Device and method for determining status in fuel cells or stacks of electrolytic cells or in fuel cells or electrolytic cells

Abstract

The invention relates to a device and a method for determining a state (100) in a stack of fuel cells or electrolytic cells or in a fuel cell or electrolytic cell, in which at least one membrane electrode unit and plates are provided, between which in each case one membrane electrode unit is arranged, the inlet of the process medium from the periphery and the outlet of the process product into the periphery as well as the electrical input and output variables are modeled by means of a first model (102), the segments of the plate are modeled by means of a second model (104), and the membrane electrode unit or the segments of the membrane electrode unit are modeled by means of a third model (106), wherein the first model (102) and the second model (104) are coupled by means of at least one coupling variable (108, 110), the second model (104) and the third model (106) are coupled in sections by means of at least one coupling variable (112, 114), at least one input variable of the first model (102) is specified, the state (100) is determined by means of the at least one input variable, the first model (102), the second model (104) and the third model (106).

Description

Technical Field

[0001] The invention relates to a device and a method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell. Background Art

[0002] When designing a polymer electrolyte membrane fuel cell, the properties of the polymer electrolyte membrane fuel cell can be determined in a simulation which takes into account the geometry of the fuel cell. The simulation requires such a large amount of computing resources that it is desirable to provide an improved simulation for determining the state of the polymer electrolyte membrane fuel cell in a polymer electrolyte membrane fuel cell stack during operation. Summary of the invention

[0003] This is achieved by the subject matter of the independent claims.

[0004] A method for determining a state in a stack of fuel cells or electrolyzers or in a fuel cell or electrolyzer, wherein at least one membrane electrode unit and plates are provided, between which each membrane electrode unit is arranged, wherein an inlet of a process medium from the surroundings and an outlet of a process product into the surroundings as well as electrical input and output variables are modeled with the aid of a first model, wherein segments of the plates are modeled with the aid of a second model, wherein the membrane electrode units or segments of the membrane electrode units are modeled with the aid of a third model, wherein the first model and the second model are coupled by at least one coupling parameter, wherein the second model and the third model are coupled in segments by at least one coupling parameter, wherein at least one input variable of the first model is predefined, wherein the state is determined with the aid of at least one input variable, the first model, the second model and the third model.

[0005] A membrane electrode unit can be understood in particular as at least one ion-conducting layer (so-called membrane) and at least one electrode layer, which is arranged on, in particular applied to, one side of the ion-conducting layer. Preferably, electrode layers are arranged or applied on both sides of the ion-conducting layer, so that the ion-conducting layer is located (preferably in a sandwich-like manner) between the two electrode layers. Preferably, the membrane electrode unit comprises a further applied porous layer, which is used to distribute (transport / remove) the reaction medium, the current and / or the heat. In this case, at least one ion-conducting layer (membrane) can be at least partially embodied as an ion-conducting polymer (for example for PEM fuel cells, PEM electrolysis, AEM fuel cells, AEM electrolysis) and / or as a non-conductive porous structure impregnated with an ion-conducting polymer and / or an ion-conducting liquid / solution (for example for liquid alkaline electrolysis or in the case of redox flow batteries) and / or as a ceramic ion conductor (for example in the case of SOFC / SOEC). Typically, the electrode layers are porous layers that fulfill the combined functions of ion transport, electron transport, transport of liquid and / or gaseous reaction media, heat transport and electrocatalysis. Depending on the technology under consideration, these combined functions can consist of any combination of electrocatalytically active materials (metals and / or metal oxides and / or ceramic materials) and / or electronically conductive (porous) support materials (metals, carbon materials, doped metal oxides, ...) and / or ion conductors (polymer ion conductors and / or liquid ion conductors and / or ceramic ion conductors). The membrane electrode unit can include additional (usually porous) functional layers, which are used, for example, to distribute the reaction medium (transporting liquid and / or gaseous reactants, transporting away liquid and / or gaseous products) and / or transporting electrons and heat. Depending on the application, at least one of the layers of the membrane electrode unit can also have a mechanical function, for example to provide a spring effect or to mechanically support adjacent layers.

[0006] Advantageously, the method can also be used to determine the state in a redox flow battery or a redox flow battery stack. The method is particularly, but not exclusively, suitable for determining the state in a NT-PEM fuel cell, a HT-PEM fuel cell, a NT-PEM electrolyzer, a HT-PEM electrolyzer, an AEM fuel cell, an AEM electrolyzer, an AEL electrolyzer (classical liquid alkaline electrolysis), a SOFC, a SOEC, a MCFC / MCEC (molten carbonate fuel cell / electrolyzer), a PAFC / PAEC (phosphoric acid fuel cell / electrolysis).

[0007] In the following, the method is explained essentially using a PEM fuel cell as an example, but the method is essentially transferable to any fuel cell, electrolysis and redox flow technology.

[0008] Preferably, the physical effects of the beam are modeled per segment or for a plurality of segments by means of the second model. This additionally improves the simulation.

[0009] Preferably, the physical effects of the membrane electrode unit or of each segment of the membrane electrode unit are modeled with the aid of the third model. This enables a simulation with particularly low demands on computing resources.

[0010] Preferably, during operation of the stack, fuel cell or electrolysis cell, a measurement characterizing the operation is detected, wherein states are determined during the operation as a function of the measurement. Thus, the operation can be influenced as a function of the simulation results.

[0011] Preferably, during operation, variables for the operation, in particular operating strategies, control variables or regulating variables, are determined as a function of the state during operation of the stack, and the stack, the fuel cell or the electrolyzer is controlled as a function of these variables. Thus, the operation is influenced as a function of the simulation results.

[0012] Preferably, as a function of the state, in particular during operation, a variable is determined that characterizes irreversible aging of the stack, fuel cell or electrolysis cell or a portion thereof or includes a prediction for maintenance of the stack, fuel cell or electrolysis cell or a portion thereof. The simulation enables this information about the state to be determined.

[0013] Preferably, design parameters for the stack, fuel cell or electrolysis cell or part thereof are determined based on the conditions. This enables a better design to be achieved more quickly.

[0014] Preferably, the first model, the second model and / or the third model include parameters, wherein training data are provided, which respectively include at least one input parameter for the first model and a reference for a state, wherein the corresponding state is determined with the aid of at least one input parameter from the training data, wherein a parameter is determined based on a deviation of the state from its corresponding reference from the training data, for which the deviation is as small as possible, wherein the state is subsequently determined based on at least one pre-given input parameter of the first model. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] A device for determining a state of a fuel cell or a stack of electrolysis cells or in a fuel cell or an electrolysis cell, in particular a virtual sensor, is designed to determine the state according to the method.

[0016] Further advantageous embodiments can be derived from the following description and the accompanying drawings. In the accompanying drawings:

[0017] Figure 1 A schematic diagram showing a model for determining the state in a fuel cell stack,

[0018] Figure 2 A schematic diagram showing a stack of a polymer electrolyte membrane fuel cell,

[0019] Figure 3 A flow chart with steps in a method for determining a state in the stack is shown. DETAILED DESCRIPTION

[0020] Polymer electrolyte membrane fuel cells convert hydrogen and oxygen into water while releasing electricity and heat. Solid oxide fuel cells convert fuel (such as methane) while releasing electricity and heat.

[0021] In the following description, the processing method is described for a polymer electrolyte membrane fuel cell. For other types of fuel cells, electrolytic cells or redox flow batteries, corresponding processing methods are provided.

[0022] As mentioned above, in particular for solid oxide electrolysis cells or polymer electrolyte membrane electrolysis cells, the treatment is carried out in a corresponding manner.

[0023] The polymer electrolyte membrane fuel cell comprises a bipolar plate in a bipolar configuration. The bipolar plate comprises a first electrode and a second electrode. A plurality of bipolar plates are arranged in series between two end plates to form a stack. In the stack, a proton-conducting polymer membrane is arranged between each two of the bipolar plates. The stack is tightened by the end plates. The two outer bipolar plates of the stack are electrically contacted by each of the end plates.

[0024] A polymer electrolyte membrane fuel cell comprises a unipolar plate in a unipolar configuration instead of the bipolar plate. The unipolar plate comprises an electrode. A plurality of unipolar plates are arranged in series between two end plates to form a stack. In this example, each fuel cell is provided with a proton-conducting polymer membrane, which is surrounded by an insulating layer outside its active surface. The stack is tightened by the end plates. The two outer unipolar plates of the stack are electrically contacted by each of the end plates. In addition, an electrical contact portion for the unipolar plate is provided, which is arranged in the stack.

[0025] In the following, bipolar plates and monopolar plates are referred to as plates. In the case of bipolar plates, the number of plates is one more than the number of membrane electrode units. In the case of monopolar plates, the number of plates is twice the number of membrane electrode units.

[0026] At least one channel for supplying a first process medium, in particular process air, is provided in the plate. A channel can be understood here as a channel in a narrow sense and as a channel in a broad sense as a continuous flow path, for example in the form of a path through an open material as in the case of a PEM electrolyzer.

[0027] At least one channel for supplying a second process medium, in particular process hydrogen, is provided in the plate.

[0028] At least one channel for a coolant, in particular water, is provided in the plate.

[0029] At least one channel is provided in the plate for discharging process products, in particular process air and product water.

[0030] exist Figure 1 An exemplary model for determining the state 100 in a stack is shown in FIG. Figure 1 , a first model 102 for the periphery of the stack and a second model 104 and a third model 106 for at least one segment in the stack are shown. In one example, the segment includes at least a portion of the anode channel, at least a portion of the membrane electrode unit, at least a portion of the cathode channel, and at least a portion of the coolant channel. This means that the segment at least partially includes two plates and a membrane electrode unit. In this example, the second model 104 models at least a portion of the anode channel consisting of two plates, at least a portion of the membrane electrode unit, at least a portion of the cathode channel, and at least a portion of the coolant channel. In this example, the third model 106 models at least a portion of the membrane electrode unit.

[0031] The first model 102 is coupled to the second model 104 via at least one first coupling parameter 108. The second model 104 is coupled to the first model 102 via at least one second coupling parameter 110. In this example, two plates in the second model 104 are combined into one plate, wherein only one coupling parameter is set for each direction for the two plates, namely the first coupling parameter 108 and the second coupling parameter. It can be provided that in the second model 104, for each plate, two segments are modeled, and these segments are coupled in each direction via their own coupling parameters.

[0032] The second model 104 is coupled to the third model 106 via at least one third coupling parameter 112. The third model 106 is coupled to the second model 104 via at least one fourth coupling parameter 114. In this example, a virtual sensor 116 is provided, which detects the state 100. In this example, the virtual sensor 116 is coupled to the third model 106 via at least one fifth coupling parameter 118. The first model 102 has an input 120 for at least one input variable of the first model 102. The first model 102 has an output 122 for outputting at least one output variable of the first model 102.

[0033] The second model 104 is configured to model physical processes, in particular transport processes, in the plate. In this example, the second model 104 models discrete segments in the plate. The transport process occurs on the one hand in the plane of the plate between the segments and on the other hand in the plane perpendicular to the plate between each segment or segment bundle from and to the membrane electrode unit. In this example, the transport process in the plate is modeled in these planes with the aid of the second model 104. The transport process in the membrane electrode unit is modeled with the aid of the third model 106.

[0034] For example, the second model 104 is designed to model the heat transport, coolant transport, gas transport and electrical potentials in each segment or in a bundle of such segments.

[0035] In this case, in particular, the medium supply, the supply of process medium (in particular reaction gas), the removal of process products (in particular liquid water in the case of PEM fuel cells) and / or heat and voltage are mapped via generalized resistors. These resistors are connected to form a resistor network. The resistors can be linear or nonlinear. In addition, the resistors used as the basis can be predefined from a physical model. For example, the physical model is discretized, for example, by a finite volume. For example, the physical model is a pre-generated table or a data-based model.

[0036] The segment is a discrete point and comprises, for example, a channel of a certain channel length. The segment may also comprise a plurality of channels.

[0037] For example, the physical processes within the segment are mapped by representative elements, such as single channels or representative channel bundles.

[0038] For example, the following parameters can be determined in a segment: gas concentration, partial pressure, voltage, plate temperature, gas temperature, liquid water saturation, coolant temperature, coolant pressure. These parameters are exemplary. Other parameters can also be determined.

[0039] For the second model 104, a mathematical description of the association can be used, for example, for gas transport a description of two-phase flow according to Darcy or Poiseuille, for voltage a description according to Ohm's law, for plate temperature a description according to the heat conduction equation, and for coolant a description as an incompressible flow.

[0040] In this example, for each segment, the third model 106 comprises a membrane electrode unit model in a plane perpendicular to the plate. The membrane electrode unit model can be implemented with different complexity.

[0041] For example, the third model 106 is a one-dimensional model which is used to approximately determine an inhomogeneous current distribution in the stack and to determine the corresponding gas turnover rate.

[0042] For example, the third model 106 is a two-dimensional model used to determine the internal state of the membrane electrode unit.

[0043] For example, the third model 106 is a three-dimensional model used to evaluate a process in the microstructure of the membrane electrode unit, for example, the flow effect along the channel flow direction.

[0044] In one example, the membrane electrode unit model models the physics of the membrane electrode unit in detail, wherein different internal states, such as membrane humidity or saturation, are automatically calculated simultaneously. For example, at least one fifth coupling parameter 118 includes at least one of these internal states. For example, aging in the segment assigned to the membrane electrode unit can be determined thereby. By abstracting into segments, the membrane electrode unit model can be implemented one-dimensionally, two-dimensionally or three-dimensionally.

[0045] In the case of PEMFC, a third model 106 models the physics of the membrane electrode unit, for example according to L. M. Patt et al., Electrochimica Acta, 326, 134963 (2019) or R. Vetter and JO Schumacher, Journal of Power Sources, 438, 227018 (2019) or AA Kulikovsky, Journal of The Electrochemical Society, 161, F263–F270 (2014).

[0046] For example, the at least one third coupling parameter 112 is a gas species concentration, a bipolar plate temperature, an electric potential. For example, the at least one fourth coupling parameter 114 is a material flow, a heat flow, an electric current. The third coupling parameter 112 and / or the fourth coupling parameter 114 couples the segment or the segmented bundle to the membrane electrode unit model.

[0047] For PEM fuel cells, in Experimental parameter uncertainty in PEM fuel cell modeling Part I: Scatter in material parameterization, R. Vetter and JO Schumacher, Journal of Power Sources, 438, 227018 (2019)

[0048] As disclosed in arXiv:1811.10091, the third model 106 is implemented according to a partial differential equation, in which a third coupling parameter 112 and a fourth coupling parameter 114 are implemented for the anode and the cathode, respectively:

[0049] In equation (1), Ohm's law is solved, where the potential is the third coupling parameter 112 and the current is the fourth coupling parameter 114 .

[0050] In equation (5), the heat equation is solved, where temperature is the third coupling parameter 112 and heat flow is the fourth coupling parameter 114 .

[0051] In equation (13), the gas transport is calculated by means of the Maxwell-Stefan equations, wherein the gas concentration is the third coupling variable 112 and the material flow is the fourth coupling variable 114 .

[0052] In addition, in this representation, the proton conduction in the ionic polymer is calculated with the help of equation (1), the water transfer in the ionic polymer is calculated with the help of equation (9), the adsorption / desorption is calculated with the help of equation (22), the evaporation / condensation is calculated with the help of equation (23), the reaction kinetics is calculated with the help of equation (1) and the contact resistance is calculated with the help of equation (S24).

[0053] For example, the first model 102 includes a collection node for a resistor network. For example, the first model 102 is configured to map the inhomogeneities between the cells of the stack. In this example, the manifold, i.e., the inlet for the process medium and the outlet for the process product and the end plates of the stack are merged. It can be set that one model is used for the inlet and outlet, and a separate model is used for the end plates. It can be set that separate models are used for the inlet, outlet and end plates. For example, the first model 102 is configured to consider the thermal and electrical characteristics of the entire stack. For example, the first model 102 is configured to model the inhomogeneities of the fluid on the channel.

[0054] In this example, in addition to the membrane electrode unit model, a first model 102 is assigned to the segment at the edge of the plate. The corresponding first and second coupling parameters model the medium supply, the gas species flow and the gas temperature. For example, the medium supply is modeled by the mass flow from the periphery into the segment or from the segment into the periphery, the operating pressure, the discharge pressure and / or the coolant temperature. This is achieved, for example, by means of numerical calculations of fluid mechanics and subsequent extraction of generalized resistors.

[0055] In this example, the first model 102 comprises at least one end plate model which is assigned to the segment on which the end plate is arranged. The respective first and second coupling variables model the conversion of the electrical requirement into the current entering the respective segment.

[0056] In order to calculate the entire stack with the aid of this discrete calculation, a plurality of individual cells of the stack can be combined into a representative cell bundle. Compared with a single cell, the cell bundle has changed properties. For example, the resistor causes a compensation current in the plane, which is mapped. In addition, the combination affects the first model 102. In this case, for this purpose, the first model 102 is constructed with the aid of corresponding coupling parameters for the cell bundle. In this way, a resistor network for the entire stack is generated, which locally provides statements about the internal state of the membrane electrode unit and the plate within the entire stack. Here, the number of cells allowed in the cell bundle is not limited. For example, the number of cells is specified according to the accuracy requirements of the application problem.

[0057] The selection of the number of single pool bundles and the number of segments is a measure of accuracy and computing time. The segmentation can be rectangular, especially square. Other geometric shapes are also possible. In this example, the geometric requirement for the segmentation is that the segmentation can achieve a complete subdivision of the plate. In one example, the segmentation combines multiple channel groups into a channel bundle. The number of merged channels can include a channel of the plate up to all channels. In particular, if the power difference that can be expected in the lateral flow direction in the channel is small or the correlation with the problem to be studied is small, it is sufficient to select only one channel bundle. Otherwise, it may be necessary to observe multiple channel bundles. The channel bundle includes, for example, 10 or more channels.

[0058] Preferably, cells at the edge of the stack are integrated into smaller cell bundles than other cell bundles comprising cells from the center of the stack, since the temperature profile there in particular generally differs from that in cells from the center of the stack.

[0059] For example, choose another discretization depending on the use case:

[0060] For simple problems with states not relevant to aging (eg polarization curves or operating strategies), it is sufficient to select, for example, 5-20 segments and 1-10 cell bundles along the flow channel.

[0061] For non-aging-related problems, in which a very accurate mapping of the local internal state of the membrane electrode unit is required, a number of segments of, for example, 100 or more segments along the flow direction of the channel is also advantageous.

[0062] The first model 102, the second model 104 and the third model 106 include parameters. In particular, the models include mutually coupled partial differential equations or are determined as analytical functions or are determined as one neural network or multiple neural networks. The parameters define the models, i.e. differential equations or analytical functions or neural networks. Differential equations, analytical functions and neural networks model electrochemical effects or physical effects in the stack. Differential equations and analytical functions include electrochemical parameters or physical parameters. Differential equations and analytical functions can also include state parameters for which there is neither an electrochemical counterpart (Entsprechung) nor a physical counterpart in the stack. The neural network includes an input terminal for an electrochemical parameter or a physical parameter and an output terminal for an electrochemical parameter or a physical parameter. The differential equations or analytical functions or neural networks are coupled via coupling parameters.

[0063] Depending on the state 100 to be modeled, the differential equations and the neural network may include different quantities, coupling quantities and / or parameters. Examples for quantities and coupling quantities are described below. In one example, the model (i.e., the differential equation, analytical function or neural network) is solved in a fully coupled manner to determine the state 100. In one example, one explicit coupling may be set or multiple explicit couplings may be set.

[0064] Optionally, the first model 102 has an interface 124 for providing data to the first model 102. Optionally, the second model 104 has an interface 126 for providing data to the second model 104. Optionally, the third model 106 has an interface 128 for providing data to the third model 106.

[0065] With the aid of these interfaces, the parameters of the corresponding models can be changed for providing data.

[0066] In order to provide data, a training can be provided. In the training, training data are provided, which respectively include at least one input variable for the first model 102 and a reference for the state 100. The reference indicates which state 100 is to be modeled with the aid of the model and the corresponding input variables.

[0067] The corresponding state 100 is determined with the aid of at least one input variable from the training data.

[0068] For example, the parameters are determined based on the deviation of the state 100 from its corresponding reference from the training data.

[0069] For example, the parameters are determined by an optimization method that minimizes the deviation, for which the deviation is as small as possible. For example, if the average value of the deviation is the smallest for the training data, the deviation is as small as possible.

[0070] In the derivation, state 100 is subsequently determined as a function of the parameters determined in the training and at least one predefined input variable of first model 102 .

[0071] exist Figure 2 Schematically shown is a membrane electrode unit 202, which is arranged in a fuel cell stack 204. The stack 204 has two ends 206, between which a plate 208 is arranged. The stack 204 is electrically contacted at its ends 206 by each end plate 210. The end plates are arranged on opposite end faces of the stack 204. The first of the plates 208 of the stack 204 is electrically connected to the first of the end plates 210, and the last of the plates 208 of the stack 204 is electrically connected to the second of the end plates 210.

[0072] The second model 104 is used to model the segment 208 - 1 of the plate 208 . The plate 208 includes channels 208 - 2 . Each segment 208 - 1 includes a portion of one of the channels 208 - 2 or a portion of a plurality of channels 208 - 2 .

[0073] An inlet 212 is arranged on the side of the stack 204, through which the process medium is supplied to the channel 208-2 of the stack 204. The process product is discharged from the channel 208-2 of the stack 204 via an outlet 214. The outlet 214 is arranged on the side of the stack 204 opposite to the inlet 212.

[0074] With the aid of first model 102 , an inlet or outlet into stack 204 is modeled as a function of at least one input variable of first model 102 .

[0075] With the aid of first model 102 , electrical input variables and output variables of stack 204 are modeled as a function of at least one input variable of first model 102 .

[0076] The segment 208 - 1 of the plate 208 of the stack 204 is modeled in the second model 104 .

[0077] With the aid of the second model 104 , the physical effects in the segment 208 - 1 are modeled.

[0078] The physical effects in the membrane electrode element 202 or in the segment 202-1 of the membrane electrode element 202 are modeled by means of the third model 106. For example, the segment 202-1 of the membrane electrode element 202 is assigned to a segment 208-1 in each of two plates 208 adjacent to the membrane electrode element 202, wherein the segments 202-1 arranged adjacent to each other can be assigned to each other.

[0079] These segments can be decoupled or coupled to each other. In this example, the number of segments in the second model 104 is equal to the number of segments in the third model 106. This is a consistent discretization. For example, the segments in the second model 104 are attached to the segments in the third model 106 by the third coupling parameter 112. For example, the segments in the third model 104 are attached to the segments in the second model 106 by the fourth coupling parameter 114. The number of segments in the second model 104 can be different from the number of segments in the third model 106. This is an inconsistent discretization. For example, the segments in the second model 104 are attached to the segments in the third model 106 by the correspondingly adapted third coupling parameter 112. For example, the segments in the third model 106 are attached to the segments in the second model 104 by the correspondingly adapted fourth coupling parameter 114.

[0080] In this example, for each segment 202 - 1 , the third model 106 includes a one-dimensional, two-dimensional or three-dimensional model, by means of which the edge conditions of the plate 208 in the segment 208 - 1 assigned to this segment 202 - 1 are modeled. This achieves a significant saving in computing time.

[0081] exist Figure 3 The steps in the method for determining the state 100 in the stack 204 are described in . The method explained below with reference to polymer electrolyte membrane fuel cells can be applied analogously to any fuel cell, electrolysis and redox flow technology.

[0082] In step 302, at least one input variable of the first model 102 is determined, for example, from a predetermined measurement on a membrane electrode unit installed in a polymer electrolyte membrane fuel cell. In one example, the measurement is detected during operation of the polymer electrolyte membrane fuel cell. In this example, the measurement characterizes the operation of the polymer electrolyte membrane fuel cell, i.e., the measurement includes at least one measurable variable that characterizes the operation.

[0083] In step 304 , state 100 is determined with the aid of at least one input variable, first model 102 , second model 104 and third model 106 . State 100 is detected with the aid of virtual sensor 116 , for example.

[0084] In this example, the virtual sensor 116 detects at least one fifth coupling variable 118. In this example, the first model 102, the second model 104 and the third model 106 are solved in a fully coupled manner for at least one input variable of the first model 102. In this case, the models are coupled via corresponding coupling variables.

[0085] Next, step 306 is performed.

[0086] In step 306, in one example, at least one output variable of the first model 102 is output. For example, the output variable is a calculated variable for the variable contained in the measurement. It can be provided that these variables are compared with one another.

[0087] In step 306, in one example, a variable for the operation of the stack 204, in particular an operating strategy, a control variable or a regulating variable, is determined as a function of the state 100, and the stack 204 is controlled as a function of this variable. For example, the variable is determined during the operation of the stack 204 as a function of a measurement detected during the operation of the stack 204 on the stack 204. For example, the stack 204 is controlled during the operation of the stack using this variable.

[0088] In step 306 , in one example, a variable is determined from state 100 of stack 204 , which variable characterizes irreversible aging of stack 204 or a portion of the stack. For example, state 100 and / or the variable are determined during operation.

[0089] In step 306, in one example, a variable is determined based on the state 100 of the stack 204: the variable includes a prediction for maintenance of the stack 204 or a portion of the stack. For example, the state 100 and / or the variable is determined during operation.

[0090] In step 306, in one example, design parameters for stack 204 or a portion of the stack are determined based on state 100 of stack 204. For example, steps 302 to 306 are repeated multiple times in a design process, wherein multiple design parameters are determined. For example, different designs are simulated using different parameters of second model 104 and / or third model 106.

[0091] For example, state 100 is determined as a function of at least one input variable for first model 102 .

[0092] In the following, an exemplary application for an exemplary state is given. In this example, with respect to stack 204, a distinction is made between a fuel cell stack of a polymer electrolyte membrane fuel cell and an electrolysis stack of a polymer electrolyte membrane electrolysis cell. The fuel cell stack includes a fuel cell. The electrolysis stack includes an electrolysis cell.

[0093] For redox flow batteries, solid oxide fuel cells or solid oxide electrolyzers, treatment is carried out in a corresponding manner.

[0094] For example, the state relates to a fuel cell stack, a fuel cell in the fuel cell stack or a part of a fuel cell. In addition to the already described part of the fuel cell stack, the fuel cell stack comprises, for example, at least one gas diffusion layer, at least one microporous layer, at least one catalyst layer, at least one inlet for a first process medium, at least one inlet for a second process medium, at least one membrane and / or at least one gas diffusion medium.

[0095] For example, state 100 is the internal state of the fuel cell stack.

[0096] The following variables are, for example, input variables of the fuel cell stack or indicate internal states of the fuel cell stack:

[0097] The gas composition of the anode gas,

[0098] Gas composition of cathode gas,

[0099] the gas pressure of the anode gas at the exhaust port for the anode gas in the fuel cell stack,

[0100] the gas mass flow rate of the anode gas at the exhaust port for the anode gas in the fuel cell stack,

[0101] the gas pressure of the cathode gas at the exhaust port for the cathode gas in the fuel cell stack,

[0102] the gas mass flow rate of the cathode gas at the exhaust port for the cathode gas in the fuel cell stack,

[0103] the gas pressure of the anode gas at the inlet for the anode gas in the fuel cell stack,

[0104] the gas mass flow rate of the anode gas at the inlet for the anode gas in the fuel cell stack,

[0105] the gas pressure of the cathode gas at the inlet for the cathode gas in the fuel cell stack,

[0106] the gas mass flow rate of the cathode gas at the inlet for the cathode gas in the fuel cell stack,

[0107] Gas temperature of anode gas

[0108] Gas temperature of cathode gas,

[0109] The temperature of the coolant,

[0110] Coolant mass flow rate,

[0111] The voltage generated by the fuel cell stack,

[0112] The electric current produced by a fuel cell stack.

[0113] Optionally, the first model 102 comprises a thermal model that models environmental conditions of the fuel cell stack, such as the temperature and / or relative humidity of the ambient air of the fuel cell stack.

[0114] For example, state 100 is the non-uniformity of the internal state of the fuel cells in the fuel cell stack.

[0115] For example, the state 100 is a non-uniformity of the internal state of at least one channel for gas, a non-uniformity of the internal state of at least one channel for coolant, or a non-uniformity of the internal state of the structure of the plate in the fuel cell stack.

[0116] For example, state 100 is the operating state of a membrane electrode unit of a fuel cell or multiple fuel cells of a fuel cell stack, such as the operating state of at least one gas diffusion carrier, at least one microporous layer, at least one catalyst layer and / or at least one polymer electrolyte membrane of the fuel cell.

[0117] State 100 is determined, for example, for drying, for a brief overload or in transient operation of the fuel cell.

[0118] For example, state 100 is a state within a fuel cell or within the fuel cell stack for water management of the fuel cell stack (eg, temperature, gas composition, saturation, liquid water content, water overflow through a polymer electrolyte membrane fuel cell).

[0119] For example, state 100 is a state that occurs when starting or stopping a fuel cell stack.

[0120] For example, state 100 is a state that is important during a cold start. In one example, a cold start is identified, in particular, based on the temperature and state 100 is detected during a cold start.

[0121] For example, the state 100 is a local state in the fuel cell stack. In one example, the impact of production fluctuations on the local state is determined based on the state 100.

[0122] In one example, the state 100 is a local temperature distribution or a local gas composition within a fuel cell and / or the fuel cell stack.

[0123] In one example, state 100 is local saturation in at least one porous layer (particularly at least one gas diffusion layer, at least one microporous layer, at least one catalyst layer and / or at least one gas channel) within a fuel cell and / or the fuel cell stack.

[0124] In one example, the state 100 is a local current density distribution or a local voltage distribution within a fuel cell and / or the fuel cell stack.

[0125] In one example, state 100 is the local water content in the membrane of the fuel cell stack.

[0126] In one example, state 100 is at least one local potential in at least one catalyst layer of the fuel cell stack.

[0127] In one example, the state 100 is the local contribution of the local reaction to the total voltage or to the total current provided by the fuel cell stack. For example, the state 100 is detected for different local reactions, and the total voltage or total current is determined based on the detected state.

[0128] In one example, state 100 is localized irreversible aging, in particular ionic polymer aging, in the fuel cell stack. Irreversible aging is, for example, catalyst aging of a catalyst layer of a fuel cell stack and / or membrane aging in at least one membrane of a fuel cell stack and / or aging in at least one gas diffusion carrier of a fuel cell stack and / or aging in at least one microporous layer of a fuel cell stack.

[0129] In one example, the state 100 is a local irreversible aging in the membrane electrode unit. For example, different states 100, ie different local irreversible aging, are detected for the membrane electrode unit, and the irreversible aging of the membrane electrode unit is determined based on the detected states.

[0130] In one example, the state 100 is detected and a particularly optimized operating strategy is determined as a function of the state 100. For example, the operating strategy is determined taking into account the efficiency and / or the service life of the fuel cell stack.

[0131] In one example, a design of the fuel cell stack optimized to achieve a desired efficiency and / or service life is determined according to state 100 .

[0132] In one example, the state 100 is determined for a plurality of different designs of the fuel cell stack and an optimized design is selected from the plurality of designs based on the state 100. This can enable a cost-effective design process.

[0133] In one example, the actual state of the fuel cell stack is determined based on the state 100. For example, the state 100 is determined based on measurements on the fuel cell stack.

[0134] For example, the actual state is the state of the fuel cell stack during operation of the fuel cell stack. For example, the state 100 is determined based on a measurement on the fuel cell stack that is detected during operation of the fuel cell stack. For example, the measurement is detected when the fuel cell stack is operated, and the state 100 is determined based on the measurement during operation of the fuel cell stack.

[0135] In one example, a control variable or a regulating variable for the operation of the fuel cell stack is determined according to state 100. For example, the control variable or the regulating variable is determined when the fuel cell stack is operated. For example, the control variable or the regulating variable is determined according to the measurement. For example, the measurement is detected when the fuel cell stack is operated, the state 100 is determined according to the measurement during the operation of the fuel cell stack, the control variable or the regulating variable is determined according to state 100 during the operation of the fuel cell stack, and the fuel cell stack is controlled according to the control variable or the regulating variable.

[0136] In one example, a prognosis for maintenance of the fuel cell stack is determined according to state 100 .

[0137] In one example, a prognosis for an adaptive change of at least one operating condition is determined based on state 100 , with the aid of which the service life and / or the operating capacity can be influenced during the operation of the fuel cell stack.

[0138] In one example, at least one input variable, for example a gas composition, is estimated for the fuel cell stack according to state 100 using a system model that models a system in which the fuel cell stack is operated.

[0139] In the following, an application involving electrolysis in an electrolysis stack is described. The electrolysis stack comprises an electrolysis cell. The electrolysis stack comprises an electrolysis stack which in this example comprises an electrolysis cell.

[0140] For example, state 100 is the internal state of the electrolytic stack.

[0141] The following variables are, for example, input variables of the electrolysis stack or indicate internal states of the electrolysis stack:

[0142] The composition of the anode fluid and cathode fluid,

[0143] the pressure of the anode fluid at the inlet for the anode fluid of the electrolysis stack,

[0144] the pressure of the anode fluid at the discharge port for the anode fluid of the electrolysis stack,

[0145] the pressure of the cathode fluid at the inlet for the cathode fluid of the electrolysis stack,

[0146] the pressure of the cathode fluid at the discharge port for the cathode fluid of the electrolysis stack,

[0147] the mass flow rate of the anode fluid at the inlet for the anode fluid of the electrolysis stack,

[0148] the mass flow rate of the anode fluid at the discharge port for the anode fluid of the electrolysis stack,

[0149] the mass flow rate of the cathode fluid at the inlet for the cathode fluid of the electrolysis stack,

[0150] the mass flow rate of the cathode fluid at the discharge port for the cathode fluid of the electrolysis stack,

[0151] The temperature of the anode fluid,

[0152] The temperature of the cathode fluid,

[0153] The voltage generated by the electrolytic stack,

[0154] The electric current produced by an electrolytic stack.

[0155] For example, state 100 is the inhomogeneity of the internal state of the electrolytic cells in the electrolytic cell stack.

[0156] For example, state 100 is an inhomogeneity of the internal state of at least one channel for gas, an inhomogeneity of the internal state of at least one channel for coolant or an inhomogeneity of the internal state of the structure of the plates in the electrolytic cell stack.

[0157] For example, state 100 is an operating state of a membrane electrode unit of one or more electrolysis cells, such as an operating state of at least one porous transport layer, at least one catalyst layer and / or the polymer electrolyte membrane electrolysis cell.

[0158] For example, a polymer electrolyte membrane electrolysis cell is part of an electrolysis device. State 100 is determined, for example, in transient operation of the electrolysis device.

[0159] In one example, state 100 is an operating state of the electrolysis cell stack, in particular in the case of load balancing, and is determined, for example, when the electrolysis cell stack is operated in overload.

[0160] In one example, state 100 is an operating state of the electrolysis cell stack that occurs when starting up, ie, starting up, or shutting down, ie, stopping, the electrolysis device.

[0161] For example, state 100 is a local state in an electrolytic cell stack. In one example, the impact of production fluctuations on the local state is determined based on state 100.

[0162] In one example, state 100 is a local temperature distribution in the electrolytic cell stack or in an electrolytic cell.

[0163] In one example, state 100 is a local fluid composition within an electrolytic cell and / or the stack of electrolytic cells.

[0164] In one example, state 100 is local saturation, ie distribution of liquid and gas phases, in at least one porous layer, in particular at least one porous transport layer, at least one catalyst layer and / or at least in at least one of the channels for fluids.

[0165] In one example, state 100 is a local current density distribution or a local voltage distribution.

[0166] In one example, state 100 is a local current density distribution or a local voltage distribution within an electrolytic cell and / or the stack of electrolytic cells.

[0167] In one example, state 100 is at least one local potential in at least one catalyst layer of the electrolytic cell stack.

[0168] In one example, the state 100 is the local contribution of the local reaction to the total voltage or to the total current provided by the electrolytic cell stack. For example, the state 100 is detected for different local reactions, and the total voltage or total current is determined based on the detected state.

[0169] In one example, state 100 is local irreversible aging, in particular ionic polymer aging, in the electrolyzer stack. Irreversible aging is, for example, catalyst aging of a catalyst layer of the electrolyzer stack and / or membrane aging in at least one membrane of the electrolyzer stack and / or aging in at least one gas diffusion support of the electrolyzer stack and / or aging in at least one microporous layer of the electrolyzer stack.

[0170] In one example, the state 100 is a local irreversible aging in the membrane electrode unit. For example, different states 100, ie different local irreversible aging, are detected for the membrane electrode unit, and the irreversible aging of the membrane electrode unit is determined based on the detected states.

[0171] In one example, state 100 is detected and a particularly optimized operating strategy is determined as a function of state 100. For example, the operating strategy is determined taking into account the efficiency and / or the service life of the electrolytic cell stack.

[0172] In one example, an optimal design of the electrolytic cell stack to achieve a desired efficiency and / or service life is determined according to state 100 .

[0173] In one example, states 100 are determined for a plurality of different designs of the electrolytic cell stack and an optimized design is selected from the plurality of designs based on states 100. This can enable a cost-effective design process.

[0174] In one example, the actual state of the electrolytic cell stack is determined based on the state 100. For example, the state 100 is determined based on measurements on the electrolytic cell stack.

[0175] For example, the actual state is the state of the electrolytic cell stack during operation of the electrolytic cell stack. For example, the state 100 is determined based on a measurement on the electrolytic cell stack that is detected during operation of the electrolytic cell stack. For example, the measurement is detected when the electrolytic cell stack is in operation, and the state 100 is determined based on the measurement during operation of the electrolytic cell stack.

[0176] In one example, a control variable or a regulating variable for the operation of the electrolytic cell stack is determined according to the state 100. For example, the control variable or the regulating variable is determined when the electrolytic cell stack is operated. For example, the control variable or the regulating variable is determined according to the measurement. For example, the measurement is detected when the electrolytic cell stack is operated, the state 100 is determined according to the measurement during the operation of the electrolytic cell stack, the control variable or the regulating variable is determined according to the state 100 during the operation of the electrolytic cell stack, and the electrolytic cell stack is controlled according to the control variable or the regulating variable.

[0177] In one example, a prognosis for maintenance of the electrolytic cell stack is determined according to state 100 .

[0178] In one example, a prognosis for an adaptive change of at least one operating condition is determined based on state 100 , with the aid of which the service life and / or the operating capacity can be influenced during the operation of the electrolytic cell stack.

[0179] In one example, at least one input variable for the electrolytic cell stack, such as the conductivity of the water or the circulating residual gas, is estimated using a system model that models the system in which the electrolytic cell stack is operated, according to state 100. The conductivity of the water changes, for example, due to contamination during operation.

Claims

1. A method for determining a state (100) in a stack (204) of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell, wherein: At least one membrane electrode unit (202) and a plate (208) are provided, between which each membrane electrode unit (202) is arranged, wherein an inlet of a process medium from the surroundings and an outlet of a process product into the surroundings as well as electrical input variables and output variables are modeled with the aid of a first model (102), wherein a segment (208-1) of the plate (208) is modeled with the aid of a second model (104), wherein the membrane electrode unit (202) or a segment (202-1) of the membrane electrode unit (202) is modeled with the aid of a third model (106), The first model (102) and the second model (104) are coupled via at least one coupling variable (108, 110), the second model (104) and the third model (106) are coupled in sections via at least one coupling variable (112, 114), at least one input variable of the first model (102) is predetermined (302), and the state (100) is determined (304) with the aid of the at least one input variable, the first model (102), the second model (104) and the third model (106).

2. The method according to claim 1, characterized in that The second model (104) is used to model the physical effects of the beam for each segment (208-1) or for a plurality of segments (208-1).

3. The method according to claim 1 or 2, characterized in that: The third model (106) is used to model physical effects of the membrane electrode unit (202) or each segment (202-1) of the membrane electrode unit (202).

4. The method according to any one of the preceding claims, characterized in that During operation of the stack (204), the fuel cell or the electrolysis cell, measurements characterizing the operation are detected (302), wherein the state (100) is determined (304) during the operation based on the measurements.

5. The method according to claim 4, characterized in that During the operation, variables for the operation, in particular an operating strategy, a control variable or a regulating variable, are determined as a function of a state (100) during the operation of the stack, and the stack (204), the fuel cell or the electrolysis cell is controlled (306) as a function of the variables.

6. The method according to claim 4 or 5, characterized in that: Depending on the state (100), in particular during the operation, a variable is determined (306) which characterizes irreversible aging of the stack (204), the fuel cell or the electrolysis cell or a part thereof or comprises a prediction for maintenance of the stack (204), the fuel cell or the electrolysis cell or a part thereof.

7. The method according to any one of the preceding claims, characterized in that Design parameters for the stack (204), the fuel cell or the electrolysis cell or a portion thereof are determined (306) based on the state (100).

8. The method according to any one of the preceding claims, characterized in that The first model (102), the second model (104) and / or the third model (106) include parameters, wherein training data are provided, the training data each including at least one input variable for the first model (102) and a reference for the state (100), wherein the corresponding state (100) is determined with the aid of at least one input variable from the training data, wherein the parameter is determined based on a deviation of the state (100) from its corresponding reference from the training data, the deviation being as small as possible for the parameter, wherein the state (100) is subsequently determined based on at least one predefined input variable of the first model (102).

9. A device, in particular a virtual sensor, for determining a state (100) of a fuel cell or a stack of electrolytic cells or in a fuel cell or electrolytic cell (202), characterized in that The device is designed to determine the state (100) according to the method of any of the preceding claims.

10. A computer program, characterized in that The computer program includes computer-readable instructions, and when the computer-readable instructions are executed by a computer, the method according to any one of claims 1 to 8 is executed.

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