Method, computer program and / or computer readable medium, data processing device and motor vehicle for diagnosing a battery cell for an energy storage device for a motor vehicle in connection with aging

Abstract

Method for diagnosing a battery cell for an energy storage device for a motor vehicle in connection with aging, wherein the method has: detecting a hysteresis which can be measured between a voltage difference of the battery cell and a state of charge and a characteristic curve of the battery cell which represents a resting voltage; determining a first reference state of charge in connection with a cathode state from the hysteresis and determining a second reference state of charge in connection with an anode state which describes a plateau switch of the characteristic curve; and determining a diagnostic indicator from the first reference state of charge and the second reference state of charge.

Description

Technical Field

[0001] This disclosure relates to a method for diagnosing, in relation to aging, battery cells of an energy storage device for motor vehicles.

[0002] This disclosure also relates to a computer program and / or a computer-readable medium, a data processing apparatus, and a motor vehicle. Background Technology

[0003] Such energy storage devices typically comprise multiple battery cells, or cells, connected in parallel and / or series, thus forming a high-voltage energy storage unit also known as a traction battery. The energy storage unit is configured to discharge the battery cells and provide electrical energy for the operation of the motor vehicle and / or, for example, to provide electrical energy to the outside of the vehicle via a charging station, and to charge the battery cells of the energy storage unit via a charging station and / or by recovering charged electrical energy during driving.

[0004] Accurate modeling of high-voltage energy storage devices (HVSDs) is crucial for their safe, continuous, and efficient use. HVSDs, or their constituent cells such as lithium-ion cells, experience capacity loss and increased internal resistance over their lifespan, a phenomenon known as aging. This can ultimately reduce the driving range and performance of vehicles when these cells are used in motor vehicles. Capacity and resistance, or their changes, can therefore be incorporated as information into the estimation of the state of the battery cells.

[0005] State estimation, used to characterize the state of individual cells and / or energy storage devices, typically employs quiescent voltage characteristics (OCV) and / or half-cell potentials (OCP), i.e., the relationship between the quiescent voltage or electrode potential of a cell and its state of charge. This allows for the estimation, in particular, of the state of charge (SOC) and / or state of health (SOH) of individual cells and / or energy storage devices. Furthermore, quiescent voltage characteristics can be used to determine charging strategies for charging energy storage devices or individual cells.

[0006] However, individual cells can have chemical compositions and / or structures that increase the complexity of modeling. For example, lithium iron phosphate (LFP) cells, as battery cells (so-called LFP cells), have relatively flat static voltage characteristic curves and hysteresis in their static voltage characteristic curves. Furthermore, various aging effects generally alter the trajectory of the static voltage characteristic curve. In particular, the relatively flat static voltage characteristic curves and hysteresis in LFP cells adversely affect currently used state-of-charge (SOC) determination methods and may, for example, lead to inaccuracies in SOC determination. This can directly impact vehicle range prediction and lead to unexpected vehicle breakdowns. Additionally, incorrect SOC determinations may adversely affect aging or health state determinations and may lead to premature greifen of protection functions and / or limitations in charging power.

[0007] Information about the current health status can be limited to the currently available capacity compared to the capacity in a new state. Health status is particularly important for vehicle operating strategies, especially for the design of charging strategies, for SOC estimation, and for safety. Health degradation, or aging, however, has a complex path dependency: individual cells can age based on different mechanisms, such as those determined by temperature and / or power call. Such mechanisms can be described by constructing 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). Here, degradation modes modelically summarize the physicochemical aging mechanisms and can be characterized by parameters, such as those describing the active materials of the electrodes and / or the loss of charge carriers like lithium. Through this degradation mode, it is possible to estimate how and when the energy storage unit will reach the end of its service life.

[0008] To characterize health status and aging, it is worthwhile to develop and provide generalizable, accurate, and reliable estimation algorithms and methods. A highly promising approach is the voltage curve reconstruction model based on half-cell potential measured under new conditions.

[0009] This reconstruction method involves the complete charging profile (see, for example, Matthieu Dubarry, Cyril Truchot, and Bor Yann Liaw, “Synthesize battery degradation modes via a diagnostic and prognostic model”, Journal of Power Sources, Vol. 219, 2012, pp. 204-216). Here, the voltage profile of the aged cell is measured at a small charging rate, the so-called C-rate, where the C-rate is equal to the current divided by the rated capacity; that is, the current rate has units of 1 / hour or generally has units of rate and is expressed in reciprocal form: how quickly the battery is charged with a given current. For example, a battery cell is fully charged in half an hour at a C-rate of 2; and in two hours at a C-rate of 1 / 2. The half-cell potential profile measured under the new conditions is shifted and compressed to reconstruct the measured voltage profile of the aged cell, such that the error between the reconstructed aged voltage profile based on the half-cell potential profile measured under the new conditions and the measured voltage profile of the aged cell is minimized.

[0010] The optimization or error minimization can also be applied to local charging curves and high C-rate conditions (see, for example, Julius Schmitt, Mathias Rehm, Alexander Karger, and Andreas Jossen, “Capacity and degradation mode estimation for lithium-ion batteries based on partial charging curves at different current rates”, Journal of Energy Storage, Vol. 59, 2023, Article 106517).

[0011] The method described in the prior art utilizes time-resolved signals to reconstruct the voltage curve. Therefore, the voltage curve contains multiple pieces of qualitative information about the voltage and allows for a more accurate relative fit to the half-cell potential curve. This can advantageously influence degradation mode estimation.

[0012] However, multiple solutions exist for different SOH values, each minimizing the error or cost function. The method is characterized by providing multiple local optima for a given charging curve, each contributing to a local minimum error. Therefore, a reliable and explicit reconstruction of the voltage curve is not possible.

[0013] Furthermore, different components of a battery cell sometimes degrade differently. For example, electrodes may age differently. Therefore, given a certain state of health, electrode-specific factors determine the state of charge of the cell, which is referred to hereinafter as electrode balance in this disclosure. Electrode balance is related to the state of health due to different degrees of degradation.

[0014] An alternative method is described in patent application DE 10 2022 129 208.8, which was not published as of the filing date of this disclosure. According to this method, it is possible to apply the relaxation voltage point along with the accumulated charge flux to reconstruct the voltage profile. This is a relatively low-cost approach for estimating aging states because the space of possible solutions for the voltage profile can be reduced so much by the predetermined amount of charge introduced, allowing for higher accuracy in SOH estimation. Summary of the Invention

[0015] In the context of the prior art, the object of this disclosure is to provide a method that extends and at least improves upon the aforementioned aspects of the prior art. In particular, the object of this disclosure is to determine the electrode balance of electrodes in relation to aging and thus extend and / or improve existing reconfiguration models.

[0016] The objective is achieved by the features of each independent claim. The dependent claims contain further extensions of this disclosure.

[0017] Accordingly, the objective is achieved according to one aspect of this disclosure by a method for diagnosing, in relation to aging, a battery cell for an energy storage device for motor vehicles, wherein the method comprises: detecting hysteresis that can be measured between the voltage difference and state of charge of the battery cell and a characteristic curve of the battery cell characterizing the resting voltage; determining a first reference state of charge related to the cathode state based on the hysteresis, and determining a second reference state of charge related to the anode state describing the plateau switching of the characteristic curve; and determining diagnostic indicators based on the first reference state of charge and the second reference state of charge.

[0018] The hysteresis can be sampled or measured using intelligent charging or diagnostic methods, for example, according to the method described in document DE 102023 118 719.8, which describes a method for characterizing the hysteresis of a battery cell in an energy storage device for an electrically driven motor vehicle. The method includes: charging the battery cell and detecting the charged cell voltage for modeling a charging voltage characteristic curve; temporarily discharging the battery cell and detecting the discharged cell voltage for modeling a discharging voltage characteristic curve; and determining the hysteresis between the charging and discharging voltage characteristic curves based on the charged cell voltage and the discharged cell voltage.

[0019] It has been recognized that the hysteresis can often be definitively determined by the cathode, for example, in the case of an LFP cell as the battery cell. Hysteresis is described here as follows: the quiescent voltage of the discharge voltage curve or discharge voltage characteristic curve differs from that of the charging voltage curve or charging voltage characteristic curve. Therefore, there are separate quiescent voltage curves for charging and discharging conditions, i.e., the so-called charging branch and discharging branch, where the charging branch describes the quiescent voltage curve under charging conditions and the discharging branch describes the quiescent voltage curve under discharging conditions. The resulting quiescent voltage is related to history, i.e., to past charging and discharging curves. The intelligent charging or diagnostic methods described above can achieve clearly defined sampling of hysteresis by limiting the sampling to a uniform history used for all samples.

[0020] Hysteresis, or the difference between the discharge and charge branches, and the transition from the discharge branch to the charge branch or vice versa, is related to the state of charge of the battery cell and has a typical curve for the cathode state. Therefore, the hysteresis can provide information about the aging of the cathode state, and a first reference state of charge can be determined based on the hysteresis.

[0021] The characteristic curves mentioned here can be either charging voltage characteristic curves or discharging voltage characteristic curves, and although they have relatively flat sections, the two plateaus, i.e., relatively flat sub-sections, within these flat sections can be different from each other. A plateau switching occurs between the two plateaus, i.e., the characteristic curve switches from a first plateau at a lower state of charge to a second plateau at a higher state of charge. It has been recognized that, in particular, the plateau switching characterizes the role of the anode. In the case of such battery cells, especially LFP cells, in addition to other effects, particularly due to the aging of the anode due to lithiation, the relationship between the cell voltage and / or half-cell potential, especially the half-cell potential defined by the anode, is related to the state of the anode. A second reference state of charge can characterize the plateau switching or be located on the state of charge axis between the lower and higher states of charge.

[0022] In other words, the first reference state of charge describes the cathode, while the second reference state of charge describes the anode. Therefore, diagnostic indicators can be derived from the first and second reference states of charge such that they describe electrode balance or that a single cell can be described by electrode-specific factors. Thus, based on the diagnostic indicators, it is possible to estimate the improved health status of a single cell. Furthermore, it is possible to improve the determination of degradation patterns.

[0023] Alternatively, the diagnostic index corresponds to a differential charge, wherein the differential charge is defined by the difference between a first reference state of charge and a second reference state of charge. It has been recognized that the differential charge advantageously describes electrode balance. The characteristics of a single cell, due not only to the anode but also to the cathode, can be characterized by the differential charge through typical parameters.

[0024] Alternatively, the method may include: determining the health status of a battery cell while considering diagnostic indices and / or determining diagnostic parameters related to the degradation mode of the battery cell while considering diagnostic indices. It has been recognized that electrode balance expressed by a reference state of charge can improve the determination of the health status. Electrode balance is related to the health status and thus allows for its inclusion in the determination of the health status. Because the reference state of charge directly describes the aging-determined characteristics of the anode and cathode, it enables the improved determination of diagnostic parameters related to the degradation mode of the battery cell. For example, the given diagnostic indices can be used to limit the parameter space or solution space of the measured partial voltage curves used to reconstruct the aging cell based on the half-cell potential. The diagnostic indices can here be used as additional boundary conditions to achieve faster convergence and higher probability in the reconstruction for finding the global optimum. In particular, the diagnostic indices can be limited to the difference between a first reference state of charge and a second reference state of charge.

[0025] Alternatively, the determination of the first reference state of charge is achieved based on the extreme values ​​of the hysteresis. It is recognized here that the hysteresis typically has global extreme values, particularly maximum values. These extreme values ​​characterize the state of the cathode. Here, the value of the voltage difference at the extreme values ​​of the hysteresis and / or the state of charge corresponding to these extreme values ​​can indicate the state of the cathode.

[0026] Alternatively, the determination of the second reference state of charge is achieved through differential voltage analysis. It has been recognized that differential voltage or differential potential analysis, i.e., analysis of the derivative of voltage or potential with respect to the state of charge, is suitable for efficiently and accurately identifying platform switching. This differential voltage analysis can be implemented with relatively low computational cost, making it possible as an onboard analysis for motor vehicles.

[0027] Alternatively, the reference state of charge corresponds to a local extremum of the derivative of the characteristic curve with respect to the state of charge. It has been recognized that platform switching can be characterized by inflection points in the characteristic curve. Inflection points of the characteristic curve can be reliably and accurately located using local extrema in the derivative of the characteristic curve.

[0028] Alternatively, the battery cell is an LFP (lithium iron phosphate) battery cell. It has been recognized that, particularly in the case of LFP cells, aging results in typical changes not only in hysteresis but also in the characteristic curves. Therefore, this method allows for efficient characterization of the electrode balance of LFP cells.

[0029] The above description can be summarized in other words and with reference to the description as a specific design scheme for non-limiting purposes of this disclosure as follows: This disclosure relates to the Delta-H-DVA method for characterizing LFP cells. The objective of this disclosure is to determine the cell-specific electrode balance between electrodes through a smart charging method and thus extend and / or improve existing reconstruction models. By adding information—hysteresis curves and differential voltage analysis regarding charge input—at least the cell-specific electrode balance between electrodes can be determined (or sometimes LLI (lithium inventory loss)), i.e., which electrode-specific SOC is active under a given overall cell SOC. This disclosure utilizes information recorded in a smart charging mode for better characterization of the cell. This further limits the solution space for determining the optimal charging curve and makes it more likely to find the global optimum. The basic principles of this disclosure are as follows: (1) The typical hysteresis, i.e., the static voltage characteristic curve of an LFP cell, is related to history (in this charging direction). (2) The hysteresis that occurs with respect to SOC or charge flux produces a peak. The hysteresis is mainly determined by the cathode material LFP. Therefore, the position and height of the peak have already led to conclusions about the state of the cathode material. (3) Hysteresis curves can be recorded using a hysteresis charging mode applicable in the vehicle. The hysteresis charging mode allows for the establishment of an absolute charging axis (which can always be referenced) and the sampling of the OCV characteristic curve along the charging direction. (4) From (3), the peak value of the hysteresis and the defined starting point of the charging can be determined. This information is required in the following text. (5) Differential voltage analysis (DVA) can be performed using the recorded charging OCV characteristic curve from (3). DVA can identify specific characteristics for the graphite lithiation (anode). This information recorded by (3) along the charging axis, together with the hysteresis information from (4), allows for conclusions regarding electrode-specific balance between the graphite anode and the LFP cathode. This information is directly incorporated into the estimation of the degradation mode. The SOH and degradation mode can now be estimated, for example, using the Delta-Q method. The relaxation voltage point and Delta-Q information are recorded in another SOC range using the hysteresis charging mode. This information, combined with the understanding of electrode balance, significantly improves the accuracy of SOH and DM estimations.

[0030] According to one aspect of this disclosure, a computer program and / or a computer-readable medium is provided. The computer program and / or computer-readable medium includes instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform steps of the method and / or method according to this disclosure. Alternatively, the computer program and / or computer-readable medium includes instructions that, when executed by a data processing apparatus, cause the data processing apparatus to perform method steps as advantageously or alternatively described, in order to achieve a technical effect associated thereto.

[0031] According to one aspect of this disclosure, a data processing apparatus for a motor vehicle is provided. This data processing apparatus is configured to implement the methods described above. Alternatively, the data processing apparatus is configured to implement the method steps and / or implement method features as advantageously or alternatively described, in order to achieve the associated technical effects.

[0032] According to one aspect of this disclosure, a motor vehicle is provided that includes the data processing apparatus described above. Alternatively, the data processing apparatus of the motor vehicle and / or the motor vehicle are configured to implement the method steps and / or implement the method features as advantageously or alternatively described, in order to achieve the technical effects associated therewith. Attached Figure Description

[0033] Each embodiment is described below with reference to the accompanying drawings.

[0034] Figure 1 A motor vehicle according to one aspect of this disclosure is schematically shown;

[0035] Figure 2 The hysteresis of a single battery cell is schematically shown as a method according to one aspect of this disclosure;

[0036] Figure 3 The diagram illustrates the relationship between the hysteresis of individual battery cells and the voltage difference and state of charge.

[0037] Figure 4 The schematic diagram shows the characteristic curves and differential voltage analysis of each individual battery cell belonging to a method according to one aspect of this disclosure;

[0038] Figure 5 A flowchart illustrating a method according to one aspect of this disclosure is shown schematically; and

[0039] Figure 6 A schematic diagram illustrating a computer program and / or a computer-readable medium in accordance with one aspect of this disclosure. Detailed Implementation

[0040] Figure 1A motor vehicle 50 is schematically shown according to one aspect of this disclosure. Motor vehicle 50 is a land vehicle. Motor vehicle 50 is a passenger car.

[0041] 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 are shown schematically only.

[0042] The energy storage device 55 or battery cell 56 is configured to load electrical energy to charge the battery cell 56, i.e., to increase the state of charge (SOC) of the battery cell 56. The energy storage device 55 or battery cell 56 is configured to provide electrical energy for operating the motor vehicle 50 and / or the electric drive 52, wherein the battery cell 56 is discharged, i.e., the SOC of the battery cell 56 decreases. The battery cell 56 may age during charging and discharging, and through time, i.e., calendar aging effects; that is, the battery cell 56 can be characterized by its state of health (SOH), which changes with time and / or the application of the energy storage device 55.

[0043] Each battery cell 56 has an anode 57, a cathode 58, and an electrolyte, along with an electrically insulated separator 59 between the anode 57 and the cathode 58 (see schematic designation of battery cell 56 in the lower left corner). For example, battery cell 56 is a lithium iron phosphate cell, i.e., an LFP cell 56a. For example, battery cell 56 is a lithium iron phosphate battery cell. Here, the cathode 58 comprises lithium iron phosphate, the anode 57 comprises graphite with deposited lithium, and the electrolyte is configured for transporting charge carriers, particularly lithium ions. The battery cells 56 of the energy storage device 55 have a hysteresis H (see example...). Figure 2 ).

[0044] according to Figure 1 The motor vehicle 50 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 to, for example, define the current characteristics with current for charging the energy storage device 55 and measure the cell voltage U of one of the individual battery cells 56 (see [link to relevant documentation]). Figures 2 to 4 ).

[0045] Figure 1 The charging station 91 and the external power grid 90 of the vehicle are also shown. The charging station 91 and the vehicle 50 are configured to be electrically and communicationally interconnected. Thus, in order to charge the battery cell 56, electrical energy can be loaded from the external power grid 90 of the vehicle to the energy storage device 55 via the charging station 91. The charging station 91 can be, for example, a wall box or wall-mounted charging station and / or a public and / or private charging point.

[0046] For charging, the data processing unit 51 can detect a charging request 75 for charging the battery cell 56 and transmit the charging request to the charging station 91. The charging request 75 is, for example, pre-determined by the user and / or by the manufacturer for diagnostic purposes. The charging request 75 is detected via a user interface (not shown) and / or via the communication interface 53 of the vehicle 50. Alternatively, the charging request 75 can be created by the data processing unit 51 itself for diagnostic purposes. Charging can be partial charging, wherein partial charging is selected such that the hysteresis H can be classified as insignificant (recorded), i.e., partial charging is implemented such that the voltage U of the battery cell 56 follows a typical charging branch (see...). Figure 2 Charging can then be achieved at a relatively slow charging rate, for example, C / 3, that is, at a charging rate at which the energy storage device 55 will be fully charged in 3 hours.

[0047] Motor vehicle 50 or data processing device 51 is configured for implementing reference Figure 5 Method 100 is described. 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, it may store hysteresis H, characteristic curve 65a (see...). Figure 2 and 4 ), reference states of charge SOC_RA, SOC_RK (see Figure 3 and 4 The relationship between the state of health (SOH) and the state of health (SOH) can be represented, for example, as a lookup table.

[0048] Alternatively or additionally, the data processing device 51 and the communication interface 53 can be technically connected. The data processing device 51 can transmit information for analysis and processing to a server (not shown) or backend outside the vehicle via the communication interface 53, wherein the server is configured to implement... Figure 5 The method has 100 steps.

[0049] Figure 2 The diagram schematically illustrates the hysteresis H of battery cell 56 according to method 100 of one aspect of this disclosure, i.e., the relationship between the voltage U and the state of charge (SOC) of battery cell 56. Such battery cell 56 refers to... Figure 1 Description. (See reference) Figure 1 Description of the situation Figure 2 .

[0050] Figure 2 The hysteresis performance of the voltage U of the battery cell 56 with respect to the state of charge (SOC) is shown, for example, for a battery cell 56 configured as an LFP cell 56a. Overvoltage is ignored in the description for better illustration.

[0051] The voltage U shown generally increases with increasing state of charge (SOC) and decreases with decreasing SOC. This voltage U exhibits hysteresis H, meaning it has a recorded correlation or a historical correlation with the curve of SOC.

[0052] The voltage characteristic curves have a charging voltage characteristic curve UKL and a discharging voltage characteristic curve UKE. The charging voltage characteristic curve UKL approximates and / or corresponds to the resting voltage OCV of the battery cell 56, which can be reached during charging. The discharging voltage characteristic curve UKE approximates and / or corresponds to the resting voltage OCV of the battery cell 56, which can be reached during discharging. A difference called hysteresis H exists between the charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE. The arrows here indicate the recording correlation of the voltage characteristic curves: the charging voltage characteristic curve UKL is sampled during charging, and the discharging voltage characteristic curve UKE is sampled immediately after discharging. The charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE coincide point-wise at, for example, 0% minimum state of charge (SOC) and at, for example, 100% maximum state of charge (SOC). The difference between the charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE affects the performance of the battery cell 56.

[0053] Here, the hysteresis H, i.e., the charging voltage characteristic curve UKL and the discharging voltage characteristic curve UKE, can be sampled by repeated temporary charging and discharging as indicated by the arrows. The temporary discharge is carried out within a predetermined state of charge (SOC) range.

[0054] First, the charging voltage characteristic curve UKL is sampled during charging, and the state of charge (SOC) increases. Then, discharging is performed, causing the SOC to decrease within the defined SOC range. The discharging voltage characteristic curve UKE is sampled during discharging. Recharging is then performed, during which the charging voltage characteristic curve UKL is sampled again, and the SOC increases again. Charging and discharging are repeated until diagnostics and / or the charging process is completed, and optionally, the desired SOC is reached. Here, the cell voltage U, i.e., the sampling voltage point, is measured on the discharging voltage characteristic curve UKE and the charging voltage characteristic curve UKL. The cell voltage U here corresponds to the relaxation voltage point. For this purpose, a predetermined relaxation time, such as approximately 1 hour, is provided after temporary charging and / or after temporary discharging, and the relaxation voltage point is detected after the relaxation time has elapsed. Overvoltage can be eliminated during the relaxation time, thereby the relaxation voltage point corresponds to the resting voltage and / or approximately resting voltage of the battery cell 56.

[0055] Alternatively, other parameters, such as charge, can be used instead of the state of charge (SOC). These other parameters can be calculated based on the SOC and vice versa.

[0056] Figure 3 The diagram schematically illustrates the relationship between the hysteresis H of cell 56 and the voltage difference DH and state of charge (SOC). (Refer to...) Figure 1 and 2 Describe the characteristics of this type of battery cell 56 and / or thereof. Reference Figure 1 and 2 describe Figure 3 .

[0057] Figure 3 The hysteresis H of the LFP cell 56a in its state of health (SOH) is shown. The static voltage characteristic curve (OCV) is related to the history (in this case, the charging direction), as shown in the reference. Figure 2 As described above. At this voltage difference DH, as... Figure 3 As shown, it varies with the state of charge (SOC). The voltage difference DH at a given SOC represents the difference between the individual cell voltages UC of the charging branch and the discharging branch at the same SOC.

[0058] The voltage difference DH has a global extremum 66 or a maximum value. The extremum 66 corresponds to approximately 30% of the state of charge (SOC). Here, the voltage difference DH and the state of charge SOC that define the extremum 66 can vary with aging and therefore with the state of the cathode 58. The state of charge SOC corresponding to the extremum 66 is defined as a first reference state of charge SOC_RK related to the state of the cathode.

[0059] Figure 4 The diagram schematically illustrates a characteristic curve 65a and a differential voltage analysis 69a for each of the battery cells 56 belonging to the method 100 according to one aspect of this disclosure. (Refer to...) Figures 1 to 3 Describe the characteristics of this type of battery cell 56 and / or thereof. (Refer to...) Figures 1 to 3 describe Figure 4 .

[0060] Figure 4 This is divided into two sections, 4(A) and 4(B). Here, section (A) exemplarily shows the curves of voltage U or half-cell potential P according to the state of charge (SOC).

[0061] Here, the half-cell potential P and voltage U, particularly the quiescent voltage, are shown as curves characterizing the state of health (SOH) of cell 56: the half-cell potential P of cathode 58 increases with increasing state of charge (SOC), while the half-cell potential P of anode 57 decreases with increasing SOC. The cell voltage U is generated by the difference in half-cell potentials U and increases with increasing SOC.

[0062] Here, the half-cell potential P and the individual cell voltage U are relatively weakly related to the state of charge (SOC) over a relatively large range of approximately 20% to 90%. The half-cell potential P and the individual cell voltage U of the anode 57 constitute two plateaus 63a and 64a within this range, in which the half-cell potential P or the individual cell voltage U changes only slightly with the SOC. Plateaus 63a and 64a are separated and distinguishable by a plateau switching 66a. The plateau switching 66a can be located with respect to the SOC via a second reference SOC_RA related to the anode state. A plateau switching 66a occurs at the second reference SOC_RA, from the first plateau 63a to the second plateau 64a. In this plateau switching 66a, or therefore the second reference SOC_RA, the state of the anode 57 is related.

[0063] Here, platform switching 66a moves with the aging process, i.e., with the decreasing state of health (SOH), as indicated by the arrow. Platform switching 66a tends to occur with a small amount of input charge in the case of a decreasing state of health (SOH). Platform switching 66a can move depending on the aging process and operating history, for example, moving to a lower state of charge (SOC) or to a higher state of charge (SOC). The half-cell potential P of anode 57 is compressed with the aging process, i.e., with the decreasing state of health (SOH). To locate platform switching 66a, differential voltage analysis 69a is performed, see section (B).

[0064] Section (B) shows the results of differential voltage analysis 69a for characteristic curve 65a of segment (A). Segment (B) thus shows a curve of voltage transformation DV or potential change (not indicated) multiplied by reference charge Q0 divided by charge change DQ with respect to state of charge (SOC). The corresponding scales for the state of charge (SOC) in segments (A) and (B) are consistent for better comparability. Based on differential voltage analysis 69a of segment (B), it can therefore be understood that characteristic curve 65a has the derivative with respect to state of charge (SOC) or the derivative of the charge parameter (or understood as being proportional to the derivative).

[0065] The inflection points of the half-cell potential P and the single-cell voltage U are formed according to the plateau switching 66a in section (A). Through differential voltage analysis 69a, the local extrema 68a, especially the maximum value, are thus obtained from the plateau switching 66a. The state of charge (SOC) value of the extremum 68a defines the second reference state of charge (SOC_RA).

[0066] In section (B), the first reference state of charge SOC_RK is also shown (see section B). Figure 3The diagnostic index DI and the differential charge DQ are defined as follows: The diagnostic index 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_RK and SOC_RA) can be converted into a charge. Therefore, the diagnostic index 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 the difference between the first reference state of charge (SOC_RK) and the second reference state of charge (SOC_RA).

[0067] Figure 5 A flowchart of method 100 according to one aspect of this disclosure is schematically shown. Figure 5 Method 100 is a method for aging-related diagnosis of battery cells 56 in an energy storage device 55 used in a motor vehicle 50. (See reference...) Figures 1 to 4 Describes such motor vehicles 50, such energy storage devices 55, such battery cells 56, and / or their characteristics. See reference. Figures 1 to 4 describe Figure 5 .

[0068] according to Figure 5 Method 100 includes: a characteristic curve 65a of the hysteresis H, which can be measured between the voltage difference DH of the battery cell 56 and the state of charge (SOC), and the static voltage (OCV) of the battery cell 56, which can be characterized by detection 110. (Refer to...) Figure 2 The description involves detecting hysteresis H through intelligent diagnostics and / or the charging process, wherein characteristic curve 65a is detected simultaneously, for example, as the charging characteristic curve UKL.

[0069] Method 100 includes: determining a first reference state of charge (SOC_RK) related to the cathode state based on hysteresis H. For this purpose, the difference between the discharge characteristic curve UKE and the charging characteristic curve UKL is formed and analyzed. The determination of the first reference state of charge (SOC_RK) 120 is achieved based on the extreme value 66 of hysteresis H or the difference (…). Figure 2 and Figure 3 ).

[0070] Method 100 includes: determining a second reference state of charge (SOC_RA) related to the anode state, 120a describing the characteristic curve 65a, and 66a. (See also...) Figure 4 The second reference state of charge (SOC_RA) is determined by differential voltage analysis (69a). The second reference state of charge (SOC_RA) corresponds to the local extremum (68a) of the derivative of the characteristic curve (65a) with respect to the state of charge (SOC) (67a).

[0071] Method 100 includes: determining a diagnostic index DI based on a first reference state of charge (SOC_RK) and a second reference state of charge (SOC_RA).

[0072] The diagnostic index DI corresponds to the differential charge DQ, wherein the differential charge DQ is defined by the difference between the first reference state of charge SOC_RK and the second reference state of charge SOC_RA.

[0073] Method 100 involves: determining the state of health (SOH) of cell 56 of cell 140, considering the diagnostic index DI, and / or determining diagnostic parameters LAM_NE, LAM_PE, and LLI associated with the degradation mode of cell 56, considering the diagnostic index DI. For example, the diagnostic index DI can here correspond to the differential charge DQ. The differential charge DQ here restricts the possible parameters used to reconstruct the partial voltage curve based on the half-cell potential P. Without such restrictions, there would be four parameters, where two parameters each characterize the compression (Alpha) and shift (Beta) of one of the half-cell potentials P. The differential charge DQ implicitly determines the shift of the half-cell potential P, i.e., the shift to which it belongs given the known and / or estimated compression. The reconstruction of the partial voltage curve thus corresponds to an optimization problem with only two parameters now used for compression. This enables a more reliable reconstruction, which also allows for a more reliable estimation of the degradation mode.

[0074] Here, those skilled in the art recognize that, according to Figure 5 Method 100 can also be implemented in a different order than that shown. In particular, it is possible that the steps of method 100 can be interchanged, moved, and / or performed simultaneously.

[0075] Figure 6 A schematic diagram of a computer program and / or computer-readable medium 200 according to one aspect of the present disclosure is shown. The computer program and / or computer-readable medium 200 includes instructions (not shown) that, upon execution of the program or instructions by the data processing device 51, cause the data processing device to perform actions according to... Figure 5 Method 100 and / or the steps of Method 100.

[0076] The instructions can exist as program code in any code or language, particularly as code suitable for controlling and / or monitoring motor vehicle 50 and / or the energy storage device 55 of said motor vehicle. The computer program and / or computer-readable medium 200 can be or include any digital data storage device, such as a USB flash drive, hard disk, CD-ROM, SD card, or SSD card. The computer program is not necessarily required to be stored on such a computer-readable storage medium, but can also be accessible via the Internet or other means.

[0077] Figure reference numerals (part of the specification)

[0078] 50 motor vehicles

[0079] 51 Data Processing Device

[0080] 52 drives

[0081] 53 Communication Interface

[0082] 54 Data Memory

[0083] 55 energy storage device

[0084] 56 cell

[0085] 56a LFP monomer

[0086] 57 anode

[0087] 58 cathode

[0088] 59 Separator

[0089] 66 extreme values

[0090] 63a First Platform

[0091] 64a Second Platform

[0092] 65a characteristic curve

[0093] 66a platform switching

[0094] 67a derivative

[0095] 68a extreme value

[0096] 69A Differential Voltage Analysis

[0097] 75 charging request

[0098] 80 State of charge estimation

[0099] 90 Electric grid outside the vehicle

[0100] 91 charging pile

[0101] 100 methods

[0102] 110 detection delay

[0103] 120 Determines the first reference state of charge.

[0104] 120a determines the second reference state of charge.

[0105] 130 Determine Diagnostic Indicators

[0106] 200 computer programs and / or computer-readable media

[0107] DI diagnostic indicators

[0108] DH voltage difference

[0109] DQ state of charge change

[0110] DV voltage change

[0111] H hysteresis

[0112] LAM_NE diagnostic parameters, loss parameters of the active material of the negative electrode.

[0113] LAM_PE diagnostic parameters, loss parameters of the active material of the positive electrode.

[0114] LLI diagnostic parameters, lithium loss parameters

[0115] OCV static voltage

[0116] P potential, half-cell potential

[0117] Q0 reference charge

[0118] Q1 First charge index

[0119] Q2 Second Charge Index

[0120] State of charge (SOC)

[0121] SOC - State of discharge

[0122] SOC_RA is a second reference state of charge related to the anode state.

[0123] SOC_RK is the first reference state of charge related to the cathode state.

[0124] SOH health status

[0125] TQ1 Partial State of Charge

[0126] U-voltage, individual cell voltage

Claims

1. A method (100) for aging-related diagnosis of a battery cell (56) in an energy storage device (55) for use in a motor vehicle (50), wherein, The method (100) has the following characteristics: -Detect (110) the characteristic curve (65a) of the hysteresis (H) between the voltage difference (DH) and the state of charge (SOC) of the battery cell (56) and the static voltage (OCV) of the battery cell (56). - Determine (120) a first reference state of charge (SOC_RK) related to the cathode state based on the hysteresis (H), and determine (120a) a second reference state of charge (SOC_RA) related to the anode state describing the plateau switching (66a) of the characteristic curve (65a); and - Determine (130) diagnostic indicators (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 the differential charge (DQ), wherein the differential charge (DQ) is defined by the 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) has the following characteristics: - Determine (140) the state of health (SOH) of the battery cell (56) in consideration of the diagnostic indicators (DI) and / or determine the diagnostic parameters (LAM_NE, LAM_PE, LLI) related to the degradation mode of the battery cell (56) in consideration of the diagnostic indicators (DI).

4. The method according to any one of the preceding claims, wherein, - The determination (120) of the first reference state of charge (SOC_RK) is achieved based on the extreme value (66) of the hysteresis (H).

5. The method according to any one of the preceding claims, wherein, The determination (120a) of the second reference state of charge (SOC_RA) is achieved through differential voltage analysis (69a).

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

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

8. A computer program and / or a computer-readable medium (200), including instructions that, when executed by a data processing device (51), cause the data processing device to perform the method (100) and / or the steps of the method (100) according to any one of claims 1 to 7.

9. A data processing device (51) for a motor vehicle (50), wherein, The data processing device (51) is configured to implement the method (100) according to any one of claims 1 to 7.

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

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