Method for modelling a battery cell of an energy storage device for a motor vehicle, computer program and / or computer-readable medium, data processing device, motor vehicle and / or server

Abstract

The invention relates to a method (100) for modelling a battery cell (56) of an energy storage device (55) for a motor vehicle (50), the method comprising: ascertaining (110), on the basis of field data (64) which characterise the battery cell, a quiescent voltage characteristic curve (65) which characterises the battery cell and is dependent on an ageing state; determining (120), on the basis of the quiescent voltage characteristic curve, diagnosis parameters which are dependent on the ageing state of the battery cell; detecting (130) a partial charge curve (60) which causes a change to the charging state; determining (140) model parameters (MP) which are dependent on the ageing state for modelling the battery cell with the current ageing state; and outputting (150) the model parameters for electrochemical modelling of the battery cell. The invention also relates to a corresponding computer program, computer-readable medium and corresponding data processing device.

Description

[0001] Method for modeling a battery cell of an energy storage device for a motor vehicle, computer program and / or computer-readable medium, data processing device, motor vehicle and / or server

[0002] The present disclosure relates to a method for modeling a battery cell of an energy storage device for a motor vehicle. The disclosure also relates to a data processing device for a motor vehicle and / or for an off-board server, and to a motor vehicle and / or an off-board server.

[0003] Such an energy storage device typically comprises a plurality of battery cells or cells connected in parallel and / or series, thus forming a high-voltage storage device of the motor vehicle, also known as a traction battery. The energy storage device is configured to discharge the battery cells and provide electrical energy to operate the motor vehicle and / or to provide electrical energy externally, for example, via a charging station, and to be supplied with electrical energy via the charging station and / or through recuperation during a journey in order to charge the battery cells of the energy storage device.

[0004] Accurate modeling of the high-voltage storage system is crucial for its safe, sustainable, and efficient use. Such high-voltage storage systems, or the cells they comprise, for example, lithium-ion cells, lose capacity over their service life, and their resistance increases, a process known as aging. This can ultimately reduce the range and performance of the vehicle when the cells are used in a motor vehicle. The capacity and resistance, or changes in them, can thus be incorporated into an estimate of the battery cell's condition.

[0005] For a state estimation to characterize the state of the cells and / or the energy storage device, an open-circuit voltage (OCV) and / or an open-circuit potential (OCP) are typically used, i.e., a relationship between a cell's open-circuit voltage or an electrode potential and the cell's state of charge. This can be used, in particular, to estimate the state of charge (SOC) and / or the aging or health status (SOH) of the cell and / or the energy storage device. The open-circuit voltage can also be used to determine a charging strategy for charging the energy storage device or the cells.

[0006] Information about a battery's current state of health can be defined as the currently available capacity compared to the capacity when new. The state of health is of utmost importance for a vehicle's operating strategy, particularly for the design of the charging strategy, for SOC estimation, and for safety. However, degradation of the state of health, or aging, exhibits a complex path dependency: the battery cell can age due to various mechanisms, for example, triggered by temperatures and / or power demands. Such mechanisms can be described by the construct of degradation modes (see CR Birkl, MR Roberts, E. McTurk, PG Bruce, DA Howey, Degradation diagnostics for lithium ion cells, Journal of Power Sources, issue 341, 2017, pages 373–386).Degradation modes model the physico-chemical aging mechanisms and can be characterized by parameters that describe, for example, the loss of active material from an electrode and / or charge carriers such as lithium. These degradation modes can be used to estimate how the storage device will behave in the future and when its service life will be reached.

[0007] To characterize health and aging, the development and provision of generalizable, accurate, and reliable estimation algorithms and methods is desirable. A promising methodology is a voltage curve reconstruction model based on measured half-cell potentials in the new state. Methods that utilize such reconstruction are known (see, for example, Matthieu Dubarry, Cyril Truchot, Bor Yann Liaw, "Synthesize battery degradation modes via a diagnostic and prognostic model," Journal of Power Sources, Issue 219, 2012, pages 204-216; and Julius Schmitt, Mathias Rehm, Alexander Karger, Andreas Jossen, "Capacity and degradation mode estimation for lithium-ion batteries based on partial charging curves at different current rates," Journal of Energy Storage, Issue 59, 2023, Article 106517).Half-cell potential curves measured in the new state are shifted and compressed to reconstruct the measured voltage curve of the aged cell in such a way that an error between an aged voltage curve reconstructed on the basis of the half-cell potential curves measured in the new state and the measured voltage curve of the aged cell is minimal.

[0008] The patent application not yet published on the filing date of the present disclosure

[0009] Patent application DE 102022 129208.8 describes an alternative method. It allows the use of relaxed stress points together with a summed charge throughput to reconstruct the stress curve. This is a comparatively low-effort method for estimating the aging state, since by specifying the amount of charge introduced, the space of possible solutions for the stress curve can be reduced to such an extent that a higher accuracy in the SOH estimation can be achieved.

[0010] Knowledge of the aging state of the cells is important for optimizing BMS functions, i.e., for applications controlled by the battery management system (BMS), such as determining charging curves, the aging state, and / or aging mechanisms. For this purpose, the battery cell can be represented by a battery model or electrochemically modeled (ECM). Such a battery model is defined by a set of model parameters. Although the battery model can be used to characterize the performance and / or the state of the cell, calibrating the model is difficult and / or is limited to a new state of the cell (beginning of life, BOL), since such models are often constructed or defined based on the new state.

[0011] Against the background of this prior art, one object of the present disclosure is to provide a method suitable for enriching the prior art and improving at least the above-mentioned aspects of the prior art. In particular, the object of the disclosure is to provide model parameters for modeling the battery cell over its lifetime.

[0012] The problem is solved by the features of the independent claims. The subclaims contain further developments of the disclosure.

[0013] According to one aspect of the disclosure, the object is achieved by a method for modeling a battery cell of an energy storage device for a motor vehicle, the method comprising: determining an open-circuit voltage characteristic curve characterizing the battery cell and dependent on an aging state of the battery cell based on field data characterizing the battery cell; determining diagnostic parameters dependent on the aging state based on the open-circuit voltage characteristic curve; detecting a partial charging curve causing a change in the state of charge; determining model parameters dependent on the aging state for the electrochemical modeling of the battery cell in the aging state; and outputting the model parameters for modeling the battery cell.

[0014] The process can be divided into several steps: In one step, field data is collected to determine the open-circuit voltage characteristic and the partial charge curve, for example, based on measurements of relaxed voltage points and a charge flow rate between the voltage points. This eliminates the need for laboratory measurements and / or factory and / or workshop measurements. The field data can be collected during regular operation of the vehicle. The open-circuit voltage characteristic can be determined from relaxed voltage points and corresponding charge flow rates.

[0015] The open-circuit voltage characteristic can be used to characterize the internal resistance and / or capacity of the battery cell. These parameters are aging-dependent, allowing diagnostic parameters to be derived from the open-circuit voltage characteristic. Different components of the battery cell can degrade at different rates. For example, the electrodes can age differently. Thus, for a given state of health, electrode-specific factors determine the cell's state of charge, which is also referred to as cell and / or electrode balancing and can be represented by the diagnostic parameters.

[0016] In a further step, the battery cell model can be adapted depending on the aging state. The aged battery cell can be modeled using a pseudo-two-dimensional model (P2D model) and / or a physicochemical model. The partial charge curve and the diagnostic parameters can be used as input variables for model adaptation, i.e., for determining the model parameters, to represent dynamic cell behavior. The partial charge curve can also be used for further parameter optimization and / or model validation.

[0017] The disclosure thus makes it possible to identify model parameters of a battery cell model over the battery cell's lifetime using field data. This allows the aged battery cell to be modeled more accurately, which improves options for operating and / or controlling the battery cell. It is possible to model the battery cell in any desired state of aging. Optionally, the method comprises transmitting the partial charging curve, the diagnostic parameters, and / or the open-circuit voltage characteristic curve from the motor vehicle and / or a motor vehicle-side data processing device to a server external to the vehicle, wherein the determination of the model parameters is performed by the server. It was recognized that determining the model parameters may be computationally and / or memory-intensive.Therefore, it may be advisable to determine the model parameters on the server, whereby a server-side data processing device can typically be more powerful than a vehicle-side data processing device. The server thus provides an image of the battery (a "battery in the cloud"), which effectively enables an in-depth analysis of the battery's condition and the associated conclusions. To determine the model parameters, the partial charge curve, in particular, can be transmitted to the server. Furthermore, the open-circuit voltage characteristic curve, based on which the server can determine the diagnostic parameters, and / or the diagnostic parameters themselves can be transmitted.

[0018] Optionally, outputting the model parameters includes transmitting them from the server to the motor vehicle and / or a data processing device on the motor vehicle. This allows the model parameters to be transmitted to the motor vehicle for further optimization of BMS functions after the server has determined the model parameters. Alternatively or additionally, outputting the model parameters can include outputting them from a software component and / or writing them to a data memory.

[0019] Optionally, the method comprises determining a boundary condition relating to the resistance of the battery cell and / or layer growth, wherein the determination of the model parameters depends on the boundary condition. It was recognized that determining the model parameters can be more effective and / or reliable if one or more boundary conditions are taken into account. The boundary condition characterizes, for example, a resistance distribution by a layer thickness (solid electrolyte interphase, SEI, and / or cathode electrolyte interphase, CEI).

[0020] Optionally, the model parameters are determined taking into account a relationship between the diagnostic parameters and the boundary condition. It was recognized that there may be a correlation, particularly between the diagnostic parameters and the boundary condition, which may facilitate the determination of the model parameters. Optionally, the method comprises: using the model parameters to predict performance and / or to determine an aging-dependent charging curve. It was recognized that a battery cell model can be used to optimize BMS functions. The performance prediction can characterize the electrical energy output by the energy storage device. The charging curve, in particular a rapid charging curve, can characterize the electrical energy absorbed by the energy storage device. This indicates an optimization of the charging strategy over the service life.

[0021] Optionally, the method comprises recalibrating, based on the model parameters, a state-of-charge estimator and / or an aging state estimator. It was recognized that the model parameters can be used, for example, to set the state-of-charge scale. This allows a state-of-charge estimator to be recalibrated to estimate the state of charge of the battery cell. The model parameters can also precisely describe the aging of the battery cell and / or its components. This allows an aging state estimator to be recalibrated to estimate the aging state of the battery cell.

[0022] In other words, the above description can be summarized as follows, with reference to a specific embodiment that is described as non-limiting for the present disclosure: The disclosure relates to a method for predicting the performance of aged cells by aging calibration using field data. Knowledge of the aging state of the cells is important for optimizing BMS functions, e.g., performance prediction, detection of the charge / aging state of the cells, and fast-charging algorithms, etc. However, there is currently no way to model all aging mechanisms of lithium-ion cells over their lifetime. An electrochemical model can be used to construct the performance of a cell. However, calibrating the model is a difficult task and is often limited for cell BOL (Begin of Life).The objective of this disclosure is to provide a method for identifying the model parameters over the lifetime using field data. The core idea is the following: Step 1: Adaptation of the open-circuit voltage characteristic curve over cell aging with input from field data, which includes relaxed OCV voltages and charge throughput between the OCV points, and with an output of a capacity of active materials (anode / cathode), loss of active materials, and loss of cyclable lithium. Step 2: Adaptation of P2D parameters over aging with input from field data as a partial curve from the charging process to represent dynamic cell behavior, in particular for parameter optimization and / or model validation, and the cell balancing from step 1. Boundary conditions can be considered, namely a resistance distribution from layer thicknesses (SEI / CEI). The output can be a cell model at any aging state.An application is an adaptation and optimization of BMS functions.

[0023] According to one aspect of the disclosure, a computer program and / or a computer-readable medium is provided. The computer program and / or the computer-readable medium comprise instructions which, when the program or instructions are executed by a data processing device, cause the device to perform the steps of the method according to the disclosure and / or steps thereof. Optionally, the computer program and / or the computer-readable medium comprises instructions which, when the program or instructions are executed by a data processing device, cause the device to perform the method steps described as advantageous or optional in order to achieve an associated technical effect.

[0024] According to one aspect of the disclosure, a data processing device for a motor vehicle and / or for an off-board server is provided. The data processing device is configured to perform the method described above and / or steps thereof. Optionally, the data processing device is configured to perform a method step described as advantageous or optional and / or to implement a method feature in order to achieve an associated technical effect.

[0025] Optionally, the data processing device is a battery management system (BMS) or is comprised thereof.

[0026] According to one aspect of the disclosure, a motor vehicle and / or off-vehicle server comprising the data processing device described above is provided. Optionally, the data processing device of the motor vehicle and / or the off-vehicle server, the motor vehicle and / or the off-vehicle server are configured to perform a method step described as advantageous or optional and / or to implement a method feature in order to achieve an associated technical effect.

[0027] In the following, one embodiment is described with reference to the figures.

[0028] Fig. 1 schematically shows a motor vehicle according to one aspect of the disclosure; Fig. 2 schematically shows features of the method according to one aspect of the disclosure; Fig. 3 schematically shows a flowchart of a method according to one aspect of the disclosure; and

[0029] Fig. 4 shows a schematic representation of a computer program and / or computer-readable medium according to one aspect of the disclosure.

[0030] Figure 1 schematically shows a motor vehicle 50 according to one aspect of the disclosure. The motor vehicle 50 is a land vehicle. The motor vehicle 50 is a passenger car.

[0031] The motor vehicle 50 has an energy storage device 55 and an electric drive 52. The energy storage device 55 has a plurality of battery cells 56, which and their number are shown only schematically.

[0032] The energy storage device 55 or the battery cells 56 are configured to be supplied with electrical energy in order to charge the battery cells 56, i.e., to increase a state of charge SOG of the battery cells 56. The energy storage device 55 or the battery cells 56 are configured to provide electrical energy for operating the motor vehicle 50 and / or the electric drive 52, wherein the battery cells 56 are discharged, i.e., the state of charge SOG of the battery cells 56 decreases. During charging and discharging, as well as over time, i.e., due to calendar effects, the battery cells 56 can age, i.e., the battery cells 56 can be characterized by a state of health SOH, which changes over time and / or with the use of the energy storage device 55.

[0033] Each of the battery cells 56 has an anode 57, a cathode 58, and an electrolyte, along with an electrically insulating separator 59 between the anode 57 and the cathode 58 (see schematic indexing of the battery cell 56 at the bottom left). For example, the battery cell 56 is a lithium-ion cell, in particular a lithium iron phosphate cell or LFP cell, and / or has a silicon-containing anode.

[0034] The motor vehicle 50 according to Figure 1 has a data processing device 51. The data processing device 51 is, for example, a battery control module and is configured to control and monitor the operation of the energy storage device 55. For this purpose, the data processing device 51 is configured, for example, to define a current profile with a current I for charging the energy storage device 55 and to measure the cell voltage U of one of the battery cells 56 (see Figure 2, sections (A), (B), and (C)). The data processing device 51 can thus acquire field data 64 that defines relaxed voltage points and a charge throughput between the charging points.

[0035] Figure 1 also shows a charging station 91 and a vehicle-external network 90. ​​The charging station 91 and the motor vehicle 50 are configured to be electrically and communicatively connected to each other. Thus, the energy storage device 55 for charging the battery cells 56 can be supplied with electrical energy from the vehicle-external network 90 via the charging station 91. The charging station 91 can be, for example, a wallbox or wall-mounted charging station and / or a public and / or private charging point.

[0036] For charging, the data processing device 51 can detect a charging request 75 for charging the battery cell 56 and transmit it to the charging station 91. The charging request 75 is specified, for example, by a user and / or by the manufacturer for charging and / or diagnostic purposes. The charging request 75 is detected by a user interface (not shown) and / or via a communication interface 53 of the motor vehicle 50. Alternatively, the charging request 75 can be generated by the data processing device 51 itself for diagnostic purposes. The charging can be a partial charge TQ, wherein the state of charge SOC of the battery cells 56 changes at least partially. During the partial charge TQ, the data processing device 51 can detect a partial charging curve 60 by measuring the cell voltage U (see Figure 2 (B)).

[0037] The data processing device 51 is configured to determine a rest voltage characteristic curve 65 (see Figure 2 (A)). For example, the data processing device 51 can measure relaxed cell voltages U as field data 64 before and / or after charging and / or discharging, or after the elapse of a relaxation time during which overvoltages can dissipate. These relaxed cell voltages correspond to the rest voltage of the battery cells 56 and / or approximate the rest voltage. Together with a charge throughput between relaxed voltage points, the rest voltage characteristic curve 65 can be reconstructed. For example, the data processing device 51 is configured to perform the process described in patent application DE 102022 129 208, which was not yet published on the filing date of the present disclosure.8 (the so-called DeltaQ method) and / or in the case of battery cells 56 exhibiting hysteresis, for example for carrying out the method described in the patent application not yet published on the filing date of the present disclosure.

[0038] DE 10 2023 118 719.8, according to which a method for characterizing a hysteresis of a battery cell of an energy storage device for an electrically driven motor vehicle is described, the method comprising: charging the battery cell and detecting a charging cell voltage to model a charging voltage characteristic; temporarily discharging the battery cell and detecting a discharging cell voltage to model a discharging voltage characteristic; and determining the hysteresis between the charging voltage characteristic and the discharging voltage characteristic based on the charging cell voltage and the discharging cell voltage.

[0039] The data processing device 51 has a data memory 54. The data memory 54 is configured to store readable information for reading and processing by the data processing device 51. For example, the open-circuit voltage characteristic curve 65 and / or the partial charging curve 60 can be stored in the data memory 54 for further processing and / or transmission.

[0040] The data processing device 51 is configured to determine the state of charge (SOC) and the state of deterioration (SOH) based on measured variables. For this purpose, the data processing device 51 is configured to implement a state of charge estimator 80 for determining the state of charge (SOC) and an aging state estimator 81 for determining the aging state (SOC).

[0041] Figure 1 also schematically illustrates a vehicle-external server 85. The vehicle-external server 85 forms a backend or cloud. The server 85 and the motor vehicle 50 are configured to communicate with each other to exchange data or information. For this purpose, the motor vehicle 50 has a vehicle-side communication interface 53, and the server 85 has a server-side communication interface 85. For processing data, the server 85 has a server-side data processing device 86.

[0042] The vehicle-side communication interface 53 is configured to be communicatively connected to the server 80 via the server-side communication interface 83 for exchanging data. For this purpose, the communication interface 53 is configured, for example, for communication via a wireless local area wireless network (WLAN) and / or a mobile radio network. The vehicle-side data processing device 51 can transmit information for evaluation to the vehicle-external server 85 via the vehicle-side communication interface 53. Alternatively, communication between the vehicle-side data processing device 51 and the server 85 can also take place via the charging station 91. The motor vehicle 50 or the data processing device 51 is configured to carry out the method 100 described with reference to Figure 3 and / or steps thereof.Alternatively or additionally, steps of the method 100 may be performed by the server 85, as described with reference to Figure 2.

[0043] Figure 2 schematically shows features of the method 100 according to one aspect of the disclosure. Figure 2 shows, in particular, the motor vehicle 50, the server 85, and features of the battery cells 56 according to Figure 1. Figure 2 is described with reference to Figure 1. Figure 2 is divided into four sections (A), (B), (C), and (D). Sections (A), (B), (C), and (D) and / or features of Figure 2 are also provided with the reference numerals of the features of the method 100 according to Figure 3 to enable better correlation between Figures 1 to 3.

[0044] Section (A) of Figure 2 shows the open-circuit voltage characteristic curve 65 and section (B) shows the partial charging curve 65. The open-circuit voltage characteristic curve 65 and the partial charging curve 60 can be detected or determined in particular by the vehicle-side data processing device 51.

[0045] The open-circuit voltage characteristic curve 65 changes with the aging of the battery cell 56, i.e., with the aging state SOH. In section (A), the open-circuit voltage U is plotted as a function of the depth of discharge (DOD). The open-circuit voltage characteristic curve 65 can also be plotted as the open-circuit voltage U as a function of the state of charge (SOC), which corresponds to a curve approximately mirrored along the x-axis.

[0046] The partial charging curve 60 in section (B) describes a charging of the battery cell 56 from an initial state of charge SOC_S to a final state of charge SOC_E that differs from the initial state of charge SOC_S and reflects dynamic properties of the battery cell 56. Section (A) thus corresponds to a determination 110 of the open-circuit voltage characteristic curve 65 characterizing the battery cell 56 and dependent on the aging state SOH of the battery cell 56. Section (B) thus corresponds to a detection 130 of the partial charging curve 60 that brings about the change in the state of charge SOC.

[0047] Using the open-circuit voltage characteristic curve 65, the half-cell potentials OOP can be reconstructed as shown in section (C). The reference parameters ANE, APE, BNE, BPE are optimized to weight the half-cell potentials OOP such that the open-circuit voltage characteristic curve 65 can be reconstructed using the weighted difference between the half-cell potentials OOP. The reference parameters ANE, APE, BNE, BPE describe in the reconstructed voltage curve 65 or in the reference voltage curve how the voltage U is composed of half-cell potentials OCP-, OCP+. The reference parameter ANE is a scaling parameter for the negative half-cell potential OCP-. The reference parameter BNE is a shift parameter for the negative half-cell potential OCP-. The reference parameter APE is a scaling parameter for the positive half-cell potential OCP+. The reference parameter BPE is a shift parameter for the positive half-cell potential OCP+.

[0048] The voltage curve 65 is reconstructed by minimizing a loss function. A function can also be maximized and / or otherwise optimized. The loss function can also include modified variants of the measured voltage curve 65, for example, a differential voltage (DVA) and / or incremental voltage (ICA). In the case of the loss function, the reference parameters ANE, APE, BNE, BPE are adjusted for reconstruction such that the value of the loss function is minimal, for example, as the difference between the reconstructed voltage curve 65 and a measured cell voltage curve (not indexed). Alternatively, the difference between a reconstructed differential and / or incremental voltage curve and a measured differential and / or incremental cell voltage curve can be minimized.

[0049] The reconstruction of the open-circuit voltage characteristic curve 65 shown in section (C) can be performed by the vehicle-side data processing device 51. Alternatively (not shown) or additionally, the open-circuit voltage characteristic curve 65 can be transmitted to the server 85, where the reconstruction of the open-circuit voltage characteristic curve 65 is performed.

[0050] Between sections (C) and (D), a determination 120 of diagnostic parameters LAM_NE, LAM_PE, LLI dependent on the aging state SOH is indicated based on the open-circuit voltage characteristic curve 65. The reference parameters ANE, APE, BNE, BPE described with reference to section (C) are now used to characterize the degradation modes based on the diagnostic parameters LAM_NE, LAM_PE, LLI.

[0051] The diagnostic parameter LAM_NE is a loss parameter of the active material of the negative electrode. The diagnostic parameter LAM_NE can be calculated as the quotient of the decrease in the capacity attributed to the negative electrode and the capacity attributed to the negative electrode in the reference state, for example, the new state.

[0052] The diagnostic parameter LAM_PE is a loss parameter of the active material of the positive electrode. The diagnostic parameter LAM_PE can be calculated as the quotient of the decrease in the capacitance attributed to the positive electrode and the capacitance attributed to the positive electrode in the reference state.

[0053] The diagnostic parameter LLI is a loss parameter of lithium. The diagnostic parameter LLI can be calculated as the quotient of the loss of the capacity attributed to lithium and the capacity attributed to lithium in the reference state.

[0054] The determination 120 of the diagnostic parameters LAM_NE, LAM_PE, LLI based on the open-circuit voltage characteristic curve 65 between sections (C) and (D) can be performed by the vehicle-side data processing device 51. Alternatively (not shown) or additionally, the open-circuit voltage characteristic curve 65 can be transmitted to the server 85, where the reconstruction of the open-circuit voltage characteristic curve 65 is performed.

[0055] Then, the partial charging curve 60 and the diagnostic parameters LAM_NE, LAM_PE, LLI are transmitted 135 from the motor vehicle 50 to the vehicle-external server 85. Alternatively or in addition to the diagnostic parameters LAM_NE, LAM_PE, LLI, the open-circuit voltage characteristic curve 65 for determining the diagnostic parameters LAM_NE, LAM_PE, LLI can be transmitted by the server 85 from the motor vehicle 50 to the server 85.

[0056] A boundary condition 70 relating to the resistance of the battery cell 56 and / or layer growth is determined 136. The information relating to the boundary condition 70 can be stored on the server 85 and / or retrieved from the server. By way of example, one or more of the following correlations or relationships can be implemented as a boundary condition 70 for modeling the battery cell 56: (i) a porosity reduction of the anode 57 is equivalent to the volume fraction of a layer thickness (SEI) in order to account for anode aging; (ii) a loss of cyclable lithium (LLI) is the source for a growth of a layer thickness (SEI) in order to account for anode aging; (iii) a loss of cathode-side active material is the source for the growth of rock salt deposits (rock salt) in order to account for cathode aging.The server 85 determines 140 model parameters MP, which are dependent on the aging state SOH, for the electrochemical modeling of the battery cell 56 in the aging state SOH. Alternatively (not shown), the determination 140 of the model parameters MP can be performed by the motor vehicle 50. The determination 140 of the model parameters MP depends on the boundary condition 70 and takes place taking into account a relationship between the diagnostic parameters LAM_NE, LAM_PE, LLI and the boundary condition 70.

[0057] The server 85 outputs 150 the model parameters MP for the electrochemical modeling of the battery cell 56. Outputting 150 of the model parameters MP includes transmitting 150a from the server 85 to the motor vehicle 50. In an alternative embodiment, outputting 150 can be performed by the motor vehicle 50, thus eliminating the need for transmission 150a, for example, storing it in a database.

[0058] The motor vehicle 50 performs a recalibration 160 based on the model parameters MP, the state of charge estimator 80, and the state of aging estimator 81. The recalibration 160 is based on the model of the battery cell 56 updated based on the model parameters MP, since the functions of the state of charge estimator 80 and the state of aging estimator 81 are model-based. Furthermore, the motor vehicle 50 performs a use 170 of the model parameters MP for power prediction and / or for determining a charging curve 61. Using the model parameters MP, the parameters of functions for power prediction and / or for determining a charging curve 61 can be updated.

[0059] Figure 3 schematically shows a flow diagram of a method 100 according to one aspect of the disclosure. The method 100 is a method 100 for modeling a battery cell 56 of an energy storage device 55 for a motor vehicle 50. Such a motor vehicle 50, such an energy storage device 55, such a battery cell 56, and / or features thereof are described with reference to Figures 1 and 2. Figure 3 is described with reference to Figures 1 and 2.

[0060] The method 100 according to Figure 3 comprises: determining 110 an open-circuit voltage characteristic curve 65 characterizing the battery cell 56 and dependent on an aging state SOH of the battery cell 56 based on field data 64 characterizing the battery cell 56. The method 100 comprises: determining 120 diagnostic parameters LAM_NE, LAM_PE, LLI dependent on the aging state SOH based on the open-circuit voltage characteristic curve 65.

[0061] The method 100 comprises: detecting 130 a partial charging curve 60 causing a change in the state of charge SOC.

[0062] The method 100 comprises: transmitting 135 the partial charging curve 60, the diagnostic parameters LAM_NE, LAM_PE, LLI and / or the open-circuit voltage characteristic curve 65 from the motor vehicle 50 and / or a motor vehicle-side data processing device 51 to a vehicle-external server 85.

[0063] The method 100 comprises: determining 136 a boundary condition 70 relating to resistances of the battery cell 56 and / or layer growth.

[0064] The method 100 comprises: determining 140 model parameters MP dependent on the aging state SOH for modeling the battery cell 56 in the aging state SOH. The determination 140 of the model parameters MP is performed by the server 85. The determination 140 of the model parameters MP depends on the boundary condition 70. The determination 140 of the model parameters MP takes place taking into account a relationship between the diagnostic parameters LAM_NE, LAM_PE, LLI and the boundary condition 70.

[0065] The method 100 comprises: outputting 150 the model parameters MP for the electrochemical modeling of the battery cell 56. The outputting 150 of the model parameters MP comprises transmitting 150a from the server 85 to the motor vehicle 50 and / or a motor vehicle-side data processing device 51.

[0066] The method 100 comprises: recalibration 160, based on the model parameters MP, a state of charge estimator 80 and / or an aging state estimator 81.

[0067] The method 100 comprises: using 170 the model parameters MP to predict performance and / or to determine a charging curve 61.

[0068] The person skilled in the art will recognize that the method 100 according to Figure 3 can also be performed in a different order than that shown. In particular, it is possible for steps of the method 100 to be interchanged, shifted, and / or performed simultaneously.

[0069] Figure 4 shows a schematic representation of a computer program and / or computer-readable medium 200 according to one aspect of the disclosure. The computer program and / or computer-readable medium 200 includes instructions (not shown) that, when executed by a data processing device 51, 86, cause the device to perform the method 100 and / or the steps of the method 100 according to Figure 3.

[0070] The instructions can be present as program code in any code or language, in particular in code suitable for controlling and / or monitoring motor vehicles 50 and / or for cloud services. The computer program and / or computer-readable medium 200 can be or comprise any digital data storage device, such as a USB stick, a hard drive, a CD-ROM, an SD card, or an SSD card. The computer program does not necessarily have to be stored on such a computer-readable storage medium, but can also be accessible via the Internet or otherwise.

[0071] Reference symbol (part of the description)

[0072] 50 motor vehicles

[0073] 51 Data processing device

[0074] 52 drive

[0075] 53 Communication interface

[0076] 54 data storage

[0077] 55 Energy storage device

[0078] 56 battery cells

[0079] 57 Anode

[0080] 58 Cathode

[0081] 59 Separator

[0082] 60 partial charging curve

[0083] 61 Charging curve

[0084] 64 field data

[0085] 65 Open-circuit voltage characteristic

[0086] 70 Boundary condition

[0087] 75 loading request

[0088] 80 state of charge estimators

[0089] 81 Ageing State Estimator

[0090] 85 vehicle-external servers

[0091] 86 server-side data processing device

[0092] 88 server-side communication interface

[0093] 90 vehicle-external network

[0094] 91 charging stations

[0095] 100 procedures

[0096] 110 Recording a cell voltage curve

[0097] 120 Determining diagnostic parameters

[0098] 130 Recording a partial charge curve

[0099] 135 Transmit 136 Determine a boundary condition

[0100] 140 Determining model parameters

[0101] 150 Issues

[0102] 150a Transmission

[0103] 160 Recalibration

[0104] 170 Use

[0105] 200 Computer program and / or computer-readable medium

[0106] ANE reference parameter, scaling parameter of the negative half-cell potential

[0107] APE reference parameter, scaling parameter of the positive half-cell potential

[0108] BNE reference parameter, shift parameter of the negative half-cell potential

[0109] BPE reference parameter, shift parameter of the positive half-cell potential DOD discharge

[0110] LAM_NE diagnostic parameters, loss parameters of the negative electrode active material

[0111] LAM_PE diagnostic parameters, loss parameters of the active material of the positive electrode

[0112] LLI diagnostic parameters, lithium loss parameters

[0113] MP model parameters

[0114] OCP half-cell potential

[0115] OCP- half-cell potential of the negative electrode

[0116] OCP+ Half-cell potential of the positive electrode

[0117] SOC state of charge

[0118] SOC_E discharge state

[0119] SOC_S Initial state of charge

[0120] SOH health status

[0121] TQ partial load

[0122] U voltage, cell voltage

Claims

Claims 1. A method (100) for modeling a battery cell (56) of an energy storage device (55) for a motor vehicle (50), the method (100) comprising: - determining (110) a rest voltage characteristic curve (65) characterizing the battery cell (56) and dependent on an aging state (SOH) of the battery cell (56) on the basis of field data (64) characterizing the battery cell (56); - determining (120) diagnostic parameters (LAM_NE, LAM_PE, LLI) dependent on the aging state (SOH) on the basis of the rest voltage characteristic curve (65); - detecting (130) a partial charging curve (60) causing a change in the state of charge (SOC); - determining (140) model parameters (MP) dependent on the aging state (SOH) for modeling the battery cell (56) in the aging state (SOH); and - Outputting (150) the model parameters (MP) for the electrochemical modeling of the battery cell (56).

2. The method (100) according to claim 1, wherein the method (100) comprises: - transmitting (135) the partial charging curve (60), the diagnostic parameters (LAM_NE, LAM_PE, LLI) and / or the rest voltage characteristic curve (65) from the motor vehicle (50) and / or a motor vehicle-side data processing device (51) to a vehicle-external server (85), wherein - the determination (140) of the model parameters (MP) is carried out by the server (85).

3. The method (100) according to claim 2, wherein the outputting (150) of the model parameters (MP) comprises transmitting (150a) from the server (85) to the motor vehicle (50) and / or a motor vehicle-side data processing device (51).

4. Method (100) according to one of the preceding claims, wherein the method (100) comprises: - determining (136) a boundary condition (70) relating to resistances of the battery cell (56) and / or layer growth, wherein - the determination (140) of the model parameters (MP) depends on the boundary condition (70).

5. The method (100) according to claim 4, wherein the determination (140) of the model parameters (MP) takes place taking into account a relationship between the diagnostic parameters (LAM_NE, LAM_PE, LLI) and the boundary condition (70).

6. The method (100) according to any one of the preceding claims, wherein the method (100) comprises: - Use (170) of the model parameters (MP) for performance prediction and / or for determining an ageing condition-dependent charging curve (61).

7. The method (100) according to any one of the preceding claims, wherein the method (100) comprises: - recalibration (160), based on the model parameters (MP), a state of charge estimator (80) and / or an ageing state estimator (81).

8. Computer program and / or computer-readable medium (200), comprising instructions which, when the program or instructions are executed by a data processing device (51, 96), cause the device (51, 96) to carry out the method (100) and / or the steps of the method (100) according to one of claims 1 to 7.

9. Data processing device (51, 96) for a motor vehicle (50) and / or for a vehicle-external server (95), wherein the data processing device (51, 81) is configured to carry out steps of the method (100) according to one of claims 1 to 7.

10. Motor vehicle (50) and / or vehicle-external server (95), comprising the data processing device (51, 81) according to claim 9.

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