Apparatus and method for determining conditions in a fuel cell or electrolysis cell stack or in a fuel cell or electrolysis 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 method for determining conditions in a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell. [Background technology]

[0002] In the design of polymer electrolyte membrane fuel cells, their behavior can be verified in simulations that take into account the geometry of the fuel cell. As this simulation requires a lot of computational resources, it is desirable to provide improved simulations to determine the state of polymer electrolyte membrane fuel cells during operation in polymer electrolyte membrane fuel cells and in stacks of polymer electrolyte membrane fuel cells.

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

[0004] A method for determining a state in a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell, comprising at least one membrane electrode unit and plates between which one membrane electrode unit is arranged, wherein a first model is used to model the inflow of process medium from the periphery and the outflow of process product to the periphery and electrical input and output variables, a second model is used to model a segment of the plate, and a third model is used to model a membrane electrode unit or a segment of a membrane electrode unit, the first and second models are coupled via at least one coupling variable, and the second and third models are coupled segment-wise via at least one coupling variable, wherein at least one input variable of the first model is predetermined, and the state is determined using the at least one input variable, the first model, the second model and the third model.

[0005] A membrane electrode unit can be understood in particular as at least one ion-conducting layer, a so-called membrane, and at least one electrode layer arranged, in particular mounted, on one side of the ion-conducting layer. In particular, electrode layers are arranged or mounted on both sides of the ion-conducting layer, so that the ion-conducting layer is located between the two electrode layers (in particular in a sandwich-like manner). Preferably, the membrane electrode unit can also include other mounted porous layers, which serve to distribute (in / out) the reaction medium, current, and / or heat. In this case, the at least one ion-conducting layer (membrane) can be at least partially formed as an ion-conducting polymer (e.g., for PEM fuel cells, PEM electrolytes, AEM fuel cells, AEM electrolytes) and / or as an electrically non-conducting porous structure impregnated with an ion-conducting polymer and / or an ion-conducting liquid / solution (e.g., for liquid alkaline electrolytes or redox flow batteries) and / or as a ceramic ion conductor (e.g., for SOFCs / SOECs). The electrode layers are typically porous layers that perform combined functions: ion transport, electron transport, transport of liquid and / or gaseous reaction medium, heat transport, and electrocatalysis. These combined functions can be realized, depending on the technology under consideration, by any combination of electrocatalytically active materials (metals and / or metal oxides and / or ceramic materials) and / or electronically conductive (porous) support materials (metal / carbon materials, doped metal oxides, ...) and / or ion conductors (polymeric ion conductors and / or liquid ion conductors and / or ceramic ion conductors). The membrane electrode unit can contain other (usually porous) functional layers, used for example for distribution of the reaction medium (inlet of liquid and / or gaseous starting materials, outlet of liquid and / or gaseous products) and / or electron and heat transport. Depending on the application, at least one of the layers of the membrane electrode unit can also have a mechanical function, such as providing a spring action or mechanical support for adjacent layers.

[0006] The method can also be advantageously used to determine conditions in redox flow cells or redox flow cell stacks. The method is particularly, but not exclusively, suitable for determining conditions in NT-PEM fuel cells, HT-PEM fuel cells, NT-PEM electrolysis cells, HT-PEM electrolysis cells, AEM fuel cells, AEM electrolysis cells, AEL electrolysis cells (classical alkaline electrolyte), SOFCs, SOECs, MCFCs / MCECs (molten carbonate fuel cells / electrolysis cells), PAFCs / PAECs (phosphoric acid fuel cells / electrolytes). Summary of the Invention

[0007] In the following, the method is substantially illustrated with reference to a PEM fuel cell, but is transferable to essentially any fuel cell, electrolyte and redox flow technology.

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

[0009] In particular, the third model is used to model the physical effects of the membrane electrode unit or of each segment of the membrane electrode unit, which allows for simulations that are particularly low in demand for computational resources.

[0010] In particular, during operation of the stack, fuel cell or electrolysis cell, measured values that characterize the operation are detected and the state during operation is determined depending on the measured values, which makes it possible to influence the operation depending on the results of the simulation.

[0011] In particular, during operation, variables for operation, in particular operating strategies, control variables or control parameters are determined depending on the operating conditions, and the stack, fuel cell or electrolysis cell is controlled depending on the variables, thereby influencing the operation depending on the results of the simulation.

[0012] In particular, depending on the operating conditions, variables are determined which characterize the irreversible aging of the stack, fuel cell or electrolysis cell, or parts thereof, or which contain predictions for the maintenance of the stack, fuel cell or electrolysis cell, or parts thereof. Simulations make it possible to determine this information about the conditions.

[0013] In particular, depending on the conditions, the design parameters of the stack, fuel cell or electrolysis cell, or parts thereof, are determined, which makes it possible to arrive at a better design more quickly.

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

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

[0016] Further advantageous embodiments can be seen from the following description and drawings. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 2 is a schematic diagram of a model for determining conditions in a fuel cell stack. [Figure 2] 1 is a schematic diagram of a polymer electrolyte membrane fuel cell stack. [Figure 3] 1 is a flowchart of method steps for determining a state in a stack. DETAILED DESCRIPTION OF THE INVENTION

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

[0019] In the following description, the procedure is described for a stack of polymer electrolyte membrane fuel cells, although corresponding procedures are contemplated for other types of fuel cells, electrolysis cells, or redox flow cells.

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

[0021] A polymer electrolyte membrane fuel cell, in a bipolar configuration, includes bipolar plates. Each bipolar plate includes 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 disposed between each pair of bipolar plates in the stack. The stack is held together by the end plates. Both outer bipolar plates of the stack are electrically contacted by each one of the end plates.

[0022] In a monopolar configuration, 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 active surface 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 contacted by each of the end plates. In addition, electrical contacts are provided for the monopolar plates arranged within the stack.

[0023] In the following, bipolar 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 as great as the number of membrane electrode units.

[0024] The plate is provided with at least one channel for the supply of a first process medium, in particular process air, which can be understood in the narrow sense as a channel and in the broad sense as a consistent flow path, for example in the form of passages, through an open porous material, as in a PEM electrolysis cell, for example.

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

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

[0027] The plate is provided with at least one channel for the discharge of process products, in particular process air and product water.

[0028] FIG. 1 illustrates an exemplary model 100 for determining conditions in a stack. In FIG. 1, there is a first model 102 for the periphery of the stack, and a second model 104 and a third model 106 for at least one segment in the stack. In one example, the segment includes at least a portion of the anode channel, at least a portion of the membrane electrode unit, at least a portion of the cathode channel, and at least a portion of the coolant channel. This means that the segment at least partially includes two plates and one membrane electrode unit. In this example, the second model 104 models at least a portion of the anode channel, at least a portion of the cathode channel, and at least a portion of the coolant channel consisting of two plates. In this example, the third model 106 models at least a portion 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, the two plates are combined into one plate in the second model 104, and both plates are provided with only one coupling variable in each direction, namely the first coupling variable 108 and the second coupling variable. It can be envisioned that in the second model 104, two segments are modeled in each plate, each coupled via its own coupling variable in each direction.

[0030] The second model 104 is coupled to the third model 106 via at least one third coupled variable 112. The third model 106 is coupled to the second model 104 via at least one fourth coupled variable 114. In this example, a virtual sensor 116 is provided to detect the state 100. In this example, the virtual sensor 116 is coupled to the third model 106 via at least one fifth coupled 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 configured to model physical processes in the plate, in particular transport processes. In this example, the second model 104 models discrete segments in the plate. Transport processes take place on the one hand in the plane of the plate between the segments and on the other hand in planes perpendicular to the plate between each segment or bundle of segments and the membrane electrode units to and from the membrane electrode units. In this example, the transport processes in the plate are modeled in these planes using the second model 104. The transport processes in the membrane electrode units are modeled using a third model 106.

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

[0033] In this case, particularly in the case of PEM fuel cells, the voltage across the medium supply, the supply of the process medium, in particular the reactant gas, the removal of the process product, in particular liquid water, and / or heat, as well as the generalized resistances are shown. These resistances are connected to form a resistor network. The resistances can be linear or nonlinear. Furthermore, the underlying resistances 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-created tabular or database model.

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

[0035] A physical process within a segment is represented, for example, by a representative element, for example a single channel or a representative bundle of channels.

[0036] In the segments, the following variables can be determined, for example: gas concentration, partial pressure, voltage, plate temperature, gas temperature, liquid water saturation, coolant temperature, and coolant pressure. These variables are by way of example only; other variables can also be determined.

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

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

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

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

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

[0042] In one example, the membrane electrode unit model models and describes the membrane electrode unit physics in detail, and various internal states, such as membrane humidity or saturation, are automatically calculated together. At least one fifth coupling variable 118, for example, includes at least one of these internal states. This allows, for example, the aging of the segment assigned to this membrane electrode unit to be determined. The segment abstraction allows the membrane electrode unit model to be implemented in one, two, or three dimensions.

[0043] The third model 106, in the case of a PEMFC, models the membrane electrode unit physics, for example according to L.M.Pant et al., Electrochimica Acta, 326, 134963 (2019) or R.Vetter and J.O.Schumacher, Journal of Power Sources, 438, 227018 (2019) or A.A.K. Kulikovsky, Journal of The Electrochemical Society, 161, F263-F270 (2014).

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

[0045] For PEM fuel cells, an implementation of the third model 106 using partial difference equations in which a third coupled variable 112 and a fourth coupled variable 114 are implemented for 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 J.O. Schumacher, Journal of Power Sources, 438, 227018 (2019) arXiv:1811.10091.

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

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

[0048] Furthermore, in this equation, proton conduction in the ionomer is calculated using equation (1), water transport in the ionomer using equation (9), adsorption / desorption using equation (22), evaporation / condensation using equation (23), reaction kinetics using equation (1), and contact resistance using equation (S24).

[0049] The first model 102 may, for example, include a set of nodes of a resistor network. The first model 102 may, for example, be configured to represent non-uniformities between cells of the stack. In this example, the manifolds, i.e., the inlet for the process medium and the outlet for the process product, are integrated with the end plates of the stack. It is contemplated that one model may be used for the inlet and outlet, and a separate model for the end plates. It is contemplated that separate models may be used for the inlet, outlet, and end plates. The first model 102 may, for example, be configured to consider the thermal and electrical behavior of the entire stack. The first model 102 may, for example, be configured to model non-uniformities in fluid flow across the channels.

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

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

[0052] To calculate the entire stack using this discretization, the individual cells of the stack can be integrated into a representative cell bundle. The cell bundle has altered characteristics compared to the individual cells. For example, the resistance in the plane results in the compensation current shown. Additionally, the integration has an effect on the first model 102. In this case, the first model 102 is formed using the corresponding coupling variables for the cell bundle. This results in a resistance network for the entire stack that provides statements about the internal state of the membrane electrode units and plates locally throughout the 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 accuracy requirements of the applied problem formulation.

[0053] The selection of the number of cell bundles and segments is based on considerations of accuracy and computation time. The segments can be rectangular, especially square. Other geometric shapes are also possible. In this example, the geometric requirement for the segments is that they allow for a complete division of the plate. In one example, a segment combines multiple channels into one channel bundle. The number of combined channels can range from one channel to all channels of the plate. In particular, if the expected performance differences transverse to the flow direction in the channels are small or not very relevant for the problem setting being examined, it is sufficient to select only one channel bundle. In other cases, it may be necessary to consider multiple channel bundles. A channel bundle may, for example, contain 10 or more channels.

[0054] The cells at the edges of the stack are preferably assembled into smaller cell bundles than other cell bundles, including the cells in the center of the stack, especially since the temperature profile there is often different from that of the cells in the center of the stack.

[0055] Another discretization may be chosen depending on the application, for example.

[0056] For simple problem settings, such as polarization curves or operating strategies using conditions not related to aging, the selection of, for example, 5-20 segments along the flow channel and, for example, 1-10 cell bundles, is sufficient.

[0057] For ageing-related problem settings, where the local internal state of the membrane electrode unit needs to be represented very accurately, a segment number of, for example, 100 or more segments along the flow direction of the channel is also advantageous.

[0058] The first model 102, the second model 104, and the third model 106 include parameters. These models include, in particular, coupled partial differential equations, or are determined as analytical functions, or as one or more neural networks. The parameters define the models, i.e., the differential equations, 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 include electrochemical or physical variables. The differential equations and analytical functions can also include state variables that have no electrochemical or physical equivalent 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 equations and neural networks may include different variables, coupling variables, and / or parameters. Examples of variables and coupling variables are described below. In one example, the models, i.e., differential equations, analytical functions, or neural networks, are fully coupled and solved to determine the state 100. In one example, one explicit coupling or multiple explicit couplings may be contemplated.

[0060] The first model 102 optionally has an interface 124 for data input 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 the parameters of each model to be modified for data input.

[0062] For data input, training can be contemplated, in which training data is provided that includes at least one input variable for the first model 102 and a criterion for the state 100. The criterion indicates which state 100 should be modeled using the model and the respective input variable.

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

[0064] The parameters are determined depending on the deviation of the state 100 from a respective reference, for example from training data.

[0065] For example, an optimization method that minimizes the deviation determines the parameters that result in the smallest possible deviation, e.g., if the average deviation of the training data is the smallest, the deviation is as small as possible.

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

[0067] 2 shows a schematic representation of membrane electrode units 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 connected at each of its ends 206 by one end plate 210, which are located on opposite ends of the stack 204. The first of the plates 208 in the stack 204 is electrically connected to the first of the end plates 210, and the last of the plates 208 in the stack 204 is electrically connected to the second of the end plates 210.

[0068] The second model 104 is used to model segments 208-1 of the plate 208. The plate 208 includes channels 208-2. Each segment 208-1 includes a portion of a respective one of the channels 208-2 or a portion of each of the channels 208-2.

[0069] An inlet 212 is arranged at the side of the stack 204, through which the process medium is supplied to the channels 208-2 of the stack 204. The process product is discharged from the channels 208-2 of the stack 204 via an outlet 214. The outlet 214 is arranged at the side of the stack 204 opposite the inlet 212.

[0070] The first model 102 is used to model the inflow or outflow to the stack 204 depending on at least one input variable of the first model 102 .

[0071] The first model 102 is used to model electrical input and output variables of the stack 204 depending on at least one input variable of the first model 102 .

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

[0073] A second model 104 is used to model the physical effects in segment 208-1.

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

[0075] These segments can be separated or combined with each other. The number of segments in the second model 104 is equal to the number of segments in the third model 106 in this example. This is an adapted discretization. The segments in the second model 104 are coupled to the segments in the third model 106, for example, via a third coupling variable 112. The segments in the third model 104 are coupled to the segments in the second model 106, for example, via a fourth coupling 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-adaptive discretization. The segments in the second model 104 are coupled to the segments in the third model 106, for example, via a correspondingly adapted third coupling variable 112. The coupling of the segments in the third model 106 to the segments in the second model 104 is effected, for example, via a correspondingly adapted fourth coupling variable 114 .

[0076] The third model 106 in this example comprises a one-, two- or three-dimensional model in which for each segment 202-1, the boundary conditions from the segment 208-1 of the plate 208 assigned to this segment 202-1 are modeled, thereby achieving a significant reduction in calculation time.

[0077] 3, steps in a method for determining the state 100 in a stack 204 are shown. In the following, the method described in the context of a polymer electrolyte membrane fuel cell can be applied equally 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 predetermined measurements, e.g., of a membrane electrode unit incorporated in a polymer electrolyte membrane fuel cell. The measurements, in one example, are detected during operation of the polymer electrolyte membrane fuel cell. In this example, the measurements characterize the operation of the polymer electrolyte membrane fuel cell, i.e., the measurements include at least one measurable variable that characterizes the operation.

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

[0080] The virtual sensor 116, in this example, 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 solved for at least one input variable of the first model 102, where the models are coupled via their respective coupling variables.

[0081] Then, 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 the variables included in the measurements. It can be intended that these variables are matched against each other.

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

[0084] In step 306, in one example, a variable characterizing irreversible aging of the stack 204 or a portion 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, in one example, variables including a forecast for maintenance of the stack 204 or a portion thereof are determined depending on the state 100 of the stack 204. The state 100 and / or variables may be determined, for example, during operation.

[0086] In step 306, design parameters for stack 204, or portions thereof, are determined, in one example, depending on state 100 of stack 204. Steps 302-306 are illustratively repeated multiple times during the design process to determine multiple design parameters, e.g., to simulate various designs with different parameters for second model 104 and / or third model 106.

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

[0088] In the following, exemplary applications of the exemplary states are described. In this example, a distinction is made with respect to stack 204 between fuel cell stacks of polymer electrolyte membrane fuel cells and electrolysis stacks of polymer electrolyte membrane electrolysis cells. Fuel cell stacks include fuel cells. Electrolysis stacks include electrolysis cells.

[0089] Redox flow batteries, solid oxide fuel cells or solid oxide electrolysis cells are treated accordingly.

[0090] The condition relates, for example, to a fuel cell stack, a fuel cell therein, or a part thereof, which may comprise, 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 a 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. Anode gas composition Cathode gas composition The gas pressure at the anode gas outlet in the fuel cell stack The mass flow rate of the anode gas at the outlet of the fuel cell stack The gas pressure at the cathode gas outlet in the fuel cell stack the gas mass flow rate at the cathode gas outlet in the fuel cell stack; The gas pressure at the anode gas inlet in the fuel cell stack The mass flow rate of the anode gas at the inlet of 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 of the fuel cell stack Anode gas temperature Cathode gas temperature Coolant temperature Refrigerant mass flow rate Voltage generated by the fuel cell stack The current produced 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, for example the temperature and / or relative humidity of the air surrounding the fuel cell stack.

[0094] For example, state 100 is the non-uniformity of the internal state of a fuel cell in a fuel cell stack.

[0095] For example, the condition 100 may be at least one gas channel, at least one coolant channel, or an internal condition non-uniformity in the structure of a plate in a fuel cell stack.

[0096] For example, state 100 is the operating state of a membrane electrode unit of one fuel cell or multiple fuel cells of 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] Condition 100 is determined for example for fuel cell drying, short term overload, or transient operation.

[0098] Conditions 100 are conditions within a fuel cell or fuel cell stack, for example, for water management in a fuel cell stack, such as temperature, gas composition, saturation, liquid water content, water overflow with a polymer electrolyte membrane fuel cell.

[0099] State 100 is a state that occurs, for example, during start-up or shutdown of a fuel cell stack.

[0100] Condition 100 is a condition of interest, for example, during a frozen start. In one example, frozen start is recognized depending on, among other things, the temperature, and condition 100 is detected during a frozen start.

[0101] The state 100 is a local state, for example in a fuel cell stack. In one example, the state 100 is dependent on which the effect of production fluctuations on the local state is determined.

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

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

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

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

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

[0107] In one example, the states 100 are the local contributions of local reactions to the total voltage or current provided by the fuel cell stack, for example, the states 100 are detected for various local reactions, and the total voltage and current are determined depending on the detected states.

[0108] In one example, condition 100 is localized irreversible aging in a fuel cell stack, in particular ionomer aging, such as catalyst aging of a catalyst layer of the fuel cell stack and / or membrane aging of at least one membrane of the fuel cell stack and / or aging of at least one gas diffusion support of the fuel cell stack and / or aging of at least one microporous layer of the fuel cell stack.

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

[0110] In one example, a condition 100 is detected and a particularly optimal operating strategy is determined depending on the condition 100. For example, the operating strategy is determined taking into account the efficiency and / or lifetime of the fuel cell stack.

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

[0112] In one example, the conditions 100 are determined for a number of different designs of fuel cell stacks, and the optimal design is selected from the number depending on the conditions 100. This allows for a cost-effective design process.

[0113] In one example, the actual state of the fuel cell stack is determined depending on the state 100. The state 100 is determined depending on, for example, measurements of the fuel cell stack.

[0114] The actual state is, for example, the state of the fuel cell stack during operation. The state 100 is determined, for example, depending on measurements of the fuel cell stack that are detected during operation. For example, measurements are detected during operation of the fuel cell stack, and the state 100 is determined depending on the measurements during operation of the fuel cell stack.

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

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

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

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

[0119] In the following, an application example of an electrolyte in an electrolysis stack is described. The electrolysis stack includes an electrolysis cell. The electrolysis stack includes one electrolysis stack, which in this example includes an electrolysis cell.

[0120] The state 100 is, for example, the internal state of an electrolysis stack.

[0121] The following variables are for example input variables of the electrolysis stack or indicate the internal state of the electrolysis stack: the composition of the anode and cathode fluids; The pressure at the anode fluid inlet of the electrolysis stack The pressure at the anode fluid outlet of the electrolysis stack The pressure at the cathode fluid inlet of the electrolysis stack The pressure at the cathode fluid outlet of the electrolysis stack The mass flow rate of the anode fluid at the inlet of the electrolysis stack the mass flow rate of the anode fluid at the outlet of the electrolysis stack The mass flow rate of the cathode fluid at the inlet of the electrolysis stack The mass flow rate of the cathode fluid at the outlet of the electrolysis stack Anode fluid temperature Cathode fluid temperature the voltage generated by the electrolytic stack; The current generated by the electrolytic stack.

[0122] For example, condition 100 is a non-uniformity of the internal conditions of an electrolysis cell in an electrolysis cell stack.

[0123] For example, the condition 100 is a channel for at least one gas, a channel for at least one coolant, or an internal condition inhomogeneity of the structure of a plate in an electrolysis cell stack.

[0124] For example, state 100 is the operating state of an electrolysis cell or membrane electrode units of multiple electrolysis cells, such as the operating state of at least one porous transport layer, at least one catalyst layer, and / or polymer electrolyte membrane electrolysis cells.

[0125] The polymer electrolyte membrane electrolysis cell is, for example, part of an electrolysis device. The state 100 is determined, for example, during transient operation of the electrolysis device.

[0126] The state 100 is, in one example, an operating state of an electrolysis cell stack, particularly in load balancing, for example, determining an overload during operation of the electrolysis cell stack.

[0127] State 100 is the operating state of the electrolysis cell stack that occurs, in one example, during start-up, ie, start, or shutdown, ie, stop, of the electrolysis system.

[0128] The state 100 is a local state, for example in an electrolysis cell stack. In one example, the state 100 is relied upon to determine the impact of production variations on the local state.

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

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

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

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

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

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

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

[0136] In one example, condition 100 is localized irreversible aging, in particular ionomer aging, in an electrolysis cell stack, such as catalyst aging of a catalyst layer of the electrolysis cell stack and / or membrane aging of at least one membrane of the electrolysis cell stack and / or aging of at least one gas diffusion support of the electrolysis cell stack and / or aging of at least one microporous layer of the electrolysis cell stack.

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

[0138] In one example, a condition 100 is detected and an optimal operating strategy is determined, inter alia, depending on the condition 100. For example, the operating strategy is determined taking into account the efficiency and / or the lifetime of the electrolysis cell stack.

[0139] In one example, the optimal electrolysis cell stack design to achieve a desired efficiency and / or life span is determined depending on the condition 100 .

[0140] In one example, the conditions 100 are determined for a number of different designs of electrolysis cell stacks, and the optimal design is selected from the number depending on the conditions 100. This allows for a cost-effective design process.

[0141] In one example, the actual state of the electrolysis cell stack is determined depending on the state 100. The state 100 is determined depending on, for example, measurements of the electrolysis cell stack.

[0142] The actual state is, for example, the state of the electrolysis cell stack during its operation. The state 100 is, for example, determined depending on measurements thereof that are detected during operation of the electrolysis cell stack. For example, measurements are detected during operation of the electrolysis cell stack, and the state 100 is determined depending on the measurements during operation of the electrolysis cell stack.

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

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

[0145] In one example, a prediction for adaptively changing at least one operating condition that can affect the lifetime and / or performance during operation of the electrolysis cell stack is determined depending on the state 100.

[0146] In one example, at least one input variable for the electrolysis cell stack, such as the conductivity of water or the circulating residual gas, is estimated using a system model that models a system for operating the electrolysis cell stack depending on the state 100. 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 Fourth Combined Variable 116 Virtual Sensors 118 Fifth Combined Variable 120 inputs 122 Output 124 Interface 126 Interface 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 electrolysis cell or in a fuel cell or electrolysis cell, comprising at least one membrane electrode unit (202) and plates (208) between which each one of the membrane electrode units (202) is arranged, wherein a first model (102) is used to model the inflow of process medium from the periphery and the outflow of process product to the periphery and 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 membrane electrode unit (202).

1. A method in which a segment (202-1) of an 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 for each segment via at least one coupling variable (112, 114), at least one input variable of the first model (102) is predetermined (302), and the state (100) is determined (304) using the at least one input variable, the first model (102), the second model (104), and the third model (106).

2. The method of claim 1, wherein the second model (104) is used to model the physical effects of each segment (208-1) or a bundle of segments (208-1).

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

4. 4. The method according to claim 1, wherein during operation of the stack (204), the fuel cell or the electrolysis cell, measured values characterizing said operation are detected (302), and wherein said state (100) is determined (304) during said operation in dependence on said measured values.

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

6. 6. The method according to claim 4 or 5, characterized in that, depending in particular on the conditions (100) during the operation, a variable is determined (306) characterizing the irreversible aging of the stack (204), the fuel cells or the electrolysis cells or parts thereof or comprising a forecast for the maintenance of the stack (204), the fuel cells or the electrolysis cells or parts thereof.

7. 7. The method according to any one of claims 1 to 6, characterized in that depending on the state (100), design parameters of the stack (204), the fuel cell or the electrolysis cell or parts thereof are determined (306).

8. 8. The method according to claim 1, wherein the first model (102), the second model (104) and / or the third model (106) comprise parameters, each of which comprises 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, parameters for which the deviation is as small as possible are determined depending on the deviation of the state (100) from its respective criterion from the training data, and subsequently the state (100) is determined depending on the predetermined at least one input variable of the first model (102).

9. 10. An apparatus for determining a state (100) of a stack of fuel cells or electrolysis cells or in a fuel cell or electrolysis cell (202), in particular a virtual sensor, characterized in that the apparatus is configured to determine the state (100) by a method according to any one of claims 1 to 8.

10. A computer program comprising computer readable instructions which, when executed by a computer, perform the method according to any one of claims 1 to 8.

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