CN116536705A - PEM (PEM) electrolyzed water control method and system based on model predictive control - Google Patents
PEM (PEM) electrolyzed water control method and system based on model predictive control Download PDFInfo
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Abstract
The invention discloses a PEM (PEM) electrolyzed water control method based on model predictive control, which comprises the following steps of S1, collecting operation data of an electrolyzed water system, wherein the data comprise electrolyzer voltage, heat exchanger power, liquid temperature, liquid flow and pump power; s2, the electrolytic cell controller estimates the internal state of the electrolytic cell according to collected operation data of the electrolytic water system, and a state estimation result is obtained, wherein the internal state comprises the heating power of the electrolytic cell and the efficiency of the electrolytic cell; s3, inputting the acquired state estimation result of the electrolytic cell into a model predictive control algorithm, and calculating the heating power of the electrolytic cell and the change trend of the temperature of the electrolytic cell; s4, tracking based on the target temperature input power of the electrolytic tank, and controlling heat exchange power of the heat exchanger and controlling water flow based on the temperature change trend so as to realize the temperature control of the electrolytic tank under the fluctuation working condition. By combining the PEM electrolyzed water control system based on model predictive control, the simple and reliable control of the electrolyzed water hydrogen production system can be realized.
Description
Technical Field
The invention relates to the field of operation control of PEM (PEM) electrolytic water systems, in particular to a PEM electrolytic water control method and system based on model predictive control.
Background
The hydrogen energy is secondary energy with rich sources, green low carbon and wide application, and can help the fields of industry, traffic, construction and the like to realize the decarburization target. The electrolytic water hydrogen production is the most environment-friendly hydrogen production mode and becomes the most important hydrogen production means in the future. The PEM electrolyzed water has the characteristics of high efficiency, low energy consumption, flexible regulation and control and the like, and is an important grip participating in peak regulation of a power grid and renewable energy consumption.
However, when the PEM water electrolysis hydrogen production system faces to fluctuation working conditions such as start-stop, power fluctuation and the like, the corresponding characteristics of each system are not different, so that the difference of the temperature, pressure and flow of each system is larger, the efficiency of the water electrolysis system is low, the service life is shortened, the energy consumption is increased, and even the system collapses, the potential safety hazards such as temperature flying, hydrogen leakage and explosion occur. Therefore, the system control strategy is necessary to adapt to the complex working condition of the electrolytic water system, so that the safe and efficient operation of the electrolytic water system is realized.
Although several patents have proposed related strategies to realize control of the electrolytic water system, for example, patent CN112481637A, CN114908356a proposes to connect with an electrolytic water power generation device or an energy storage module to regulate and control front-end power to be stably input so as to realize system regulation, and patent CN113373458B proposes to add a multistage voltage-stabilizing regulation subsystem in the water vapor separation subsystem to realize multistage regulation of hydrogen pressure so as to improve system stability and efficiency; patent CN112134497a proposes to implement stabilization of the input power of the system by electrical conversion of the fluctuating power supply; patent CN113481539a forms an electrolytic water system by a plurality of sets of electrolytic tank units to achieve smooth operation of the system. However, these strategies still have the following problems: (1) Introducing new modules or units can greatly increase system complexity and energy consumption; (2) The above strategy, while enhancing system regulation by other means, does not address the problem of smooth system operation from the perspective of system control; (3) The stability of the electrolytic water system is not only in the stable operation of each unit of the system, but also in the cooperative constraint control of the system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a simple and reliable PEM (proton exchange membrane) electrolyzed water control method and system based on model predictive control, which are applied to a electrolyzed water hydrogen production system.
The technical scheme for achieving the purpose is as follows: a PEM electrolyzed water control method based on model predictive control comprises the following steps:
s1, collecting running data of an electrolytic water system;
s2, estimating the internal state of the electrolytic tank according to collected operation data of the electrolytic water system, and obtaining a state estimation result;
s3, inputting the acquired state estimation result of the electrolytic cell into a model predictive control algorithm, and calculating the heating power of the electrolytic cell and the change trend of the temperature of the electrolytic cell;
s4, tracking the input power of the electrolyzed water based on the temperature of the electrolyzer, and controlling the heat exchange power of the heat exchanger and the water flow based on the change trend of the temperature of the electrolyzer to realize the temperature control of the electrolyzer under the fluctuation working condition.
Further, the operation data of the water electrolysis system in the S1 comprises the voltage of the electrolytic cell, the current of the electrolytic cell, the temperature of the electrolytic cell, the flow rate of hydrogen at the outlet of the electrolytic cell, the flow rate of water at the inlet of the electrolytic cell and the power of a heat exchanger; the model predictive control algorithm calculates through a pre-established predictive model, and the predictive model is established with a three-order linear state space model of the water electrolysis system, an input/output model of the water electrolysis system and performance indexes of the water electrolysis system;
the expression of the third-order linear state space model of the electrolyzed water control system is as follows:
wherein T is stack For the temperature of the electrolytic cell, V stack For the cell voltage, I stack The electric current of the electrolytic cell is supplied,is a first derivative of the temperature of the electrolyzer, +.>Is the first derivative of the hydrogen flow at the outlet of the electrolyzer,/->Is the first derivative of the cell voltage, V flow For the flow of electrolyzer inlet water, W heat-ex For heat exchanger power, A 3×3 As a first coefficient matrix, B 3×2 Is a second coefficient matrix;
the input of the input/output model of the water electrolysis system is the input current of the electrolytic tank, the output is the efficiency of the electrolytic tank, and the expression of the input/output model of the water electrolysis system is as follows:
u＝I stack
T stack ＝ψ(I stack ，V stack ，V flow ，W heat-ex )
wherein u is an input/output model of the water electrolysis systemIs input as the cell current I stack Psi is the input current I of the electrolytic cell input Water flow V flow Heat exchanger power W heat-ex And cell voltage V stack To the temperature T of the electrolytic cell stack Is mapped to;
performance index Z of the electrolytic water system p The calculated expression of (2) is:
wherein P is Net For generating hydrogen power of the electrolytic tank, T stack Is the temperature of the electrolytic cell. Further, an optimal control law of the prediction model is solved by adopting a particle swarm algorithm, and the optimal control law is applied to the water electrolysis system.
Further, in S2, an unscented kalman filter algorithm is used to estimate the internal state of the electrolyzed water.
A system of PEM electrolyzed water control method comprises an electrolyzed water system operation data module, an electrolyzer state estimation module, a model predictive control algorithm module and a working condition control module;
the water electrolysis system operation data module is used for collecting water electrolysis system operation data of the water electrolysis controller;
the electrolytic tank state estimation module estimates the internal state of the electrolytic tank according to the running data of the electrolytic water system, acquires a state estimation result, and inputs the estimation result into the model predictive control algorithm module;
the model predictive control algorithm module calculates the heating power of the electrolytic cell and the temperature change trend of the electrolytic cell;
the working condition control module controls heat exchange power of the heat exchanger and water flow based on the temperature change trend, so that temperature control of the electrolytic tank under the fluctuation working condition is realized.
5. The system of a PEM electrolyzed water control method according to claim 4 wherein the electrolyzed water system operational data module comprises an electrolyzed water controller and a data acquisition module connected via a CAN bus.
6. The system of a PEM electrolyzed water control method according to claim 4 wherein the operating mode control module comprises an air compressor controller and a DC/DC controller.
Compared with the prior art, the invention has the following advantages:
(1) The invention comprehensively considers the power tracking of the electrolytic cell, the efficiency of the electrolytic cell and the constraint of the system, adopts a model predictive control algorithm to control the heat exchanger and the pump based on the temperature tracking of the electrolytic cell, can control the system to quickly and accurately track the heating of the electrolytic cell, ensures the system to work in a safe area, and improves the power of the system.
(2) According to the invention, the three-order linear state space model of the water electrolysis system is used as a prediction model, and the heat exchanger power and the pump power are calculated based on the output of the prediction model, so that the calculated amount can be greatly reduced, and the method is easy to realize in a real vehicle.
(3) The unscented Kalman filtering algorithm is used for the optimal estimation of the undetectable state of the input current of the electrolytic cell, and the problems that the measured value of the input current of the electrolytic cell is interfered and part of the states are not measurable are solved.
Drawings
FIG. 1 is a schematic diagram of a model predictive control-based electrolyzed water hydrogen production system of the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples:
examples:
the embodiment provides an electrolytic water system control method based on model predictive control, which adopts the concept of topological association, takes the current input into an electrolytic tank as an initial variable, the gas flow at the outlet of the electrolytic tank as an intermediate variable, takes the power of a heat exchanger, the flow of a pump and the like as operating variables, takes the efficiency of the electrolytic water system as an index, designs a model predictive control algorithm of the electrolytic water hydrogen production system, and uses a particle swarm algorithm for solving optimal control.
The electrolyzed water control method based on model predictive control of the embodiment is used in an electrolyzed water control system.
As shown in FIG. 1, the model predictive control-based electrolytic water hydrogen production system comprises an AC/DC (alternating current/direct current) transformation module, a fluid conveying module, a heat exchange module, an electrolytic tank module and a gas-liquid separation module. The water electrolysis hydrogen production control system comprises a data acquisition module and a model prediction control module. The data acquisition module is respectively connected with the electrolytic tanks, the heat exchanger, the heat exchange module and the fluid input module.
Specifically, the electrolyzed water control method comprises the following steps:
s1, a data acquisition module sends access signals to an AC/D module, a fluid conveying module, a heat exchange module and an electrolytic cell module data acquisition module through a bus to acquire required data, wherein the data comprise electrolytic cell voltage, heat exchanger power, liquid temperature, liquid flow and pump power.
S2, the electrolyzed water controller judges whether the data required by control is received completely, if yes, the step 3 is executed, and if not, the step 1 is executed.
S3, the electrolyzer controller controls the electrolyzer according to T stack For the temperature of the electrolytic cell, V stack For the cell voltage, I stack And estimating the internal state of the electrolyzed water by using an unscented Kalman filtering algorithm to obtain a state estimation result, wherein the internal state comprises the heating power of the electrolyzer and the efficiency of the electrolyzer.
S4, the electrolyzer controller calculates the heating power and the electrolysis efficiency of the electrolyzer by adopting a model predictive control algorithm based on the AC/DC data acquisition result.
S5, based on the calculated heating power and electrolysis efficiency of the electrolytic cell and the comparison of the estimated state data, calculating the temperature change trend and the efficiency change trend of the electrolytic cell.
S6, based on the temperature change trend and the efficiency change trend of the electrolytic cell, the power of the heat exchanger and the power of the pump are adjusted, for example, the temperature of the electrolytic cell is increased, namely, the heating power of the heat exchanger is reduced, and the flow is increased, so that the purpose of cooling the electrolytic cell is realized.
The steps are described in detail below.
Step S3, the temperature controller of the electrolytic tank comprises a heat exchanger feeding according to the data required by controlOutlet temperature T heat-in 、T heat-out 、V stack Cell voltage, AC/DC output power I input Temperature T of liquid flow， Flow V of liquid flow Estimating the internal state of the electrolytic cell by adopting an unscented Kalman filtering algorithm, wherein the internal state comprises the heating temperature W of the electrolytic cell stack Electrolytic cell outlet hydrogen flow V stack-Hout Cell voltage V stack ，
The unscented Kalman filtering method comprises the following specific steps:
the state variable x is an n-dimensional random variable and its mean is knownAnd covariance P, u (k) is the input to the system.
A number of Sigma points, i.e. sampling points, were calculated:
wherein x is (i) To obtain 2n+1 s igma points with distributed sampling,matrix square root of (n+λ) P> Represents the ith row of (n+λ) P.
Selection of weight w corresponding to each Sigma point:
wherein m represents the mean value and c represents the covariance; the parameter λ=α2 (n+κ) -n; the selection of alpha controls the distribution state of sampling points; kappa is the undetermined parameter, typically 0, beta is the state distribution parameter, and is optimal for gaussian distribution beta=2.
At time k, a set of Sigma points was obtained using the equation:
wherein x is i (k|k) is the Sigma point obtained at time k,p (k|k) is the variance of the state variable at time k, which is the mean of the state variable at time k.
Updating sampling points according to a state equation of the system:
x i (k+1|k)＝f(k，x i (k|k)，u(k))+w(k)
wherein u (k) is the input of the system at the moment k, and the input of the system comprises the current input of the electrolytic cell, the water flow and the power of the heat exchangerf(k，x i (k|k), u (k)) is the state equation at system k time, and W (k) is process white noise.
The linear continuous state equation of the system is:
the one-step estimation of the system state at time k+1 is:
the covariance matrix of the system at time k+1 is:
where Q is the variance matrix of the process white noise W (k).
Calculating a one-step estimated value of the observed value according to an output equation of the system:
y i (k+1|k)＝g(x i (k+1|k),u(k))+V(k)
wherein y is i (k+ 1|k) is the observed value at system k time, g (y) i (k+ 1|k), u (k)) is the observation equation at system k time, and V (k) is the observation white noise.
The mean and covariance of the system observations are calculated by:
wherein R is observed white noise V (k) Is a matrix of variances of (a),for the variance matrix of the observations, +.>Is the covariance matrix of observables and state quantities.
Calculating a Kalman gain matrix:
computing state optimal estimation at time k+1 of systemAnd covariance matrix P (k+1):
and step S4, specifically, the controller calculates the heating power of the electrolytic tank and the efficiency of the electrolytic tank by adopting a model predictive control algorithm according to the received data and the estimated data.
The model predictive control algorithm calculates through a pre-established predictive model, and the predictive model is established with a three-order linear state space model of the water electrolysis system, an input-output model of the water electrolysis system and performance indexes of the water electrolysis system;
the method specifically comprises the following steps:
offline calculation: and determining the optimal peroxy ratio corresponding to the net output power of the electrolytic water system. The optimal peroxy ratio is the peroxy ratio corresponding to the minimum working current of the constant-time net output power of the electrolytic water system.
The prediction model is used for obtaining a third-order linear state space model of the electrolytic water air supply system based on reasonable assumption according to the lumped parameter model of the electrolytic water air supply system without considering an air compressor
In which W is stack For the heating power of the electrolytic tank, V stack-Hout For the outlet hydrogen flow of the electrolytic tank, V stack The cell voltage is applied to the cell in a manner,is the first derivative of the heating power of the electrolytic cell, < >>Is the first differential of the hydrogen flow at the outlet of the electrolyzer,is the first derivative of the cell voltage, I input To supply current to the electrolytic cell, V stack-Hout For the outlet hydrogen flow of the electrolytic tank, A 3×3 As a first coefficient matrix, B 3×3 Is a second coefficient matrix.
And using the model as a prediction model, and predicting the future state and output of the electrolytic water system according to the current state and the assumed input of the electrolytic water system. The electrolytic water system state comprises the outlet pressure of an air compressor, the cathode flow passage pressure of the electrolytic water and the partial pressure of oxygen.
The input of the system is the heating power of the electrolytic cell:
the output of the water electrolysis system is the efficiency eta of the electrolytic tank stack ：
η stack ＝ψ(I input ，V stack-out ，V stack )
Wherein, psi is the input current I of the electrolytic cell input Hydrogen flow V at the outlet of the electrolyzer stack-Hout And cell voltage V stack To the efficiency eta of the electrolytic cell stack Is mapped to the mapping of (a).
And (3) rolling optimization, and solving an optimal control law by using a particle swarm algorithm. The optimal control law refers to optimizing the performance function of the water electrolysis system in the Np time domain under the action of Nc control signals in the future. The optimized performance function is as follows:
wherein Np is a prediction step length, nc is a control step length, and NP is more than or equal to Nc. zr is a reference track, qz, and Rz is a weighting matrix of the corresponding dimension.
After the optimal control law is calculated, the first element of the control law is applied to the system.
And (3) feedback correction, wherein in the next control period, the difference between the predicted output of the prediction model and the actual output of the electrolytic water system is used as an error correction prediction model.
And S5, the controller calculates the temperature change trend of the electrolytic tank and the efficiency trend of the electrolytic tank according to the input current of the electrolytic tank.
And S6, controlling the power of the heat exchanger and the water flow change of the electrolytic tank by the controller to finish the temperature control of the electrolytic tank.
The electrolytic water system comprises an AC/DC (alternating current/direct current) transformation module, a fluid conveying module, a heat exchange module, an electrolytic tank module and a gas-liquid separation module. Based on the temperature change trend and the efficiency change trend of the electrolytic cell, the power of the heat exchanger and the power of the pump are adjusted, for example, the temperature of the electrolytic cell is increased, namely, the heating power of the heat exchanger is reduced, and the flow is increased so as to realize the purpose of cooling the electrolytic cell, thereby completing the control of an electrolytic water system.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (6)
1. A model predictive control-based PEM electrolyzed water control method, which is characterized by comprising the following steps:
s1, collecting running data of an electrolytic water system;
s2, estimating the internal state of the electrolytic tank according to collected operation data of the electrolytic water system, and obtaining a state estimation result;
s3, inputting the acquired state estimation result of the electrolytic cell into a model predictive control algorithm, and calculating the heating power of the electrolytic cell and the change trend of the temperature of the electrolytic cell;
s4, tracking the input power of the electrolyzed water based on the temperature of the electrolyzer, and controlling the heat exchange power of the heat exchanger and the water flow based on the change trend of the temperature of the electrolyzer to realize the temperature control of the electrolyzer under the fluctuation working condition.
2. The model predictive control-based PEM electrolyzed water control method of claim 1 wherein the electrolyzed water system operating data in S1 comprises electrolyzer voltage, electrolyzer current, electrolyzer temperature, electrolyzer outlet hydrogen flow, electrolyzer inlet water flow and heat exchanger power; the model predictive control algorithm calculates through a pre-established predictive model, and the predictive model is established with a three-order linear state space model of the water electrolysis system, an input/output model of the water electrolysis system and performance indexes of the water electrolysis system;
the expression of the third-order linear state space model of the electrolyzed water control system is as follows:
wherein T is stack For the temperature of the electrolytic cell, V stack For the cell voltage, I stack The electric current of the electrolytic cell is supplied,is a first derivative of the temperature of the electrolyzer, +.>Is the first derivative of the hydrogen flow at the outlet of the electrolyzer,/->Is the first derivative of the cell voltage, V flow For the flow of electrolyzer inlet water, W heat-ex For heat exchanger power, A 3×3 As a first coefficient matrix, B 3×2 Is a second coefficient matrix;
the input of the input/output model of the water electrolysis system is the input current of the electrolytic tank, the output is the efficiency of the electrolytic tank, and the expression of the input/output model of the water electrolysis system is as follows:
u＝I stack
T stack ＝ψ(I stack ，V stack ，V flow ，W heat-ex )
wherein u is the input of the input/output model of the water electrolysis system and is the current I of the electrolytic tank stack Psi is the input current I of the electrolytic cell input Water flow V flow Heat exchanger power W heat-ex And cell voltage V stack To the temperature T of the electrolytic cell stack Is mapped to;
performance index Z of the electrolytic water system p The calculated expression of (2) is:
wherein P is Net For generating hydrogen power of the electrolytic tank, T stack Is the temperature of the electrolytic cell. Further, an optimal control law of the prediction model is solved by adopting a particle swarm algorithm, and the optimal control law is applied to the water electrolysis system.
3. The PEM electrolyzed water control method based on model predictive control according to claim 1, wherein in S2, the internal state of the electrolyzed water is estimated using an unscented kalman filter algorithm.
4. The system of the PEM electrolyzed water control method is characterized by comprising an electrolyzed water system operation data module, an electrolyzer state estimation module, a model predictive control algorithm module and a working condition control module;
the water electrolysis system operation data module is used for collecting water electrolysis system operation data of the water electrolysis controller;
the electrolytic tank state estimation module estimates the internal state of the electrolytic tank according to the running data of the electrolytic water system, acquires a state estimation result, and inputs the estimation result into the model predictive control algorithm module;
the model predictive control algorithm module calculates the heating power of the electrolytic cell and the temperature change trend of the electrolytic cell;
the working condition control module controls heat exchange power of the heat exchanger and water flow based on the temperature change trend, so that temperature control of the electrolytic tank under the fluctuation working condition is realized.
5. The system of a PEM electrolyzed water control method according to claim 4 wherein the electrolyzed water system operational data module comprises an electrolyzed water controller and a data acquisition module connected via a CAN bus.
6. The system of a PEM electrolyzed water control method according to claim 4 wherein the operating mode control module comprises an air compressor controller and a DC/DC controller.
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| CN117071000A (en) * | 2023-10-17 | 2023-11-17 | 深圳润世华研发科技有限公司 | Remote safety monitoring system for PEM (PEM) water electrolysis hydrogen production equipment |
| CN117904674A (en) * | 2024-01-29 | 2024-04-19 | 北京氢羿能源科技有限公司 | Multilayer control system and method for hydrogen production by PEM (PEM) electrolysis of water |
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| CN117071000A (en) * | 2023-10-17 | 2023-11-17 | 深圳润世华研发科技有限公司 | Remote safety monitoring system for PEM (PEM) water electrolysis hydrogen production equipment |
| CN117071000B (en) * | 2023-10-17 | 2023-12-15 | 深圳润世华研发科技有限公司 | Remote safety monitoring system for PEM (PEM) water electrolysis hydrogen production equipment |
| CN117904674A (en) * | 2024-01-29 | 2024-04-19 | 北京氢羿能源科技有限公司 | Multilayer control system and method for hydrogen production by PEM (PEM) electrolysis of water |
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