintain high-precision SOC estimation, which can make up for the defect of the open-loop PCT method that is too sensitive to external conditions. In addition, the dynamic correction of errors in the non-equilibrium dynamic process of the MH solid-state hydrogen storage system can improve the operating stability of the MH solid-state hydrogen storage system.

[0050] 3. By estimating the SOC of the MH solid-state hydrogen storage system through the extended Kalman filter algorithm, the hydrogen storage / desorption process can be managed more intelligently according to the current state, the hydrogen supply can be optimized, and material degradation caused by excessive hydrogen storage / desorption can be prevented, thereby extending the service life of solid-state hydrogen storage materials such as MH.

[0051] 4. The invented extended Kalman filter algorithm is not only suitable for SOC estimation of MH solid-state hydrogen storage system, but also can be used for nonlinear processes in hydrogen energy systems based on solid-state hydrogen storage. It can estimate the hydrogen content state of the system in real time under dynamic conditions and guide the dynamic control of system operation, reduce errors caused by measurement noise and model uncertainty, and provide theoretical basis and data support for the large-scale application of hydrogen energy technology in a wider range of fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the structure of the MH solid-state hydrogen storage system;

[0053] Figure 2 This is a model diagram of a solid-state hydrogen storage tank;

[0054] Figure 3 It is a schematic diagram of the structure of the MH solid-state hydrogen storage system during the hydrogen release process;

[0055] Figure 4 It is a linearization flow chart of the MH solid-state hydrogen storage system model of the present invention;

[0056] Figure 5 It is a flow chart of SOC estimation of the MH solid-state hydrogen storage system of the present invention. DETAILED DESCRIPTION

[0057] The structural diagram of MH solid-state hydrogen storage system is as follows: Figure 1 As shown, the solid-state hydrogen storage system includes a solid-state hydrogen storage tank 1, a hydrogen delivery pipeline 2, a hydrogen valve 3, a hydrogen buffer tank 4, a thermal fluid valve 5, a thermal fluid pipeline 6, a thermal management device 7 (such as: an external heat source, a heat exchanger, a fuel cell waste heat supply device, etc.) and a hydrogen-using device 8 (such as: a fuel cell, etc.). The hydrogen channel of the solid-state hydrogen storage tank 1 is connected to the first passage of the hydrogen buffer tank 4 through the hydrogen delivery pipeline 2 and the hydrogen valve 3, the second passage of the hydrogen buffer tank 4 is connected to the hydrogen production device, and the third passage of the hydrogen buffer tank 4 is connected to the hydrogen-using device 8; the thermal fluid in the solid-state hydrogen storage tank 1 is provided by the external thermal management device 7, and the heat exchange medium can be water or other heat-conducting fluids. By controlling the opening of the thermal fluid valve 5, the heat enters the solid-state hydrogen storage tank 1 through the thermal fluid pipeline, and flows back to the thermal management device 7 after heat exchange, forming a closed thermal management. When storing hydrogen, the hydrogen produced by the hydrogen production equipment can be directly supplied to the hydrogen-using equipment 8 or enter the solid-state hydrogen storage tank 1 through the hydrogen buffer tank 4; when releasing hydrogen, the second passage of the hydrogen buffer tank 4 is closed, and the hydrogen released by the solid-state hydrogen storage tank 1 first enters the hydrogen buffer tank 4, and then is supplied to the hydrogen-using equipment 8. The function of the hydrogen buffer tank 4 is to stabilize the hydrogen flow rate and reduce pressure fluctuations, which can improve the stability problem caused by the poor dynamic responsiveness of solid-state hydrogen storage.

[0058] The model of the solid hydrogen storage tank 1 is as follows Figure 2As shown, the solid hydrogen storage tank 1 is a cylindrical structure, with metal hydride and other hydrogen storage materials inside. The top of the tank is a hydrogen channel for the flow of hydrogen during the storage / discharge process. The radial direction of the solid hydrogen storage tank 1 is the r direction, and the direction along the central axis is the z direction. During the dehydrogenation process, the MH solid hydrogen storage system is as follows Figure 3 As shown, assuming that the solid-state hydrogen storage tank 1 has uniform boundary conditions and infinite thermal conductivity, the model can be simplified to a lumped parameter model. The present invention describes the temperature and pressure changes inside the solid-state hydrogen storage tank 1 by simplified partial differential equations, and represents the reaction evolution process with average temperature and pressure. This method focuses on overall performance rather than details, and is suitable for quickly and comprehensively obtaining the operating status of the solid-state hydrogen storage system.

[0059] This method for estimating the capacity state of a solid-state hydrogen storage system first simplifies the high-dimensional numerical calculation model into a lumped parameter model based on the chemical reaction kinetic equation of solid-state hydrogen storage / release, and then performs linearization processing to obtain the state space equation and linearization model of the solid-state hydrogen storage system; then, an extended Kalman filter for state monitoring of the solid-state hydrogen storage system is designed, and the extended Kalman filter algorithm is used to estimate the state of the solid-state hydrogen storage system at future moments, and the hydrogen content SOC state of the solid-state hydrogen storage system is output accordingly; finally, the prediction and update of the state of the solid-state hydrogen storage system is performed through cyclic iteration, and the SOC estimation value of the solid-state hydrogen storage system is adjusted in real time to ensure its dynamic correction and accuracy. The details are as follows:

[0060] Step 1, according to the chemical reaction kinetic equation of solid-state hydrogen storage / desorption, the high-dimensional numerical calculation model is simplified into a lumped parameter model, and then linearized to obtain the state space equation and linearized model of the MH solid-state hydrogen storage system.

[0061] The linear system state space can be expressed as follows:

[0062]

[0063] Where: x, u, y are state vector, input vector, and output vector respectively; A, B, C, and D are state matrix, input matrix, output matrix, and feedforward matrix respectively; is the time derivative of the state vector x.

[0064] In order to make the state variables fully reflect the current storage / desorption state, the temperature of the thermal fluid outside the solid-state hydrogen storage tank (taking water as an example), the density of the metal hydride hydrogen storage material, and the temperature in the hydrogen storage tank are defined as state variables, and the thermal power provided by the thermal management device, the hydrogen buffer tank pressure, and the storage / desorption flow rate are defined as control variables, measurable interference quantities, and control target quantities, respectively. Therefore, the specific expression of the state space of the solid-state hydrogen storage / desorption model is described as follows:

[0065]

[0066] Where: x, u, y are the state vector, input vector, and output vector respectively; x′ represents the first-order derivative vector of the state vector with respect to time; δ represents the measurable interference vector; Indicates the temperature of the hot fluid, unit K; ρ MH Indicates the density of metal hydride hydrogen storage materials, in kg / m 3 ; T MH Indicates the temperature inside the hydrogen storage tank, unit K; Indicates the hydrogen flow rate at the hydrogen channel of the hydrogen storage tank, in kg / s; P heat Indicates the thermal power provided by the thermal management device, in kW; p Bt Indicates the pressure in the hydrogen buffer tank, unit: Pa.

[0067] The linearization flow chart of MH solid-state hydrogen storage system model is as follows: Figure 4 As shown, the state matrix A, input matrix B, output matrix C and feedforward matrix D in the state space of the linear system are obtained by first-order Taylor expansion under the stable working state of the system, as follows:

[0068] When the system is in stable working state (such as x0 = [T 0,H2O ,ρ 0,MH ,T 0,MH ]、u0=[P 0,heat ]、δ0=p 0,Bt ) Performing a first-order Taylor expansion on the state space, the values ​​of each dynamic matrix can be obtained, and the expressions are as follows:

[0069]

[0070] According to the obtained dynamic matrix, the transfer function of the system can be obtained by performing inverse Fourier transform on the state space, which is expressed as:

[0071]

[0072] Where: Y(s) and U(s) are the Laplace transforms of the input and output; s is a complex frequency domain variable.

[0073] Step 2, design an extended Kalman filter for MH solid-state hydrogen storage system status monitoring, use the extended Kalman filter algorithm to estimate the state of the solid-state hydrogen storage system at future times, and output the hydrogen content SOC state of the MH solid-state hydrogen storage system based on this.

[0074] MH solid-state hydrogen storage system SOC estimation flow chart as follows Figure 5 As shown, the details are as follows:

[0075] Step 2-1, initialization:

[0076] First, determine the initial state estimate In addition to considering the temperature of the hot fluid, the density of the metal hydride hydrogen storage material, and the temperature in the hydrogen storage tank, the initial SOC should also be estimated based on the initial conditions and prior knowledge of the system, which is set as The initial state estimate is set to a combination of guess values ​​close to the true value, that is,

[0077] Secondly, determine the initial covariance matrix, which represents the uncertainty of the initial state estimation, and also needs to consider the uncertainty of SOC. The initial covariance matrix is ​​also set based on prior knowledge and the expected error of the system, expressed as:

[0078]

[0079] Step 2-2, prediction:

[0080] First, the state of the system is predicted, and the state vector is expanded to the following form including SOC:

[0081]

[0082] Among them, the prediction of SOC is simply estimated based on the system's hydrogen storage / desorption model and the SOC at the previous moment. For example, according to the flow integration method or the open-circuit PCT method, the change of SOC in a short period of time is related to the hydrogen storage / desorption rate at the previous moment.

[0083] Secondly, the system covariance prediction is performed, and the covariance matrix is ​​also expanded and updated accordingly to include the following form of SOC:

[0084]

[0085] Step 2-3, Update:

[0086] The output vector also needs to include the measured or estimated value of SOC, expressed as:

[0087]

[0088] in is the measurement quantity related to the hydrogen storage / discharge flow rate, y soc,k It can be an SOC value estimated by other indirect methods or a quantity related to SOC.

[0089] The Kalman gain calculation is transformed into

[0090]

[0091] The system status is updated as:

[0092]

[0093] For the update of SOC, adjustments are made based on the Kalman gain and the difference between the measured value and the predicted value to ensure a more accurate SOC estimation.

[0094] The system covariance is updated as:

[0095]

[0096] Step 2-4, loop iteration:

[0097] The above prediction and update steps are continuously iterated in a cycle to continuously adjust and optimize the estimated value of the SOC of the hydrogen storage system. At the same time, the estimated state variables such as the temperature of the hot fluid, the density of the metal hydride hydrogen storage material and the temperature inside the hydrogen storage tank are also updated to achieve accurate monitoring of the state of the MH solid-state hydrogen storage system and real-time estimation of the SOC.

[0098] The present invention is further described below by taking LaNi5 as the hydrogen storage alloy material in the metal hydride solid hydrogen storage tank and the hydrogen release process as an example. The solid hydrogen storage tank 1 has a diameter of 50 mm and a height of 60 mm, the hydrogen channel at the top of the tank has a diameter of 5 mm and a height of 10 mm, and the heat exchange medium is water.

[0099] According to the basic equation and heat balance equation of the metal hydride hydrogen storage tank, the detailed expressions of the first-order derivatives of each state variable in the system state space can be deduced as follows:

[0100]

[0101]

[0102] Where: U is the heat transfer coefficient, unit W / (m 2 ·K); A MH is the heat exchange area of ​​the solid hydrogen storage tank, in m 2 ; is the specific heat capacity of water, in J / (kg·K); is the mass of water, in kg; C des is the hydrogen desorption constant, unit s -1 ; E des is the desorption activation energy, in J / mol; R is the universal gas constant, in J / (kg·K); ρ MH,emp is the density of MH when hydrogen is completely desorbed, in kg / m 3 ; a, b, φ, φ0, α1, α2, β are empirical constants; ρ MH,sat is the density of MH when hydrogen is completely adsorbed, in kg / m 3 ; ΔH is the reaction enthalpy change, unit J / kg; C p,aveis the effective volume average specific heat of hydrogen and MH, in J / (m 3 ·K).

[0103] Select the system stable operating point (such as SOC = 50%), and obtain x0 = [312.2K, 7238.9kg / m 3 ,311.8K],u0=[6.34kW],δ0=[2.8atm]. The state matrix A, input matrix B, output matrix C and feedforward matrix D in the expression are obtained by first-order Taylor expansion under stable working state:

[0104]

[0105] The eigenvalues ​​of the state matrix A are [-2.48; -0.09; -3.97×10 -7 ] are all negative values. Therefore, according to the Lyapunov stability theory, the system is locally stable, and the transfer function of the hydrogen release process can be obtained by the inverse Laplace transform:

[0106]

[0107] Assume that the process noise covariance matrix is:

[0108]

[0109] The measurement noise covariance matrix R = [0.05].

[0110] The initial state is estimated to be

[0111]

[0112] The initial covariance matrix is

[0113]

[0114] After continuous estimation, updating and iteration, the estimated value of SOC gradually converges to a range closer to the true value. The accuracy of the estimation can be evaluated by comparing it with the actual measured hydrogen content. The actual measured SOC of the embodiment is 45% after a certain period of time, while the estimated value is 44.5%, with a small error of 0.5%, indicating that this estimation method is more accurate.

[0115] In response to the problem of SOC estimation in MH solid-state hydrogen storage systems, the present invention can avoid the phenomenon of error accumulation in hydrogen flow integration and improve the accuracy of SOC estimation. At the same time, it can achieve real-time estimation of the system hydrogen content state under dynamic conditions and guide the dynamic control of system operation, reduce errors caused by measurement noise and model uncertainty, and provide a theoretical basis and data support for the large-scale application of hydrogen energy technology in a wider range of fields.

Claims

1. A method for estimating the capacity state of a solid-state hydrogen storage system, characterized in that: The specific steps include: Step 1, according to the chemical reaction kinetic equation of solid-state hydrogen storage / desorption, the high-dimensional numerical calculation model is simplified into a lumped parameter model, and then linearized to obtain the state space equation and linearized model of the MH solid-state hydrogen storage system; The linear system state space representation is as follows: Where: x, u, y are state vector, input vector, and output vector respectively; A, B, C, and D are state matrix, input matrix, output matrix, and feedforward matrix respectively; is the derivative of the state vector x with respect to time; The temperature of the thermal fluid outside the solid-state hydrogen storage tank, the density of the metal hydride hydrogen storage material, and the temperature in the hydrogen storage tank are defined as state variables. The thermal power provided by the thermal management device, the pressure of the hydrogen buffer tank, and the storage / desorption flow rate are defined as control variables, measurable interference quantities, and control target quantities, respectively. The specific expression of the state space of the solid-state hydrogen storage / desorption model is described as follows: Where: x, u, y are the state vector, input vector, and output vector respectively; x′ represents the first-order derivative vector of the state vector with respect to time; δ represents the measurable disturbance vector; T H2O Indicates the temperature of the hot fluid, unit K; ρ MH Indicates the density of metal hydride hydrogen storage materials, in kg / m 3 ; T MH Indicates the temperature inside the hydrogen storage tank, unit K; W MH,H2 Indicates the hydrogen flow rate at the hydrogen channel of the hydrogen storage tank, in kg / s; P heat Indicates the thermal power provided by the thermal management device, in kW; p Bt Indicates the pressure in the hydrogen buffer tank, in Pa; The state matrix A, input matrix B, output matrix C and feedforward matrix D in the state space of the linear system are obtained by first-order Taylor expansion under the stable working state of the system; Step 2, use the extended Kalman filter algorithm to estimate the state of the solid-state hydrogen storage system at the future moment, and output the hydrogen content SOC state of the MH solid-state hydrogen storage system based on this; through the loop iteration execution system state prediction and update, the system SOC estimation value is adjusted in real time; Step 2-1, initialization: First, determine the initial state estimate When the initial SOC is estimated based on the initial conditions and prior knowledge of the system, it is set as The initial state estimate is set to a combination of guess values ​​close to the true value, that is, Secondly, determine the initial covariance matrix, which is set according to prior knowledge and the expected error of the system and is expressed as: Step 2-2, prediction: First, the state of the system is predicted, and the state vector is expanded to the following form including SOC: Among them, the prediction of SOC is estimated based on the system's hydrogen storage / release model and the SOC at the previous moment; Next, the system covariance prediction is performed, and the covariance matrix is ​​expanded and updated accordingly to include the following form of SOC: Step 2-3, Update: The output vector is augmented with the measured or estimated value of SOC, expressed as: in It is a measurement quantity related to the hydrogen storage / release flow rate; The Kalman gain calculation is transformed into The system status is updated as follows: For the update of SOC, adjustments are made based on the Kalman gain and the difference between the measured value and the predicted value; The system covariance is updated as: Step 2-4, loop iteration: The above prediction and update steps are continuously iterated in a loop to continuously adjust and optimize the estimated value of the SOC of the hydrogen storage system, while also updating the estimate of the state variables, so as to achieve accurate monitoring of the state of the MH solid-state hydrogen storage system and real-time estimation of the SOC.

2. The method for estimating the capacity state of a solid-state hydrogen storage system according to claim 1, characterized in that: In Step 1, the state matrix A, input matrix B, output matrix C and feedforward matrix D in the state space of the linear system are obtained by first-order Taylor expansion under the stable working state of the system. The specific steps are as follows: When the system is in stable working state, x0=[T 0,H2O ,ρ 0,MH ,T 0,MH ]、u0=[P 0,heat ]、δ0=p 0,Bt , perform a first-order Taylor expansion on the state space to obtain the values ​​of each dynamic matrix, the expression is as follows: According to the obtained dynamic matrix, the state space is inversely transformed by Fourier to obtain the transfer function of the system, which is expressed as: Where: Y(s) and U(s) are the Laplace transforms of the input and output; s is a complex frequency domain variable.

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