Method, apparatus and system for classifying battery cells of a battery pack for cell health

Abstract

The invention relates to a method (10) for classifying battery cells (1100) of a battery pack (1001) for their cell health. In the method (10), a cell voltage (VZell) and a current (IZell) corresponding thereto are captured for each battery cell (1100) to be classified. An internal resistance (iR) of the respective battery cell (1100) is thus determined. Based on the determined internal resistance (iR), for each battery cell (1100), when the deviation [Delta] iR of the respective internal resistance (iR) relative to the internal resistance reference value (iRW) is within a normal range, it is then classified as healthy. In this case, the internal resistance reference value (iRW) is set to vary over time. The classification of the battery cells (1100) is output in the form of a classification result (KE). The invention also relates to a computer program product, a device (100) and a battery system (1000), each implementing the aforementioned method (10).

Description

Technical Field

[0001] This invention relates to a method and apparatus for classifying individual battery cells in a battery pack according to their individual health status. The invention also relates to a computer program product for executing the method of the invention on a computer, and a battery system having the apparatus of the invention. Background Technology

[0002] For batteries to be used in a variety of applications, safety during use is paramount. For example, batteries with high energy storage capacity are used in conjunction with electric vehicles and residential or industrial photovoltaic systems. Lithium-ion batteries are frequently used due to their relatively high energy density compared to other battery types. To further increase energy density, high-voltage batteries with an operating voltage of at least 60V DC can be employed.

[0003] However, as energy density increases, so do the potential risks associated with such energy storage devices. Particularly problematic is the possibility of battery burn-through or thermal runaway, also known as "thermal runaway."

[0004] Specifically, thermal runaway describes a self-reinforcing heat-generating process. This process is accelerated by rising temperatures, which in turn releases heat, further increasing the temperature and thus accelerating the process even more. This can lead to battery overheating, potentially causing affected batteries to catch fire and / or even explode. In such cases, extinguishing the battery fire or interrupting the process chain is often nearly impossible or impossible.

[0005] In batteries, especially lithium-ion batteries, thermal runaway can occur due to localized overheating or internal short circuits in the electrodes. In such a short circuit, the resulting short-circuit current heats the environment around the localized connection points between the electrodes, allowing the heating process to locally expand and release additional stored energy.

[0006] Internal short circuits can have a variety of causes. Contamination of the electrode separator (e.g., trapped foreign particles) or mechanical damage to the separator can both lead to short circuits. Furthermore, short circuits can also occur during normal use. During repeated charging and discharging of the battery, so-called dendrite growth can occur on the battery electrodes. When the formed dendrites pierce the separator, this dendrite growth can cause a short circuit. Typically, as charge-discharge cycles progress and the battery ages, the deposition of materials on the electrodes (such as dendrite growth) intensifies.

[0007] Existing technologies know that thermal runaway problems are addressed through intensive monitoring of battery temperature. A drawback of this solution is the need for a dense network of temperature sensors to monitor the temperature of individual battery cells. This stems from the fact that, due to the high thermal insulation between battery cells, localized hotspots are often impossible to detect when using only a small number of sensors, or even a single centralized temperature sensor. Furthermore, dense temperature sensor networks involve high construction and cost. Another disadvantage is that the danger of thermal runaway is only identified at a point when it is no longer possible to prevent it.

[0008] One method for detecting short circuits in a battery cell, described from other known solutions in the prior art, involves determining the cell's internal resistance and comparing it to a manufacturing-specific internal resistance reference value. A drawback of this solution is that, due to numerous influencing factors, the internal resistance can typically only be estimated with very low precision. Consequently, it is impossible to determine critical changes in the internal resistance with sufficient accuracy and speed. As a consequence, even using these solutions, it is impossible to identify the risk of thermal runaway at a point when it is still avoidable. Summary of the Invention

[0009] Therefore, the objective of this invention is to at least partially eliminate the aforementioned drawbacks. In particular, the objective of this invention is to provide a method and apparatus capable of reliably identifying problematic changes within a battery at an early stage. Specifically, changes that could lead to thermal runaway should be identified as early as possible.

[0010] The above-mentioned task is accomplished by a method having the features of claim 1, a computer program product having the features of claim 11, an apparatus having the features of claim 12, and a battery system having the features of claim 14.

[0011] Other advantages and features of the invention are derived from the dependent claims, the specification, and the drawings. Hereinafter, the features and details described in relation to the method of the invention also apply to the computer program products, devices, and battery systems of the invention, and vice versa; therefore, the disclosures regarding various aspects of the invention are always cross-referenced or can be cross-referenced.

[0012] This invention relates to a method for classifying the individual battery cells of a battery pack according to their individual health conditions. The method includes acquiring the cell voltage and corresponding current for each battery cell to be classified. Furthermore, the internal resistance of each battery cell is determined based on the cell voltage and the acquired current. The method also includes classifying each battery cell based on the determined internal resistance, classifying it as healthy when the deviation ΔiR of the corresponding internal resistance from a reference value is within the normal range. The internal resistance reference value varies over time. The classification of the battery cells is output as the classification result.

[0013] In other words, the present invention provides a method by which individual battery cells in a battery pack can be classified according to their individual health status.

[0014] Within the scope of this invention, "cell battery" specifically refers to the smallest unit of a battery used for electrochemical power generation. A cell battery preferably has at least two electrodes, an electrolyte, a separator, and a casing.

[0015] A "battery pack" preferably refers to multiple interconnected battery cells. Series and parallel connections are possible. A group of interconnected battery cells can be arranged within a battery module. A battery pack may have one or more battery modules. The battery pack and / or battery modules can be controlled separately by a battery management system (BMS).

[0016] Within the scope of this invention, the "cell health" of a battery cell preferably refers to the physical state of the battery cell. Specifically, the state in which the predetermined electrochemical power generation process within the battery can be normally performed can be described by cell health. Therefore, cell health reflects the current functionality and performance of the battery cell. For example, cell health can be determined based on the state of key components of the battery cell (e.g., electrodes or separators) and / or based on the state of the chemical components used within the battery cell (e.g., catalysts or electrolytes). Preferably, cell health can also be determined based on one or more cell parameters, such as the cell's internal resistance, cell voltage, cell current, cell temperature, state of charge (SOC), one or more concentrations of substances present within the battery cell, and / or state of health (SoH). A battery cell with poor cell health may exhibit abnormal behavior, i.e., deviation of at least one cell parameter from expected behavior.

[0017] This method collects the cell voltage and its corresponding current for each battery cell to be classified.

[0018] Within the scope of this invention, it is conceivable to classify all battery cells in the battery pack, or to classify only individual battery cells, wherein at least two battery cells must be classified. Within the scope of this invention, "collection" specifically refers to providing or acquiring measurement data. Preferably, the current can be collected for each battery cell individually or for all battery cells together. This may depend, for example, on the electrical connection of the battery cells to each other, i.e., whether they are connected in series or in parallel. It is preferable to collect both cell voltage and current simultaneously.

[0019] This method determines the internal resistance of each battery cell to be classified based on the collected cell voltage and current.

[0020] Within the scope of this invention, "determine" preferably means calculation, approximation, and / or estimation. The internal resistance of a battery cell is the impedance of the battery cell to the flow of current.

[0021] Furthermore, in this method, based on a determined internal resistance, for each battery cell to be classified, it is classified as healthy when the deviation ΔiR of the corresponding internal resistance relative to the internal resistance reference value is within the normal range. Here, the internal resistance reference value is set to vary over time. The classification of the battery cell is output as the classification result.

[0022] The method configuration of this invention allows for the assessment and classification of cell health within a battery pack at the individual cell level based on changes in cell internal resistance over time. This invention cleverly utilizes the direct interplay between changes in cell internal resistance and changes in cell health. Internal resistance also exhibits a strong dependence on the current temperature of each individual cell. The temporal variability of the reference value used to calculate the changes in internal resistance allows environmental conditions, operating conditions, or aging and change processes to be incorporated into the calculation of these changes, thus taking them into account during classification. Therefore, in this method, the progression of cell health towards a severely deteriorated state (in which thermal runaway occurs) can be accurately, early, and cell-specifically acquired and identified. Affected cells can be tagged with corresponding classifications. The correspondingly classified cells can be disabled, for example, by the BMS, thereby preventing thermal runaway in these cells; alternatively, adjacent cells or the entire battery pack can also be disabled. Therefore, unlike prior art, temperature sensors for each individual cell can be eliminated.

[0023] According to a preferred design, the method may include a determination step in which a reference value for the internal resistance is determined. The reference value determination step is preferably performed before the classification step.

[0024] Preferably, the internal resistance reference value may depend on a previous, preferably most recent, classification result. More preferably, the internal resistance reference value may be an average value derived from the determined internal resistances of battery cells most recently classified as healthy. Here, the average value may be, for example, an arithmetic mean, a geometric mean, or a weighted average.

[0025] With these configurations, the most recently determined classification result can be incorporated into the current classification of the battery cells. Therefore, when determining the internal resistance reference value, only those battery cells that most recently appeared to be operating normally are considered. Accordingly, the internal resistance change is also determined only relative to a reference value that represents normal operation of a single battery cell. A particular advantage of using an average internal resistance reference value for the difference formation of this invention is that the deviation ΔiR primarily reflects changes attributable to the state changes of individual battery cells. Conversely, difference formation eliminates internal resistance fluctuations attributable to factors that affect all battery cells equally. These could be measurement errors, expected temperature fluctuations, or cell aging effects.

[0026] The internal resistance reference value can be the same or different for each individual cell.

[0027] Therefore, battery pack configurations using different battery types or other configurations could also be considered.

[0028] Alternatively or additionally, the initial internal resistance reference value may be the average of the determined internal resistances of each individual battery cell. Alternatively, the initial internal resistance reference value may be the internal resistance determined during the manufacturing of the battery pack. Preferably, the internal resistance reference value may also be the average of the internal resistances of the individual battery cells determined during the manufacturing of the battery pack.

[0029] This provides a reference value that can be used when the battery pack is put into operation.

[0030] Alternatively or additionally, the deviation ΔiR can be the deviation of the internal resistance of each individual cell relative to an internal resistance reference value, based on that internal resistance reference value. Preferably, the deviation ΔiR can be a percentage deviation. More preferably, the deviation ΔiR can be determined by dividing the difference (i.e., the difference between the internal resistance of each individual cell and the internal resistance reference value) by the internal resistance reference value.

[0031] Based on the relativization of the deviation ΔiR to the internal resistance reference value, abnormal deviations from the normal state become particularly clear and quickly identifiable. Such "normalizations" also have the advantage that they form dimensionless quantities and are mostly within the range of -1 to +1. Accordingly, the critical deviation range can be set once without variable adjustments. Another advantage is that the internal resistance reference value changes over time, so the deviation ΔiR can change in a time-adaptive manner. Consequently, the method can rely on a one-time calibration that can be used throughout operation. These advantages are further enhanced, in particular, by using the average value derived from the internal resistance of the most recently classified healthy battery cells as the internal resistance reference value.

[0032] According to another preferred design, the method may further include determining the actual operating mode of the battery pack from at least the current curves sampled for individual battery cells. Preferably, this determination step may also be performed from at least one sampled cell voltage curve. Here, the actual operating mode may include a constant operating mode for a segment of the current curve, which has a constant or substantially constant trend and preferably has no zeros. The constant operating mode may be, for example, a battery charging operating mode and / or a battery discharging operating mode. Alternatively or additionally, the actual operating mode may include a dynamic operating mode for a segment of the current curve, which has at least one current pulse and preferably at least one zero. The dynamic operating mode may be, for example, a driving operating mode. Alternatively or additionally, the actual operating mode may include an unloaded operating mode for a segment of the current curve, which has no or almost no current flow.

[0033] This allows us to determine the different operating modes of the battery pack and incorporate these determinations into the classification.

[0034] Preferably, the internal resistance of a single battery cell can be determined based on the actual operating mode of the battery pack. For example, in a constant operating mode, the internal resistance can be determined from the potential difference between the actual open-circuit voltage and the sampled cell voltage of the battery cell, as well as from the sampled current. Preferably, the actual open-circuit voltage is determined based on the sampled state of charge of the battery cell. Conversely, in a dynamic operating mode, the internal resistance can be determined from the difference between the cell voltages sampled at the beginning and end of a current pulse, and from the current sampled at the end of the current pulse.

[0035] This allows the internal resistance of individual battery cells to be determined based on the operating mode. This enables the consideration of the individual cell physical characteristics present in each specific operating mode. Consequently, accurate and meaningful determination of the internal resistance can be achieved.

[0036] According to a preferred design, for each battery cell, the current can be sampled at the electrodes of the battery pack or at the electrodes of the individual battery cell.

[0037] Therefore, the current corresponding to the voltage of a single cell can be either the battery pack current or the single cell current belonging to the battery cell.

[0038] According to another preferred design, each battery cell can be classified as abnormal based on a predetermined internal resistance when the deviation ΔiR exceeds the normal range. Specifically, each battery cell classified as abnormal can be classified as severely aged when the deviation ΔiR is greater than an aging deviation limit. Alternatively or additionally, each battery cell classified as abnormal can be classified as having a short-circuit hazard when the deviation ΔiR is negative and less than a short-circuit deviation limit, and preferably, the deviation ΔiR has a rate of change greater than a rate of change limit. Alternatively or additionally, each battery cell classified as abnormal can be classified as overheating when the deviation ΔiR is negative and less than a temperature deviation limit, and at least one sampled battery pack temperature exceeds a temperature limit.

[0039] This allows for the identification of the causes of cell health deterioration and the corresponding responses. The present invention cleverly utilizes, for example, the characteristic that internal resistance increases with cell aging or decreases during a short circuit. The strong temperature dependence of internal resistance allows for risk balancing and determination of whether a cell's current or subsequent cycles will lead to thermal runaway.

[0040] According to a preferred design, the normal range may have an upper and a lower limit, which are preferably defined according to the construction structure of the battery cell. Preferably, the values ​​within the normal range, in absolute terms, may be less than aging deviation limits, short-circuit deviation limits, or temperature deviation limits. For example, the normal range may be a symmetrical interval, such as a deviation of -5% to 5% relative to the internal resistance reference value.

[0041] This allows for the advantageous identification of unhealthy battery cells.

[0042] According to another preferred design, the method may include a calibration step in which at least a normal range is set. Preferably, other aforementioned limits may also be determined in the calibration step, such as aging deviation limits, short-circuit deviation limits, rate of change limits, temperature deviation limits, or temperature limits.

[0043] This allows for direct testing of limit ranges on the battery pack, thereby improving the accuracy of the method.

[0044] According to a preferred design, the method of the present invention may include a verification step in which the range determined during calibration is verified using comparative data. Here, the comparative data may be, for example, derived from simulation or measurement.

[0045] This allows for the direct determination of the limit range on the battery pack, thereby improving the accuracy of the method.

[0046] According to another preferred design, the output step may include: outputting information about the classification results (particularly information about the number and identification numbers of battery cells classified as healthy, and information about the total number of battery cells), warnings, and / or control signals for transmission to the battery pack control device to selectively activate or disable battery cells in the battery pack based on the corresponding classification results. Preferably, the output is sent to an output device for the user.

[0047] Preferably, the method may further include a step of analyzing the classification results. In particular, it can be determined whether the number of battery cells classified as healthy exceeds a minimum number.

[0048] Preferably, a warning may be issued if one or more battery cells are repeatedly classified as unhealthy. Alternatively or additionally, a warning may be issued if an excessive number of battery cells are classified as unhealthy. Preferably, the necessary limits for assessing such events can be identified through calibration and / or set by the user.

[0049] This allows for the issuance of warnings and feedback to users.

[0050] According to a preferred design, the method can be configured to classify individual battery cells during the operation of the battery pack.

[0051] The battery pack may be a lithium-ion battery pack. In particular, the battery pack may be a high-voltage battery pack for electric vehicles. Preferably, the battery pack may have at least 100 or 200 battery cells, which are preferably connected in series within the battery pack. Alternatively or additionally, the battery pack may have a voltage sensor for each battery cell to be classified, in order to acquire the corresponding cell voltage. This method may, for example, be a battery pack diagnostic method. The method may also be configured and / or designed for thermal runaway prediction.

[0052] The method of the present invention can be advantageously implemented in combination with the above-described configurations, but is not limited to these configurations.

[0053] Another aspect of the present invention relates to a computer program product having instructions that cause the computer to perform the aforementioned method when the program is executed by the computer.

[0054] Another aspect of the present invention relates to an apparatus for classifying the individual battery cells of a battery pack according to their individual health conditions. The apparatus includes a data acquisition module for acquiring the cell voltage and corresponding current for each battery cell to be classified. Furthermore, the apparatus includes a determination module for determining the internal resistance of each individual battery cell based on the acquired cell voltage and current. The apparatus also includes a classification module for classifying each battery cell based on the determined internal resistance. The classification module is configured to classify the battery cell as healthy when the deviation ΔiR of the corresponding internal resistance from a reference value of internal resistance is within a normal range, wherein the reference value of internal resistance varies over time. The apparatus also includes an output module for outputting the classification of the battery cells as a classification result.

[0055] The device is particularly capable of performing and / or setting up for performing the methods of the present invention.

[0056] According to a preferred design, the device may include an operating mode determination module for determining the actual operating mode of the battery pack. This module utilizes at least current curves acquired for individual battery cells, and preferably also utilizes at least one acquired voltage curve for a single cell. Preferably, the device may be a diagnostic or predictive device for thermal runaway.

[0057] Another aspect of the present invention relates to a battery system. The battery system has at least one battery pack having a plurality of battery cells preferably connected in series. The battery system also has a voltage sensor for acquiring the cell voltage of each battery cell to be classified and at least one current sensor for acquiring the current. Furthermore, the battery system has the aforementioned means for classifying the battery cells of the battery pack according to their individual health conditions.

[0058] Preferably, the battery system may have a user-specific output device for outputting classification results.

[0059] The same technical effects and advantages as those described regarding the control method can be achieved using the aforementioned computer program products, devices, and battery systems. Therefore, the following descriptions will only refer to the relevant explanations.

[0060] In a preferred design, the battery system may have at least one circuit breaker with a switchable disconnect for interrupting charge transfer within one or more battery cells, wherein the circuit breaker is connected to the device of the present invention so as to switch the circuit breaker according to the classification result.

[0061] This allows for the failure of poorly functioning battery cells when necessary, thereby preventing thermal runaway. Attached Figure Description

[0062] Other advantages, features, and details of the present invention are derived from the following detailed description of embodiments of the invention with reference to the accompanying drawings. Wherein: Figure 1 This illustration schematically shows one embodiment of the method of the present invention. Figure 2 This illustration schematically shows another embodiment of the method of the present invention. Figure 3 This illustration schematically shows another embodiment of the device and battery system of the present invention. Figure 4 This illustration schematically shows another embodiment of the battery system of the present invention. Figure 5 An example of voltage and current curves for a single battery cell is shown schematically. Detailed Implementation

[0063] Figures 1 to 5 Different aspects and embodiments of the invention are shown.

[0064] One aspect of the present invention relates to a method 10 for classifying the individual battery cells 1100 of a battery pack 1001 according to their individual health. Figure 1 and Figure 2 Exemplary embodiments of the method 10 of the present invention are shown respectively. The battery cell 1100 may in particular be a lithium-ion battery cell for electric vehicles.

[0065] Here, Figure 1 Method 10 shown comprises four steps. Specifically, in the acquisition step S20, the cell voltage V_Zell and its corresponding current I_Zell are acquired for each battery cell 1100 to be classified. Figure 1In the diagram, multiple pairs of acquired values ​​are represented as an array within parentheses “<<..:>>”. When battery cells 1100 are connected in series, the acquired current I_Zell can be, for example, the battery pack current. Besides the cell voltage V_Zell and current I_Zell, other parameters can also be acquired. For example, cell temperature and / or state of charge (SOC) can be acquired. Voltage, current, and / or temperature values ​​can be acquired, for example, by a battery management system (BMS) or dedicated sensors. In the internal resistance determination step S30, the corresponding internal resistance iR of each battery cell 1100 can be determined based on the corresponding value pairs of the cell voltage V_Zell and the acquired current I_Zell. In the classification step S40, each battery cell 1100 is classified based on the determined internal resistance iR. In the classification step S40, when the deviation ΔiR of the corresponding internal resistance iR from the time-varying internal resistance reference value iRW is within the normal range, the battery cell 1100 is classified as healthy. In the output step S50, the classification of the battery cell 1100 is output as the classification result KE. Therefore, method 10 can be used, for example, as a diagnostic method for the battery cell 1100.

[0066] Figure 2 Another embodiment of method 10 is shown, which includes other preferred method steps in addition to the steps S20 to S50 already described.

[0067] Specifically, Figure 2 The exemplary method 10 includes a calibration step S10, in which parameters such as the measurement accuracy, measurement frequency, measurement window size, data buffer size, signal filter, and other parameters to be set can be determined and configured. Specifically, for example, the upper and lower limits of the normal range can be determined in calibration step S10.

[0068] After the optional parameter settings in calibration step S10, at least the individual cell voltage V_Zell and its corresponding current I_Zell are acquired in acquisition step S20 as described above.

[0069] In the operation mode determination step S31, the current actual operation mode of the battery pack 1001 or each individual battery cell 1100 can be determined based on the curves of each collected parameter.

[0070] Figure 5The diagram illustrates such curves for the cell voltage V_Zell and the acquired current I_Zell. For the battery pack 1001 of an electric vehicle, there are typically at least three distinguishable actual operating modes IBM. This invention is not limited to the actual operating modes IBM described below, but merely illustrates these modes exemplarily. For example, during discharge operation, a segment VK of the current I_Zell curve with a nearly constant trend and no zeros may occur. A corresponding situation exists during charging operation; therefore, these operating modes are also referred to as constant operating modes. During normal driving operation, the current I_Zell curve is often characterized by a segment VD with at least one current pulse SP and preferably at least one zero. Since dynamism can often be identified in such signal curves, this operating mode is also referred to as a dynamic operating mode. Furthermore, there is an unloaded operating mode, in which almost no current flows in a segment of the current I_Zell curve.

[0071] exist Figure 2 In step S30, which shows the determination of internal resistance, the internal resistance iR of each individual battery cell 1100 is determined considering the actual operating mode IBM. For the constant operating mode, the internal resistance iR can be continuously determined in step S301 based on Ohm's law, from the potential difference between the determined actual open-circuit voltage of the battery cell 1100 and the acquired cell voltage V_Zell, and from the acquired current I_Zell. The actual open-circuit voltage can be determined, for example, from a lookup table based on the state of charge (SOC).

[0072] The method for determining the internal resistance iR in dynamic operating mode is different. Here, for example, the internal resistance iR is calculated separately from the values ​​of the cell voltage V_Zell and current I_Zell present at the beginning and end of the current pulse SP. Figure 5 In this process, the start of the current pulse SP is displayed as time point t_0, and its end is displayed as time point t_1. This is implemented accordingly in step S302. Step S303 skips the determination of the internal resistance iR in the no-load operation mode. Of course, other implementations for determining the internal resistance iR are also conceivable.

[0073] Before classifying the battery cell 1100 in classification step S40, the value of the internal resistance reference value iRW can be determined in reference value determination step S41. For example, when method 10 is executed for the first time, the internal resistance reference value iRW can be determined in the initial reference value determination step S411. At this time, for example, the value of the internal resistance iR of the battery cell 1100 specified by the manufacturer can be used. For subsequent cycles of method 10, alternatively, the internal resistance reference value iRW can be repeatedly re-determined in the running reference value determination step S412. For this purpose, for example, the internal resistance reference value iRW can depend on the previous classification result KE, wherein the internal resistance reference value iRW is assigned by the arithmetic mean of the internal resistance iR of the battery cells 1100 that were most recently classified as healthy.

[0074] In deviation determination step S42, the deviation ΔiR can be determined. Preferably, the deviation ΔiR can be the relative deviation of the internal resistance iR of each individual battery cell 1100 relative to the internal resistance reference value iRW, with the internal resistance reference value iRW as a reference. For example, the deviation ΔiR of each battery cell 1100 at time point t can be determined by the following formula: .

[0075] Here, the subscript n represents the number of each individual battery cell 1100.

[0076] In particular, when the internal resistance reference value iRW is taken as the arithmetic mean of the internal resistance iR of the battery cell 1100 that was most recently classified as healthy (i.e., at time point t-1), we can obtain: .

[0077] Subsequently, in classification step S40, the battery cells 1100 are classified according to their cell health using the deviation ΔiR. Here, each deviation ΔiR is compared with the normal range. Preferably, further classification and comparison with other limit ranges can be performed to more finely assess cell health or identify the causes of potential deterioration in cell health.

[0078] In output step S50, the classification result from step S40 is output. For example, a message or warning may be issued to the user. Alternatively, the classification result KE may be provided to the subsequent bias determination step S42.

[0079] Alternatively or additionally, diagnostic and / or analytical steps S60 may be performed to derive further information from the classification result KE. Based on this, for example, a control signal KS may be generated in control step S70 to drive the affected battery cell 1100 ( Figure 2 (Not shown). Furthermore, it is conceivable that the classification result KE be checked in verification step S80 to verify the initial calibration from calibration step S10, and adjustments be made if necessary.

[0080] based on Figure 1 and Figure 2 It also clearly shows how possible implementations of method 10 can be designed as computer program products.

[0081] Figure 3 and Figure 4 Embodiments of the apparatus 100 of the present invention for classifying the individual battery cells 1100 of the battery pack 1001 according to their individual health are shown respectively.

[0082] Figure 3In particular, one possible structure and signal flow of the device 100 are shown. Here, the device 100 has a data acquisition module 120 for acquiring the cell voltage V_Zell and current I_Zell of each battery cell 1100 to be classified. Figure 3 In the diagram, the signal flow between the battery pack 1001 and / or individual battery cells 1100 is represented by dashed lines. The device 100 preferably includes an operating mode determination module 131 for determining the actual operating mode IBM of the battery pack 1001 based on the curve of the current I_Zell collected for each individual battery cell 1100. Furthermore, in the determination module 130, the internal resistance iR of each individual battery cell 1100 is determined based on the collected cell voltage V_Zell and the collected current I_Zell. A classification module 140 is provided for classifying each individual battery cell 1100 based on the determined internal resistance iR. This classification module is configured to classify the individual battery cell 1100 as healthy when the deviation ΔiR of the corresponding internal resistance iR relative to the internal resistance reference value iRW is within the normal range. Here, the internal resistance reference value iRW varies over time. The device 100 also includes an output module 150 for outputting the classification result KE. Preferably, the device may also include a diagnostic module 160, which, for example, evaluates the classification result KE and generates a control signal KS accordingly. Here, in addition to the control signal KS, the diagnostic module 160 preferably also outputs the classification result KE to an output device 1800 (e.g., a display).

[0083] Device 100 may be part of a battery management system or a standalone computing unit, for example, that may be integrated into a server or battery pack 1001.

[0084] Figure 3 and Figure 4 The battery system 1000 of the present invention is also shown. The battery system 1000 includes the aforementioned device 100 and a battery pack 1001 having a plurality of battery cells 1100. The battery cells 1100 are shown in series with their respective battery cell electrodes 1102, 1103. The battery pack 1001 also has electrodes 1002, 1003. Each battery cell 1100 has a voltage sensor 1101 for acquiring the cell voltage V_Zell. Furthermore, at least one current sensor 1200 is provided for acquiring the corresponding current I_Zell. Figure 4 In this configuration, the current I_Zell is the same as the battery pack current. Therefore, the current I_Zell only needs to be sampled once for all battery cells 1100, without needing to sample each battery cell 1100 individually. Thus, to determine the internal resistance iR, the battery pack current can be applied to each battery cell 1100.

[0085] also, Figure 4As exemplarily shown, in addition to outputting the classification result KE to the output device 1800, a control signal KS can also be output to the circuit breaker device 1500 to, for example, protect defective battery cells 1100 from short circuits, or to disable them in the battery system 1100 before thermal runaway occurs. For this purpose, the circuit breaker device 1500 may have a separation section 1501 that, when activated by the control signal KS, can electrically separate the battery cell electrodes 1102, 1103 within the battery cell 1100.

[0086] The foregoing description of the embodiments is merely illustrative of the invention. It is obvious that the various features of the embodiments can be freely combined with each other without departing from the scope of the invention, provided it is technically reasonable.

[0087] List of reference numerals

[0088] 10 methods

[0089] S10 Calibration Steps

[0090] S20 Data Acquisition Steps

[0091] S30 Internal Resistance Determination Steps

[0092] S301 Determination of Constant Operating Mode

[0093] S302 Determination of Dynamic Operating Mode

[0094] S303 Determination of No-load Operation Mode

[0095] S31 Operating Mode Determination Steps

[0096] S40 Classification Steps

[0097] S41 Reference Value Determination Steps

[0098] S411 Initial Reference Value Determination Steps

[0099] S412 Running Reference Value Determination Steps

[0100] S42 Deviation Determination Steps

[0101] S50 Output Steps

[0102] S60 Diagnostic and / or analytical steps

[0103] S70 Control Procedure

[0104] S80 Verification Steps

[0105] 100 devices

[0106] 120 data acquisition module

[0107] 130 Determine Module

[0108] 131 Operation Mode Determination Module

[0109] 140 Category Modules

[0110] 150 Output Module

[0111] 160 Diagnostic Module

[0112] 1000 Battery System

[0113] 1001 Battery Pack

[0114] 1002 and 1003 battery pack electrodes

[0115] 1100 battery cell

[0116] 1101 Voltage Sensor

[0117] 1102 and 1103 battery cell electrodes

[0118] 1200 Current Sensor

[0119] 1500 Circuit Breaker

[0120] 1501 Separation Section

[0121] 1800 Output Device

[0122] KS control signal

[0123] V_Zell unit voltage

[0124] I_Zell refers to the current of a single cell or the battery pack.

[0125] iR internal resistance

[0126] iRW internal resistance reference value

[0127] ΔiR deviation

[0128] IBM's actual operating model

[0129] KE classification results

[0130] t_0 Current pulse begins

[0131] t_1 Current pulse ends

[0132] Dynamic segment of the VD curve

[0133] Constant section of the VK curve

[0134] SP current pulse

Claims

1. A method (10) for classifying the individual battery cells (1100) of a battery pack (1001) according to their individual health, comprising the following steps: For each battery cell (1100) to be classified, the cell voltage (V_Zell) and its corresponding current (I_Zell) are collected. The internal resistance (iR) of each individual battery cell (1100) is determined based on the collected cell voltage (V_Zell) and the collected current (I_Zell). Based on the determined internal resistance (iR), for each of the battery cells (1100), when the deviation ΔiR of the corresponding internal resistance (iR) from the internal resistance reference value (iRW) is within the normal range, it is classified as healthy, and The classification output of the battery cell (1100) is a classification result (KE). Its features are, The internal resistance reference value (iRW) changes over time.

2. The method (10) according to claim 1, characterized in that, The internal resistance reference value (iRW) It depends on the previous, preferably the most recent, classification result (KE). It is an average value derived from the determined internal resistance (iR) of the most recently classified healthy battery cells (1100). For each of the aforementioned battery cells (1100), they may be the same or different, and / or The initial value is the average value derived from the determined internal resistance (iR) of each of the battery cells (1100), or the initial value is the internal resistance determined during the manufacture of the battery pack, preferably the average value derived from the internal resistance (iR) of the battery cells (1100) determined during the manufacture of the battery pack.

3. The method (10) according to claim 1 or 2, characterized in that, The deviation ΔiR is a percentage deviation of the internal resistance (iR) of each individual battery cell (1100) relative to the internal resistance reference value (iRW), and preferably, the deviation ΔiR is determined by dividing a difference result by the internal resistance reference value (iRW), which is the difference between the internal resistance (iR) of each individual battery cell (1100) and the internal resistance reference value (iRW).

4. The method (10) according to any one of the preceding claims, characterized in that, The actual operating mode (IBM) of the battery pack (1001) is determined at least from the current (I_Zell) curves collected for the battery cell (1100), and preferably also from the voltage (V_Zell) curves of at least one collected cell, wherein the actual operating mode (IBM) includes: o Constant operating mode, especially battery charging operating mode and / or battery discharging operating mode, which corresponds to the segment (VK) of the current (I_Zell) curve with a constant or substantially constant trend and preferably without zero points. o Dynamic operating mode, especially driving operating mode, which corresponds to a segment (VD) of the current (I_Zell) curve having at least one current pulse (SP) and preferably at least one zero point, and / or o No-load operation mode, which corresponds to the section of the current (I_Zell) curve where there is no or almost no current flow.

5. The method (10) according to claim 4, characterized in that, The internal resistance (iR) of the individual battery cell (1100) is determined based on the actual operating mode (IBM) of the battery pack (1001). In the constant operating mode, the internal resistance (iR) is determined from the potential difference between the actual open-circuit voltage of the battery cell (1100) and the acquired cell voltage (V_Zell), and from the acquired current (I_Zell). Preferably, the actual open-circuit voltage is determined based on the acquired state of charge of the battery cell (1100), and / or In the dynamic operation mode, the internal resistance (iR) is determined by the difference between the unit voltage (V_Zell) collected at the beginning (t_0) and the end (t_1) of the current pulse (SP), and the current (I_Zell) collected at the end of the current pulse (SP).

6. The method (10) according to any one of the preceding claims, characterized in that, For each of the battery cells (1100), the current (I_Zell) is collected at the electrodes (1002, 1003) of the battery pack (1001) or at the electrodes (1102, 1103) of the respective battery cell (1100).

7. The method (10) according to any one of the preceding claims, characterized in that, Based on the determined internal resistance (iR), for each of the battery cells (1100), when the deviation ΔiR exceeds the normal range, it is classified as abnormal, and preferably further includes: For each battery cell (1100) classified as abnormal, if the deviation ΔiR is greater than the aging deviation limit, it is classified as severely aged. For each battery cell (1100) classified as abnormal, if the deviation ΔiR is negative and less than the short-circuit deviation limit, and preferably the deviation ΔiR has a rate of change greater than the rate of change limit, it is classified as having a short-circuit hazard. For each battery cell (1100) classified as abnormal, it is classified as overheated when the deviation ΔiR is negative and less than the temperature deviation limit, and at least one collected battery pack (1001) temperature exceeds the temperature limit.

8. The method (10) according to any one of the preceding claims, characterized in that, The normal range has an upper limit and a lower limit, which are preferably defined according to the structural design of the battery cell (1100). Preferably, the value within the normal range is less than the aging deviation limit, the short circuit deviation limit, or the temperature deviation limit in absolute terms.

9. The method (10) according to any one of the preceding claims, characterized in that, Preferably, the output is directed to the user's output device (1800): Information regarding the classification results, particularly regarding the number of battery cells (1100) classified as healthy, and preferably regarding their identification numbers, as well as information regarding the total number of said battery cells (1100). o warning, and / or o control signal (KS) is transmitted to the control device of the battery pack (1001) to selectively activate or disable the battery cells (1100) of the battery pack (1001) according to the corresponding classification results.

10. The method (10) according to any one of the preceding claims, characterized in that, The method (10) is configured to classify the individual battery cells (1100) during operation of the battery pack (1001). The battery pack (1001) is equipped with a voltage sensor (1101) for each battery cell (1100) to be classified, so as to collect the individual cell voltage (V_Zell). The battery pack (1001) has at least 100 or 200 individual battery cells (1100). The battery pack (1001) is a lithium-ion battery pack. The battery pack (1001) is a high-voltage battery pack for electric vehicles, and / or The battery cell (1100) is connected in series in the battery pack (1001).

11. A computer program product having instructions that, when executed by a computer, cause the computer to perform the method (10) according to any one of the preceding claims.

12. An apparatus (100) for classifying the individual battery cells (1100) of a battery pack (1001) according to their individual health, comprising: The acquisition module (120) is used to acquire the cell voltage (V_Zell) and its corresponding current (I_Zell) for each cell to be classified (1100). The determination module (130) is used to determine the internal resistance (iR) of each individual battery cell (1100) based on the collected cell voltage (V_Zell) and the collected current (I_Zell). A classification module (140) is used to classify each battery cell (1100) based on a determined internal resistance (iR), and The output module (150) is used to output the classification results (KE) of the battery cells (1100). Its features are, The classification module (140) is configured to classify the battery cell (1100) as healthy when the deviation ΔiR of the corresponding internal resistance (iR) relative to the internal resistance reference value (iRW) is within the normal range, wherein the internal resistance reference value (iRW) changes over time.

13. The apparatus (100) according to claim 12, characterized in that, An operating mode determination module (131) is provided for determining the actual operating mode (IBM) of the battery pack (1001) from at least the curve of current (I_Zell) collected for the battery cell (1100) and preferably also from the curve of voltage (V_Zell) of at least one cell.

14. A battery system (1000) comprising: At least one battery pack (1001) having a plurality of battery cells (1100) preferably connected in series. A voltage sensor (1101) for acquiring the cell voltage (V_Zell) of each battery cell (1100) to be classified, and at least one current sensor (1200) for acquiring the current (I_Zell). Its features are, The device (100) has, according to claim 12 or 13, a means (100) for classifying the individual battery cells (1100) of the battery pack (1001) according to their individual health.

15. The battery system (1000) according to claim 14, characterized in that, Given: At least one circuit breaker (1500) having a switchable separation section (1501) for interrupting charge transfer within one of the battery cells (1100), wherein the circuit breaker (1500) is connected to the device (100) to switch the circuit breaker (1500) according to a classification result (KE), and / or The output device (1800) for the user is used to output the classification result (KE).

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