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

Abstract

A method for aging-dependent diagnosis of a battery cell for an energy storage device for a motor vehicle, wherein the method comprises: detecting a hysteresis which can be measured between a voltage difference and a state of charge of the battery cell and a characteristic curve of the battery cell which characterizes an open-circuit voltage; determining a cathode-state-dependent first reference state of charge based on the hysteresis, and determining an anode-state-dependent second reference state of charge which describes a plateau change in the characteristic curve; and determining a diagnosis indicator based on the first reference state of charge and the second reference state of charge.

Description

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

[0002] The present disclosure relates to a method for age-dependent diagnosis of a battery cell for an energy storage device for a motor vehicle.

[0003] The disclosure also relates to a computer program and / or computer-readable medium, a data processing device and a motor vehicle.

[0004] 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, 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 to the vehicle, for example, via a charging station. The energy storage device is configured 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.

[0005] 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.

[0006] 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 the open-circuit voltage of a cell 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 state (SOH) of the cell and / or the energy storage device. The open-circuit voltage characteristic can also be used to determine a charging strategy for charging the energy storage device or the cells.

[0007] However, the cells may have a cell chemistry and / or structure that increases the complexity of modeling. For example, lithium iron phosphate cells used as battery cells (so-called LFP cells) exhibit a comparatively flat open-circuit voltage characteristic curve in some sections, as well as a hysteresis of the open-circuit voltage characteristic curve. In addition, various aging effects generally alter the course of the open-circuit voltage characteristic curve. In particular, the comparatively flat open-circuit voltage characteristic curve in some sections and the hysteresis of LFP cells have a negative impact on the currently used state determination methods and can, for example, lead to inaccuracies in determining the state of charge. This can have a direct impact on the vehicle's range forecast and lead to an unexpected breakdown of the vehicle.In addition, the incorrect determination of the state of charge can have a negative impact on the determination of the age or state of health and can lead to premature activation of protective functions and / or limitations in the charging performance.

[0008] 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, due to 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.

[0009] 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.

[0010] Methods that use this type of reconstruction rely on 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, Issue 219, 2012, pages 204-216). Here, the voltage curves of an aged cell are measured at low charging rates, so-called C-rates. A C-rate is equal to a current divided by a nominal capacity, i.e., the current rate has the unit 1 / h or, in general, a rate, and reciprocally indicates how quickly the battery is fully charged with a specific 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 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.

[0011] This optimization or error minimization can also be applied to partial charging curves and even 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).

[0012] The aforementioned state-of-the-art methods utilize time-resolved signals to reconstruct the voltage curve. Thus, these voltage curves contain a wealth of information about the qualitative voltage profile, allowing for a precise relative fit of the half-cell potential curves. This can have a positive impact on the estimation of degradation modes.

[0013] However, there are multiple solutions at different SOH values ​​that minimize the error function or cost function. A characteristic of the aforementioned methods is that there can be multiple local optima for a specific charging curve, each of which can lead to a local minimum error. This means that the voltage curve cannot be reliably and unambiguously reconstructed. Furthermore, different components of the battery cell sometimes degrade differently. 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 referred to below in this disclosure as electrode balancing. Due to the varying degradation, the electrode balancing depends on the state of health.

[0014] Patent application DE 102022 129208.8, 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 voltage points together with a summed charge throughput to reconstruct the voltage curve. This is a comparatively low-cost way to estimate the aging state, since by specifying the amount of charge introduced, the space of possible solutions for the voltage curve can be reduced to such an extent that a higher accuracy in the SOH estimation can be achieved.

[0015] 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 determine an aging-dependent electrode balancing of the electrodes and thus to expand and / or improve existing reconstruction models.

[0016] The problem is solved by the features of the independent claims. The subclaims contain further developments of the disclosure.

[0017] According to one aspect of the disclosure, the object is achieved by a method for the age-dependent diagnosis of a battery cell for an energy storage device for a motor vehicle, the method comprising: detecting a hysteresis measurable between a voltage difference and a state of charge of the battery cell and a characteristic curve of the battery cell characterizing a rest voltage; determining a cathode-state-dependent first reference state of charge based on the hysteresis, and determining a second reference state of charge based on the anode state, describing a plateau change in the characteristic curve; and determining a diagnostic indicator based on the first reference state of charge and the second reference state of charge. The hysteresis can be sensed or measured by an intelligent charging or diagnostic method, for example, according to the method according to 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.

[0018] It was recognized that hysteresis can typically be largely determined by the cathode, for example in the case of an LFP cell as a battery cell. Hysteresis describes the difference between the open-circuit voltages of the discharge voltage curve or discharge voltage characteristic and the charge voltage curve or charge voltage characteristic. Thus, there is a closed-circuit voltage curve for charging and discharging, i.e., a so-called charging branch that describes the open-circuit voltage curve during charging, and a discharge branch that describes the open-circuit voltage curve during discharging. The resulting open-circuit voltage depends on the history, i.e., on the past course of charging and discharging. The intelligent charging and diagnostic procedure described above enables well-defined sampling of the hysteresis by defining a standardized history for all samples.

[0019] The hysteresis, or the difference between the discharge and charge states, and the transition from the discharge state to the charge state, or vice versa, depends on the battery cell's state of charge and exhibits a characteristic curve for the cathode's condition. Thus, the hysteresis can provide information about the cathode's condition as a function of age, and the first reference state of charge can be determined based on the hysteresis.

[0020] The characteristic curve can be the charge-voltage characteristic curve or the discharge-voltage characteristic curve and, although it has a comparatively flat section, two plateaus, i.e. comparatively flat subsections, can be distinguished from one another within the flat section. A plateau change occurs between the two plateaus, i.e. a change in the characteristic curve from a first plateau at a lower state of charge to a second plateau at a higher state of charge. It was recognized that the plateau change, in particular, is characteristic of the effect of the anode. In such a battery cell, in particular an LFP cell, aging occurs, among other influences, in particular through lithiation of the anode of the battery cell. The relationship between the cell voltage and / or a half-cell potential, in particular a half-cell potential defined with reference to the anode, depends on the condition of the anode.The second reference state of charge can characterize the plateau change or locate it on the state of charge axis between the lower state of charge and the higher state of charge.

[0021] In other words, the first reference state of charge describes the cathode, and the second reference state of charge describes the anode. Thus, the diagnostic indicator can be determined from the first reference state of charge and the second reference state of charge in such a way that the diagnostic indicator can describe the electrode balancing or the cell can be described by electrode-specific factors. This allows the diagnostic indicator to improve the estimation of the cell's state of health. It also enables the determination of degradation modes to be improved.

[0022] Optionally, the diagnostic indicator corresponds to a differential charge, where the differential charge is defined as the difference between the first reference charge level and the second reference charge level. It was recognized that the differential charge advantageously describes the electrode balancing. The differential charge allows cell properties determined by both the anode and cathode to be represented by a characteristic quantity.

[0023] Optionally, the method comprises determining a state of health of the battery cell taking into account the diagnostic indicator and / or diagnostic parameters relating to degradation modes of the battery cell taking into account the diagnostic indicator. It was recognized that electrode balancing, expressed by the reference charge states, can improve the determination of the state of health. Electrode balancing depends on the state of health and thus enables inclusion of electrode balancing in the determination of the state of health. Since the reference charge states directly reflect aging-related properties of the anode and cathode, reference charge states enable an improved determination of the diagnostic parameters relating to degradation modes of the battery cell.For example, a parameter space or solution space for reconstructing a measured partial voltage curve of an aged cell with a given diagnostic indicator can be restricted using half-cell potentials. The diagnostic indicator can be used as an additional boundary condition to achieve faster convergence and a higher probability of finding a global optimum during reconstruction. In particular, the diagnostic indicator can be defined as the difference between the first reference state of charge and the second reference state of charge.

[0024] Optionally, the first reference state of charge is determined based on a hysteresis extremum. It has been recognized that hysteresis typically has a global extremum, particularly a maximum. This extremum is characteristic of the cathode state. The value of the hysteresis voltage difference at the extremum and / or the state of charge corresponding to the extremum can be indicative of the cathode state.

[0025] Optionally, the reference state of charge can be determined using a differential voltage analysis. It has been recognized that a differential voltage or differential potential analysis, i.e., an analysis of the derivative of the voltage or potential with respect to the state of charge, is suitable for effectively and accurately identifying the plateau transition. The differential voltage analysis can be performed with comparatively low computational effort, making it possible to perform differential voltage analysis as an onboard analysis by the vehicle.

[0026] Optionally, the reference state of charge corresponds to a local extremum of a derivative of the characteristic curve with respect to the state of charge. It was recognized that the plateau transition can be characterized by an inflection point in the characteristic curve. The inflection point of the characteristic curve can be reliably and precisely located by the local extremum in the derivative of the characteristic curve.

[0027] Optionally, the battery cell is an LFP cell. It has been recognized that, particularly in LFP cells, characteristic changes occur with aging, both in the hysteresis and in the characteristic curve. This method can thus effectively characterize the electrode balancing of LFP cells.

[0028] 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 Delta-H-DVA method for characterizing LFP cells. The aim of the disclosure is to determine the cell-specific electrode balancing of the electrodes relative to one another using an intelligent charging method and thus to expand and / or improve existing reconstruction models. By adding information - hysteresis curve via charge input and differential voltage analysis - at least the cell-specific electrode balancing relative to one another (or sometimes also LLI) can be determined, i.e. which electrode-specific SOC is effective at a certain full-cell SOC. The disclosure uses the information, recorded during an intelligent charging mode, to better characterize these cells.This further limits the solution space for the optimization for determining the charging curve and makes finding the global optimum more likely. The basic principle of the disclosure is as follows: (1) the characteristic hysteresis of LFP cells, i.e. the open-circuit voltage characteristic, depends on the history (here, charging direction). (2) the occurring hysteresis plotted against the SOC or the charge throughput results in a peak. The hysteresis is primarily caused by the LFP cathode material. Thus, the position and height of the peak already provide information about the condition of the cathode material. (3) The hysteresis curve can be recorded using a hysteresis charging mode, which can be used in the vehicle. The hysteresis charging mode can be used to create an absolute charge axis (which can be referenced again and again later), and secondly to scan the OCV characteristic curve in the charging direction.(4) From (3), the hysteresis peak and a defined starting point of the charge can be determined. This information is required later. (5) From (3), a differential voltage analysis (DVA) can be performed using the recorded charge-OCV characteristic curve. Using DVA, a marker specific to the graphite lithiation (anode) can be identified. This information, plotted on the charge axis from (3) and together with the hysteresis information from (4), allows conclusions to be drawn about the electrode-specific electrode balancing of the graphite anode and LFP cathode. This information flows directly into the estimation of the degradation modes. Now, for example, the SOH and the degradation modes can be estimated using the Delta-Q method. The hysteresis charging mode records relaxed voltage points and Delta-Q information over a wide SOC range.This information combined with knowledge of electrode balancing significantly increases the accuracy of SOH and DM estimation.

[0029] 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 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.

[0030] According to one aspect of the disclosure, a data processing device for a motor vehicle is provided. The data processing device is configured to perform the method described above. 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.

[0031] According to one aspect of the disclosure, a motor vehicle comprising the data processing device described above is provided. Optionally, the data processing device of the motor vehicle and / or the motor vehicle 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.

[0032] In the following, one embodiment is described with reference to the figures.

[0033] Fig. 1 schematically shows a motor vehicle according to one aspect of the disclosure;

[0034] Fig. 2 schematically shows a hysteresis of a battery cell associated with a method according to one aspect of the disclosure;

[0035] Fig. 3 shows schematically a relationship between a voltage difference and a state of charge according to a hysteresis of a battery cell;

[0036] Fig. 4 schematically shows a characteristic curve of a battery cell and differential voltage analysis associated with a method according to one aspect of the disclosure;

[0037] Fig. 5 schematically shows a flow diagram of a method according to one aspect of the disclosure; and

[0038] Fig. 6 shows a schematic representation of a computer program and / or computer-readable medium according to one aspect of the disclosure.

[0039] 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. 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.

[0040] 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 SOC 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 SOC 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 that changes over time and / or with the use of the energy storage device 55.

[0041] 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 cathode 58 (see schematic indexing of the battery cell 56 bottom left). For example, the battery cell 56 is a lithium iron phosphate cell, i.e., an LFP cell 56a. For example, the battery cell 56 is a lithium iron phosphate cell. The cathode 58 comprises lithium iron phosphate, the anode 57 comprises graphite with embedded lithium, and the electrolyte is designed to transport charge carriers, in particular lithium ions. The battery cells 56 of the energy storage device 55 have a hysteresis H (see, for example, Figure 2).

[0042] 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 for charging the energy storage device 55 and to measure the cell voltage U of one of the battery cells 56 (see Figures 2 to 4).

[0043] 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.

[0044] 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 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, wherein the partial charge is selected such that the hysteresis H can be classified as insignificant, i.e., the partial charge is carried out such that the voltage U of the battery cell 56 follows a characteristic charging path (see Figure 2).Charging can then be carried out using a comparatively slow charging rate of, for example, C / 3, i.e., a charging rate at which the energy storage device 55 would be fully charged in 3 hours.

[0045] The motor vehicle 50 or the data processing device 51 is configured to carry out the method 100 described with reference to Figure 5. For this purpose, 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, a relationship between the hysteresis H, a characteristic curve 65a (see Figures 2 and 4), reference charge states SOC_RA, SOC_RK (see Figures 3 and 4), and the state of health SOH can be stored, for example as a look-up table.

[0046] Alternatively or additionally, the data processing device 51 can be communicatively connected to the communication interface 53. Via the communication interface 53, the data processing device 51 can transmit information for evaluation to a vehicle-external server (not shown) or a backend, wherein the server is configured to perform steps of the method 100 according to Figure 5.

[0047] Figure 2 schematically shows a hysteresis H of a battery cell 56 associated with a method 100 according to one aspect of the disclosure, i.e., a relationship between a voltage U and a state of charge SOC of the battery cell 56. Such a battery cell 56 is described with reference to Figure 1. Figure 2 is described with reference to Figure 1.

[0048] Figure 2 illustrates the hysteresis behavior of the voltage U of a battery cell 56 as a function of the state of charge SOC, for example for battery cells 56 designed as LFP cells 56a. For better illustration, overvoltages are neglected in the description.

[0049] The voltage U shown generally increases with increasing SOC and decreases with decreasing SOC. The voltage U exhibits a hysteresis H, i.e., it is protocol-dependent, or rather, dependent on the history of the SOC.

[0050] The voltage characteristic curve has a charging voltage characteristic curve UKL and a discharging voltage characteristic curve UKE. The charging voltage characteristic curve UKL approximates and / or corresponds to an open-circuit voltage OCV of the battery cell 56 that can be achieved during charging. The discharging voltage characteristic curve UKE approximates and / or corresponds to an open-circuit voltage OCV of the battery cell 56 that can be achieved during discharging. There is a difference referred to as hysteresis H between the charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE. The arrows illustrate the protocol dependence of the voltage characteristic curve: during charging, the charging voltage characteristic curve UKL is sampled, and during subsequent discharging, the discharging voltage characteristic curve UKE is sampled. At a minimum state of charge SOC of, for example, 0% and a maximum state of charge SOC of, for example, 100%, the charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE coincide at certain points.The difference between the charging voltage characteristic UKL and the discharging voltage characteristic UKE has an influence on the performance of the battery cell 56.

[0051] The hysteresis H, i.e., the charging voltage characteristic UKL and the discharging voltage characteristic UKE, can be sampled by repeated temporary charging and discharging, as illustrated by the arrows. The temporary discharging is carried out over a predetermined discharge state of charge range SOC-.

[0052] During charging, the charging voltage characteristic UKL is first sampled and the state of charge SOC increases. Then, discharging occurs, whereby the state of charge SOC decreases through the state of charge range SOC- defined. During discharging, the discharge voltage characteristic UKE is sampled. Charging occurs again, during which the charging voltage characteristic UKL is again sampled and the state of charge SOC increases again. Charging and discharging are repeated until the diagnostic and / or charging process is completed and, optionally, a requested state of charge SOC is reached. In this process, cell voltages U are measured on the discharge voltage characteristic UKE and the charging voltage characteristic UKL, i.e. voltage points are sampled. The cell voltages U correspond to relaxed voltage points.For this purpose, a predetermined time, for example a relaxation time of approximately 1 hour, is provided after the temporary charging and / or discharging, after which the relaxed voltage point is recorded. During the relaxation time, overvoltages can be reduced so that the relaxed voltage point corresponds to a resting voltage of the battery cell 56 and / or approximates the resting voltage.

[0053] Instead of the state of charge SOC, another quantity can be plotted, such as a charge, which can be calculated based on the state of charge SOC and vice versa.

[0054] Figure 3 schematically shows a relationship between a voltage difference DH and a state of charge SOC according to a hysteresis H of a battery cell 56. 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.

[0055] Figure 3 illustrates the hysteresis H of an LFP cell 56a, which is characteristic of a state of health SOH. The open-circuit voltage characteristic OCV depends on the history (here, the charging direction), as described with reference to Figure 2. The voltage difference DH changes with the state of charge SOC, as shown in Figure 3. The voltage difference DH at a specific state of charge SOC is the difference between the cell voltage U of the charging branch and the cell voltage U of the discharging branch, each at the state of charge SOC.

[0056] The voltage difference DH has a global extremum 66 or maximum. The extremum 66 corresponds to a state of charge SOC of approximately 30%. The voltage difference DH defining the extremum 66 and the state of charge SOC can change with aging and thus with the state of the cathode 58. The state of charge SOC corresponding to the extremum 66 is defined as a cathode-state-dependent first reference state of charge SOC_RK. Figure 4 schematically shows a characteristic curve 65a of a battery cell 56 and a differential voltage analysis 69a belonging to a method according to one aspect of the disclosure. Such a battery cell 56 and / or features thereof are described with reference to Figures 1 to 3. Figure 4 is described with reference to Figures 1 to 3.

[0057] Figure 4 is divided into two sections, 4 (A) and 4 (B). Section (A) shows an example of a voltage U or a half-cell potential P as a function of the state of charge SOC.

[0058] The half-cell potentials P and the voltage II, in particular a resting voltage, each illustrate a characteristic curve for the state of health SOH of the cell 56: The half-cell potential P of the cathode 58 increases with the state of charge SOC, while the half-cell potential P of the anode 57 decreases with the state of charge SOC. The cell voltage U results from a difference between the half-cell potentials U and increases with the state of charge SOC.

[0059] The half-cell potentials P and the cell voltage U are comparatively weakly dependent on the state of charge SOC over a comparatively large range of the state of charge SOC from approximately 20% to 90%. The half-cell potential P of the anode 57 and the cell voltage U form two plateaus 63a, 64a in this range, in which the half-cell potentials P and the cell voltage U change only slightly with the state of charge SOC. The plateaus 63a, 64a are separated and distinguishable from one another by a plateau change 66a. The plateau change 66a can be localized with regard to the state of charge SOC by an anode state-dependent second reference state of charge SOC_RA. At the second reference state of charge SOC_RA, the plateau change 66a takes place from the first plateau 63a to the second plateau 64a. The plateau change 66a, or thus the second reference state of charge SOC_RA, depends on the state of the anode 57.

[0060] As illustrated by arrows, the plateau transition 66a migrates with the aging process, i.e., with a decreasing state of health SOH. With a decreasing state of health SOH, the plateau transition 66a tends to occur with a lower amount of charge applied. Depending on the aging process and operating history, the plateau transition 66 can shift, for example, to lower states of charge SOC or to higher states of charge SOC. The half-cell potential P of the anode 57 compresses with the aging process, i.e., with a decreasing state of health SOH. To locate the plateau transition 66a, a differential voltage analysis 69a is performed, see section (B).

[0061] Section (B) shows the result of a differential voltage analysis 69a of the characteristic curve 65a of section (A). Section (B) thus shows a curve of a voltage change DV or a potential change (not indexed) multiplied by a reference charge QO divided by a charge change DQ as a function of the state of charge SOC. The respective scales of the state of charge SOC of sections (A) and (B) match for better comparability. The differential voltage analysis 69a according to section (B) can thus be understood as a derivative (or as proportional to a derivative) of the characteristic curve 65a with respect to the state of charge SOC or a charge quantity.

[0062] The plateau transition 66a according to section (A) forms an inflection point of the half-cell potential P and the cell voltage U. Through the differential voltage analysis 69a, the plateau transition 66a thus results in a local extremum 68a, in particular a maximum. The value of the state of charge SOC of the extremum 68a defines the second reference state of charge SOC_RA.

[0063] Section (B) also illustrates the first reference state of charge SOC_RK (see Figure 3), a diagnostic indicator DI, and a differential charge DQ. The diagnostic indicator DI is the difference between the first reference state of charge SOC_RK and the second reference state of charge SOC_RA. Each of the reference states of charge SOC_RA and SOC_RK can be converted into a charge. Therefore, the diagnostic indicator DI, or the difference between the reference states of charge SOC_RA and SOC_RK, can be converted into the differential charge DQ. The differential charge DQ is thus defined by a difference between the first reference state of charge SOC_RK and the second reference state of charge SOC_RA.

[0064] Figure 5 schematically shows a flow diagram of a method 100 according to one aspect of the disclosure. The method 100 according to Figure 5 is a method 100 for the age-dependent diagnosis of a battery cell 56 for 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 to 4. Figure 5 is described with reference to Figures 1 to 4. The method 100 according to Figure 5 comprises: detecting 110 a hysteresis H measurable between a voltage difference DH and a state of charge SOC of the battery cell 56 and a characteristic curve 65a of the battery cell 56 characterizing an open-circuit voltage OCV. The hysteresis H is detected by an intelligent diagnostic and / or charging process, as described with reference to Figure 2, wherein the characteristic curve 65a is detected simultaneously, for example as a charging characteristic curve UKL.

[0065] The method 100 comprises: determining 120 a cathode-state-dependent first reference state of charge SOC_RK based on the hysteresis H. For this purpose, the difference between the discharge characteristic curve UKE and the charging characteristic curve UKL is calculated and analyzed. The determination 120 of the first reference state of charge SOC_RK is performed based on an extremum 66 of the hysteresis H or the difference (Figures 2 and 3).

[0066] The method 100 comprises: determining 120a an anode state-dependent second reference state of charge SOC_RA (see Figure 4) describing a plateau change 66a of the characteristic curve 65a. The second reference state of charge SOC_RA is determined 120a by a differential voltage analysis 69a. The second reference state of charge SOC_RA corresponds to a local extremum 68a of a derivative 67a of the characteristic curve 65a with respect to the state of charge SOC.

[0067] The method 100 comprises: determining 130 a diagnostic indicator DI based on the first reference state of charge SOC_RK and the second reference state of charge SOC_RA.

[0068] The diagnostic indicator DI corresponds to a differential charge DQ, where the differential charge DQ is defined by a difference between the first reference state of charge SOC_RK and the second reference state of charge SOC_RA.

[0069] The method 100 comprises: determining 140 a state of health SOH of the battery cell 56 taking into account the diagnostic indicator DI and / or diagnostic parameters LAM_NE, LAM_PE, LLI relating to degradation modes of the battery cell 56 taking into account the diagnostic indicator DI. For example, the diagnostic indicator DI can correspond to the differential charge DQ. The differential charge DQ restricts possible parameters for reconstructing a partial voltage curve based on half-cell potentials P. Without such a restriction, there would be four parameters, with two parameters each characterizing the compression (alpha) and the shift (beta) of the half-cell potential P. The differential charge DQ determines the shifts of the half-cell potentials P implicitly, ie, if the compression is known and / or estimated, the associated shifts result.The reconstruction of the partial stress distribution thus corresponds to an optimization problem with only two parameters for compression. This enables a more reliable reconstruction, which also allows for a more reliable estimation of degradation modes.

[0070] The person skilled in the art will recognize that the method 100 according to Figure 5 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.

[0071] Figure 6 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) which, when the program or instructions are executed by a data processing device 51, cause the device 51 to perform the method 100 and / or the steps of the method 100 according to Figure 5.

[0072] The instructions can be present as program code in any code or in any language, in particular in code suitable for controlling and / or monitoring motor vehicles 50 and / or their energy storage device 55. 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.

[0073] Reference symbol (part of the description)

[0074] 50 motor vehicles

[0075] 51 Data processing device

[0076] 52 drive

[0077] 53 Communication interface

[0078] 54 data storage

[0079] 55 Energy storage device

[0080] 56 battery cells

[0081] 56a LFP cell

[0082] 57 Anode

[0083] 58 Cathode

[0084] 59 Separator

[0085] 66 Extremum

[0086] 63a first plateau

[0087] 64a second plateau

[0088] 65a characteristic curve

[0089] 66a Plateau change

[0090] 67a Derivation

[0091] 68a Extremum

[0092] 69a differential stress analysis

[0093] 75 loading request

[0094] 80 State of charge estimation

[0095] 90 vehicle-external network

[0096] 91 charging stations

[0097] 100 procedures

[0098] 110 Detecting a hysteresis

[0099] 120 Determining a first reference state of charge

[0100] 120a Determining a second reference state of charge

[0101] 130 Determining a diagnostic indicator 200 Computer program and / or computer-readable medium

[0102] DI diagnostic indicator

[0103] DH voltage difference

[0104] DQ state of charge change

[0105] DV voltage change

[0106] H Hysteresis

[0107] LAM_NE diagnostic parameters, loss parameters of the negative electrode active material

[0108] LAM_PE diagnostic parameters, loss parameters of the active material of the positive electrode

[0109] LLI diagnostic parameters, lithium loss parameters

[0110] OCV resting voltage

[0111] P potential, half-cell potential

[0112] Q0 reference charge

[0113] Q1 first charge indicator

[0114] Q2 second charge indicator

[0115] SOC state of charge

[0116] SOC- discharge state of charge range

[0117] SOC_RA anode state-dependent second reference state of charge

[0118] SOC_RK cathode state-dependent first reference state of charge

[0119] SOH health status

[0120] TQ1 Partial charge level

[0121] U voltage, cell voltage

Claims

Claims 1. A method (100) for the age-dependent diagnosis of a battery cell (56) for an energy storage device (55) for a motor vehicle (50), the method (100) comprising: - detecting (110) a hysteresis (H) measurable between a voltage difference (DH) and a state of charge (SOC) of the battery cell (56) and a characteristic curve (65a) of the battery cell (56) characterizing an open-circuit voltage (OCV); - determining (120) a cathode state-dependent first reference state of charge (SOC_RK) based on the hysteresis (H), and determining (120a) an anode state-dependent second reference state of charge (SOC_RA) describing a plateau change (66a) of the characteristic curve (65a); and - Determining (130) a diagnostic indicator (DI) based on the first reference state of charge (SOC_RK) and the second reference state of charge (SOC_RA).

2. The method (100) according to claim 1, wherein the diagnostic indicator (DI) corresponds to a differential charge (DQ), wherein the differential charge (DQ) is defined by a difference between the first reference state of charge (SOC_RK) and the second reference state of charge (SOC_RA).

3. The method (100) according to claim 1 or 2, wherein the method (100) comprises: - Determining (140) a state of health (SOH) of the battery cell (56) taking into account the diagnostic indicator (DI) and / or diagnostic parameters (LAM_NE, LAM_PE, LLI) relating to degradation modes of the battery cell (56) taking into account the diagnostic indicator (DI).

4. Method according to one of the preceding claims, wherein - the determination (120) of the first reference state of charge (SOC_RK) is carried out on the basis of an extremum (66) of the hysteresis (H).

5. Method according to one of the preceding claims, wherein the determination (120a) of the second reference state of charge (SOC_RA) is carried out by a differential voltage analysis (69a).

6. The method (100) according to claim 5, wherein the second reference state of charge (SOC_RA) corresponds to a local extremum (68a) of a derivative (67a) of the characteristic curve (65a) according to the state of charge (SOC).

7. The method according to one of the preceding claims, wherein the battery cell (56) has an LFP cell (56a).

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), cause the device (51) 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) for a motor vehicle (50), wherein the data processing device (51) is configured to carry out the method (100) according to one of claims 1 to 7.

10. Motor vehicle (50) comprising the data processing device (51) according to claim 9.

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