Apparatus and method for determining the state in a stack of fuel cells or electrolytic cells, or in a fuel cell or electrolytic cell.

Abstract

An apparatus and method for determining a state (100) in a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell, comprising membrane electrode units and plates between which one membrane electrode unit is arranged, wherein a first model (102) is used to model the inflow of process medium from the periphery and the outflow of process products to the periphery and electrical input and output variables, a second model (104) is used to model the segments of the plates, and a third model (106) is used to model the membrane electrode units or the plates. Segments of the membrane electrode unit are modeled, a first model (102) and a second model (104) are coupled via at least one coupling variable (108, 110), and the second model (104) and a third model (106) are coupled for each segment via at least one coupling variable (112, 114), at least one input variable of the first model (102) is predetermined, and a state (100) is determined using the at least one input variable, the first model (102), the second model (104), and the third model (106).

Description

Technical Field

[0001] The present invention relates to an apparatus and a method for determining the state in a stack of fuel cells or electrolytic cells, or in a fuel cell or an electrolytic cell.

Background Art

[0002] In the design of a polymer electrolyte membrane fuel cell, its behavior can be confirmed in a simulation considering the geometric shape of the fuel cell. Since this simulation requires a large amount of computing resources, 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 (and) in a stack of polymer electrolyte membrane fuel cells.

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

[0004] A method for determining the state in a stack of fuel cells or electrolytic cells, or in a fuel cell or an electrolytic cell, comprising at least one membrane electrode unit and plates disposed between each of the membrane electrode units, wherein, using a first model, the inflow of a process medium from a peripheral part and the outflow of a process product to the peripheral part are modeled together with electrical input variables and output variables, using a second model, segments of the plates are modeled, using a third model, a membrane electrode unit or segments of the membrane electrode unit are modeled, the first model and the second model are coupled via at least one coupling variable, the second model and the third model are coupled segmentwise via at least one coupling variable, at least one input variable of the first model is predefined, and the state is determined using 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-conductive layer, so-called membrane, and at least one electrode layer, particularly mounted, positioned on one side of the ion-conductive layer. In particular, electrode layers are positioned or mounted on both sides of the ion-conductive layer, thereby positioning the ion-conductive layer (particularly in a sandwich configuration) between the two electrode layers. Preferably, the membrane electrode unit may include other mounted porous layers, which are used to distribute (import / export) reaction media, electric current, and / or heat. In this case, the at least one ion-conductive layer (membrane) can be formed at least partially as an ion-conductive polymer (e.g., for PEM fuel cells, PEM electrolytes, AEM fuel cells, and AEM electrolytes), and / or as an electrically non-conductive porous structure impregnated with an ion-conductive polymer and / or an ion-conductive liquid / solution (e.g., for liquid alkaline electrolytes, or in the case of redox flow batteries), and / or as a ceramic ion conductor (e.g., in the case of SOFC / SOEC). Electrode layers are typically porous layers that perform combination functions, namely ion transport, electron transport, transport of liquid and / or gaseous reaction media, heat transport, and electrocatalysis. Depending on the technology under consideration, these combination functions can consist of any combination of electrocatalytically active materials (metals and / or metal oxides and / or ceramic materials) and / or electronically conductive (porous) carrier materials (metal / carbon materials, doped metal oxides, etc.) and / or ion conductors (polymer ion conductors and / or liquid ion conductors and / or ceramic ion conductors). A membrane electrode unit may include other (usually porous) functional layers used, for example, for the distribution of reaction media (introduction of liquid and / or gaseous starting materials, excretion of liquid and / or gaseous products) and / or for electron and heat transport. Depending on the application, at least one of the layers of the membrane electrode unit may also have a mechanical function, such as providing spring action or mechanical support for adjacent layers.

[0006] The method can also be advantageously used to determine the state in a redox flow cell or redox flow cell stack. The method is particularly suitable for determining the state in NT-PEM fuel cells, HT-PEM fuel cells, NT-PEM electrolytic cells, HT-PEM electrolytic cells, AEM fuel cells, AEM electrolytic cells, AEL electrolytic cells (classical alkaline electrolyte), SOFCs, SOECs, MCFCs / MCECs (molten carbonate fuel cells / electrolytic cells), and PAFCs / PAECs (phosphate fuel cells / electrolytes), but is not limited to these. [Overview of the project]

[0007] In the following, the method will be explained using a PEM fuel cell as an example, but it is essentially applicable to any fuel cell, electrolyte, and redox flow technology.

[0008] In particular, the second model is used to model the physical effects of each segment, or a bundle of segments. This further improves the simulation.

[0009] In particular, the third model is used to model the physical effects of the membrane electrode unit, or each segment of the membrane electrode unit. This enables simulations with particularly low computational resource requirements.

[0010] In particular, during the operation of a stack, fuel cell, or electrolytic cell, measurements characterizing the operation are detected, and the operating state is determined based on these measurements. This makes it possible to influence the operation based on the results of simulations.

[0011] In particular, during operation, the variables for operation, especially the operation strategy, control variables, or control set, are determined depending on the operating state, and the stack, fuel cell, or electrolytic cell is controlled depending on these variables. Thus, the operation is influenced depending on the results of the simulation.

[0012] In particular, variables are determined that characterize the irreversible aging degradation of the stack, fuel cell, or electrolytic cell, or parts thereof, especially depending on the operating conditions, or that include forecasts for maintenance of the stack, fuel cell, or electrolytic cell, or parts thereof. Simulation makes it possible to determine this information about the conditions.

[0013] In particular, the design parameters of the stack, fuel cell, or electrolytic cell, or parts thereof, are determined depending on the state. This allows for the achievement of better designs more quickly.

[0014] In particular, the first model, the second model, and / or the third model include parameters, each provided with training data that includes at least one input variable and a state criterion of the first model, the respective state is determined using at least one input variable from the training data, the parameter with the smallest possible deviation is determined depending on the deviation of the state from each state criterion from the training data, and then the state is determined depending on at least one predetermined input variable of the first model.

[0015] A device for determining the state of a stack of fuel cells or electrolytic cells, or in a fuel cell or electrolytic cell, particularly a virtual sensor, is configured to determine the state by means of a method.

[0016] Other advantageous embodiments can be found in the following description and drawings. [Brief explanation of the drawing]

[0017] [Figure 1] This is a schematic diagram of a model for determining the state of a fuel cell stack. [Figure 2] This is a schematic diagram of a polymer electrolyte membrane fuel cell stack. [Figure 3] This is a flowchart of the steps involved in determining the state of a stack. [Modes for carrying out the invention]

[0018] Polymer electrolyte membrane fuel cells convert hydrogen and oxygen into water under the supply of electrical and thermal energy. Solid oxide fuel cells convert fuels such as methane under the supply of electrical and thermal energy.

[0019] The following description details the procedure for a stack consisting of a polymer electrolyte membrane fuel cell. Appropriate procedures are intended for other types of fuel cells, electrolytic cells, or redox flow cells.

[0020] In particular, solid oxide type electrolytic cells or polymer electrolyte membrane electrolytic cells are treated accordingly as described above.

[0021] A polymer electrolyte membrane fuel cell includes bipolar plates in a bipolar structure. Each bipolar plate contains a first electrode and a second electrode. Multiple bipolar plates are arranged in series between two end plates to form a stack. A proton-conducting polymer membrane is placed between each pair of bipolar plates in the stack. The stack is held in place by the end plates. Both outer bipolar plates of the stack are electrically connected by one end plate on each side.

[0022] In a monopolar structure, a polymer electrolyte membrane fuel cell includes monopolar plates instead of bipolar plates. The monopolar plates contain electrodes. Multiple monopolar plates are arranged in series between two end plates to form a stack. Each fuel cell, in this example, contains a proton-conducting polymer membrane, the outside of which is surrounded by an insulating layer. The stack is held together by the end plates. Both outer monopolar plates of the stack are electrically connected by one of each end plate. In addition, electrical contacts are provided for the monopolar plates located within the stack.

[0023] 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 greater 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.

[0024] The plates are provided with at least one channel for the supply of a first process medium, in particular process air. In this case, the channel can be understood as a channel in the narrow sense and as a continuous flow path in the broad sense, for example in the form of a passage, by an open porous material such as in a PEM electrolysis cell.

[0025] The plates are provided with at least one channel for the supply of a second process medium, in particular process hydrogen.

[0026] The plates are provided with at least one channel for a coolant, in particular water.

[0027] The plates are provided with at least one channel for the discharge of process products, in particular process air and generated water.

[0028] In FIG. 1, an exemplary model 100 for determining the state in the stack is shown. In FIG. 1, there is a first model 102 for the peripheral part of the stack and second and third models 104 and 106 for at least one segment in the stack. A segment, in one example, includes at least a part of the anode channel, at least a part of the membrane electrode unit, at least a part of the cathode channel, and at least a part of the coolant channel. This means that a segment at least partially includes two plates and one membrane electrode unit. The second model 104 models, in this example, at least a part of the anode channel, at least a part of the cathode channel, and at least a part of the coolant channel consisting of two plates. The third model 106 models, in this example, at least a part of the membrane electrode unit.

[0029] 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 this example, two plates are integrated as one plate in the second model 104, and both plates have only one coupling variable in each direction, namely the first coupling variable 108 and the second coupling variable. In the second model 104, it can be assumed that two segments are modeled in each plate, and these are each coupled in each direction via their own coupling variables.

[0030] The second model 104 is coupled to the third model 106 via at least one third coupling variable 112. The third model 106 is coupled to the second model 104 via at least one fourth coupling variable 114. In this example, a virtual sensor 116 is provided to detect a state 100. In this example, the virtual sensor 116 is coupled to the third model 106 via at least one 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.

[0031] The second model 104 is designed to model physical processes in the plate, particularly transport processes. In this example, the second model 104 models discrete segments in the plate. Transport processes occur, on the one hand, in the plane of the plate between segments, and on the other hand, in the plane perpendicular to the plate between each segment or bundle of segments and the membrane electrode unit, and to the membrane electrode unit. In this example, the transport processes in the plate are modeled using the second model 104 in these planes. Transport processes in the membrane electrode unit are modeled using the third model 106.

[0032] The second model 104 is, for example, formed to model heat transport, refrigerant transport, gas transport, and potential in each individual segment or a bundle of such segments.

[0033] In that case, particularly in the case of a PEM fuel cell, the flow of the medium, especially the process medium, especially the reaction gas, the discharge of process products, especially liquid water, and / or heat, and the voltage through generalized resistors are shown. These resistors are connected to form a resistor network. The resistors can be linear or nonlinear. Furthermore, the underlying resistors can be specified from a physical model. The physical model is discretized, for example, by the finite volume method. The physical model is, for example, a pre-prepared table or database model.

[0034] A segment is a discrete point, for example, containing a channel of a specific channel length. A segment can also contain multiple channels.

[0035] The physical processes within a segment are represented, for example, by representative elements, such as a single channel or a representative channel bundle.

[0036] Within a segment, for example, the following variables can be determined: gas concentration, partial pressure, voltage, plate temperature, gas temperature, liquid water saturation, refrigerant temperature, and refrigerant pressure. These are just examples; other variables can also be determined.

[0037] In the case of the second model 104, mathematical descriptions of the relationships can be used, for example, a two-phase flow description by Darcy or Poisseuille for gas transport, a description by Ohm's law for voltage, a description by the heat conduction equation for plate temperature, and a description of the refrigerant as an incompressible flow.

[0038] The third model, 106, in this example, includes a membrane electrode unit model in a plane perpendicular to the plate of each segment. This membrane electrode unit model can be implemented with varying degrees of complexity.

[0039] The third model, 106, is a one-dimensional model for, for example, approximately determining the non-uniform flow distribution in a stack and the corresponding gas flow (Gasumsatz).

[0040] The third model 106 is a two-dimensional model for determining, for example, the internal state of a membrane electrode unit.

[0041] The third model, 106, is a three-dimensional model for evaluating processes in the microstructure of, for example, a membrane electrode unit. The process is, for example, a flow effect along the channel flow direction.

[0042] In one example, a membrane electrode unit model is created, the membrane electrode unit physics is described in detail, and various internal states, such as membrane humidity or saturation, are automatically calculated together. At least one fifth coupled variable 118 includes, for example, at least one of these internal states. This makes it possible to determine, for example, the degradation over time in the segment assigned to this membrane electrode unit. By abstracting to segments, the membrane electrode unit model can be run in one, two, or three dimensions.

[0043] The third model 106, in the case of PEMFC, models the membrane electrode unit physics using, for example, LMPant et al., Electrochimica Acta, 326, 134963 (2019), or R. Vetter and JOSchumacher, Journal of Power Sources, 438, 227018 (2019), or AAKulikovsky, Journal of The Electrochemical Society, 161, F263-F270 (2014).

[0044] At least one third coupling variable 112 is, for example, the concentration of a gas species, the bipolar plate temperature, or the potential. At least one fourth coupling variable 114 is, for example, the mass flow, the heat flow, or the electric current. The third coupling variable 112 and / or the fourth coupling variable 114 couple a segment or bundle of segments to a membrane electrode unit model.

[0045] For PEM fuel cells, the implementation of a third model 106, which uses partial difference equations in which a third coupling variable 112 and a fourth coupling variable 114 are applied to the anode and cathode respectively, is disclosed in Experimental parameter uncertainty in PEM fuel cell modeling Part I: Scatter in material parameterization, R. Vetter and JOSchumacher, Journal of Power Sources, 438, 227018 (2019) arXiv:1811.10091.

[0046] In equation (1), Ohm's law is solved, and the potential represents the third coupling variable 112, and the current represents the fourth coupling variable 114.

[0047] In equation (5), the heat equation is solved, with temperature representing the third coupling variable 112 and heat flow representing the fourth coupling variable 114. In equation (13), gas transport is calculated using the Maxwell-Stefan equation, where the gas concentration represents the third coupled variable 112 and the mass flow represents the fourth coupled variable 114.

[0048] Furthermore, in this equation, proton conduction in the ionomer is calculated using equation (1), water transport (Wasseruebertrag) 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 resistance is calculated using equation (S24).

[0049] The first model 102 includes, for example, a collection of nodes in a resistance network. The first model 102 is formed, for example, to show heterogeneity between cells in a stack. In this example, a manifold, i.e., the inlet for the process medium and the outlet for the process products, and the end plates of the stack are integrated. It is conceivable to use one model for the inlet and outlet, and separate models for the end plates. It is conceivable to use separate models for the inlet, outlet, and end plates. The first model 102 is formed, for example, to consider the thermal and electrical behavior of the entire stack. The first model 102 is formed, for example, to model fluid heterogeneity across channels.

[0050] In this example, a first model 102 is assigned to the segments at the edge of the plate, in addition to the membrane electrode unit model. The corresponding first and second coupled variables model the medium supply, gas species flow, and gas temperature. The medium supply is modeled, for example, by the mass flow rate from the periphery to the segments or from the segments to the periphery, operating pressure, discharge pressure, and / or refrigerant temperature. This is done, for example, by numerical calculation of a generalized resistance flow mechanism and subsequent extractions.

[0051] In this example, the first model 102 includes at least one endplate model assigned to the segment in which the endplates are located. The corresponding first and second coupled variables model the conversion of the electrical demand to current to the respective segments.

[0052] To compute the entire stack using this discretization, multiple individual cells of the stack can be integrated into a representative cell bundle. The cell bundle has different characteristics compared to the individual cells. For example, the resistance in the plane results in a given compensation current. Additionally, the integration has an effect on a first model 102, in which case the first model 102 is formed using corresponding coupling variables for the cell bundle. This results in a resistance network for the entire stack that provides local statements about the internal state of the film electrode units and the plate across the entire stack. In this case, there is no limit to the number of cells allowed in the cell bundle. The number of cells is determined, for example, by the precision requirements of the application problem setting.

[0053] The selection of the number of cell bundles and segments is considered in terms of accuracy and computation time. Segments can be rectangular, particularly square. Other geometric shapes are also possible. In this example, the geometric requirement for a segment is that it allows for a complete division of the plate. A segment, in one example, consolidates multiple channels into a single channel bundle. The number of channels consolidated can range from one channel to all channels in the plate. In particular, if the expected difference in capacity is small lateral to the flow direction in the channels, or is not very relevant to the problem setting being verified, then selecting only one channel bundle may suffice. In other cases, it may be necessary to consider multiple channel bundles. A channel bundle may contain, for example, 10 or more channels.

[0054] Cells at the edges of the stack are preferably incorporated into smaller cell bundles than the other cell bundles, particularly because their temperature profiles often differ from those of the cells in the center of the stack.

[0055] Another discretization method can be selected, for example, depending on the application.

[0056] For simple problem settings such as polarization curves or operating strategies that use conditions unrelated to aging, it is sufficient to select, for example, 5 to 20 segments along a flow channel and, for example, 1 to 10 cell bundles.

[0057] For aging-related problem settings where the local internal state of the membrane electrode unit needs to be shown with great accuracy, a segment count of 100 or more segments along the channel flow direction is also advantageous.

[0058] The first model 102, the second model 104, and the third model 106 include parameters. These models are determined in particular by including coupled partial differential equations, or as analytical functions, or as one neural network or multiple neural networks. The parameters define the model, i.e., the differential equations, analytical functions, or neural networks. The differential equations, analytical functions, and neural networks model the electrochemical or physical effects in the stack. The differential equations and analytical functions include electrochemical or physical variables. The differential equations and analytical functions may also include state variables that have neither electrochemical nor physical equivalents in the stack. The neural network includes inputs for the electrochemical or physical variables and outputs for the electrochemical or physical variables. The differential equations, analytical functions, or neural networks are coupled via coupling variables.

[0059] Depending on the state 100 to be modeled, the differential equation and neural network can include different variables, coupling variables, and / or parameters. Examples of variables and coupling variables are described below. In one example, to determine state 100, the model, i.e., the differential equation, analytical function, or neural network, is solved in a fully coupled manner. In one example, one or more explicit couplings may be intended.

[0060] The first model 102 optionally has an interface 124 for data input (Bedatung) of the first model 102. The second model 104 optionally has an interface 126 for data input of the second model 104. The third model 106 optionally has an interface 128 for data input of the third model 106.

[0061] These interfaces allow you to change the parameters of each model for data input.

[0062] Training can be initiated for data input. During training, training data is provided, each including at least one input variable for the first model 102 and a criterion for state 100. The criterion indicates which state 100 should be modeled using the model and its respective input variable.

[0063] Each state 100 is determined using at least one input variable from the training data.

[0064] The parameters are determined, for example, by the deviation of 100 from each criterion in the training data.

[0065] For example, an optimization method that minimizes deviation determines the parameter with the smallest possible deviation. For instance, if the mean of the deviation in the training data is minimized, the deviation is as small as possible.

[0066] In inference, the state 100 is then determined depending on the parameters determined in training and at least one specified input variable of the first model 102.

[0067] Figure 2 schematically shows a membrane electrode unit 202 arranged in a fuel cell stack 204. The stack 204 has two ends 206, with a plate 208 positioned between these ends. The stack 204 is electrically connected at its ends 206 by one end plate 210 each. These end plates are positioned on the end faces of the stack 204 that are opposite each other. The first plate of the stack 204's plate 208 is electrically connected to the first end plate of the end plate 210, and the last plate of the stack 204's plate 208 is electrically connected to the second end plate of the end plate 210.

[0068] Using the second model 104, segment 208-1 of plate 208 is modeled. Plate 208 contains channel 208-2. Each segment 208-1 contains a portion of one or more channels 208-2.

[0069] An inlet 212 is located on the side of the stack 204, and the process medium is supplied to the channel 208-2 of the stack 204 through this inlet. The process product is discharged from the channel 208-2 of the stack 204 via an outlet 214. The outlet 214 is located on the side of the stack 204, opposite the inlet 212.

[0070] Using the first model 102, the inflow or outflow into the stack 204 is modeled depending on at least one input variable of the first model 102.

[0071] Using the first model 102, the electrical input and output variables of the stack 204 are modeled depending on at least one input variable of the first model 102.

[0072] Segment 208-1 of plate 208 of stack 204 is modeled in the second model 104.

[0073] The physical effects in segment 208-1 are modeled using the second model 104.

[0074] A third model 106 is used to model the physical effects in the membrane electrode element 202 or segment 202-1 of the membrane electrode element 202. Segment 202-1 of the membrane electrode element 202 can be assigned to, for example, one segment 208-1 of each of two plates 208 adjacent to the membrane electrode element 202, and adjacent segments 202-1 can be assigned to each other.

[0075] These segments can be separated or joined to one another. 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 fitted discretization. The joining of segments in the second model 104 to segments in the third model 106 is performed, for example, via a third joining variable 112. The joining of segments in the third model 104 to segments in the second model 106 is performed, for example, via a fourth joining variable 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 a non-fitted discretization. The joining of segments in the second model 104 to segments in the third model 106 is performed, for example, via a correspondingly fitted third joining variable 112. The joining of a segment in the third model 106 to a segment in the second model 104 is performed, for example, via a appropriately fitted fourth joining variable 114.

[0076] The third model 106, in this example, includes a one-dimensional, two-dimensional, or three-dimensional model for each segment 202-1, which models the boundary conditions from segment 208-1 of plate 208 assigned to segment 202-1. This achieves a significant reduction in computation time.

[0077] Figure 3 shows the steps in the method for determining state 100 in stack 204. The method described below, based on a polymer electrolyte membrane fuel cell, can be similarly applied to any fuel cell, electrolyte, and redox flow technology.

[0078] In step 302, at least one input variable of the first model 102 is determined from a predetermined measurement of a membrane electrode unit incorporated into, for example, a polymer electrolyte membrane fuel cell. In one example, the measurement is detected during the 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.

[0079] In step 304, state 100 is determined using at least one input variable, the first model 102, the second model 104, and the third model 106. State 100 is detected, for example, using a virtual sensor 116.

[0080] 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 fully coupled and uncoupled for at least one input variable of the first model 102. In this case, the models are coupled through their respective coupling variables.

[0081] Next, step 306 is executed.

[0082] In step 306, in one example, at least one output variable of the first model 102 is output. The output variable is, for example, a calculated variable of a variable included in the measurement. These variables can be intended to be compared with each other.

[0083] In step 306, for example, depending on state 100, variables for the operation of stack 204, in particular the operation strategy, control variables, or control variables, are determined, and stack 204 is controlled depending on these variables. The variables are determined, for example, during the operation of stack 204, depending on measurements of stack 204 detected during its operation. Stack 204 is controlled, for example, using these variables during its operation.

[0084] In step 306, in one example, a variable characterizing the irreversible aging of the stack 204 or a part thereof is determined, depending on the state 100 of the stack 204. The state 100 and / or the variable are determined, for example, during operation.

[0085] In step 306, for example, a variable containing a forecast for maintenance of stack 204 or a part thereof is determined depending on the state 100 of stack 204. The state 100 and / or the variable are determined, for example, during operation.

[0086] In step 306, for example, the design parameters of stack 204 or a part thereof are determined depending on the state 100 of stack 204. Steps 302-306 are repeated many times in the design process, as an example, and a large number of design parameters are determined. Different designs are simulated, for example, with different parameters of a second model 104 and / or a third model 106.

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

[0088] The following describes an exemplary application of the exemplary states. In this example, with respect to stack 204, a distinction is made between the fuel cell stack of a polymer electrolyte membrane fuel cell and the electrolytic stack of a polymer electrolyte membrane electrolytic cell. The fuel cell stack includes a fuel cell. The electrolytic stack includes an electrolytic cell.

[0089] Redox flow batteries, solid oxide fuel cells, or solid oxide electrolytic cells are handled accordingly.

[0090] The description relates, for example, to a fuel cell stack, a fuel cell therein, or a part thereof. The fuel cell stack includes, in addition to the parts of the fuel cell stack already described, 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.

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

[0092] The following variables are, for example, input variables for the fuel cell stack, or they indicate the internal state of the fuel cell stack. gas composition of anode gas Cathode gas composition The gas pressure at the outlet of the anode gas in the fuel cell stack The gas mass flow rate at the outlet of the anode gas in the fuel cell stack The gas pressure at the cathode gas outlet in the fuel cell stack The mass flow rate of the cathode gas at the outlet in the fuel cell stack. The gas pressure at the anode gas inlet in the fuel cell stack The gas mass flow rate at the anode gas inlet in the fuel cell stack. The gas pressure at the cathode gas inlet in the fuel cell stack The mass flow rate of the cathode gas at the inlet in the fuel cell stack. Anode gas temperature Cathode gas temperature Refrigerant temperature Refrigerant mass flow rate Voltage generated by fuel cell stack The electric current generated by the fuel cell stack.

[0093] The first model 102 optionally includes a thermal model that models the environmental conditions of the fuel cell stack, such as the temperature and / or relative humidity of the air surrounding the fuel cell stack.

[0094] For example, state 100 is the heterogeneity of the internal state of the fuel cell in the fuel cell stack.

[0095] For example, state 100 is heterogeneity of the internal state of the structure of the plates in the fuel cell stack, such as channels for at least one gas, channels for at least one refrigerant, or the structure of the plates in the fuel cell stack.

[0096] For example, state 100 is the operating state of a membrane electrode unit of one or more fuel cells in a fuel cell stack, e.g., the operating state of at least one gas diffusion support, at least one microporous layer, at least one catalyst layer, and / or at least one polymer electrolyte membrane of the fuel cell.

[0097] State 100 is determined, for example, for fuel cell drying, short-term overload, or transient operation.

[0098] State 100 is, for example, a state within the fuel cell or fuel cell stack for water management in a fuel cell stack, such as temperature, gas composition, saturation, liquid water content, and water overflow in a polymer electrolyte membrane fuel cell.

[0099] State 100 is, for example, a state that occurs when the fuel cell stack is started or stopped.

[0100] State 100 is an important state, for example, during frozen state initiation. In one example, frozen state initiation is recognized in a temperature-dependent manner, and state 100 is detected during frozen state initiation.

[0101] State 100 is, for example, a local state in a fuel cell stack. In one example, the impact of production fluctuations on the local state is determined depending on state 100.

[0102] In one example, state 100 is the local temperature distribution or local gas composition within the fuel cell and / or fuel cell stack.

[0103] 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 the fuel cell and / or fuel cell stack.

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

[0105] In one example, state 100 is the local water content in the 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 the local contribution of local reactions to the total voltage or total current provided by the fuel cell stack. For example, state 100 is detected for various local reactions, and the total voltage and total current are determined depending on the detected state.

[0108] In one example, state 100 is localized irreversible aging in the fuel cell stack, particularly ionomatous aging. Irreversible aging includes, for example, catalytic aging in the 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.

[0109] In one example, state 100 is localized irreversible aging degradation in the membrane electrode unit. For example, various states 100 of the membrane electrode unit, i.e., various localized irreversible aging degradations, are detected, and the irreversible aging degradation of the membrane electrode unit is determined depending on the detected state.

[0110] In one example, state 100 is detected, and a particularly optimal operating strategy is determined depending on state 100. For example, the operating strategy is determined considering the efficiency and / or service life of the fuel cell stack.

[0111] In one example, the optimal fuel cell stack design to achieve the desired efficiency and / or service life is determined depending on state 100.

[0112] In one example, state 100 is determined for numerous different fuel cell stack designs, and the optimal design is selected from among them depending on state 100. This enables a cost-effective design process.

[0113] In one example, the actual state of the fuel cell stack is determined by state 100, which is determined, for example, by a measurement of the fuel cell stack.

[0114] The actual state is, for example, the state of the fuel cell stack during operation. State 100 is determined, for example, by a measurement detected during the operation of the fuel cell stack. For example, the measurement is detected during the operation of the fuel cell stack, and state 100 is determined by the measurement during the operation of the fuel cell stack.

[0115] In one example, control variables for the operation of the fuel cell stack are determined depending on state 100. The control variables are determined, for example, during the operation of the fuel cell stack. The control variables are determined, for example, depending on a measured value. For example, the measured value is detected during the operation of the fuel cell stack, state 100 is determined depending on the measured value during the operation of the fuel cell stack, the control variables are determined depending on state 100 during the operation of the fuel cell stack, and the fuel cell stack is controlled depending on the control variables.

[0116] In one example, the forecast for fuel cell stack maintenance is determined depending on state 100.

[0117] In one example, a forecast for adaptively changing at least one operating condition that may affect the lifespan and / or performance of the fuel cell stack is determined depending on state 100.

[0118] In one example, a system model that models the system that operates the fuel cell stack depending on state 100 is used to estimate at least one input variable for the fuel cell stack, such as the gas composition.

[0119] The following describes application examples of electrolytes in electrolytic stacks. An electrolytic stack includes an electrolytic cell. In this example, the electrolytic stack includes one electrolytic stack containing an electrolytic cell.

[0120] State 100 is, for example, the internal state of an electrolytic stack.

[0121] The following variables are, for example, input variables for an electrolytic stack, or they indicate the internal state of an electrolytic stack. Composition of anode fluid and cathode fluid, The pressure at the inlet of the anode fluid in the electrolytic stack The pressure at the outlet of the anode fluid in the electrolytic stack The pressure at the cathode fluid inlet of the electrolytic stack The pressure at the outlet of the cathode fluid in the electrolytic stack The mass flow rate at the inlet of the anode fluid in the electrolytic stack The mass flow rate of the anode fluid at the outlet of the electrolytic stack The mass flow rate at the inlet of the cathode fluid in the electrolytic stack The mass flow rate of the cathode fluid at the outlet of the electrolytic stack Temperature of the anode fluid Cathode fluid temperature Voltage generated by the electrolytic stack, Current generated by an electrolytic stack.

[0122] For example, state 100 is the heterogeneity of the internal state of the electrolytic cells in the electrolytic cell stack.

[0123] For example, state 100 is a heterogeneity of the internal state of the structure of the plate in an electrolytic cell stack, such as a channel for at least one gas, a channel for at least one refrigerant, or a channel for at least one gas.

[0124] For example, state 100 is the operating state of a membrane electrode unit of one or more electrolytic cells, such as at least one porous transport layer, at least one catalyst layer, and / or the operating state of a polymer electrolyte membrane electrolytic cell.

[0125] A polymer electrolyte membrane electrolytic cell is, for example, part of an electrolytic device. State 100 is determined, for example, during the transient operation of the electrolytic device.

[0126] State 100, in one example, is the operating state of an electrolytic cell stack, particularly in load balancing, where, for instance, an overload is determined during the operation of the electrolytic cell stack.

[0127] State 100 is, in one example, the operating state of the electrolytic cell stack that occurs when the electrolytic device is started, i.e., at startup, or stopped, i.e., at shutdown.

[0128] State 100 is, for example, a local state in an electrolytic cell stack. In one example, the effect of production fluctuations on the local state is determined depending on state 100.

[0129] In one example, state 100 is the local temperature distribution in an electrolytic cell stack or electrolytic cell.

[0130] In one example, state 100 is the local fluid composition within the electrolytic cell and / or electrolytic cell stack.

[0131] In one example, state 100 is local saturation, i.e., the distribution of liquid and gas phases in at least one porous layer, particularly at least one porous transport layer, at least one catalyst layer and / or channels for at least one fluid.

[0132] In one example, state 100 is a local current density distribution or a local voltage distribution.

[0133] In one example, state 100 is a local current density distribution or local voltage distribution within an electrolytic cell and / or electrolytic cell stack.

[0134] In one example, state 100 is at least one local potential in at least one catalyst layer of the electrolytic cell stack.

[0135] In one example, state 100 is the local contribution of a local reaction to the total voltage or total current provided by the electrolytic cell stack. For example, state 100 is detected for various local reactions, and the total voltage and total current are determined depending on the detected state.

[0136] In one example, state 100 is localized irreversible aging in an electrolytic cell stack, particularly ionomer aging. Irreversible aging includes, for example, catalytic aging of the catalyst layer of the electrolytic cell stack and / or membrane aging in at least one membrane of the electrolytic cell stack and / or aging in at least one gas diffusion support of the electrolytic cell stack and / or aging in at least one microporous layer of the electrolytic cell stack.

[0137] In one example, state 100 is localized irreversible aging degradation in the membrane electrode unit. For example, various states 100 of the membrane electrode unit, i.e., various localized irreversible aging degradations, are detected, and the irreversible aging degradation of the membrane electrode unit is determined depending on the detected state.

[0138] In one example, state 100 is detected, and the optimal operating strategy is determined in particular, depending on state 100. For example, the operating strategy is determined considering the efficiency and / or service life of the electrolytic cell stack.

[0139] In one example, the optimal design of the electrolytic cell stack to achieve the desired efficiency and / or service life is determined depending on state 100.

[0140] In one example, state 100 is determined for numerous different designs of electrolytic cell stacks, and the optimal design is selected from among them depending on state 100. This enables a cost-effective design process.

[0141] In one example, the actual state of the electrolytic cell stack is determined by state 100. State 100 is determined, for example, by a measurement of the electrolytic cell stack.

[0142] The actual state is, for example, the operating state of the electrolytic cell stack. State 100 is determined, for example, by a measurement detected during the operation of the electrolytic cell stack. For example, the measurement is detected during the operation of the electrolytic cell stack, and state 100 is determined by the measurement during the operation of the electrolytic cell stack.

[0143] In one example, a control variable for the operation of the electrolytic cell stack is determined depending on state 100. The control variable is determined, for example, during the operation of the electrolytic cell stack. The control variable is determined, for example, depending on a measured value. For example, the measured value is detected during the operation of the electrolytic cell stack, state 100 is determined depending on the measured value during the operation of the electrolytic cell stack, the control variable is determined depending on state 100 during the operation of the electrolytic cell stack, and the electrolytic cell stack is controlled depending on the control variable.

[0144] In one example, the forecast for maintenance of the electrolytic cell stack is determined depending on state 100.

[0145] In one example, a forecast for adaptively changing at least one operating condition that may affect the lifespan and / or performance of the electrolytic cell stack is determined depending on state 100.

[0146] In one example, a system model is used to model the system that operates the electrolytic cell stack, depending on state 100, and at least one input variable for the electrolytic cell stack, such as the conductivity of water or the circulating residual gas, is estimated. The conductivity of water changes, for example, due to contamination during operation. [Explanation of Symbols]

[0147] 100 status 102 First Model 104 Second Model 106 Third Model 108 First binding variable 110 Second binding variable 112 Third binding variable 114 The fourth join variable 116 Virtual Sensors 118 The fifth linking variable 120 inputs 122 Output 124 Interfaces 126 Interfaces 128 Interfaces 202 Membrane electrode unit 202-1 Segment 204 Fuel Cell Stack 206 Edge 208 Plate 208-1 Segment 208-2 Channel 210 End Plate 212 Inlet 214 Outlet 302 steps 304 steps 306 steps

Claims

1. A method for determining a state (100) in a stack (204) of a fuel cell or electrolytic cell, wherein the stack (204) comprises at least one membrane electrode unit (202) and a plate (208) in which each membrane electrode unit (202) is positioned, wherein a first model (102) is used to model the inflow of process medium from the periphery and the outflow of process products to the periphery, as well as electrical input and output variables, a second model (104) is used to model a segment (208-1) of the plate (208), and a third model (106) is used to model the membrane electrode unit (202) or the front A segment (202-1) of a film electrode unit (202) is modeled, 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 segment by segment via at least one coupling variable (112, 114), at least one input variable of the first model (102) is predetermined (302), 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) (304), The first model (102), the second model (104), and / or the third model (106) each include parameters, each of which is provided with training data including at least one input variable of the first model (102) and a criterion for the state (100), the respective state (100) is determined using the at least one input variable from the training data, the parameter with the smallest possible deviation is determined depending on the deviation of the state (100) from the respective criterion for the state (100) from the training data, and the state (100) is subsequently determined depending on the predetermined at least one input variable of the first model (102). method.

2. The method according to claim 1, characterized in that the physical effects of each segment (208-1) or a bundle of multiple segments (208-1) are modeled using the second model (104).

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

4. The method according to claim 1 or 2, characterized in that, during the operation of the stack (204), the fuel cell, or the electrolytic cell, a measurement value characterizing the operation is detected (302), and in that case, the state (100) is determined during the operation depending on the measurement value (304).

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

6. The method according to claim 4, characterized in that, depending on the state (100) during operation, variables are determined (306) that characterize the irreversible aging degradation of the stack (204), the fuel cell, or the electrolytic cell or a part thereof, or that include a forecast for maintenance of the stack (204), the fuel cell, or the electrolytic cell or a part thereof.

7. The method according to claim 5, characterized in that, depending on the state (100), variables are determined (306) that characterize the irreversible aging degradation of the stack (204), the fuel cell, or the electrolytic cell or a part thereof, or that include a forecast for maintenance of the stack (204), the fuel cell, or the electrolytic cell or a part thereof.

8. The method according to claim 1 or 2, characterized in that the design parameters of the stack (204), the fuel cell, or the electrolytic cell or a part thereof are determined (306) depending on the state (100).

9. An apparatus for determining the state (100) of a stack of fuel cells or electrolytic cells, or in a fuel cell or electrolytic cell (202), characterized in that it is configured to determine the state (100) by the method described in claim 1 or 2.

10. A computer program characterized by including a computer-readable instruction that, when executed by a computer, performs the method described in claim 1 or 2.

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