Method and device for providing a self-discharge characteristic map of a battery type of a device battery for carrying out a diagnosis

Abstract

The invention relates to a method, in particular an at least partially computer-implemented method, for determining a self-discharge characteristic map for diagnosing and monitoring device batteries (41) of battery-operated devices (4), comprising the following steps: - After a predetermined minimum duration of an inactivity phase of one or more device batteries (41), determining (S4) one or more measuring points at one or more measuring times, wherein the measuring points each indicate a self-discharge depending on a duration of the inactivity phase at the measuring time and depending on at least one parameter; - Providing (S6) the self-discharge characteristic map which takes the measuring points into account, wherein an entry is made in the self-discharge characteristic map if a corresponding previously entered value is greater than the self-discharge of the current measuring point; - Carrying out a diagnosis or monitoring of at least one of the device batteries (41) depending on the self-discharge characteristic map.

Description

Technical area

[0001] The invention relates to portable batteries, and in particular to the monitoring of portable batteries of the same battery type based on their self-discharge. The invention further relates to methods for diagnosing the function of portable batteries based on their self-discharge. Technical background

[0002] To monitor and diagnose portable batteries, the aging state and / or electrochemical battery conditions are typically continuously determined and monitored. The aging state is often determined during active operation of the portable battery, i.e., during a charging and discharging process, based on suitable aging state models, and the temporal development of the aging state is evaluated.

[0003] If the device battery is frequently inactive, i.e. no battery current flows, the aging states determined with previous models are generally not very reliable, so that no reliable diagnosis of the device battery's function is possible based on a temporal progression of an aging state determined in this way. Disclosure of the invention

[0004] According to the invention, a method for determining a self-discharge characteristic map for self-discharge rates of device batteries of a specific battery type for use in diagnosis and anomaly detection in a device battery according to claim 1 as well as a corresponding device and a battery control unit according to the independent claims are provided.

[0005] Further embodiments are specified in the dependent claims.

[0006] According to a first aspect, a method, in particular an at least partially computer-implemented method, is provided for determining a self-discharge characteristic map for diagnosing and monitoring device batteries of battery-operated devices, comprising the following steps: - After a predetermined minimum duration of an inactivity phase of one or more device batteries, determining one or more measuring points at one or more measuring times, wherein the measuring points each indicate a self-discharge depending on a duration of the inactivity phase at the corresponding measuring time and depending on at least one parameter; - Providing the self-discharge map that takes the measuring points into account, whereby an entry is made in the self-discharge map if a corresponding previously entered value is greater than the self-discharge of the current measuring point; - Carrying out a diagnosis or monitoring of at least one of the device batteries depending on the self-discharge map.

[0007] In addition to using the modeled aging state and / or the internal electrochemical states of a portable battery for diagnostics or monitoring, it may also be possible to derive information about the condition of a portable battery from its self-discharge rate during a period of inactivity. Self-discharge exists in all battery types and is generally very low in fault-free portable batteries.

[0008] The self-discharge of a portable battery manifests itself in a decrease in cell voltage, which, for example, in lithium-ion batteries, amounts to approximately 0.5 to 2% per month. Furthermore, self-charging is highly temperature-dependent, so that, for example, a temperature increase of approximately 10°K causes the self-discharge rate to double. Furthermore, the self-discharge rate increases with the charge level of the portable battery. A more severely degraded portable battery also exhibits a higher self-discharge rate than a newer one.

[0009] A relatively long period of inactivity is necessary to reliably determine the self-discharge rate of a portable battery. For example, an electric vehicle must be left idle for at least two weeks to reliably demonstrate a self-discharge effect in the portable battery. Such long periods of inactivity only occur, for example, when the electric vehicle is parked during a vacation or when it is used infrequently as a second vehicle.

[0010] To date, the self-discharge rate has not been used for diagnosis because it depends significantly on the battery condition and therefore threshold values ​​for anomaly detection or diagnosis cannot be determined with reasonable effort using heuristic monitoring methods.

[0011] Battery diagnostics or anomaly detection methods are currently often performed based on a temporal change in the aging state, taking into account internal electrochemical battery states. This allows deviations from the normal operating behavior of the device battery to be detected early and signaled accordingly. Slowly developing errors, such as gradual cell defects, are difficult to detect using monitoring based on a change in the aging state, as these are best diagnosed during or after a prolonged period of inactivity of the device battery. However, such periods of inactivity do not occur frequently in device batteries used in technical devices, as a device battery is typically used regularly and frequently undergoes a discharge or charge cycle.

[0012] The self-discharge dU of a device battery corresponds to a voltage difference of the terminal voltage between a voltage value at the beginning of the inactivity phase and at the currently considered time during the inactivity phase and depends on a large number of influencing parameters and increases with the duration of the inactivity phase t: dU=f(SoH,SoC,T,t)

[0013] Self-charging is also strongly dependent on the battery temperature T, the state of charge SoC, and the aging state SoH. Self-discharge can be expressed as a change in the terminal voltage for a certain duration of the inactivity phase.

[0014] The objective of the above method is to determine a self-discharge map for a large number of portable batteries of the same battery type during extended periods of inactivity, for example, a given period of more than two weeks, and to supplement or improve the map from this. Due to the high dependence of self-discharge on the aging state, the state of charge, the temperature, and the duration of the inactivity phase, applying a self-discharge map is very complex.

[0015] The core of the above method consists in first generating a self-discharge characteristic map by using a large number of portable batteries of the same type, in which the parameters of an aging state, a state of charge, a battery temperature and a time period since the last current flow are determined for a specific portable battery that is in an inactivity phase at a specific time and a voltage change is recorded, which form a measuring point that is entered into the characteristic map according to the parameters.

[0016] For monitoring purposes, the self-discharge map can be determined separately for one or more battery cells and / or the entire battery module.

[0017] The self-discharge map is generated by adopting the self-discharge dU as an entry in the map or by updating an entry in the map if the previous value of the self-discharge with comparable parameterization is higher than the current value to be entered.

[0018] Using such a self-discharge map, it is then possible to better monitor and predict the behavior of the device battery. Monitoring the device battery based on self-discharge makes it possible to diagnose potential cell defects, such as leakage currents in the battery cell, detect electronic faults in the battery management system, or locate leakage currents from electrical loads.

[0019] In addition, other characteristics that influence self-discharge can be recorded at the evaluation times during the inactivity phase. These can include: - voltage differences (especially related to a period of time); - Differences in state of charge (especially related to a period of time); - Voltage gradients, smoothed e.g. with a moving average; - State of charge gradients, smoothed e.g. with a moving average; - Balancing frequency per time interval (excluding rest periods) - Balancing amount per time interval (excluding rest periods); - Electrochemical model parameters, including: equilibrium parameters such as cyclable lithium, kinetic parameters such as diffusion coefficients and / or internal battery states of a nonlinear coupled differential equation such as SEI thickness.

[0020] The current aging state can be determined using conventional and known models for determining the aging state. In particular, an electrochemical aging state model can be used, which is fundamentally based on an electrochemical battery model. Such an electrochemical battery model can comprise a system of differential equations that, based on differential equations parameterized via model parameters, models internal battery states, in particular equilibrium states and, if applicable, kinetic states, using a time integration method and provides a relationship between operating parameter profiles of the device battery, namely a battery current, a battery voltage, a battery temperature, and a state of charge of the device battery, and the internal battery state. Such electrochemical battery models are known, for example, from the publications US 2016 / 023,566, US 2016 / 023,567, and US 2020 / 150,185.An aging state can be derived from the internal battery states.

[0021] The aging state at the beginning of the inactivity phase can be used as an aging state parameter for creating the self-discharge map.

[0022] Due to the high dimensionality of the dependencies of the self-discharge characteristic map, it is intended to transmit the measurement points to a central unit remote from the device and store them there during an inactivity phase.

[0023] In the central unit, a self-discharge model can now be provided using the self-discharge map determined in this way, which can, for example, be data-based and indicate a self-discharge dU depending on the aging state, the state of charge, the battery temperature and the duration of the inactivity phase.

[0024] Furthermore, an autoencoder or a variational autoencoder can be trained as an anomaly detection model on the entries of the self-discharge characteristic map in order to determine an anomaly detection based on a reconstruction error or based on a deviation measure that results with regard to the latent state vector in the latent state space. In this case, an encoder is used to compress the original state space into a latent state space, the embedding space. The deviation measure can be a distance measure of a deviation of the state vector determined at a measuring point in the latent state space from a centroid of the normal cluster resulting across all measuring points entered in the self-discharge characteristic map. If the distance measure is smaller than a threshold value, the latent state vector is evaluated as "normal." Advantageously, a confidence can also be taken into account here, so that, for example,A quantile evaluation with threshold adjustment is performed. Possible distance measures include: Euclidean distance, measured from the centroid, Manhattan distance with respect to the centroid, cosine similarity to the centroid vector, etc.

[0025] The training of such an anomaly detection model can be carried out in the central unit, whereby the trained anomaly detection model can be implemented in the control unit of the device so that anomaly detection can also be carried out without a communication connection to the central unit remote from the device.

[0026] To obtain measurement points for creating the self-discharge map, a wake-up process can be performed at regular intervals in a device battery control unit starting at a minimum duration of an inactivity phase. This process determines the corresponding parameters and the current self-discharge as the difference between the value at the beginning of the inactivity phase and the measurement time. In particular, the current terminal voltage at the cell and / or module level, the aging states, the charge states, and the temperature are recorded as parameters, assigned to the corresponding duration of the inactivity phase, and transmitted to the central unit as measurement points. This allows a larger number of measurement points to be obtained during a longer inactivity phase.

[0027] It can be provided that the self-discharge characteristic map has a characteristic map discretization of the duration of the inactivity phase of between 12 hours and one week and in particular has a characteristic map discretization of the state of charge of between 0.5 and 5%, the ageing state of between 0.5% and 5% and the battery temperature of between 1°C and 10°C.

[0028] By accumulating these measurement points, a characteristic map can be created that discretizes the parameters into parameter ranges for making a corresponding entry. For example, the discretization of the aging state can be 1% SOH-C, the state of charge 1% SOC, the temperature 1°C, and the duration of the inactivity phase can cover one day. This self-discharge characteristic map can then be described with the measurement points, each of which specifies a voltage difference between a terminal voltage at the beginning of the inactivity phase and the terminal voltage at the time of measurement. If a smaller voltage difference results for an entry in the characteristic map than previously entered, the entry in the characteristic map can be changed accordingly.

[0029] If the voltage difference at a measuring point is higher than the corresponding value previously entered in the self-discharge map by more than a predetermined tolerance, such as 5%, an error or anomaly can be signaled for the corresponding device battery. For example, a user of the device can then be signaled that the device battery needs to be serviced for inspection. Brief description of the drawings

[0030] Embodiments are explained in more detail below with reference to the attached drawings. They show: Fig. 1 a schematic representation of a system for providing battery-related operating variables for determining a self-discharge characteristic map for the diagnosis and anomaly detection of a vehicle battery; Fig. 2 is a flowchart illustrating a method for creating a self-discharge map and performing anomaly monitoring. Description of embodiments

[0031] The method according to the invention is described below using vehicle batteries as device batteries in a variety of motor vehicles as similar devices. For this purpose, one or more electrochemical battery models are evaluated in the central unit based on operating parameter profiles, and a self-discharge map is created.

[0032] The above example represents a multitude of stationary or mobile devices with grid-independent energy supply, such as vehicles (electric vehicles, pedelecs, etc.), systems, machine tools, household appliances, IOT devices and the like, which are connected to a device-external central unit (cloud) via a corresponding communication connection (e.g. LAN, Internet).

[0033] Fig. 1 shows a system 1 for collecting fleet data in a central unit 2 to create a self-discharge map.

[0034] Fig. 1 shows a vehicle fleet 3 with several motor vehicles 4. One of the motor vehicles 4 is in Fig. 1. The motor vehicles 4 each have a vehicle battery 41 with battery cells 45, an electric drive motor 42, and a control unit 43. The control unit 43 is connected to a communication device 44, which is suitable for transmitting data between the respective motor vehicle 4 and a central unit 2 (a so-called cloud).

[0035] The control unit 43 is designed in particular to record operating variables of the vehicle battery 41 recorded with the aid of a battery management system 46 with a high temporal resolution, such as between 1 and 50 Hz, such as 10 Hz, and to transmit these to the central unit 2 via the communication device 44.

[0036] The motor vehicles 4 send the operating variables F to the central unit 2, which at least indicate variables with which the aging state of the vehicle battery 41 and the state of charge can be determined, which are required as parameters for creating a self-discharge. In the case of a vehicle battery, the operating variables F can indicate an instantaneous battery current, an instantaneous battery voltage, an instantaneous battery temperature, and an instantaneous state of charge (SOC), both at the pack, module, and / or cell level. The operating variables F are recorded as operating variable profiles in a fast time frame of 0.1 Hz to 50 Hz and can be regularly transmitted to the central unit 2 in uncompressed and / or compressed form.For example, the time series can be transmitted to the central unit 2 in blocks at intervals of 10 minutes up to several hours using compression algorithms to minimize data traffic to the central unit 2.

[0037] The central unit 2 comprises a data processing unit 21 in which part of the method described below can be carried out, and a database 22 for storing measuring points and the like.

[0038] The central unit 2 is designed to receive the operating variable profiles F. The central unit 2 can determine a current aging state from the operating variable profiles for the respective vehicle battery 41 in a manner known per se, e.g., using an aging state model.

[0039] Fig.2 schematically shows, using a flow chart, the sequence of a method for creating a self-discharge characteristic map in a central unit remote from the vehicle.

[0040] In step S1, for an inactive vehicle, the time of parking is recorded as the start of the inactivity phase and a terminal voltage is stored at both the module and cell level for the individual battery cells.

[0041] In step S2, an aging state is determined according to a suitable aging state model in a manner known per se.

[0042] In step S3, a check is made to determine whether a minimum duration of the inactivity phase has been reached or exceeded. If this is the case (alternative: Yes), the method continues with step S4. Otherwise (alternative: No), the method continues with step S3 and waits until the minimum duration is reached.

[0043] If the inactivity phase is temporarily terminated by starting a charging or discharging process, the procedure is aborted.

[0044] Now, in step S4, a wake-up program is started in the control unit of vehicle 4 at regular times, such as every 24 hours, and measurement points are recorded. The measurement points correspond to a self-discharge as the voltage difference between the terminal voltage at the current measurement time and the terminal voltage at the beginning of the inactivity phase, the stored aging state, a current state of charge, a battery temperature, and a duration of the inactivity phase. The measurement points can be recorded at the cell and / or module level and stored in a separate self-discharge map for each cell and module.

[0045] In step S5, the measuring points are transmitted to the remote central unit 2.

[0046] Alternatively, if the vehicle battery 41 is also connected to the central unit 2 for determining the aging state, the aging state of the respective vehicle battery can also be assigned to the measuring point in the central unit. The state of charge can also be determined from the terminal voltage and the OCV characteristic curve, which is aging-dependent and therefore requires information about the aging state.

[0047] In step S6, the measurement points are transferred to a self-discharge map in the central unit 2. The self-discharge map discretizes the ranges of the characteristics of the aging state, state of charge, and battery temperature into value ranges of, for example, 1-5% SOH, 1-5% SOC, 1-10°C.

[0048] If necessary, further internal battery states from the aging state model or an electrochemical battery model still executed in the central unit can be used as additional dimensions in the self-discharge map.

[0049] The self-discharge map is created by entering the lowest self-discharge for each measurement point in the self-discharge map. If the self-discharge for the respective self-discharge map deviates by more than a predetermined amount, such as 5%, from the value entered there, an error or anomaly can be signaled.

[0050] Alternatively or additionally, in step S7, an autoencoder or a variational autoencoder can be trained as an anomaly detection model based on the self-discharge map. The model parameters of the anomaly detection model can be transmitted back to the vehicles to perform anomaly detection based on an evaluation of the autoencoder or the variational autoencoder, respectively, by evaluating a reconstruction loss or, in the case of a variational autoencoder, a deviation value / distance measure with respect to the latent state vector. QUOTES CONTAINED IN THE DESCRIPTION

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

[0000] US 2016 / 023,566

[0020] US 2016 / 023,567

[0020] US 2020 / 150,185

[0020]

Claims

[1] Method, in particular an at least partially computer-implemented method, for determining a self-discharge characteristic map for diagnosing and monitoring device batteries (41) of battery-operated devices (4), comprising the following steps: - After a predetermined minimum duration of an inactivity phase of one or more device batteries (41), determining (S4) one or more measuring points at one or more measuring times, wherein the measuring points each indicate a self-discharge depending on a duration of the inactivity phase at the measuring time and depending on at least one parameter; - Providing (S6) the self-discharge characteristic map which takes the measuring points into account, wherein an entry is made in the self-discharge characteristic map if a corresponding previously entered value is greater than the self-discharge of the current measuring point; - Carrying out a diagnosis or monitoring of at least one of the device batteries (41) depending on the self-discharge characteristic map. [2] The method according to claim 1, wherein the parameters of the one or more measuring points each comprise at least one of the following values: an aging state, a state of charge, a battery temperature, a voltage difference relative to a predetermined period of time, a state of charge difference relative to a predetermined period of time, a voltage gradient, a state of charge gradient, a balancing frequency per time interval, a balancing amount per time interval, and one or more electrochemical model parameters. [3] Method according to claim 1 or 2, wherein an anomaly of a specific device battery (41) is detected when the self-discharge at a measuring point of the specific device battery (41) is greater than the corresponding value in the self-discharge map by more than a predetermined relative or absolute amount. [4] Method according to claim 1 or 2, wherein an anomaly detection model is trained or provided using the self-discharge map, in particular in the form of an autoencoder or a variational autoencoder, wherein entries in the self-discharge map correspond to the training data of the anomaly detection model, wherein an anomaly of a specific device battery is detected using a measuring point for a self-discharge using the anomaly detection model. [5] Method according to one of claims 1 to 4, wherein the one or more measuring points are determined wholly or partly in the device (4) and are transmitted to a central unit (2) remote from the device, wherein the self-discharge characteristic map is created and provided in the central unit (2). [6] Method according to one of claims 1 to 5, wherein the self-discharge characteristic map has a characteristic map discretization of the duration of the inactivity phase of between 12 hours and one week and in particular has a characteristic map discretization of the state of charge of between 0.5 and 5%, the state of age of between 0.5% and 5% and the battery temperature of between 1°C and 10°C. [7] Method according to one of claims 1 to 6, wherein the self-discharge characteristic map is determined for the battery cells (45) of the device batteries (41) and / or for the entire battery modules of the device batteries (41). [8] Apparatus for carrying out one of the methods according to one of claims 1 to 7. [9] Computer program product comprising instructions which, when the program is executed by at least one data processing device, cause the device to carry out the steps of the method according to one of claims 1 to 7. [10] Machine-readable storage medium comprising instructions which, when executed by at least one data processing device, cause it to carry out the steps of the method according to one of claims 1 to 7.

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