CN111146478B - Method for predicting residual service life of proton exchange membrane fuel cell stack - Google Patents

Method for predicting residual service life of proton exchange membrane fuel cell stack Download PDF

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CN111146478B
CN111146478B CN201911332428.5A CN201911332428A CN111146478B CN 111146478 B CN111146478 B CN 111146478B CN 201911332428 A CN201911332428 A CN 201911332428A CN 111146478 B CN111146478 B CN 111146478B
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张存满
欧明阳
杨代军
张若凡
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    • H01—ELECTRIC ELEMENTS
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Abstract

The invention relates to a method for predicting the residual service life of a proton exchange membrane fuel cell stack, which comprises a training stage and a prediction stage, wherein the training stage comprises the following steps: (A1) constructing a voltage prediction model of the proton exchange membrane fuel cell; (A2) obtaining the operating parameters of the proton exchange membrane fuel cell and constructing a training sample; (A3) training a voltage prediction model based on the training samples; the prediction phase comprises the steps of: (B1) setting a cycle working condition of a prediction stage; (B2) acquiring the single-chip average voltage of the proton exchange membrane fuel cell to be predicted by using a voltage prediction model; (B3) and taking the single-chip average voltage corresponding to the current density under the rated power as a reference voltage, and obtaining the time corresponding to 10% reduction of the single-chip average voltage of the proton exchange membrane fuel cell to be predicted relative to the reference voltage, so as to obtain the residual service life of the fuel cell stack. Compared with the prior art, the method has high prediction accuracy.

Description

Method for predicting residual service life of proton exchange membrane fuel cell stack
Technical Field
The invention belongs to the field of proton exchange membrane fuel cells, and particularly relates to a method for predicting the residual service life of a proton exchange membrane fuel cell stack.
Background
The proton exchange membrane fuel cell is an electrochemical power generation device, has the advantages of high energy efficiency, environmental friendliness, low noise and the like, and is considered to be a new generation of automobile power source with great potential in the future. However, the short service life is one of the technical bottlenecks that restrict the development of the fuel cell. In order to prolong the service life of the fuel cell stack, it is important to build an accurate life prediction model.
The vehicle fuel cell needs to deal with different working road conditions in the actual working process, such as start-stop, idling, rated working conditions, overload operation and the like, and the change of the working conditions causes physical and chemical damages to components such as a catalyst, a proton exchange membrane, a gas diffusion layer and the like in the fuel cell, and some components are even irreversible. The existing service life prediction methods mainly comprise three methods: model-driven, data-driven, and model-data hybrid driving methods. The model driving method needs to have systematic cognition on the constitution and the failure mode of the fuel cell, and brings difficulty to the construction of the model. The data-driven method does not need to know the system of the fuel cell, but the method has poor effect in long-term prediction, and the method based on data analysis depends on the repeatability of a fading mechanism, and parameters also need to be adjusted when the electricity of the fuel cell changes and the operation condition is adjusted. Moreover, the existing durability test applied to the fuel cell stack is mostly in a constant working condition or a simple dynamic working condition, has a larger difference with the running condition of the vehicle-mounted fuel cell stack, and has no reliability.
The invention patent CN109683093A of lie jian qiu et al proposes to classify the operation condition data of the fuel cell, and obtain the total accumulated start-stop time t1 and the accumulated large-load operating time t2 during the operation of the fuel cell vehicle according to the operation condition of the fuel cell vehicle. Where t1 represents the accumulated time of the larger output voltage, which is the main cause of the increase in the battery impedance; t2 represents the cumulative time of the greater current density, which is the primary cause of the electrochemically active area decay. And constructing a voltage decline model, and obtaining key parameters in the fuel cell voltage decline model by using data fitting. The invention is data obtained by operating a fuel cell bus, but the influence factors considered in the model are few, the reason of the performance reduction of the cell is simply classified as the change of the internal resistance and the electrochemical active area, and the decline model of the internal resistance and the electrochemical active area is simple, thereby influencing the prediction precision on the whole.
The invention patent CN109696638A of zuirkun et al proposes a method for predicting the life of a molten carbonate fuel cell, in which, during a preset operation time, when the average voltage of a single cell is greater than or equal to the lowest operating voltage, the power attenuation rate μ of the molten carbonate fuel cell under the current discharge current is calculated, and the time life of the molten carbonate fuel cell which can continue to operate under the current working condition is calculated according to the relationship between the power attenuation rate μ and the operation time, so as to calculate the total life cycle of the fuel cell. The invention is easy to realize, but the cause of the performance degradation of the battery cannot be analyzed from the mechanism, and the repeatability is low.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing a method for predicting the remaining service life of a pem fuel cell stack.
The purpose of the invention can be realized by the following technical scheme:
a method for predicting the residual service life of a proton exchange membrane fuel cell stack comprises a training phase and a prediction phase, wherein,
the training phase comprises the following steps:
(A1) constructing a voltage prediction model of the proton exchange membrane fuel cell;
(A2) obtaining the operating parameters of the proton exchange membrane fuel cell and constructing a training sample;
(A3) training a voltage prediction model based on the training samples;
the prediction phase comprises the following steps:
(B1) setting a cycle working condition of a prediction stage;
(B2) acquiring the single-chip average voltage of the proton exchange membrane fuel cell to be predicted by using a voltage prediction model;
(B3) and taking the single-chip average voltage corresponding to the current density under the rated power as a reference voltage, and obtaining the time corresponding to 10% reduction of the single-chip average voltage of the proton exchange membrane fuel cell to be predicted relative to the reference voltage, so as to obtain the residual service life of the fuel cell stack.
The voltage prediction model established in the step (a1) is:
Figure BDA0002330016510000021
wherein E is the average voltage of the single piece of the proton exchange membrane fuel cell, epsilon is the recovery factor of the electric pile, and Ueq0In order to represent the average voltage value of the battery in an ideal state, theta is an aging parameter of charge transfer coefficients of an anode and a cathode of the fusion battery, k is an electrochemical active area degradation coefficient, t is the accumulated running time of the battery, I is current density, I is current corresponding to the current density, R is0As an initial value of the equivalent impedance of the battery, delta1、δ2、δ3As equivalent impedance decay model parameters, BcIn order to cause the aging parameters of the non-uniform distribution of the current density on the surface of the electrode, F is a Faraday constant, R is a common gas constant, T is the temperature of the electric pile,
Figure BDA0002330016510000031
is the oxygen diffusion coefficient, LGDLIs the thickness of the gas diffusion layer,
Figure BDA0002330016510000032
is the partial pressure of oxygen during the reaction.
The operating parameters of the pem fuel cell obtained in step (a2) include the average voltage of the single plate, the current density, the current corresponding to the current density, the accumulated operating time of the cell, the temperature of the stack, and the oxygen partial pressure during the reaction process.
The Faraday constant F is 96485C.mol-1。
The common gas constant R is 8.3145J.mol-1.K-1。
Oxygen diffusion coefficient
Figure BDA0002330016510000033
And thickness L of gas diffusion layerGDLIs a constant corresponding to a proton exchange membrane fuel cell.
Step (A3) of training a voltage prediction model to obtain unknown parameters in the voltage prediction model, wherein the unknown parameters comprise a pile recovery factor epsilon and an average voltage value U used for representing the ideal state of the batteryeq0Aging parameter theta of charge transfer coefficients of anode and cathode of fusion battery, electrochemical active area degradation coefficient k and initial value R of equivalent impedance of battery0Aging parameter B causing non-uniform distribution of current density on electrode surfacecAnd equivalent impedance decay model parameter δ1、δ2、δ3。
The cycle condition of the prediction stage set in the step (B1) comprises current density, load current corresponding to the current density, cycle times of the working condition, accumulated running time of the battery, time interval of simulated shutdown and rest, temperature of the electric pile and oxygen partial pressure in the reaction process.
Compared with the prior art, the invention has the following advantages:
(1) the voltage prediction model of the invention introduces the pile restorability factor which represents the pile performance restoration, the shutdown and the restorability of the pile in the operation process can cause the reduction of the Faraday impedance value, and the catalyst activity is restored, so the pile performance is raised to a certain extent, and the introduction of the restorability factor can truly restore the pile operation condition in the residual service life prediction stage and improve the prediction accuracy.
(2) The invention can obtain reliable aging parameters (including theta and B) through model trainingc) The test working condition and the test condition can generate different influences on the degradation and aging of the proton exchange membrane fuel cell, and the voltage model provided by the invention introduces a new aging parameter on the basis of fusing common aging parameters, so that the aging parameter value is obtained by calculating the collected running data of the proton exchange membrane fuel cell, model fitting is carried out, the change rule is summarized, the degradation condition of the cell under a certain test working condition and a certain test condition can be accurately reflected, and the prediction result is more accurate.
Drawings
FIG. 1 is a block flow diagram of a method for predicting remaining useful life of a PEM fuel cell stack according to the present invention;
FIG. 2 is a schematic diagram of current density variation under a single condition of NEDC;
FIG. 3 is a schematic diagram of a predicted result of any single operating condition in the validation set;
FIG. 4 is a graph showing that the current density was 0.7A/cm2The voltage prediction result of (1) is shown schematically.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, a method for predicting the remaining service life of a pem fuel cell stack includes a training phase and a prediction phase, wherein,
the training phase comprises the following steps:
(A1) constructing a voltage prediction model of the proton exchange membrane fuel cell;
(A2) obtaining the operating parameters of the proton exchange membrane fuel cell and constructing a training sample;
(A3) training a voltage prediction model based on the training samples;
the prediction phase comprises the following steps:
(B1) setting a cycle working condition of a prediction stage;
(B2) acquiring the single-chip average voltage of the proton exchange membrane fuel cell to be predicted by using a voltage prediction model;
(B3) and taking the single-chip average voltage corresponding to the current density under the rated power as a reference voltage, and obtaining the time corresponding to 10% reduction of the single-chip average voltage of the proton exchange membrane fuel cell to be predicted relative to the reference voltage, so as to obtain the residual service life of the fuel cell stack.
The voltage prediction model established in the step (a1) is:
Figure BDA0002330016510000041
wherein E is the average voltage of the single piece of the proton exchange membrane fuel cell, epsilon is the recovery factor of the electric pile, and Ueq0In order to represent the average voltage value of the battery in an ideal state, theta is an aging parameter of charge transfer coefficients of an anode and a cathode of the fusion battery, k is an electrochemical active area degradation coefficient, t is the accumulated running time of the battery, I is current density, I is current corresponding to the current density, R is0As an initial value of the equivalent impedance of the battery, delta1、δ2、δ3As equivalent impedance decay model parameters, BcIn order to cause the aging parameters of the non-uniform distribution of the current density on the surface of the electrode, F is a Faraday constant, R is a common gas constant, T is the temperature of the electric pile,
Figure BDA0002330016510000055
is the oxygen diffusion coefficient, LGDLIs the thickness of the gas diffusion layer,
Figure BDA0002330016510000053
the partial pressure of oxygen in the reaction process, wherein the Faraday constant F is 96485C.mol-1The common gas constant R is 8.3145J-1.K-1Oxygen diffusion coefficient
Figure BDA0002330016510000054
And thickness L of gas diffusion layerGDLIs a constant corresponding to a proton exchange membrane fuel cell.
In order to train the voltage prediction model, the operating parameters of the pem fuel cell obtained in step (a2) include the average voltage of the single plate, the current density, the current corresponding to the current density, the accumulated operating time of the cell, the temperature of the stack, and the oxygen partial pressure during the reaction process. Step (A3) of training a voltage prediction model to obtain unknown parameters in the voltage prediction model, wherein the unknown parameters comprise a pile recovery factor epsilon and an average voltage value U used for representing the ideal state of the batteryeq0Aging parameter theta of charge transfer coefficients of anode and cathode of fusion battery, electrochemical active area degradation coefficient k and initial value R of equivalent impedance of battery0Aging parameter B causing non-uniform distribution of current density on electrode surfacecAnd equivalent impedance decay model parameter δ1、δ2、δ3。
In order to obtain unknown parameters in the process of training the voltage prediction model, under the condition of temporarily not considering a cell stack recovery factor epsilon, the voltage prediction model is deformed into the following steps:
Figure BDA0002330016510000051
wherein, will
Figure BDA0002330016510000052
By utilizing a regular operation equation, the U corresponding to different moments can be obtained through calculationeq0、θ×k、θ、Req、BcValues where U is obtained using linear fittingeq0And BcThe change rule of the electrochemical active area degradation coefficient k and the aging parameter theta of the charge transfer coefficient of the anode and the cathode of the fusion battery can be obtained by combining the change rule of theta multiplied by k and theta, and the equivalent impedance ReqBy using gradient descent algorithm, equivalent impedance decay model parameter delta can be obtained1、δ2、δ3。
The actual operation condition of the proton exchange membrane fuel cell stack is combined, namely the shutdown and the rest are carried out after the operation for a certain time, so that a performance recovery factor epsilon of the stack is introduced. And comparing the average voltage recovery proportion of the galvanic pile before and after shutdown and shutdown, and summarizing the relationship between the recovery proportion and the shutdown time by combining the shutdown time. In the residual service life prediction stage, the time for simulating the shutdown of the galvanic pile is fixed, so that the restorability factor epsilon can be determined according to the obtained relation.
In the prediction stage, the trained voltage prediction model is used for obtaining the single-chip average voltage of the proton exchange membrane fuel cell to be predicted, and then the residual service life of the proton exchange membrane fuel cell stack is predicted. The cycle condition of the prediction stage set in the step (B1) includes current density, load current corresponding to the current density, cycle number of the operating condition, accumulated running time of the battery, time interval of the simulated shutdown and rest, temperature of the electric pile and oxygen partial pressure in the reaction process, wherein the temperature of the electric pile and the oxygen partial pressure in the reaction process are set to be constant.
After the aging parameters in the model are determined in the training stage, the data of the verification set are substituted into the final voltage prediction model, and the accuracy of the prediction method is verified.
The current density of the test stack under a single NEDC condition varies as shown in figure 2. Correspondingly, in the verification process, any single working condition is selected, and the prediction result obtained by utilizing the model provided by the invention is shown in the attached figure 3. It can be seen that the predicted value of the model in a single working condition can accurately follow the actual voltage variation trend no matter the duration of the training set is 183 hours or 280 hours. When the training set has more data, the predicted result is more accurate.
In this embodiment, the current density corresponding to the rated power is 1000mA/cm2And selecting the output voltage of the electric pile corresponding to the current density. The training set data duration is 280 hours, the validation set data duration is 180 hours, and the prediction phase duration is 500 hours. The result is shown in fig. 4, and it can be seen from the figure that the predicted voltage value corresponding to the verification set not only accurately follows the variation trend, but also the mean square error of the predicted value reaches 0.898%, which indicates that the model has achieved a relatively ideal prediction effect. In the 500-hour prediction stage, the performance degradation rate of the proton exchange membrane fuel cell is 3.7%, the comprehensive degradation rate of the proton exchange membrane fuel cell in 960 hours is 6.6%, and the predicted service life of the proton exchange membrane fuel cell under the NEDC working condition is 1419 hours by taking degradation 10% as the service life end point.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.

Claims (6)

1. A prediction method for the residual service life of a proton exchange membrane fuel cell stack is characterized by comprising a training phase and a prediction phase, wherein,
the training phase comprises the following steps:
(A1) constructing a voltage prediction model of the proton exchange membrane fuel cell;
(A2) obtaining the operating parameters of the proton exchange membrane fuel cell and constructing a training sample;
(A3) training a voltage prediction model based on the training samples;
the prediction phase comprises the following steps:
(B1) setting a cycle working condition of a prediction stage;
(B2) acquiring the single-chip average voltage of the proton exchange membrane fuel cell to be predicted by using a voltage prediction model;
(B3) taking the single-chip average voltage corresponding to the current density under the rated power as a reference voltage, and obtaining the time corresponding to 10% reduction of the single-chip average voltage of the proton exchange membrane fuel cell to be predicted relative to the reference voltage, so as to obtain the residual service life of the fuel cell stack;
the voltage prediction model established in the step (a1) is:
Figure FDA0002725974720000011
wherein E is the average voltage of the single piece of the proton exchange membrane fuel cell, epsilon is the recovery factor of the electric pile, and Ueq0In order to represent the average voltage value of the battery in an ideal state, theta is an aging parameter of charge transfer coefficients of an anode and a cathode of the fusion battery, k is an electrochemical active area degradation coefficient, t is the accumulated running time of the battery, I is current density, I is current corresponding to the current density, R is0As an initial value of the equivalent impedance of the battery, delta1、δ2、δ3As equivalent impedance decay model parameters, BcIn order to cause the aging parameters of the non-uniform distribution of the current density on the surface of the electrode, F is a Faraday constant, R is a common gas constant, T is the temperature of the electric pile,
Figure FDA0002725974720000013
is the oxygen diffusion coefficient, LGDLIs the thickness of the gas diffusion layer,
Figure FDA0002725974720000012
is the oxygen partial pressure during the reaction;
step (A3) of training a voltage prediction model to obtain unknown parameters in the voltage prediction model, wherein the unknown parameters comprise a pile recovery factor epsilon and an average voltage value U used for representing the ideal state of the batteryeq0Aging parameter theta of charge transfer coefficients of anode and cathode of fusion battery, electrochemical active area degradation coefficient k and initial value R of equivalent impedance of battery0Aging parameter B causing non-uniform distribution of current density on electrode surfacecAnd equivalent impedance decay model parameter δ1、δ2、δ3。
2. The method of claim 1, wherein the operating parameters of the pem fuel cell obtained in step (a2) include average voltage of each sheet, current density, current corresponding to the current density, accumulated operating time of the cell, temperature of the stack, and oxygen partial pressure during the reaction process.
3. The method of claim 1, wherein the faradaic constant F is 96485c.mol-1。
4. The method of claim 1, wherein the common gas constant R is 8.3145J.mol-1.K-1。
5. The method of claim 1, wherein the oxygen diffusion coefficient is determined by a method of predicting the remaining useful life of the pem fuel cell stack
Figure FDA0002725974720000021
And thickness L of gas diffusion layerGDLIs a constant corresponding to a proton exchange membrane fuel cell.
6. The method of claim 1, wherein the operating conditions of the cycle in the prediction stage set in step (B1) include current density, load current corresponding to the current density, operating cycle number, accumulated cell operating time, time interval of simulated shutdown and rest, temperature of the stack, and oxygen partial pressure during the reaction process.
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