CN116050587A - PEM electrolytic tank service life prediction method and device for variable power operation - Google Patents

Abstract

The invention provides a PEM electrolytic cell life prediction method and device for variable power operation, firstly, considering the non-monotonic characteristic of PEM electrolytic cell degradation data, fitting the trace of the degradation data by a polynomial, superposing the upper limit and the lower limit of a certain confidence interval of fitting error distribution, and establishing a PEM electrolytic cell performance degradation model represented by the interval; based on the method, a Hyper-CuboidalVolume acceleration model is adopted to describe the influence of power fluctuation amplitude and fluctuation time scale on the degradation process of the electrolytic cell, and then an acceleration model parameter estimation method based on optimization is provided; finally, a random power sequence time-scale fluctuation segment statistical method is provided to predict the service life of the PEM electrolytic tank operated with variable power. According to the invention, the non-monotonic characteristic of the PEM electrolytic tank degradation data is considered in the life prediction, and the PEM electrolytic tank degradation data is closer to a real model, so that the life prediction is finer and more accurate.

Description

A method and device for predicting the life of a PEM electrolyzer with variable power operation

Technical Field

The invention belongs to the field of life prediction of power system equipment, and in particular relates to a method and device for predicting the life of a PEM electrolyzer operating with variable power.

Background Art

Faced with the problems of ultra-large-scale and ultra-high proportion of new energy grid connection, insufficient balance support capacity of new power systems, and difficulty in ensuring power supply in extreme cases, the demand for flexible resources has increased dramatically. The proton exchange membrane (PEM) electrolyzer has characteristics such as second-level response (hot start from standby state to rated production state in 1 to 5 seconds, with a ramp rate of 100% per second) and wide adjustment (the adjustment range of the electric hydrogen production system is 10% to 200%), which can provide auxiliary services such as frequency modulation and peak regulation for the power grid, and can provide considerable adjustment capacity for the system for its large-scale grid connection. The wide adjustment of PEM electrolyzer supports the stable and economic operation of the new power system. However, long-term operation or frequent cycle operation of PEM electrolyzers will lead to degradation of electrolyzer performance, which includes reversible and irreversible parts; the microscopic phenomena of degradation mainly include cation contamination, thinning of proton exchange membrane, corrosion of catalysts, oxidation of titanium-based components, and macroscopic manifestations include abnormal working temperature, flooding, leakage, low purity of hydrogen production, and increased working voltage (i.e. "overall upward shift of polarization curve"), etc., which ultimately lead to failure of electrolyzer performance. There are many factors that affect the performance and life of PEM electrolyzers, mainly including intrinsic factors, system factors, and environmental factors. Intrinsic factors involve the structure and design parameters of the equipment itself, such as the loading of platinum and iridium catalysts, the thickness of the proton exchange membrane, the plate material, etc., which are related to the manufacturing process and selection of the electrolyzer. System factors mainly include improper parameter settings or subsystem failures of the multi-energy flow management subsystems such as water, heat, electricity, and gas. Environmental factors mainly include changes in the operating temperature, pressure, and input power of the PEM electrolyzer. Among them, parameters such as operating temperature and pressure are controllable parameters, and when directly connected to fluctuating power sources such as wind power and photovoltaics to produce hydrogen, the input power of the PEM electrolyzer often changes with the power supply. Therefore, it is of great significance to establish a performance degradation model and life prediction model for variable power operation electrolyzers.

At present, many studies have discussed the impact mechanism of variable operating power on the working performance and life of PEM electrolyzers. However, the current studies have not taken the non-monotonic decreasing law of PEM electrolyzer performance into account in its life prediction model. The life prediction model of variable power operating PEM hydrogen production system needs to be further refined.

Summary of the invention

In order to overcome the shortcomings of the prior art, the present invention proposes a method and device for predicting the life of a PEM electrolyzer operating with variable power, which is closer to the actual model and makes the life prediction more precise and accurate.

In order to achieve the above object, the technical solution adopted by the present invention is:

A method for predicting the life of a PEM electrolyzer operating at variable power comprises the following steps:

1) Obtain the performance degradation trajectory of the PEM electrolyzer by fitting the degradation data with a polynomial;

2) Based on the performance degradation trajectory of the PEM electrolyzer obtained in step 1), the upper and lower confidence limits of a certain confidence level of the fitting error distribution are superimposed to establish an interval-characterized PEM electrolyzer performance degradation model;

3) Based on the PEM electrolyzer performance degradation model obtained in step 2), the influence of the dual stress of power fluctuation amplitude and fluctuation time scale on the electrolyzer degradation process is described, and a Hyper-CuboidalVolume acceleration model is established;

4) Based on the Hyper-CuboidalVolume acceleration model obtained in step 3), the Hyper-CuboidalVolume acceleration model parameters are calculated based on an optimization method;

5) Based on the PEM electrolyzer performance degradation model, Hyper-CuboidalVolume acceleration model and parameters obtained in step 2), step 3) and step 4), the life of the PEM electrolyzer under different stresses is calculated.

Furthermore, the step 1) comprises the following steps:

The variation of degradation data in different periods is characterized by Ito random process, as shown in formula (1):

dA＝μ(A,θ)dt+δ(A,θ)dW _t (1)

In formula (1), A represents the rate of change of degradation data; μ(A,θ)dt is the drift term, which represents the changing trend of the average attenuation rate of electrolytic cell degradation; δ(A,θ) _dWt is the diffusion term, which represents the random uncertainty of electrolytic cell degradation; θ represents the random variable, and _Wt is the standard Wiener process.

The experimental degradation data are fitted by a polynomial function as shown in formula (2), and the parameters of the drift term are derived and calculated:

In formula (2), M represents the highest order of the polynomial, _wj is the coefficient of the ^xj term, and x is the time variable.

Furthermore, the step 2) comprises the following steps:

Generate the fitting error sequence ε according to formula (3), _y0 is the actual measurement data; assume that the fitting error obeys a normal distribution with a mean value of u and a variance of m:

ε _i =y ₀ (x _i )-y(x _i ,w) (3)

Where I is the number of fitting points.

The lower limit c ₁ and upper limit c ₂ of a confidence interval of the error distribution are superimposed on the fitting curve, as shown in formula (6), to generate the performance degradation change trajectory of the electrolytic cell represented by the interval:

In formula (7), y- represents the lower limit of the electrolytic cell fitting life interval, and y+ represents the upper limit of the electrolytic cell fitting life interval; when the confidence interval is calculated for a large sample, r is a constant determined according to the confidence interval.

Furthermore, the step 3) comprises the following steps:

The Hyper-Cuboidal Volume acceleration model is shown in formula (7):

为连续相乘符号。Where A(S ₁ ,S ₂ ,…,S _n ) is the life of the product under the combined action of accelerated stresses S ₁ ~S _n ; a _i (i＝0,1,…,n) is the coefficient to be determined; P(S _i ) represents the function of each accelerated stress,

is the continuous multiplication symbol.

Take n = 2, take the logarithm of both sides of equation (8) and simplify it to equation (9), which is easy to solve:

ln(A(S ₁ ,S ₂ ))＝a ₁ +a ₂ lnS ₁ +a ₃ lnS ₂ (9)

In formula (9), a ₁ , a ₂ and a ₃ are acceleration model parameters to be estimated.

Furthermore, the step 4) comprises the following steps:

Taking the minimum difference between the actual value and the estimated value as the optimization goal, the parameter optimization model of the Hyper-CuboidalVolume acceleration model is constructed, namely, equations (10) to (11), and the genetic algorithm is used to solve it:

a ₁ +a ₂ ln(s _1,n )+a ₃ ln(s _2,n )-ln(y _n )＝d _n (10)

In formula (10), y _n is the life under different stresses, which is obtained by the performance degradation model, a _i,max is the upper limit of each parameter, s _1,n and s _2,n are the values of stress 1 and stress 2 corresponding to the nth group of degradation data.

Furthermore, the step 5) comprises the following steps:

The lifespan is evaluated by the attenuation rate of each fluctuation within the detailed operation cycle. The specific steps are as follows:

Step 1: Define the fluctuation segment: Let the power at time t be Pt. If the power at the next time _Pt+1 is greater than or equal to Pt, then the period from time t to time t+1 is defined as the power rising period, otherwise Pt is defined as the power falling period; if the power rises continuously from time t to time t+n, and then the power starts to fall after time t+n, then the period from t to t+n is defined as a rising fluctuation segment; if the power falls continuously from time t to time t+n, then the period from t to t+n is defined as a falling fluctuation segment;

Step 2: Input the new energy output sequence, remove the segments where the power is continuously zero; count the duration and fluctuation amplitude of the rising or falling fluctuation segments of the new energy output sequence;

Step 3: Calculate the difference Δdg,i between the average attenuation rate at the initial moment and the final moment of each fluctuation segment according to formula (12), and then calculate the attenuation dg,i within the fluctuation segment, as shown in formula (13);

Δd _g,i =|D/(A(T _i,t ,V _i,t0 ))-D/A(T _i,t ,V _i,tend )|(12)

d _g,i = k·Δd _g,i (13)

In formula (11), D is the failure threshold of degradation, Ti _,t is the cycle period of the ith fluctuation, _Vi,t0 and Vi _,tend are the working voltages at the initial and final moments of the ith fluctuation; k is the duration of the fluctuation period, in hours;

Step 4: Calculate the life of the PEM electrolyzer as shown in formula (14):

In formula (14), I is the number of rising fluctuation segments and falling fluctuation segments.

The present invention also provides a PEM electrolyzer life prediction device for variable power operation, comprising:

A degradation trajectory fitting module is used to obtain the performance degradation trajectory of the PEM electrolyzer by fitting the degradation data with a polynomial;

The acceleration model parameter optimization calculation module is used to superimpose the upper and lower confidence limits of a certain confidence level of the fitting error distribution to establish an interval-characterized PEM electrolyzer performance degradation model; describe the influence of the dual stress of power fluctuation amplitude and fluctuation time scale on the electrolyzer degradation process, and establish a Hyper-Cuboidal Volume acceleration model;

The life prediction module is used to calculate the parameters of the Hyper-Cuboidal Volume acceleration model based on the optimization method, and to calculate the life of the PEM electrolyzer under different stresses based on the obtained PEM electrolyzer performance degradation model, Hyper-Cuboidal Volume acceleration model and parameters.

The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned evaluation method when executing the computer program.

The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the above-mentioned evaluation method is implemented.

Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Beneficial effects:

The present invention takes the non-monotonic characteristics of the degradation data of the PEM electrolyzer into consideration in its life prediction, which is closer to the actual model and makes its life prediction more precise and accurate. The present invention can be promoted and applied in the field of electrolytic hydrogen production from new energy sources such as wind power and photovoltaics, and is of great significance. The present invention has the following advantages over the existing PEM electrolyzer life assessment method: 1) The impact of the non-monotonic decreasing characteristics of the change in the performance degradation data of the electrolyzer on its performance degradation trajectory is considered, making its life prediction assessment results more accurate; 2) The proposed PEM electrolyzer life prediction model can be directly used for the life assessment of PEM electrolyzers in wind farms and photovoltaic power stations, and the reasonable replacement of the electrolyzers can better ensure the safety of the power system where the electrolyzers are located; 3) The proposed PEM electrolyzer life prediction model can directly guide the economic planning of PEM electrolyzers in various application scenarios and promote their application development.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method for predicting the life of a PEM electrolyzer operating at variable power according to the present invention.

Figure 2 is a schematic diagram of the degradation principle of a variable power PEM electrolyzer.

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

The present invention provides a method and device for predicting the life of a PEM electrolyzer under variable power operation. First, the non-monotonic characteristics of the degradation data of the PEM electrolyzer are considered, and the polynomial fitting degradation data is used as the basic degradation trajectory. The upper and lower confidence limits of a certain confidence level of the fitting error distribution are superimposed to establish an interval-characterized PEM electrolyzer performance degradation model; based on this, the fluctuation characteristics of the random input power at multiple time scales are considered, and the Hyper-Cuboidal Volume acceleration model is adopted to describe the influence of the dual stress of the power fluctuation amplitude and the fluctuation time scale on the degradation process of the electrolyzer; then, combined with the acceleration model parameter estimation principle, an acceleration model parameter estimation method based on optimization is proposed; finally, a fluctuation fragment statistical method of the random power sequence at different time scales is proposed to predict the life of the PEM electrolyzer under variable power operation. The present invention takes the non-monotonic characteristics of the degradation data of the PEM electrolyzer into consideration in its life prediction, which is closer to the actual model and makes its life prediction more precise and accurate. The above scheme is introduced in detail below.

As shown in FIG1 , the steps of the variable power operation PEM electrolyzer life prediction method of the present invention are as follows:

Step 1) considering the non-monotonic characteristics of the degradation data of the PEM electrolyzer, a polynomial is used to fit the degradation data to obtain the performance degradation trajectory of the PEM electrolyzer;

Step 2) Based on the performance degradation trajectory of the PEM electrolyzer obtained in step 1), the upper and lower confidence limits of a certain confidence level of the fitting error distribution are superimposed to establish an interval-characterized PEM electrolyzer performance degradation model;

Step 3) Based on the PEM electrolyzer performance degradation model obtained in step 2), the influence of the dual stress of power fluctuation amplitude and fluctuation time scale on the electrolyzer degradation process is described, and a Hyper-CuboidalVolume acceleration model is established;

Step 4) based on the Hyper-CuboidalVolume acceleration model obtained in step 3), the Hyper-CuboidalVolume acceleration model parameters are calculated based on an optimization method;

Step 5) Based on the PEM electrolyzer performance degradation model, Hyper-Cuboidal Volume acceleration model and parameters obtained in step 2), step 3) and step 4), the life of the PEM electrolyzer under different stresses is calculated.

Specifically, the step 1) of fitting the performance degradation trajectory of the PEM electrolyzer under variable power operation includes the following steps:

By observing the degradation data of the accelerated test of PEM electrolyzer, it is found that its attenuation process is nonlinear. The variation of degradation data at different time periods does not show a strictly monotonous increasing or decreasing law, and can be characterized by the Ito random process, as shown in formula (1).

dA＝μ(A,θ)dt+δ(A,θ)dW _t (1)

Where A represents the rate of change of degradation data; μ(A,θ)dt is the drift term, which represents the changing trend of the average attenuation rate of electrolytic cell degradation; δ(A,θ) _dWt is the diffusion term, which represents the random uncertainty of electrolytic cell degradation; θ represents the random variable, and _Wt is the standard Wiener process.

The experimental degradation data can be fitted by a polynomial function as shown in formula (2) to derive the parameters of the drift term.

Where M represents the highest order of the polynomial, _wj is the coefficient of the ^xj term, and x is the time variable.

The step 2) of establishing the PEM electrolyzer performance degradation model of interval characterization comprises the following steps:

The fitting error sequence ε is generated according to formula (3), where _y0 is the actual measurement data; it is assumed that the fitting error obeys a normal distribution with a mean of u and a variance of m.

ε _i =y ₀ (x _i )-y(x _i ,w) (3)

Where I is the number of fitting points.

The lower limit c ₁ and the upper limit c ₂ of a confidence interval of the error distribution are respectively superimposed on the fitting curve, as shown in formula (6), to generate the trajectory of the performance degradation variation of the PEM electrolyzer represented by the interval.

In the formula, y- represents the lower limit of the electrolytic cell fitting life interval, and y+ represents the upper limit of the electrolytic cell fitting life interval; when the confidence interval is calculated for a large sample, r is a constant determined according to the confidence interval.

The step 3) of establishing the Hyper-Cuboidal Volume acceleration model includes the following steps:

The Hyper-Cuboidal Volume acceleration model is often used to describe the comprehensive impact of multiple stresses on the product performance degradation process, as shown in formula (7).

为连续相乘符号。由于PEM电解槽变功率运行主要是循环周期与循环电压两个变量影响，此处n＝2，即可将式(8)两边取对数简化为式(9)，便于求解。Where A(S ₁ ,S ₂ ,…,S _n ) is the life of the product under the combined action of accelerated stresses S ₁ ~S _n , a _i (i＝0,1,…,n) is the coefficient to be determined; P(S _i ) represents the function of each accelerated stress,

is the continuous multiplication symbol. Since the variable power operation of PEM electrolyzer is mainly affected by two variables: cycle period and cycle voltage, n = 2 here, and the logarithm of both sides of equation (8) can be simplified to equation (9), which is easy to solve.

ln(A(S ₁ ,S ₂ ))＝a ₁ +a ₂ lnS ₁ +a ₃ lnS ₂ (9)

Where a ₁ , a ₂ and a ₃ are the acceleration model parameters to be estimated.

Calculating the Hyper-CuboidalVolume acceleration model parameters based on the optimization method in step 4) includes the following steps:

Based on the principle of accelerating model parameter estimation, it is proposed to take the minimum difference between the actual value and the estimated value as the optimization goal and construct an accelerating model parameter optimization model, as shown in equations (10) to (11).

a ₁ +a ₂ ln(s _1,n )+a ₃ ln(s _2,n )-ln(y _n )＝d _n (10)

In formula (10), y _n is the life under different stresses, which is obtained by the performance degradation model, a _i,max is the upper limit of each parameter, s _1,n and s _2,n are the values of stress 1 and stress 2 corresponding to the nth group of degradation data.

The calculation of the life of the PEM electrolyzer under different stresses in step 5) comprises the following steps:

Since the performance attenuation of PEM electrolyzers is affected by the cycle period and fluctuation amplitude, in order to achieve a more accurate life assessment, it is necessary to fine-tune the attenuation rate of each fluctuation within the operating cycle. The specific steps are as follows:

Step 1: Define the fluctuation segment. Let the power at time t be Pt. If the power at the next time Pt+1 is greater than or equal to Pt, then the period from time t to time t+1 is defined as the power rising period, otherwise Pt is defined as the power falling period; if the power rises continuously from time t to time t+n, and then the power starts to fall after time t+n, then the period from t to t+n is defined as a rising fluctuation segment; if the power falls continuously from time t to time t+n, then the period from t to t+n is defined as a falling fluctuation segment.

Step 2: Input the new energy output sequence and remove the segments where the power is continuously zero; count the duration and fluctuation amplitude of the rising or falling fluctuation segments of the new energy output sequence.

Step 3: Calculate the difference Δdg,i between the average attenuation rate at the initial moment and the final moment of each fluctuation segment according to formula (12), and then calculate the attenuation dg,i within the fluctuation segment, as shown in formula (13);

d _g,i = k·Δd _g,i (13)

Where D is the failure threshold of degradation, Ti,t is the cycle period of the ith fluctuation, Vi,t0 and Vi,tend are the working voltages at the initial and final moments of the ith fluctuation; k is the duration of the fluctuation period in hours.

Step 4: Calculate the life of the PEM electrolyzer as shown in formula (14).

Where I is the number of rising and falling fluctuation segments.

The present invention also provides a PEM electrolyzer life prediction device for variable power operation, comprising:

Degradation trajectory fitting module, used to obtain the performance degradation trajectory of PEM electrolyzer by fitting degradation data with polynomials

The acceleration model parameter optimization calculation module is used to superimpose the upper and lower confidence limits of a certain confidence level of the fitting error distribution to establish an interval-characterized PEM electrolyzer performance degradation model; describe the influence of the dual stress of power fluctuation amplitude and fluctuation time scale on the electrolyzer degradation process, and establish a Hyper-Cuboidal Volume acceleration model;

The life prediction module is used to calculate the parameters of the Hyper-Cuboidal Volume acceleration model based on the optimization method, and to calculate the life of the PEM electrolyzer under different stresses based on the obtained PEM electrolyzer performance degradation model, Hyper-Cuboidal Volume acceleration model and parameters.

The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned evaluation method when executing the computer program.

The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the above-mentioned evaluation method is implemented.

It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims (10)

1. A method for predicting the life of a PEM electrolyzer operated at variable power comprising the steps of:

1) Fitting degradation data by using a polynomial to obtain a performance degradation track of the PEM electrolytic cell;

2) Based on the performance degradation track of the PEM electrolytic cell obtained in the step 1), overlapping the upper and lower confidence limits of a certain confidence level of the fitting error distribution, and establishing a PEM electrolytic cell performance degradation model with interval characterization;

3) Describing the influence of power fluctuation amplitude and fluctuation time scale double stress on the degradation process of the electrolytic cell based on the PEM electrolytic cell performance degradation model obtained in the step 2), and establishing a Hyper-Cuboidal Volume acceleration model;

4) Based on the Hyper-Cuboidal Volume acceleration model obtained in the step 3), calculating parameters of the Hyper-Cuboidal Volume acceleration model based on an optimization method;

5) And calculating the service life of the PEM electrolytic cell under different stresses based on the PEM electrolytic cell performance degradation model obtained in the step 2), the step 3) and the step 4) and the Hyper-Cuboidal Volume acceleration model and parameters.

2. A PEM electrolyser life prediction method for variable power operation according to claim 1, characterized in that: the step 1) comprises the following steps:

the variation of the degradation data of different periods is characterized by an illite random process, as shown in a formula (1):

dA＝μ(A,θ)dt+δ(A,θ)dW _t (1)

in the formula (1), a represents a rate of change of degradation data; μ (A, θ) dt drift term, representing cell withdrawalChanging the change trend of the average attenuation rate; delta (A, theta) dW _t As a diffusion term, representing the random uncertainty of the cell degradation; θ represents a random variable, W _t Is a standard wiener process;

parameters for calculating drift terms are derived by fitting experimental degradation data to polynomial functions as shown in equation (2):

in the formula (2), M represents the highest order, w, of the polynomial _j The coefficients of the xj term, x being the time variable.

3. A PEM electrolyser life prediction method for variable power operation according to claim 2, characterized in that: the step 2) comprises the following steps:

generating a fitting error sequence epsilon, y according to the formula (3) ₀ Is the actual measurement data; assuming that the fitting error obeys a normal distribution with an average value u and a variance m:

ε _i ＝y ₀ (x _i )-y(x _i ,w) (3)

wherein I is the fitting point number;

distributing the error to a certain lower limit c of confidence interval ₁ Upper limit c ₂ Respectively superposing the characteristic curves with the fitting curves, and generating a characteristic degradation variation track of the electrolytic tank represented by the interval as shown in the formula (6):

in the formula (7), y-represents a lower limit value of the fitting life span of the electrolytic cell, and y+ represents an upper limit value of the fitting life span of the electrolytic cell; when calculating the confidence interval for a large sample, r is a constant that depends on the confidence interval.

4. A PEM electrolyser life prediction method for variable power operation according to claim 3, characterized in that: the step 3) comprises the following steps:

the Hyper-Cuboidal Volume acceleration model is shown in formula (7):

wherein A (S) ₁ ,S ₂ ,…,S _n ) For the product under acceleration stress S ₁ ～S _n Lifetime under coaction a _i (i=0, 1, …, n) is a coefficient to be determined;

P(S _i ) A function representing the stress of each acceleration,

for successive multiplied symbols;

taking n=2, and taking logarithms from two sides of the formula (8) is simplified into a formula (9), so that the solution is convenient:

ln(A(S ₁ ,S ₂ ))＝a ₁ +a ₂ lnS ₁ +a ₃ lnS ₂ (9)

in the formula (9), a ₁ ，a ₂ And a ₃ Is the acceleration model parameter to be estimated.

5. A PEM electrolyser life prediction method for variable power operation according to claim 4, wherein: the step 4) comprises the following steps:

taking the minimum difference between the actual value and the estimated value as an optimization target, constructing a parameter optimization model of the Hyper-Cuboidal Volume acceleration model, namely formulas (10) to (11), and adopting a genetic algorithm to solve:

a ₁ +a ₂ ln(s _1,n )+a ₃ ln(s _2,n )-ln(y _n )＝d _n (10)

s.t.0≤a _i ≤a _i,max ,i＝1,2,3

in the formula (10), y _n For the service life under different stresses, a is obtained by a performance degradation model _i,max Is the upper limit of each parameter; s is(s) _1,n And s _2,n The values of stress 1 and stress 2 corresponding to the nth set of degradation data.

6. A PEM electrolyser life prediction method for variable power operation according to claim 5, characterized in that: said step 5) comprises the steps of:

the life evaluation is carried out by the attenuation rate of each fluctuation in the fine operation period, and the specific steps are as follows:

step 1: defining a fluctuation segment: let the power at time t be Pt, if the power at the next time P _t+1 More than or equal to Pt, defining a power rising period from a time t to a time t+1, otherwise defining a power falling period by Pt; if the power continuously rises from the time t to the time t+n and starts to drop after the time t+n, defining the time period t to t+n as an ascending fluctuation segment; if the power continuously drops from the time t to the time t+n, defining a period from t to t+n as a drop fluctuation segment;

step 2: inputting a new energy output sequence, and removing fragments with continuous zero power; counting the duration time and the fluctuation amplitude of the fluctuation segment of the rising or falling of the new energy output sequence;

step 3: calculating the average attenuation rate difference delta dg and i between the initial time and the final time of each fluctuation segment according to the formula (12), and further calculating the attenuation quantity dg and i in the fluctuation segment, wherein the attenuation quantity dg and i is shown in the formula (13);

d _g,i ＝k·Δd _g,i (13)

in the formula (11), D is a degradation failure threshold value, T _i,t For the cycle period of the ith fluctuation, V _i,t0 And V is equal to _i,tend The working voltage is the working voltage at the initial moment and the final moment of the ith fluctuation; k is the duration of the fluctuation period in hours;

step 4: the lifetime of the PEM electrolyzer is calculated as shown in equation (14):

in the formula (14), I is the number of rising and falling wave segments.

7. A PEM electrolyser life prediction apparatus for variable power operation comprising:

the degradation track fitting module is used for obtaining the performance degradation track of the PEM electrolytic cell by polynomial fitting degradation data;

the acceleration model parameter optimization calculation module is used for superposing the upper and lower confidence limits of a certain confidence level of fitting error distribution and establishing a PEM electrolytic cell performance degradation model with interval characterization; describing the influence of power fluctuation amplitude and fluctuation time scale double stress on the degradation process of the electrolytic cell, and establishing a Hyper-Cuboidal Volume acceleration model;

the life prediction module is used for calculating parameters of the Hyper-Cuboidal Volume acceleration model based on an optimization method, and calculating the life of the PEM electrolytic tank under different stresses based on the obtained PEM electrolytic tank performance degradation model, the Hyper-Cuboidal Volume acceleration model and the parameters.

8. A PEM electrolyser life prediction device for variable power operation according to claim 7, wherein said degradation trajectory fitting module is in particular adapted to:

the variation of the degradation data of different periods is characterized by an illite random process, as shown in a formula (1):

dA＝μ(A,θ)dt+δ(A,θ)dW _t (1)

in the formula (1), a represents a rate of change of degradation data; μ (a, θ) dt drift term indicating a trend of change in the average decay rate of the cell degradation; delta (A, theta) dW _t As a diffusion term, representing the random uncertainty of the cell degradation; θ represents a random variable, W _t Is a standard wiener process;

parameters for calculating drift terms are derived by fitting experimental degradation data to polynomial functions as shown in equation (2):

in the formula (2), M represents the highest order, w, of the polynomial _j Is x ^j The coefficients of the term, x, are time variables.

9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the computer program, implements a PEM electrolyser life prediction method of variable power operation according to any one of claims 1 to 6.

10. A readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of a PEM electrolyser life prediction method of variable power operation according to any of claims 1 to 6.

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