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

Abstract

A method for diagnosing a battery cell for an energy storage device for a motor vehicle, comprising: capturing a cell voltage profile of the battery cell during a partial charge process to charge the battery cell from an initial state of charge to a final state of discharge; reconstructing a voltage curve corresponding to a health state based on the cell voltage profile, taking into account that the partial charge depends on the health state, the initial state of charge, and the final state of discharge; determining half-cell potentials based on the voltage curve; and determining diagnostic parameters relating to degradation modes of the battery cell based on the half-cell potentials.

Description

[0001] The present disclosure relates to a method for diagnosing a battery cell for an energy storage device for a motor vehicle. The disclosure also relates to a computer program and / or computer-readable medium, a data processing device for an external server and / or for a motor vehicle, and an external server and / or a motor vehicle.

[0002] Such an energy storage device typically comprises a plurality of battery cells connected in parallel and / or series, thus forming a high-voltage storage device also known as a traction battery. The energy storage device is designed to discharge the battery cells and provide electrical energy to operate the vehicle and / or to supply 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 driving in order to charge the battery cells of the energy storage device.

[0003] Accurate modeling of the high-voltage storage system is crucial for its safe, sustainable, and efficient use. Such a high-voltage storage system, or rather the cells it comprises, for example, lithium-ion cells, lose capacity and increase their resistance over their lifespan, a process known as aging. This can ultimately reduce the vehicle's range and performance when the cells are used in a motor vehicle. The capacity and resistance, or rather their changes, can therefore be incorporated into an assessment of the battery cell's condition.

[0004] For state estimation to characterize the condition of the cells and / or the energy storage device, an open-circuit voltage (OCV) characteristic curve and / or a half-cell potential (OCP) are typically used, i.e., a relationship between the open-circuit voltage of a cell or the potential of an electrode and the cell's state of charge. This allows, in particular, the estimation of 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 characteristic curve can also be used to determine a charging strategy for charging the energy storage device or the cells.

[0005] Information about a battery's current state of health can be defined as the currently available capacity compared to its capacity when new. State of health is of paramount importance for a vehicle's operating strategy, particularly for designing the charging strategy, estimating the state of charge (SOC), and ensuring safety. However, degradation of state of health, or aging, exhibits a complex path dependency: the battery cell can age due to various mechanisms, such as temperature 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, Vol. 341, 2017, pp. 373–386).Degradation modes summarize the physicochemical aging mechanisms in a model and can be characterized by parameters that describe, for example, the loss of active material in an electrode and / or charge carriers such as lithium. These degradation modes allow for an estimation of how the storage device will behave in the future and when its end of life will be reached.

[0006] To characterize health status and aging, the development and provision of generalizable, accurate, and reliable estimation algorithms and methods is desirable. A promising methodology is a voltage-time reconstruction model based on measured half-cell potentials in the initial state.

[0007] Methods that utilize such a reconstruction refer to complete charging curves (see, for example, Matthieu Dubarry, Cyril Truchot, Bor Yann Liaw, "Synthesize battery degradation modes via a diagnostic and prognostic model," Journal of Power Sources, Vol. 219, 2012, pp. 204-216). These methods measure the voltage profiles of an aged cell at low charging rates, known as C-rates. A C-rate is equal to the current divided by the nominal capacity; that is, the current rate has the unit 1 / h, or more generally, a rate, and inversely indicates how quickly the battery is fully charged with a given current. For example, a cell is fully charged in half an hour at a C-rate of two; at a C-rate of one and a half, a cell is fully charged in two hours.Half-cell potential profiles measured in the new state are shifted and compressed to reconstruct the measured voltage profile of the aged cell in such a way that the error between an aged voltage profile reconstructed on the basis of the half-cell potential profiles measured in the new state and the measured voltage profile of the aged cell is minimal.

[0008] This optimization or error minimization can also be applied to partial charging curves and at higher C-rates (see, for example, 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).

[0009] The aforementioned state-of-the-art methods utilize time-resolved signals to reconstruct the voltage curve. These voltage curves thus contain a wealth of information about the qualitative behavior of the voltage, and an accurate relative fit of the half-cell potential curves can be achieved. This can have a positive impact on the estimation of degradation modes.

[0010] However, there are multiple solutions for different SOH values ​​that minimize the error function or cost function. A characteristic of the aforementioned methods is that there can be several local optima for a given charging curve, each potentially leading to a local minimum error. Therefore, the voltage profile cannot be reliably and uniquely reconstructed. Furthermore, these methods do not consider any information about the charged capacity.

[0011] Patent application DE 10 2022 129 208.8, which was not yet published on the filing date of the present disclosure, describes an alternative method. According to this method, it is possible to use relaxed stress points together with a summed charge throughput to reconstruct the stress curve. This is a comparatively low-effort way to estimate the state of aging, since by specifying the amount of charge applied, the space of possible solutions for the stress curve can be reduced to such an extent that higher accuracy can be achieved in estimating the state of aging (SOH). However, it can be more difficult to achieve accurate results in estimating the degradation modes.

[0012] In light of this prior art, one objective of the present disclosure is to provide a method suitable for enriching the prior art and improving at least the aforementioned aspects of the prior art. In particular, the disclosure aims to provide a reliable and unambiguous method for determining degradation modes.

[0013] The problem is solved by the features of the independent claims. The dependent claims contain further developments of the disclosure.

[0014] The problem is then solved according to one aspect of the disclosure by a method for diagnosing a battery cell for an energy storage device for a motor vehicle, the method comprising: recording a cell voltage profile of the battery cell during a partial charging process with a partial charge to charge the battery cell from an initial state of charge to a final state of discharge; reconstructing a voltage curve corresponding to a health state based on the cell voltage profile, taking into account that the partial charge depends on the health state, the initial state of charge and the final state of discharge; determining half-cell potentials based on the voltage curve; and determining diagnostic parameters relating to degradation modes of the battery cell based on the half-cell potentials.

[0015] It was discovered that partial charging can be sufficient for battery reconstruction. In this process, the battery cell is charged from its initial state of charge to its final state of charge. The difference between the final state of charge and the initial state of charge corresponds to the partial charge, i.e., the amount of charge introduced. During the partial charging process, the cell voltage profile is recorded. This cell voltage profile thus indicates the cell voltage as a function of the state of charge between the initial and final states of charge.

[0016] It was recognized that a unique voltage curve corresponding to the cell's health status can be reconstructed based on the cell voltage profile. This takes into account that the partial charge depends on the cell's health status, initial state of charge, and final state of discharge. This dependence of the partial charge on the cell's health status, initial state of charge, and final state of discharge can be considered an additional constraint during the reconstruction process to ensure that information relating to the partial charge can be used. Alternatively or additionally, the dependence of the partial charge on the cell's health status, initial state of charge, and final state of discharge can be formulated more generally as a dependency between the partial charge, the cell's health status, the initial state of charge, and the final state of discharge.In particular, the dependence can be formulated as a dependence of any one of the mentioned quantities on the other mentioned quantities.

[0017] In addition to the displacement and / or scaling achieved through reconstruction, the initial and final charge states are also considered, taking into account the dependence of the partial charge. This allows for unambiguous, highly reliable, and effective reconstruction.

[0018] Based on the reconstructed voltage curve of the battery cell in its healthy state, half-cell potentials can be determined, and these half-cell potentials can then be used to determine the diagnostic parameters. The diagnostic parameters can be determined such that each corresponds to a degradation mode. It was found that the degradation modes, like the half-cell potentials, are related to the electrodes or charge carriers of the battery cell. The unambiguous reconstruction of the voltage curve corresponding to the healthy state thus allows for the unambiguous determination of the half-cell potentials and therefore the diagnostic parameters. This enables the reliable and unambiguous determination of the degradation and / or the degradation modes.

[0019] Optionally, the voltage curve is reconstructed under the assumption that the partial charge is proportional to the product of the state of health and the difference between the initial and final states of charge. It was recognized that a proportionality of the partial charge to the product of the state of health and the difference between the initial and final states of charge can reflect the fact that the state of health can be defined as the quotient of the current capacity of the battery cell corresponding to the state of health and the capacity of the battery cell in its new state. Alternatively, the partial charge is equal to the product of the state of health and the difference between the initial and final states of charge, where the partial charge can be normalized by the cell's capacity in its new state.

[0020] Optionally, the voltage curve can be reconstructed using a reference voltage curve. This allows for a comparison of the battery cell to a reference state. The reference voltage curve can be a voltage curve of the battery cell recorded in its new state, enabling a comparison to that new state. The reference voltage curve can also include reference half-cell potentials.

[0021] Optionally, the voltage curve can be reconstructed taking into account a plurality of reference parameters. These reference parameters can, in particular, define a scaling and / or shift of the reference voltage curve.

[0022] Optionally, the voltage curve can be reconstructed by minimizing a loss function. The loss function, or error function and / or cost function, can be used to reconstruct the voltage curve by minimizing it so that the measured cell voltage profile corresponds as closely as possible to the reconstructed voltage curve.

[0023] Optionally, a dependency of the partial charge on the health status, the initial state of charge, and the final state of charge can be a constraint for minimization. This allows the minimization to lead to a unique solution and thus reconstruction of the voltage curve.

[0024] Optionally, each diagnostic parameter defines a scaling and / or a shift of the half-cell potentials. This makes it possible to calculate the diagnostic parameters directly from values ​​that can be read from the half-cell potentials or measured numerically.

[0025] The above description can be summarized in other words, and with reference to a specific implementation described as not limiting to the present disclosure, as follows: The disclosure relates to estimating state of health (SOH) and degradation modes using a combination of partial charge curves and Coulomb counting. The background is that a high-voltage storage system, or rather the lithium-ion cells it contains, lose capacity and increase resistance over its lifetime. Information about the current state of health (SOH), typically defined as the currently available capacity compared to the capacity when new, is of paramount importance for a vehicle's operating strategy (e.g., design of charging algorithms, state of charge (SOC) estimation, safety, etc.). Furthermore, the complex path dependency of aging must be considered. Degradation modes represent an attempt to capture this path dependency.This information allows for a better assessment of the storage device's future behavior and its expected end of life. Developing generalizable, accurate, and reliable estimation algorithms is essential. A promising methodology is voltage curve reconstruction based on measured half-cell potentials in the new state. Methods utilizing such a reconstruction technique refer to complete charge curves. At low C-rates, the half-cell potential curves measured in the new state are shifted and compressed to minimize the error between the reconstructed, aged voltage curve and the measured, aged voltage curve. This optimization is also applied to partial charge curves, even at higher C-rates. The aforementioned methods use time-resolved signals to reconstruct the voltage curve.Thus, this information contains data on the qualitative voltage profile, and an accurate relative fit of the half-cell potential curves can be achieved. This can have a positive impact on the degradation mode estimation. However, there are multiple solutions for different SOH values ​​that minimize the cost function, meaning multiple optima for a given charging curve. Furthermore, no information on the charged capacity is included. Additionally, the "Delta-Q" method, as described in patent application DE 10 2022 129 208.8 (not yet published on the filing date of the present disclosure), can be applied. This method uses relaxed voltage points together with the summed charge throughput to reconstruct the voltage curve.The DeltaQ method offers a low-effort way to estimate the state of health (SOH) because specifying the amount of charge applied reduces the space of possible solutions, thus enabling higher accuracy in the SOH estimation. However, achieving accurate results in degradation mode estimation is more difficult. The discovery combines the two models mentioned above. An optimizer now not only solves the displacement and compression parameters but also the initial and discharge states of the charging curve. An additional constraint forces the optimizer algorithm to also use the "DeltaQ" information. The idea is to incorporate the condition "(SOC_end - SOC_start) * SOH = DeltaQ / C_N" into the model.Here, SOC_start is the starting state of charge, SOC_end the final state of charge, SOH a current health status defined as the current capacity compared to the capacity when new, DeltaQ a measured charge throughput within the limits of SOC_start and SOC_end, and C_N the cell's capacity when new. As an example, a cell in a high-voltage storage system (HVS) with a specific cell technology is measured when new: half-cell potentials and voltage curves during a full charge at a specific current value, which can correspond to that of a wallbox. A user now wants to characterize their older vehicle quickly and easily. They initiate a diagnostic mode overnight at the wallbox. The vehicle is fully charged as usual. The recorded voltage and current trajectories are transmitted to the algorithm, which operates, for example, in the backend. This algorithm uses the time-resolved voltage data, as well as the cumulative charge (current times time), to start the optimization process.After completion, an updated SOH and degradation mode value, as well as an updated OCV characteristic curve, are available. These can now be used by the other vehicle functions.

[0026] 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 includes instructions that, when executed by a data processing device, cause the device to perform the method according to the disclosure and / or steps thereof. Optionally, the computer program and / or the computer-readable medium includes instructions that, when executed by a data processing device, cause the device to perform the process steps described as advantageous or optional in order to achieve an associated technical effect.

[0027] According to one aspect of the disclosure, a data processing device is provided for an external server and / or for a motor vehicle. The data processing device is configured to perform the procedure described above. Optionally, the data processing device is configured to perform a procedure step and / or implement a procedure feature described as advantageous or optional in order to achieve an associated technical effect.

[0028] According to one aspect of the disclosure, an off-vehicle server and / or a motor vehicle comprising the data processing device described above is provided. Optionally, the data processing device, the off-vehicle server, and / or the motor vehicle is configured to perform a process step and / or implement a process feature described as advantageous or optional in order to achieve an associated technical effect.

[0029] One embodiment of each is described below with reference to the figures. Fig. Figure 1 schematically shows an external server and an electrically powered motor vehicle, each according to an aspect of the disclosure; Fig. 2 schematically shows a flowchart of a procedure according to an aspect of the revelation; Fig. Figure 3 shows a schematic representation of a computer-readable medium according to one aspect of the revelation; Fig. Figure 4 shows, by way of example, a cell voltage and half-cell potentials as a function of a state of charge as reference voltage curves for a method according to an aspect of the disclosure; Fig. Figure 5 shows an example of a cell voltage curve and a reconstruction of a voltage curve corresponding to a health condition according to a method according to an aspect of the disclosure; Fig. Figure 6 shows, by way of example, a cell voltage and half-cell potentials as a function of a state of charge for determining diagnostic parameters according to a method according to an aspect of the disclosure; Fig. Figure 7 shows an example of the result of an aging condition assessment using a state-of-the-art method; and Fig. Figure 8 shows an example of the result of an aging state assessment using a method according to one aspect of the disclosure.

[0030] Fig. Figure 1 schematically shows a vehicle-external server 80 and an electrically powered motor vehicle 50 according to one aspect of the disclosure.

[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, the number of which is 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 the state of charge (SOC) of the battery cells 56. The energy storage device 55, or the battery cells 56, are also configured to provide electrical energy for operating the motor vehicle 50 and / or the electric drive 52, whereby the battery cells 56 are discharged, i.e., the state of charge (SOC) of the battery cells 56 decreases.

[0033] The battery cells 56 of the energy storage device 55 exhibit a cell voltage profile 60, i.e., a relationship between a measured voltage U and the state of charge (SOC) of the battery cell 56, as with reference to Fig. 4 and Fig. 5 described, wherein the cell voltage profile 60 depends on a health state SOH of the battery cells 56.

[0034] The motor vehicle 50 according to Fig. Figure 1 includes 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 charging profile with a current I and to measure a cell voltage U of one of the battery cells 56 for charging the energy storage device 55. In particular, a partial charging process can be defined in which a partial charge DQ is supplied to the battery cells 56, which may correspond to a fraction of the capacity of the battery cells 56.

[0035] Fig. Figure 1 also shows a charging station 91 and an external power supply network 90. ​​The charging station 91 and the vehicle 50 are configured to be electrically and communicatively connected. This allows the energy storage device 55 to be supplied with electrical energy from the external power supply network 90 via the charging station 91 to charge the battery cells 56.

[0036] The vehicle-external server 80 has a server-side data processing device 81 and a server-side communication interface 83. The vehicle 50 has a vehicle-side communication interface 53. The vehicle-side data processing device 51 is connected to the vehicle-side communication interface 53. The vehicle-side communication interface 53 is configured to communicate with the server 80 via the server-side communication interface 83 for the exchange of data. For this purpose, the communication interface 53 is configured, for example, for communication via a wireless local area network (WLAN) and / or a mobile network. Alternatively, communication between the vehicle-side data processing device 51 and the server 80 can also take place via the charging station 91.

[0037] Server 80 according to Fig. 1 or the server-side data processing device 81 is configured to perform the procedure 100 according to Fig. 2. In particular, the data processing device 81 is configured to perform tasks related to Fig. Sections 2 and 4 to 6 describe how to determine the diagnostic parameters LAM_NE, LAM_PE, and LLI.

[0038] In another embodiment, the motor vehicle 50 or the data processing device 51 is configured to process the data relating to Fig. 2 described procedures to be carried out 100 times.

[0039] Fig. Figure 2 schematically shows a flowchart of a procedure 100 according to one aspect of the disclosure. The procedure 100 according to Fig. 2 is a method 100 for diagnosing a battery cell 56 for an energy storage device 55 for a motor vehicle 50. Such a battery cell 56, such an energy storage device 55 and such a motor vehicle 50 are related to Fig. 1 described. Fig. 2 is referred to Fig. 1 described.

[0040] The procedure 100 according to Fig. 2 indicates: Recording 110 of a cell voltage profile 60 of the battery cell 56 during a partial charging process with a partial charge DQ to charge the battery cell 56 from an initial state of charge SOC_S to a final state of charge SOC_E. Such a cell voltage profile 60 is in Fig. 5 shown and with reference to Fig. 5 described.

[0041] The procedure 100 according to Fig. 2 shows: Reconstruction 120 of a voltage curve 65 corresponding to a health state SOH based on the cell voltage profile 60, taking into account that the partial charge DQ depends on the health state SOH, the initial state of charge SOC_S, and the final state of charge SOC_E. Such a reconstructed voltage curve 65 is in Fig. 5 shown and with reference to Fig. 5 described.

[0042] The reconstruction of the voltage curve 65 is carried out under the assumption that the partial charge DQ is proportional to the product of the state of health (SOH) and the difference between the initial state of charge (SOC_S) and the final state of charge (SOC_E). In particular, it can be assumed that the partial charge DQ is equal to the product of the state of health (SOH) and the difference between the initial state of charge (SOC_S) and the final state of charge (SOC_E), where the partial charge DQ is normalized to the nominal capacity of the battery cell 56 in the reference state and / or in its new state.

[0043] The reconstruction 120 of the voltage curve 65 is performed taking into account a reference voltage curve 66. The reference voltage curve 66 is, for example, a relationship between voltage U and state of charge (SOC) in the reference state, such as the new state. Such a reference voltage curve 66 is in Fig. 4 shown and with reference to Fig. 4 described.

[0044] The reconstruction 120 of the voltage curve 65 is carried out taking into account a plurality of reference parameters ANE, APE, BNE, BPE. The reference parameters ANE, APE, BNE, BPE describe in the reconstructed voltage curve 65 or in the reference voltage curve 66 how the voltage U is composed of half-cell potentials OCP-, OCP+.

[0045] The reconstruction 120 of the voltage curve 65 is performed by minimizing a loss function L. Alternatively, a function can also be maximized and / or otherwise optimized. The loss function can also include modified versions of the measured voltage curve 65, for example, a differential voltage (DVA) and / or incremental voltage (ICA). In the case of the loss function L, the reference parameters ANE, APE, BNE, BPE are adjusted for reconstruction 120 such that the value of the loss function L, for example, as the difference between the reconstructed voltage curve 65 and the measured cell voltage profile 60, is minimal. Alternatively, the difference between a reconstructed differential and / or incremental voltage curve and a measured differential and / or incremental cell voltage profile can be minimized.The dependence of the partial charge DQ on the health state SOH, the initial charge state SOC_S, and the final charge state SOC_E is a constraint for minimizing the charge. This allows a unique minimum to be found.

[0046] The procedure 100 according to Fig. 2 indicates: Determine 130 half-cell potentials 70 from the voltage curve 65. The reference parameters ANE, APE, BNE, BPE, fitted by minimizing the loss function L, are used to determine the half-cell potentials 70 from the voltage curve 65. Such half-cell potentials 70 are in Fig. 6 shown and with reference to Fig. 6 described.

[0047] The procedure 100 according to Fig. 2 indicates: Determine 140 of the battery cell degradation modes 56 related to diagnostic parameters LAM_NE, LAM_PE, LLI based on the half-cell potentials 70. Each of the diagnostic parameters LAM_NE, LAM_PE, LLI defines a scaling and / or a shift of the half-cell potentials 70. The diagnostic parameters LAM_NE, LAM_PE, LLI are related as follows: Fig. 6 described in connection with capacities CLIT, CPE, CNE.

[0048] Fig. Figure 3 shows a schematic representation of a computer-readable medium 200 according to one aspect of the disclosure. The computer-readable medium 200 comprises instructions (not shown) which, when the program or instructions are executed by a data processing device 51, 81, cause it to execute the method 100 and / or the steps of the method 100 according to Fig. 2 to be carried out.

[0049] The commands can be in the form of program code in any code or language, in particular code suitable for controlling and / or monitoring motor vehicles. The computer-readable medium can be or comprise any digital data storage device, such as a USB flash drive, hard drive, CD-ROM, SD card, or SSD. The computer program does not necessarily have to be stored on such a computer-readable storage medium, but can also be accessed via the internet or otherwise.

[0050] Fig. Figure 4 shows an example of a cell voltage U and half-cell potentials OCP+, OCP- each as a function of a state of charge SOC as reference voltage curves 66 for a method 100 according to an aspect of the disclosure. Fig. 4 is referred to Fig. 1 to 3 described.

[0051] The cell voltage U and the half-cell potentials OCP+, OCP- of battery cell 56, for example, were measured in their new state with a predetermined charging rate. The solid line shows the cell voltage U as a function of the state of charge (SOC). The dotted line shows the half-cell potential OCP- of the negative electrode. The dashed line shows the half-cell potential OCP+ of the positive electrode. The cell voltage U can also be calculated as the difference between the half-cell potentials OCP-, OCP+.

[0052] The reference parameters ANE, APE, BNE, and BPE describe how the reference voltage curve 66 is composed of the two half-cell potentials OCP- and OCP+. The reference parameters ANE, APE, BNE, and BPE can thus be numerically extracted from the half-cell potentials OCP- and OCP+ and the cell voltage U.

[0053] The reference parameter ANE is a scaling parameter of the negative half-cell potential OCP-. The reference parameter BNE is a shift parameter of the negative half-cell potential OCP-. The reference parameter APE is a scaling parameter of the positive half-cell potential OCP+. The reference parameter BPE is a shift parameter of the positive half-cell potential OCP+.

[0054] Fig. Figure 5 shows an example of a cell voltage curve 60 and a reconstruction of a voltage curve 65 corresponding to a health state SOH according to a method 100 in accordance with an aspect of the disclosure. Fig. 5 is referred to Fig. 1 to 4 described.

[0055] The cell voltage curve 60 is a voltage U measured during a partial charging process of battery cell 56. Here, the cell voltage curve 60 is represented as voltage U as a function of a charge Q and the state of charge (SOC). For an aged battery cell 56, a voltage U is recorded over the partial charging process.

[0056] As in Fig. Figure 5 shows that during the partial charging process, battery cell 56 is imparted with a partial charge DQ, thereby increasing the charge quantity of battery cell 56 from an initial charge Q0 to a final discharge Q0+DQ. The state of charge (SOC) thereby increases from an initial state of charge SOC_S to a final discharge state SOC_E.

[0057] The cell voltage profile 60 can be used to reconstruct the voltage curve 65 corresponding to the state of health (SOH). The reconstructed voltage curve 65 extends over the entire capacity C of the battery cell 56, since the reference voltage curves 66 are used as a reference. Fig. 4 is the basis.

[0058] The reconstruction 120 of the voltage curve 65 is carried out taking into account a plurality of reference parameters ANE, APE, BNE, BPE as well as the initial state of charge SOC_S and the final state of charge SOC_E or the partial charge DQ. The voltage curve 65 is derived from the half-cell potentials OCP-, OCP+ or the voltage U as the reference voltage curve 66 in the new state (see Fig. 4) reconstructed by compression or scaling and translation.

[0059] Fig. Figure 6 shows an example of a cell voltage U and half-cell potentials OCP-, OCP+ each as a function of a state of charge SOC for determining diagnostic parameters LAM_NE, LAM_PE, LLI according to a method 100 according to an aspect of the disclosure. Fig. 6 is referred to Fig. 1 to 5 described.

[0060] In this process, by reconstructing the stress curve 65, a set of parameters belonging to the reconstructed stress curve 65 is determined analogously to the reference parameters ANE, APE, BNE, BPE belonging to the reference stress curve 66.

[0061] In Fig. Figure 6 shows the cell voltage U and the half-cell potentials OCP-, OCP+ according to the reconstructed voltage curve 65, each represented by a dotted and / or dashed line. For comparison, the cell voltage U and the half-cell potentials OCP-, OCP+ according to the reference voltage curve 66 are shown with solid lines. Based on the state of charge (SOC) range, the capacities CLI, CNE, and CPE can be determined from the cell voltage U and the half-cell potentials OCP+-. Here, CLI is the capacity attributed to the lithium, CNE is the capacity attributed to the negative electrode, and CPE is the capacity attributed to the positive electrode.

[0062] This information can now be used to characterize the degradation modes based on diagnostic parameters LAM_NE, LAM_PE, and LLI. The state of health (SOH) is then calculated as the quotient of the current capacity C of battery cell 56 and the capacity C of battery cell 56 when new.

[0063] The diagnostic parameter LAM_NE is a loss parameter of the active material of the negative electrode. LAM_NE can be calculated as the quotient of the decrease in the capacitance CNE attributed to the negative electrode and the capacitance CNE attributed to the negative electrode in the reference state, i.e., LAM_NE = (CNE_I - CNE) / CNE_I, where CNE is the current capacitance attributed to the negative electrode and CNE_I is the capacitance attributed to the negative electrode in the reference state.

[0064] 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 CPE attributed to the positive electrode and the capacitance CPE attributed to the positive electrode in the reference state, i.e., LAM_PE = (CPE_I - CPE) / CPE_I, where CPE is the current capacitance attributed to the positive electrode and CPE_I is the capacitance attributed to the positive electrode in the reference state.

[0065] The diagnostic parameter LLI is a loss parameter of lithium. The diagnostic parameter LLI can be calculated as the quotient of the decrease in the capacity CLI attributed to the lithium and the capacity CLI attributed to the lithium in the reference state, i.e., LLI = (CLI_I - CLI) / CLI_I, where CLI is the current capacity attributed to the lithium and CLI_I is the capacity attributed to the lithium in the reference state.

[0066] In this description, the terms voltage curve and open-circuit voltage (OCV) curve were used synonymously. Likewise, the terms half-cell potential and open-circuit potential (OCP) were used synonymously.

[0067] Fig. Figure 7 shows an example of the result of an aging state estimation using a prior art method 100. The state of health (SOH) is shown as a function of a number of intermediate measurements Z, whereby aging of the battery cell 56 has occurred between two intermediate measurements Z, for example, through several charging cycles. Within a charging cycle, the state of charge (SOC) increases from 10% to 90% at a rate of C / 25. The state of health (SOH) decreases with the number of intermediate measurements Z. The actual state of health (SOH) is represented by a dotted line, and the state of health (SOH) determined according to the prior art is represented by a solid line. It becomes apparent that the state of health (SOH) determined according to the prior art develops rather abruptly with each intermediate measurement Z and only approximately follows the actual state of health (SOH).

[0068] Fig. Figure 8 shows an example of a result of an aging state assessment according to a method 100 in accordance with one aspect of the disclosure. Fig. 8 is referred to Fig. 1 to 7 described, whereby to determine the Fig. 8 a with the protocol for determining the Fig. 7 a comparable protocol was used, whereby in Fig. 8 the reconstruction 120 was carried out according to the disclosed procedure 100. In this process, Fig. Figure 8 shows the state of health (SOH) as a function of a number of intermediate measurements Z. Within a charging cycle, the state of charge (SOC) increases from 10% to 90% at a rate of C / 25. The state of health (SOH) decreases with the number of intermediate measurements Z. The actual state of health (SOH) is shown with a dotted line and is determined according to procedure 100. Fig.The determined health status SOH is represented by a solid line. This shows that the health status SOH determined according to procedure 100 corresponds relatively closely to the actual health status SOH.

[0069] This shows that the dependence of the partial charge DQ on the health state SOH, the initial state of charge SOC_S, and the final state of charge SOC_E as a constraint further improves the accuracy of the SOH health state estimation for smaller SOC windows, i.e., differences between the initial state of charge SOC_S and the final state of charge SOC_E. This example is intended to clearly demonstrate that even with a large state-of-charge range of 10 to 90%, the error can be reduced.

[0070] Method 100 enables the battery cell 56 to be measured in a reference state, for example, in its new state: Half-cell potentials OCP+, OCP-, and voltage U are recorded during a full charge with a specific current, which can correspond to that of a wallbox. A user can request a low-effort and rapid characterization of the vehicle 50 or the energy storage device 55. A diagnostic mode is initiated, for example, overnight at the wallbox. The energy storage device 55 is charged as usual. The recorded voltage and current trajectories, i.e., the cell voltage profile 60 and the partial charge DQ, are transmitted to the server 80 or a backend. The server 80 uses the time-resolved data of the voltage U, as well as the cumulative charge DQ, i.e., current I times time, to initiate optimization, for example, minimization.After completion, an updated SOH health status and the diagnostic parameters LAM_NE, LAM_PE, and LLI are available. These can now be used by other vehicle functions, for example, to coordinate power demands and / or to schedule a charging process. Reference symbol (part of the description) 50 motor vehicles 51 Data processing device 52 Drive 53 Communication interface 55 Energy storage device 56 battery cells 60 Cell voltage curve 65 Voltage curve 66 Reference voltage curve 70 half-cell potential 80 vehicle-external servers 81 Data processing device 83 Communication interface 90 vehicle-external network 91 charging station 100 procedures 110 Capture 120 Reconstruction 130 Determine 140 Determine 200 computer-readable media ANE reference parameter, scaling parameter of the negative half-cell potential APE reference parameter, scaling parameter of the positive half-cell potential BNE reference parameter, shift parameter of the negative half-cell potential BPE reference parameter, shift parameter of the positive half-cell potential C capacity CLI capacity, lithium CNE capacity, negative electrode CPE capacity, positive electrode DQ partial load I current L loss function LAM_NE diagnostic parameters, loss parameters of the active material of the negative electrode LAM_PE diagnostic parameters, loss parameters of the active material of the positive electrode LLI diagnostic parameters, lithium loss parameters OCP half-cell potential of the negative electrode OCP+ half-cell potential of the positive electrode Q Charge Q0 Initial charge SOH health status SOC_E discharge state SOC_S Initial state of charge Voltage, cell voltage Z Intermediate measurement QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2022 129 208.8 [0011, 0025] Cited non-patent literature

[0000] C. R. Birkl, M. R. Roberts, E. McTurk, P. G. Bruce, D. A. Howey, Degradation diagnostics for lithium ion cells, Journal of Power Sources, Ausgabe 341, 2017, Seiten 373-386

[0005] Matthieu Dubarry, Cyril Truchot, Bor Yann Liaw, „Synthesize battery degradation modes via a diagnostic and prognostic model“, Journal of Power Sources, Ausgabe 219, 2012, Seiten 204-216

[0007] 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, Ausgabe 59, 2023, Artikel 106517

[0008]

Claims

[1] Method (100) for diagnosing a battery cell (56) for an energy storage device (55) for a motor vehicle (50), the method (100) comprising: - detecting (110) a cell voltage curve (60) of the battery cell (56) during a partial charging process with a partial charge (DQ) for charging the battery cell (56) from an initial state of charge (SOC_S) to a final state of charge (SOC_E); - reconstruction (120) of a voltage curve (65) corresponding to a state of health (SOH) based on the cell voltage curve (60), taking into account that the partial charge (DQ) depends on the state of health (SOH), the initial state of charge (SOC_S) and the final state of charge (SOC_E); - determining (130) half-cell potentials (70) based on the voltage curve (65); and - Determining (140) diagnostic parameters (LAM_NE, LAM_PE, LLI) relating to degradation modes of the battery cell (56) based on the half-cell potentials (70). [2] Method (100) according to claim 1, wherein the reconstruction (120) of the voltage curve (65) is carried out under the assumption that the partial charge (DQ) is proportional to a product of the state of health (SOH) and the difference between the initial state of charge (SOC_S) and the final state of charge (SOC_E). [3] Method (100) according to claim 1 or 2, wherein the reconstruction (120) of the voltage curve (65) takes place taking into account a reference voltage curve (66). [4] Method (100) according to one of the preceding claims, wherein the reconstruction (120) of the voltage curve (65) takes place taking into account a plurality of reference parameters (ANE, APE, BNE, BPE). [5] Method (100) according to one of the preceding claims, wherein the reconstruction (120) of the voltage curve (65) is carried out by minimizing a loss function (L). [6] Method (100) according to claim 5, wherein a dependence of the partial charge (DQ) on the state of health (SOH), the initial state of charge (SOC_S) and the final state of charge (SOC_E) is a constraint for the minimization. [7] Method (100) according to one of the preceding claims, wherein each of the diagnostic parameters (LAM_NE, LAM_PE, LLI) defines a scaling and / or a shift of the half-cell potentials (70). [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, 81), cause the device (51, 81) 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, 81) for a vehicle-external server (80) and / or for a motor vehicle (50), wherein the data processing device (51, 81) is configured to carry out the method (100) according to one of claims 1 to 7. [10] Vehicle-external server (80) and / or motor vehicle (50), comprising the data processing device (51, 91) according to claim 9.

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