Device and method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or an electrolysis cell

Abstract

The invention relates to a device and method for determining a state (100) in a stack of fuel cells or electrolysis cells or in a fuel cell or an electrolysis cell, wherein a membrane-electrode unit and plates are provided, between which a membrane-electrode unit is arranged, wherein inflows of process media from a peripheral and outflows of a process product into the peripheral and electrical input and output variables are modelled using a first model (102), wherein segments of the plates are modelled using a second model (104), wherein the membrane-electrode unit or segments of the membrane-electrode unit are modelled using a third model (106), wherein the first model (102) and the second model (104) are coupled by at least one coupling variable (108, 110), wherein the second model (104) and the third model (106) are coupled in segments via at least one coupling variable (112, 114), wherein at least one input variable of the first model (102) is predetermined, wherein the state (100) is determined with the at least one input variable, the first model (102), the second model (104) and the third model (106).

Description

[0001] Description

[0002] title

[0003] Apparatus and method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or a

[0004] State of the art

[0005] The invention relates to a device and method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or an electrolysis cell.

[0006] When designing a polymer electrolyte membrane fuel cell, its behavior can be determined in a simulation that takes the fuel cell geometry into account. This simulation requires so many computational resources that it is desirable to provide an improved simulation for determining the state of the polymer electrolyte membrane fuel cell during operation in a polymer electrolyte membrane fuel cell stack.

[0007] Disclosure of the invention

[0008] This is achieved by the subject matter of the independent claims.

[0009] A method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell, wherein at least one membrane electrode assembly and plates are provided, between each of which a membrane electrode assembly is arranged, wherein a first model models inflows of process media from a periphery and outflows of a process product into the periphery and electrical input and output variables, wherein a second model models segments of the plates, wherein a third model models the membrane electrode assembly or segments of the membrane electrode assembly, wherein the first model and the second model are coupled via at least one coupling variable, wherein the second model and the third model are coupled segment-wise via at least one coupling variable, wherein at least one input variable of the first model is specified,wherein the state is determined with the at least one input variable, the first model, the second model and the third model.,

[0010] A membrane-electrode unit can be understood in particular as at least one ion-conducting layer, the so-called membrane, and at least one electrode layer arranged, in particular applied, on one side of the ion-conducting layer. Preferably, an electrode layer is 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. The membrane-electrode unit can preferably comprise further applied porous layers which serve to distribute (transport / removal) the reaction media, the current and / or the heat. The at least one ion-conducting layer (membrane) can here at least partially be made of an ion-conducting polymer (e.g.The electrode layers can be designed as a single-layer (e.g., for a PEM fuel cell, PEM electrolysis, AEM fuel cell, AEM electrolysis) and / or as an electrically non-conductive porous structure impregnated with an ion-conducting polymer and / or an ion-conducting liquid / solution (e.g., for liquid alkaline electrolysis or in the case of redox flow batteries) and / or as a ceramic ion conductor (e.g., in SOFC / SOEC). The electrode layers are typically porous layers that fulfill the combined functions of ion transport, electron transport, transport of the liquid and / or gaseous reaction media, heat transport, and electrocatalysis. Depending on the technology under consideration, these combined functions can be achieved with 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, etc.).) and / or ion conductors (polymeric ion conductors and / or liquid ion conductors and / or ceramic ion conductors). The membrane electrode assembly can comprise additional (usually porous) functional layers, which serve, for example, to distribute the reaction media (transport of liquid and / or gaseous reactants, removal of liquid and / or gaseous products) and / or to transport electrons and heat. Depending on the application, at least one of the layers of the membrane electrode assembly can also have mechanical functions, e.g., providing a spring effect or mechanical support for adjacent layers.

[0011] The method can also advantageously be used to determine a state in a redox flow cell or a redox flow cell stack. The method is particularly, but not exclusively, suitable for determining a state in a low-temperature PEM fuel cell, a high-temperature PEM fuel cell, a low-temperature PEM electrolysis cell, a high-temperature PEM electrolysis cell, an AEM fuel cell, an AEM electrolysis cell, an AEL electrolysis cell (classical liquid alkaline electrolysis), an SOFC, an SOEC, an MCFC / MCEC (molten carbonate fuel cell / electrolysis cell), and a PAFC / PAEC (phosphoric acid fuel cell / electrolysis).

[0012] In the following, the process is essentially explained using the example of the PEM fuel cell, but is basically transferable to any fuel cell, electrolysis and redox flow technologies.

[0013] Preferably, the second model models a physical effect for each segment or for a bundle of multiple segments. This further improves the simulation.

[0014] Preferably, the third model models a physical effect of the membrane electrode assembly or of each segment of the membrane electrode assembly. This enables a simulation with particularly low computing requirements.

[0015] Preferably, during operation of the stack, fuel cell, or electrolysis cell, a measurement is recorded that characterizes the operation, wherein the state during operation is determined depending on the measurement. This allows the operation to be influenced depending on a simulation result. Preferably, during operation, a variable for the operation, in particular an operating strategy, a control variable, or a controlled variable, is determined depending on the state during its operation, and the stack, fuel cell, or electrolysis cell is controlled depending on the variable. This allows the operation to be influenced depending on a simulation result.

[0016] Preferably, depending on the condition, particularly during operation, a variable is determined that characterizes irreversible aging of the stack, fuel cell, or electrolysis cell, or a part thereof, or that includes a prediction for maintenance of the stack, fuel cell, or electrolysis cell, or a part thereof. The simulation makes it possible to determine this information about the condition.

[0017] Preferably, a design parameter is determined for the stack, fuel cell, or electrolysis cell, or a part thereof, depending on the condition. This allows for a better design to be achieved more quickly.

[0018] Preferably, the first model, the second model and / or the third model comprises parameters, wherein training data is provided which each comprise at least one input variable for the first model and a reference for the state, wherein the respective states are determined from the training data using the at least one input variable, and wherein, depending on a deviation of the states from their respective reference from the training data, the parameters for which the deviation is as small as possible are determined, and wherein the state is subsequently determined depending on the predetermined at least one input variable of the first model.

[0019] A device, in particular a virtual sensor, for determining a state of a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell is designed to determine the state according to the method.

[0020] Further advantageous embodiments can be found in the following description and the drawing. The drawing shows: Fig. 1 a schematic representation of models for determining a state in a fuel cell stack,

[0021] Fig. 2 is a schematic representation of a stack of a polymer electrolyte membrane fuel cell,

[0022] Fig. 3 is a flowchart showing steps in a method for determining a state in the stack.

[0023] A polymer electrolyte membrane fuel cell converts hydrogen and oxygen into water, releasing electrical and thermal energy. A solid oxide fuel cell converts a fuel such as methane, releasing electrical and thermal energy.

[0024] The procedure is described below for stacks of polymer electrolyte membrane fuel cells. A corresponding procedure is provided for other types of fuel cells, electrolysis cells, or redox flow cells.

[0025] As mentioned above, the same procedure is followed, particularly for a solid oxide electrolysis cell or a polymer electrolyte membrane electrolysis cell.

[0026] The polymer electrolyte membrane fuel cell comprises a bipolar plate in a bipolar structure. The bipolar plate comprises a first electrode and a second electrode. Several bipolar plates are arranged in series to form a stack between two end plates. A proton-conducting polymer membrane is arranged between each pair of the bipolar plates in the stack. The stack is held together by the end plates. The two outer bipolar plates of the stack are each electrically connected by one of the end plates.

[0027] The polymer electrolyte membrane fuel cell comprises a monopolar plate instead of a bipolar plate in a monopolar design. The monopolar plate comprises an electrode. Several monopolar plates are arranged in series between two end plates to form a stack. In this example, each fuel cell contains a proton-conducting polymer membrane surrounded by an insulator layer outside its active area. The stack is held together by the end plates. The two outer monopolar plates of the stack are each electrically contacted by one of the end plates. Electrical contacts are also provided for monopolar plates arranged within the stack.

[0028] Bipolar plates and monopolar plates are referred to as plates below. In the case of bipolar plates, the number of plates is one greater than the number of membrane electrode assemblies. In the case of monopolar plates, the number of plates is twice the number of membrane electrode assemblies.

[0029] At least one channel is provided in the plate for supplying a first process medium, in particular process air. A channel can be understood in the narrower sense, and a channel in the broader sense as a continuous flow path, for example, in the form of a path through an open-pore material, such as in a PEM electrolysis cell.

[0030] At least one channel for supplying a second process medium, in particular process hydrogen, is provided in the plate.

[0031] At least one channel for a coolant, in particular water, is provided in the plate.

[0032] At least one channel for the removal of a process product, in particular process air and product water, is provided in the plate.

[0033] Figure 1 shows exemplary models for determining a state 100 in the stack. Figure 1 shows a first model 102 for a periphery of the stack, as well as a second model 104 and a third model 106 for at least one segment in the stack. In one example, the segment comprises at least part of an anode channel, at least part of a membrane electrode assembly, at least part of a cathode channel, and at least part of a coolant channel. This means that the segment at least partially comprises two plates and a membrane electrode assembly. In the example, the second model 104 models at least part of the anode channel, at least part of the cathode channel, and at least part of the coolant channel made up of two plates. In the example, the third model 106 models at least part of the membrane electrode assembly.

[0034] The first model 102 is coupled to the second model 104 via at least one first coupling variable 108. The second model 104 is coupled to the first model 102 via at least one second coupling variable 110. In the example, the two plates in the second model 104 are combined as one plate, with only one coupling variable per direction being provided for both plates, i.e., the first coupling variable 108 and the second coupling variable. It can be provided that two segments are modeled per plate in the second model 104, each of which is coupled via its own coupling variable in each direction.

[0035] The second model 104 is coupled to the third model 106 via at least a third coupling variable 112. The third model 106 is coupled to the second model 104 via at least a fourth coupling variable 114. In the example, a virtual sensor 116 is provided, which detects the state 100. In the example, the virtual sensor 116 is coupled to the third model 106 via at least a fifth coupling variable 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.

[0036] The second model 104 is designed to model physical processes, in particular transport processes, in the plate. In the example, the second model 104 models discrete segments in the plate. The transport processes take place, on the one hand, in a plane of the plate between the segments and, on the other hand, in a plane perpendicular to the plate between each segment or a bundle of segments from and to a membrane electrode assembly. In the example, the transport processes in the plate are modeled in these planes using the second model 104. The transport processes in the membrane electrode assembly are modeled using the third model 106.

[0037] The second model 104 is designed, for example, to model heat transport, coolant transport, gas transport, and an electrical potential in one segment each or a bundle of such segments. In particular, media supply, supply of process media, in particular reaction gases, removal of process products, in particular liquid water, especially in a PEM fuel cell, and / or heat, and electrical voltage are represented by generalized resistors. These resistors are connected to form resistor networks. The resistors can be linear or nonlinear. Furthermore, the underlying resistors can be specified from physical models. The physical models are, for example, discretized, e.g., by finite volumes. The physical models are, for example, pre-generated tables or data-based models.

[0038] A segment is a discretization point and includes, for example, a channel of a certain length. The segment can also include multiple channels.

[0039] The physical processes within a segment are represented, for example, by a representative element, such as a single channel or a representative channel bundle.

[0040] For example, the following variables can be determined in a segment: a gas concentration, a partial pressure, an electrical voltage, a plate temperature, a gas temperature, a liquid water saturation, a coolant temperature, and a coolant pressure. These variables are examples. Other variables can also be determined.

[0041] For the second model 104, mathematical descriptions of the relationships can be used, e.g. for gas transport a description of two-phase flow according to Darcy or Poisseuille, for electrical voltage a description according to Ohm's law, for the plate temperature a description according to the heat conduction equation, for the coolant a description as incompressible flow.

[0042] In the example, the third model 106 comprises a membrane electrode assembly model per segment in the plane perpendicular to the plate. This membrane electrode assembly model can be implemented in varying degrees of complexity. The third model 106 is, for example, a one-dimensional model for the approximate determination of inhomogeneous current distributions in the stack and for determining a corresponding gas conversion.

[0043] The third model 106, for example, is a two-dimensional model for determining internal states of the membrane electrode assembly.

[0044] The third model 106, for example, is a three-dimensional model for evaluating processes in the microstructure of a membrane electrode assembly. The processes include, for example, flow effects along a channel flow direction.

[0045] In one example, the membrane electrode unit model models the physics of the membrane electrode unit in detail, with various internal states such as membrane humidity or saturation being automatically calculated. The at least one fifth coupling variable 118 comprises, for example, at least one of these internal states. This makes it possible to determine, for example, aging in a segment assigned to this membrane electrode unit. By abstracting into segments, the membrane electrode unit model can be implemented in one-dimensional, two-dimensional, or three-dimensional form.

[0046] The third model 106 models the membrane electrode assembly physics in the case of a PEMFC, e.g. according to LM Pant 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).

[0047] The at least one third coupling variable 112 is, for example, a gas species concentration, a bipolar plate temperature, or an electrical potential. The at least one fourth coupling variable 114 is, for example, a material flow, a heat flow, or an electrical current. The third coupling variable 112 and / or the fourth coupling variable 114 couple the segments or bundles of segments to the membrane electrode assembly models.

[0048] For a PEM fuel cell, an implementation of the third model 106 using partial differential equations, in which the third coupling quantity 112 and the fourth coupling quantity 114 are each implemented for an anode and a cathode, is disclosed 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) arXiv: 1811.10091:

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

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

[0051] In equation (13), the gas transport is calculated using the Maxwell-Stefan equation, where the gas concentration represents the third coupling variable 112 and the material flow the fourth coupling variable 114.

[0052] Furthermore, in this formulation, proton conduction in an ionomer is calculated using equation (1), water transfer in the ionomer is calculated using equation (9), adsorption / desorption is calculated using equation (22), evaporation / condensation is calculated using equation (23), reaction kinetics is calculated using equation (1), and contact resistances are calculated using equation (S24).

[0053] The first model 102 comprises, for example, a collecting node for a resistor network. The first model 102 is designed, for example, to represent an inhomogeneity between cells of the stack. In this example, the manifold, i.e. the inlet for process media and the outlet for a process product, and the end plates of the stack are combined. It can be provided that one model is used for the inlet and outlet and a separate model for the end plates. It can be provided that separate models are used for the inlet, outlet, and end plates. The first model 102 is designed, for example, to take into account the thermal and electrical behavior of the entire stack. The first model 102 is designed, for example, to model an inhomogeneity of fluids across the channels.

[0054] In the example, the first model 102 is assigned to segments at the edge of the plate in addition to a membrane-electrode unit model. Corresponding first and second coupling variables model a media supply, gas species flow, and a gas temperature. The media supply is modeled, for example, by mass flows from the periphery into the segment or from the segment to the periphery, an operating pressure, an outlet pressure, and / or a coolant temperature. This is done, for example, by means of a numerical calculation of fluid mechanics and a subsequent extraction of generalized resistances.

[0055] In the example, the first model 102 comprises at least one end plate model assigned to segments where the end plates are arranged. Corresponding first and second coupling variables model the conversion of electrical demands into electrical currents in the respective segments.

[0056] In order to calculate the entire stack using this discretization, several individual cells of the stack can be combined to form representative cell bundles. The cell bundle has different properties compared to the individual cell. For example, a resistance in the plane causes a compensating current that is mapped. The combination also affects the first model 102. In this case, the first model 102 is designed with appropriate coupling variables for cell bundles. This creates a resistance network for the entire stack that provides local information about the internal states of the membrane-electrode assembly and the plates across the entire stack. There is no limit to the permissible number of cells in a cell bundle. The number of cells is determined, for example, according to the accuracy requirements of the application.

[0057] The choice of cell bundles and number of segments is a trade-off between accuracy and computing time. The segments can be rectangular, or in particular square. Other geometric shapes are also possible. In the example, one geometric requirement for the segments is that they allow the plate to be completely subdivided. In one example, the segments combine several channels into a channel bundle. The number of combined channels can range from one to all of the channels in the plates. In particular, the choice of just one channel bundle is sufficient if the expected performance differences perpendicular to a flow direction in the channels are small or of little relevance to the question under investigation. Otherwise, it may be necessary to consider several channel bundles. A channel bundle, for example, comprises 10 or more channels.

[0058] The cells at the edge of the stack are preferably integrated in smaller cell bundles than other cell bundles that include cells from the middle of the stack, since the temperature profiles there often differ from those in the cells in the middle of the stack.

[0059] A further discretization is chosen, for example, depending on use cases:

[0060] For simple questions such as polarization curves or operating strategies with non-aging relevant conditions, a selection of e.g. 5-20 segments along the flow channels, as well as e.g. 1-10 cell bundles is sufficient.

[0061] For age-related questions in which local internal states of a membrane electrode assembly must be mapped very accurately, a number of segments of, for example, 100 or more segments along the flow direction of the channels is also advantageous.

[0062] The first model 102, the second model 104, and the third model 106 comprise parameters. The models, in particular, comprise mutually coupled partial differential equations or are defined as analytical functions or as one or more neural networks. The parameters define the models, i.e., the differential equations, the analytical functions, or neural networks. The differential equations, analytical functions, and neural networks model electrochemical or physical effects in the stack. The differential equations and analytical functions comprise electrochemical or physical quantities. The differential equations and analytical functions can also comprise state quantities for which there is neither an electrochemical nor a physical equivalent in the stack.The neural networks comprise inputs for electrochemical or physical quantities and outputs for electrochemical or physical quantities. The differential equations, or analytical functions, or neural networks are coupled via the coupling quantities.

[0063] Depending on the state 100 to be modeled, the differential equations and the neural networks can include various variables, coupling variables, and / or parameters. Examples of the variables and coupling variables are described below. In one example, the models—that is, the differential equations, the analytical functions, or the neural networks—are solved in a fully coupled manner to determine state 100. In one example, one or more explicit couplings may be provided.

[0064] The first model 102 optionally has an interface 124 for parameterizing the first model 102. The second model 104 optionally has an interface 126 for parameterizing the second model 104. The third model 106 optionally has an interface 128 for parameterizing the third model 106.

[0065] With these interfaces, the parameters of the respective models can be changed for calibration purposes.

[0066] Training may be provided for the purpose of data acquisition. During training, training data is provided, each of which includes at least one input variable for the first model 102 and a reference for the state 100. The reference specifies which state 100 is to be modeled with the models and the respective input variables.

[0067] The respective states 100 are determined using at least one input variable from the training data.

[0068] For example, the parameters are determined depending on a deviation of the states 100 from their respective reference from the training data.

[0069] For example, an optimization method that minimizes the deviation determines the parameters for which the deviation is as small as possible. The deviation is as small as possible when, for example, the mean value of the deviations for the training data is minimal. In an inference, the state 100 is then determined depending on the parameters determined during training and at least one predefined input variable of the first model 102.

[0070] Figure 2 schematically shows membrane electrode assemblies 202 arranged in a fuel cell stack 204. The stack 204 has two ends 206, between which plates 208 are arranged. The stack 204 is electrically contacted at each of its ends 206 by an end plate 210. These are arranged on opposite end faces of the stack 204. A first of the plates 208 of the stack 204 is electrically connected to a first of the end plates 210, and a last of the plates 208 of the stack 204 is electrically connected to a second of the end plates 210.

[0071] The second model 104 models segments 208-1 of the plate 208. The plates 208 include channels 208-2. Each segment 208-1 includes a portion of one of the channels 208-2 or portions of several channels 208-2.

[0072] An inlet 212 is arranged on the side of stack 204, through which channels 208-2 of stack 204 are supplied with process media. A process product is discharged from channels 208-2 of stack 204 via an outlet 214. Outlet 214 is arranged on the side of stack 204 opposite inlet 212.

[0073] With the first model 102, inflows or outflows into the stack 204 are modeled depending on at least one input variable of the first model 102.

[0074] The first model 102 models electrical input and output variables of the stack 204 depending on at least one input variable of the first model 102.

[0075] The segments 208-1 of the plates 208 of the stack 204 are modeled in the second model 104. Physical effects in the segments 208-1 are modeled using the second model 104.

[0076] The third model 106 models physical effects in membrane electrode elements 202 or in segments 202-1 of the membrane electrode elements 202. The segments 202-1 of a membrane electrode element 202 are each assigned, for example, to a segment 208-1 of the two plates 208 adjacent to the membrane electrode element 202, wherein adjacent segments 202-1 can be assigned to one another.

[0077] These segments can be decoupled or coupled to one another. In the example, the number of segments in the second model 104 is equal to the number of segments in the third model 106. This represents a conformal discretization. The segments in the second model 104 are connected to the segments in the third model 106, for example, via the third coupling variable 112. The segments in the third model 104 are connected to the segments in the second model 106, for example, via the fourth coupling variable 114. The number of segments in the second model 104 can differ from the number of segments in the third model 106. This represents a non-conforming discretization. A connection of the segments in the second model 104 to the segments in the third model 106 is made, for example, via a correspondingly adapted third coupling variable 112. A connection of the segments in the third model 106 to the segments in the second model 104 is made, for example, via a correspondingly adapted fourth coupling variable 114.

[0078] In the example, the third model 106 comprises a one-dimensional, a two-dimensional, or a three-dimensional model for each segment 202-1, with which boundary conditions from the segment 208-1 of the plate 208 assigned to this segment 202-1 are modeled. This achieves a significant saving in computing time.

[0079] Figure 3 describes steps in a method for determining the state 100 in the stack 204. The method explained below using the polymer electrolyte membrane fuel cell as an example can be applied analogously to any fuel cell, electrolysis, and redox flow technologies. In a step 302, at least one input variable of the first model 102 is determined, e.g., from a predetermined measurement on a membrane electrode assembly installed in the polymer electrolyte membrane fuel cell. In one example, the measurement is recorded during operation of the polymer electrolyte membrane fuel cell. In the example, the measurement characterizes the operation of the polymer electrolyte membrane fuel cell, i.e., the measurement comprises at least one measurable variable that characterizes the operation.

[0080] In a step 304, the state 100 is determined using the at least one input variable, the first model 102, the second model 104, and the third model 106. The state 100 is detected, for example, using the virtual sensor 116.

[0081] In the example, the virtual sensor 116 detects at least one fifth coupling variable 118. In the example, the first model 102, the second model 104, and the third model 106 are solved in a fully coupled manner for the at least one input variable of the first model 102. The models are coupled via the respective coupling variables.

[0082] A step 306 is then executed.

[0083] In one example, in step 306, the at least one output variable of the first model 102 is output. The output variable is, for example, a calculated variable for a variable contained in the measurement. It may be provided that these variables are compared with each other.

[0084] In step 306, in one example, depending on the state 100, a variable for the operation of the stack 204, in particular an operating strategy, a control variable, or a controlled variable, is determined, and the stack 204 is controlled depending on the variable. The variable is determined, for example, during the operation of the stack 204 depending on a measurement on the stack 204 that is recorded during the operation of the stack 204. The stack 204 is controlled with the variable, for example, during its operation. In step 306, in one example, depending on the state 100 of the stack 204, a variable is determined that characterizes irreversible aging of the stack 204 or one of its parts. The state 100 and / or the variable is determined, for example, during operation.

[0085] In step 306, in one example, a variable is determined depending on the state 100 of the stack 204, which variable comprises a prediction for maintenance of the stack 204 or one of its parts. The state 100 and / or the variable is determined, for example, during operation.

[0086] In step 306, in one example, a design parameter for the stack 204 or one of its parts is determined depending on the state 100 of the stack 204. Steps 302 to 306 are repeated multiple times, for example, in a design process, with a plurality of design parameters being determined. For example, different designs are simulated using different parameters of the second model 104 and / or the third model 106.

[0087] The state 100 is determined, for example, depending on the at least one input variable for the first model 102.

[0088] The following describes exemplary applications for exemplary states. In the example, with regard to stack 204, a distinction is made between a fuel cell stack of the polymer electrolyte membrane fuel cell and an electrolysis stack of the polymer electrolyte membrane electrolysis cell. The fuel cell stack comprises fuel cells. The electrolysis stack comprises electrolysis cells.

[0089] The same procedure applies to a redox flow battery, a solid oxide fuel cell or a solid oxide electrolysis cell.

[0090] The conditions relate, for example, to the fuel cell stack, fuel cells therein, or parts thereof. In addition to the parts of the fuel cell stack already described, 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 the first process medium, at least one inlet for the second process medium, at least one membrane, and / or at least one gas diffusion medium.

[0091] For example, state 100 is an internal state of the fuel cell stack.

[0092] The following variables are, for example, input variables of the fuel cell stack or indicate the internal state of the fuel cell stack: a gas composition of anode gas, a gas composition of cathode gas, a gas pressure of anode gas at an outlet in the fuel cell stack for this, a gas mass flow of anode gas at an outlet in the fuel cell stack for this, a gas pressure of cathode gas at an outlet in the fuel cell stack for this, a gas mass flow of cathode gas at an outlet in the fuel cell stack for this, a gas pressure of anode gas at an inlet in the fuel cell stack for this, a gas mass flow of anode gas at an inlet in the fuel cell stack for this, a gas pressure of cathode gas at an inlet in the fuel cell stack for this, a gas mass flow of cathode gas at an inlet in the

[0093] Fuel cell stack therefor, a gas temperature of anode gas, a gas temperature of cathode gas, a temperature of the coolant, a mass flow of the coolant, an electrical voltage generated by the fuel cell stack, an electrical current generated by the fuel cell stack.

[0094] The first model 102 optionally includes a thermal model that models an ambient condition of the fuel cell stack, e.g., a temperature and / or a relative humidity of the ambient air of the fuel cell stack.

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

[0096] For example, the state 100 is an inhomogeneity of an internal state of at least one channel for gas, at least one channel for coolant or a structure of the plate in the fuel cell stack.

[0097] For example, the state 100 is an operating state of the membrane electrode unit of a fuel cell or of several fuel cells of the fuel cell stack, for example 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.

[0098] The state 100 is determined, for example, for drying, for a short-term overload or in a transient operation of the fuel cell.

[0099] The state 100 is, for example, a state within a fuel cell or within the fuel cell stack for water management in the fuel cell stack, e.g., a temperature, a gas composition, a saturation, a liquid water content, a water transfer through the polymer electrolyte membrane fuel cell.

[0100] For example, state 100 is a state that occurs when the fuel cell stack is started or stopped.

[0101] For example, state 100 is a state that is important during a freeze start. In one example, the freeze start is detected depending on a temperature, and state 100 is recorded during the freeze start.

[0102] State 100 is, for example, a local state in the fuel cell stack. In one example, an effect of production fluctuations on the local state is determined depending on state 100. In one example, state 100 is a local temperature distribution or a local gas composition within a fuel cell and / or the fuel cell stack.

[0103] In one example, the state 100 is a local saturation within a fuel cell and / or the fuel cell stack in at least one porous layer, in particular at least one gas diffusion layer, at least one microporous layer, at least one catalyst layer and / or at least one gas channel.

[0104] In one example, state 100 is a local current density distribution or a local voltage distribution within a fuel cell and / or the fuel cell stack.

[0105] In one example, state 100 is a local water content in a membrane of the fuel cell stack.

[0106] In one example, state 100 is at least one local potential in at least one catalyst layer of the fuel cell stack.

[0107] In one example, state 100 is a local contribution of a local reaction to a total voltage or current delivered by the fuel cell stack. For example, state 100 is recorded for various local reactions, and the total voltage and current are determined based on the recorded states.

[0108] In one example, state 100 is local irreversible aging, in particular ionomer aging in the fuel cell stack. The irreversible aging is, for example, catalyst aging of a catalyst layer of the fuel cell stack and / or membrane aging in at least one membrane of the fuel cell stack and / or aging in at least one gas diffusion support of the fuel cell stack and / or aging in at least one microporous layer of the fuel cell stack. In one example, state 100 is local irreversible aging in a membrane electrode assembly. For example, different states 100, i.e., different local irreversible agings, are detected for the membrane electrode assembly, and the irreversible aging of the membrane electrode assembly is determined depending on the detected states.

[0109] In one example, the state 100 is detected and a particularly optimal operating strategy is determined depending on the state 100. For example, the operating strategy is determined taking into account the efficiency and / or lifetime of the fuel cell stack.

[0110] In one example, an optimal design of the fuel cell stack for achieving a desired efficiency and / or lifetime is determined depending on the state 100.

[0111] In one example, state 100 is determined for a plurality of different fuel cell stack designs, and the optimal design is selected from the plurality based on state 100. This enables a cost-efficient design process.

[0112] In one example, an actual state of the fuel cell stack is determined depending on state 100. State 100 is determined, for example, depending on a measurement on the fuel cell stack.

[0113] The actual state is, for example, a state of the fuel cell stack during its operation. State 100 is determined, for example, based on a measurement of the fuel cell stack acquired during its operation. For example, the measurement is acquired during the operation of the fuel cell stack, and state 100 is determined during the operation of the fuel cell stack based on the measurement.

[0114] In one example, a control variable or a controlled variable for the operation of the fuel cell stack is determined depending on the state 100. The control variable or the controlled variable is determined, for example, during operation of the fuel cell stack. The control variable or the controlled variable is determined, for example, depending on the measurement. For example, the measurement is recorded during operation of the fuel cell stack, the state 100 is determined during operation of the fuel cell stack depending on the measurement, the control variable or the controlled variable is determined depending on the state 100 during operation of the fuel cell stack, and the fuel cell stack is controlled depending on the control variable or the controlled variable.

[0115] In one example, a prediction for maintenance of the fuel cell stack is determined depending on the state 100.

[0116] In one example, depending on the state 100, a prediction is determined for an adaptive change in at least one operating condition with which a lifetime and / or performance during operation of the fuel cell stack can be influenced.

[0117] In one example, depending on the state 100, at least one input variable for the fuel cell stack is estimated using a system model that models a system in which the fuel cell stack is operated, e.g., a gas composition.

[0118] The following describes use cases involving electrolysis in an electrolysis stack. The electrolysis stack includes electrolysis cells. The electrolysis stack comprises an electrolysis stack, which in the example includes the electrolysis cells.

[0119] For example, state 100 is an internal state of the electrolysis stack.

[0120] The following quantities are, for example, input quantities of the electrolysis stack or indicate the internal state of the electrolysis stack: a composition of anode and cathode fluids, a pressure of an anode fluid at an inlet of the electrolysis stack therefor, a pressure of an anode fluid at an outlet of the electrolysis stack therefor, a pressure of a cathode fluid at an inlet of the electrolysis stack therefor, a pressure of a cathode fluid at an outlet of the electrolysis stack therefor, a mass flow of the anode fluid at the inlet of the electrolysis stack therefor, a mass flow of the anode fluid at the outlet of the electrolysis stack therefor, a mass flow of the cathode fluid at the inlet of the electrolysis stack therefor, a mass flow of the cathode fluid at the outlet of the electrolysis stack therefor, a temperature of the anode fluid, a temperature of the cathode fluid, an electrical voltage generated by the electrolysis stack, an electrical current generated by the electrolysis stack.

[0121] For example, state 100 is an inhomogeneity of an internal state of an electrolysis cell in the electrolysis cell stack.

[0122] For example, the state 100 is an inhomogeneity of an internal state of at least one channel for gas, at least one channel for coolant or a structure of the plate in the electrolysis cell stack.

[0123] For example, the state 100 is an operating state of the membrane electrode unit of an electrolysis cell or several electrolysis cells, for example the operating state of at least one porous transport layer, at least one catalyst layer and / or the polymer electrolyte membrane electrolysis cell.

[0124] The polymer electrolyte membrane electrolysis cell, for example, is part of an electrolyzer. The state 100 is determined, for example, during transient operation of the electrolyzer.

[0125] In one example, the state 100 is an operating state of the electrolysis cell stack, in particular during load balancing, and is determined, for example, during operation of the electrolysis cell stack in overload.

[0126] In one example, state 100 is an operating state of the electrolysis cell stack that occurs when the electrolyzer is started or shut down. State 100 is, for example, a local state in the electrolysis cell stack. In one example, the effect of production fluctuations on the local state is determined depending on state 100.

[0127] In one example, state 100 is a local temperature distribution in the electrolytic cell stack or in an electrolytic cell.

[0128] In one example, state 100 is a local fluid composition within an electrolysis cell and / or the electrolysis cell stack.

[0129] In one example, the state 100 is a local saturation, ie a distribution of liquid and gas phases, in at least one porous layer, in particular in at least one porous transport layer, at least one catalyst layer and / or in at least one of the channels for a fluid.

[0130] In one example, state 100 is a local current density distribution or a local voltage distribution.

[0131] In one example, state 100 is a local current density distribution or a local voltage distribution within an electrolytic cell and / or the electrolytic cell stack.

[0132] In one example, state 100 is at least a local potential in at least one catalyst layer of the electrolysis cell stack.

[0133] In one example, state 100 is a local contribution of a local reaction to a total voltage or current delivered by the electrolysis cell stack. For example, state 100 is recorded for various local reactions, and the total voltage and current are determined based on the recorded states.

[0134] In one example, state 100 is local irreversible aging, in particular ionomer aging in the electrolysis cell stack. The irreversible aging is, for example, catalyst aging of a catalyst layer of the electrolysis cell stack and / or membrane aging in at least one membrane of the electrolysis cell stack and / or aging in at least one gas diffusion support of the electrolysis cell stack and / or aging in at least one microporous layer of the electrolysis cell stack.

[0135] In one example, state 100 is a local irreversible aging process in a membrane electrode assembly. For example, different states 100, i.e., different local irreversible aging processes, are detected for the membrane electrode assembly, and the irreversible aging process of the membrane electrode assembly is determined based on the detected states.

[0136] In one example, the state 100 is detected and a particularly optimal operating strategy is determined depending on the state 100. For example, the operating strategy is determined taking into account the efficiency and / or lifetime of the electrolysis cell stack.

[0137] In one example, an optimal design of the electrolysis cell stack for achieving a desired efficiency and / or lifetime is determined depending on the state 100.

[0138] In one example, state 100 is determined for a plurality of different electrolysis cell stack designs, and the optimal design is selected from the plurality based on state 100. This enables a cost-efficient design process.

[0139] In one example, an actual state of the electrolysis cell stack is determined depending on the state 100. The state 100 is determined, for example, depending on a measurement on the electrolysis cell stack.

[0140] The actual state is, for example, a state of the electrolysis cell stack during its operation. The state 100 is determined, for example, depending on a measurement on the electrolysis cell stack that is recorded during its operation. For example, the measurement is recorded during operation of the electrolysis cell stack, and the state 100 is determined during operation of the electrolysis cell stack depending on the measurement. In one example, a control variable or a controlled variable for the operation of the electrolysis cell stack is determined depending on the state 100. The control variable or the controlled variable is determined, for example, during operation of the electrolysis cell stack. The control variable or the controlled variable is determined, for example, depending on the measurement.For example, the measurement is recorded during operation of the electrolysis cell stack, the state 100 during operation of the electrolysis cell stack is determined depending on the measurement, the control variable or the controlled variable is determined depending on the state 100 during operation of the electrolysis cell stack, and the electrolysis cell stack is controlled depending on the control variable or the controlled variable.

[0141] In one example, a prediction for maintenance of the electrolysis cell stack is determined depending on the state 100.

[0142] In one example, depending on the state 100, a prediction is determined for an adaptive change in at least one operating condition with which a lifetime and / or performance during operation of the electrolysis cell stack can be influenced.

[0143] In one example, depending on state 100, at least one input variable for the electrolysis cell stack is estimated using a system model that models a system in which the electrolysis cell stack is operated, e.g., a water conductivity or a circulating residual gas. The water conductivity changes, for example, due to contamination during operation.

Claims

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 an electrolysis cell, wherein at least one membrane electrode assembly (202) and plates (208) are provided, between each of which a membrane electrode assembly (202) is arranged, wherein inflows of process media from a periphery and outflows of a process product into the periphery and electrical input and output variables are modeled with a first model (102), wherein segments (208-1) of the plates (208) are modeled with a second model (104), wherein the membrane electrode assembly (202) or segments (202-1) of the membrane electrode assembly (202) are modeled with a third model (106), wherein the first model (102) and the second model (104) are coupled via at least one coupling variable (108, 110), wherein the second model (104) and the third model (106) segmentally over at least one coupling size (112,114), wherein at least one input variable of the first model (102) is predetermined (302), wherein the state (100) is determined (304) with the at least one input variable, the first model (102), the second model (104) and the third model (106)., 2. Method according to claim 1, characterized in that the second model (104) models a physical effect for each segment (208-1) or for a bundle of several segments (208-1).

3. Method according to claim 1 or two, characterized in that the third model (106) models a physical effect of the membrane electrode unit (202) or of each segment (202-1) of the membrane electrode unit (202).

4. Method according to one of the preceding claims, characterized in that during operation of the stack (204), the fuel cell or the electrolysis cell, a measurement is recorded (302) which determines the operation characterized, whereby the state (100) is determined during operation depending on the measurement (304).

5. The method according to claim 4, characterized in that during operation a variable for the operation, in particular an operating strategy, a control variable or a controlled variable, is determined depending on the state (100) during its operation, and the stack (204), the fuel cell or the electrolysis cell is controlled depending on the variable (306).

6. The method according to claim 4 or 5, characterized in that depending on the state (100), in particular during 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. Method according to one of the preceding claims, characterized in that depending on the state (100) a design parameter for the stack (204), the fuel cell or the electrolysis cell or a part thereof is determined (306).

8. Method according to one of the preceding claims, characterized in that the first model (102), the second model (104) and / or the third model (106) comprise parameters, wherein training data is provided which each comprise at least one input variable for the first model (102) and a reference for the state (100), wherein the respective states (100) are determined using the at least one input variable from the training data, and wherein, depending on a deviation of the states (100) from their respective reference from the training data, the parameters for which the deviation is as small as possible are determined, and wherein the state (100) is then determined depending on the predetermined at least one input variable of the first model (102).

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

10. Computer program, characterized in that the A computer program comprising computer-readable instructions, when executed by a computer, a method according to any one of claims 1 to 8 is carried out.

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