WO2023052521A1 - Détermination d'une valeur de vieillissement pour des batteries faisant intervenir des séries chronologiques de courant-tension dans le domaine temporel et le domaine de charge - Google Patents

Détermination d'une valeur de vieillissement pour des batteries faisant intervenir des séries chronologiques de courant-tension dans le domaine temporel et le domaine de charge Download PDF

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WO2023052521A1
WO2023052521A1 PCT/EP2022/077141 EP2022077141W WO2023052521A1 WO 2023052521 A1 WO2023052521 A1 WO 2023052521A1 EP 2022077141 W EP2022077141 W EP 2022077141W WO 2023052521 A1 WO2023052521 A1 WO 2023052521A1
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battery
time
time series
values
measured
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PCT/EP2022/077141
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German (de)
English (en)
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Manuel WANISCH
Klara Neumayr
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TWAICE Technologies GmbH
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent

Definitions

  • Various examples of the disclosure relate to the characterization of batteries. Various examples relate to determining a aging value of a rechargeable battery. Various examples relate to the pre-processing of measured operational observables of a battery, such as measured current values or measured voltage values, in order to obtain an appropriate input to the machine-learned algorithm.
  • Rechargeable batteries are used in a wide variety of application scenarios. Examples include traction batteries for electric vehicles, static energy storage, etc.
  • Various techniques are known for determining an aging value of batteries. For example, some techniques are based on classical modeling of battery behavior. For example, physico-chemical models of the battery tery can be used to determine battery aging as a function of load factors. Empirical models can also be used. An example is eg Schmalumble, Johannes, et al. "A holistic aging model for Li (NiMnCo) 02 based 18650 lithium-ion batteries.” Journal of Power Sources 257 (2014): 325-334. Typically, with such a classic modeling, a significant effort is required for the parameterization of the corresponding models. For example, complex laboratory tests may be necessary.
  • Machine-learned algorithms are also known to determine an aging value of the battery. See e.g. CN111832220A - or see Ungurean, Lucian; Micea, Mihai V. Online state of health prediction method for lithium-ion batteries, based on gated recurrent unit neural networks International Journal of Energy Research 44(8) 6767-6777 - or see Xiang, Ming, et al. "State-of-health prognosis for lithium-ion batteries considering the limitations in measurements via maximum information entropy and collective sparse variational gaussian process.” IEEE Access 8 (2020): 188199-188217 - or see Fan, Yaxiang, et al. "A novel deep learning framework for state of health estimation of lithium-ion battery.” Journal of Energy Storage 32 (2020): 101741. It has been observed that such techniques are sometimes imprecise or require a particularly computationally intensive implementation
  • a computer-implemented method for determining an aging value of a battery includes obtaining measured current values of an operating current of the battery and measured voltage values of an operating voltage of the battery.
  • the current values give the operating current during an observation period at.
  • the voltage values indicate the operating voltage during the same observation period.
  • a first time series of first current values and first voltage values is determined based on the measured current values and the measured voltage values.
  • the first time series includes the first points in time of the first current values and the first voltage values in the observation period. These first points in time are arranged according to a first sampling scheme defined in the time domain.
  • the sampling scheme can define an arrangement of the first points in time in the time domain.
  • a sampling scheme can define the arrangement of corresponding points in time in the corresponding domain.
  • the sampling scheme could define a relative spacing of the corresponding time points in the corresponding domain from each other.
  • the sampling scheme it would be possible for the sampling scheme to define the exact positions of the corresponding time points of the corresponding domain.
  • the sampling scheme defines a tolerated variance of the corresponding points in time in relation to reference points in time.
  • the sampling scheme could determine a number of data points of a time series (i.e. the sampling points).
  • the sampling scheme could determine the sampling points depending on a length of a data structure used as input to an ML algorithm.
  • the first sampling scheme it would be conceivable for the first sampling scheme to define an equidistant arrangement of the first points in time in the time domain.
  • An equidistant arrangement in the time domain means that the first points in time all have the same time distance from one another, for example each 5 minutes apart, to give just one example.
  • a second time series of second current values and second voltage values is also determined based on the measured current values and the measured voltage values.
  • the second time series is different from the first time series.
  • the second time series includes second points in time of the second current values and the second voltage values in the observation period.
  • the second points in time are arranged according to a second sampling scheme in a domain associated with an operating load parameter of the battery (load domain). It would again be conceivable for the second points in time to be arranged equidistantly in the load domain.
  • the operating load parameter can therefore denote a load size of the battery during active operation, that is to say in particular it can be different from the mere passage of time.
  • Examples of operating load parameters are the charge throughput or the power throughput of operating the battery.
  • the first time series can depict characteristic features in the operating current and in the operating voltage, which essentially describe a calendar aging of the battery.
  • the second time series can depict characteristic features in the operating current and in the operating voltage, which essentially describe stress aging of the battery due to charging or discharging processes. This is achieved by using the first and second sampling schemes defined in the time domain and the load domain.
  • the aging value can be determined particularly reliably by the combined use of both time series.
  • complex pre-processing of the measured current values or the measured voltage values for example filtering or recognition of specific signatures and extraction of local measurement points—can be omitted. This simplifies the preprocessing.
  • the data basis can also be compressed before input into the ML algorithm. For example, it may be possible to sample the measured current values comparatively sparsely over the measured voltage values in order to obtain the first time series and the second time series. This means that for each current value or voltage value, the first time series and the second time series have one large number of measured current values and measured voltage values can be present.
  • the aging value can be determined, for example, by a capacity of the battery and/or by an impedance of the battery.
  • the aging value can in particular indicate an absolute aging since the battery was put into service. This means that not only the relative aging can be determined during the observation period, but rather the absolute aging value of the battery. For example, the absolute capacity of the battery could be determined (rather than just determining the relative decrease in capacity over the observation period). The absolute impedance could be determined.
  • first time series and the second time series can be used directly as input to the ML algorithm. It would be possible to concatenate the first time series and the second time series (i.e. "join" each other in a matrix structure) to obtain such a data structure, which then serves as input to the machine-learned algorithm. This means that a particularly compact and efficient pre-processing can take place.
  • the data structure can, for example, include time stamps for the first points in time in the first time series and time stamps for the second points in time in the second time series.
  • time stamps for the first points in time in the first time series
  • time stamps for the second points in time in the second time series.
  • Examples of ML algorithms would be a recurrent neural network, such as a long-short-term memory (LSTM) network or a gated recurrent unit (GRU) network. See for example: Chung, Junyoung, et al. "Empirical evaluation of gated recurrent neural networks on sequence modeling.” arXiv preprint arXiv: 1412.3555 (2014).
  • the first time series and the second time series can be determined, for example, using an interpolation operation applied to the measured current values and the voltage values, respectively. For example, linear interpolation can be used.
  • an interpolation operation might or might not be used depending on the sampling scheme; If no interpolation operation is used, such measured current values and measured voltage values could be selected for the first and second time series that are as close as possible to a reference time defined by the respective sampling scheme.
  • the sampling scheme could define an equidistant arrangement of the points in time in the respective domain, for example the time domain (for the first sampling scheme); but at the same time define a tolerated variance that is greater than or of the same order of magnitude as the distance between measured operating observables (e.g. operating current and operating voltage) of the battery.
  • the measurement data sample the observation period sufficiently densely - the closest values of the respective operating observables can be taken over. Even if the arrangement of the points in time is then not perfectly equidistant from one another, the variance in the distances between the points in time is within a tolerance defined by the respective sampling scheme.
  • an interpolation operation can be comparatively less computationally intensive, so that the pre-processing of the measured current values and measured voltage values can be implemented particularly efficiently even when using the interpolation operation.
  • the first time series and the second time series to be determined on such a data processing system that is arranged locally at the battery.
  • the aging value can then be determined on a further data processing system that is not arranged locally at the battery is.
  • the ML algorithm could be run on a central server, for example.
  • the interpolation operation can take into account a length of the data structure as a boundary condition. This means that for observation periods of different lengths, a distance between the points in time can be varied so that the entire observation period can be covered while the length of the data structure is maintained at the same time.
  • the data can be compressed, ie the data structure has a specific size, regardless of the number of measured current values or voltage values.
  • the interpolation operation could result in a reduction of the first current value and the first voltage value with respect to the measured current value and the measured voltage value by a factor of 1000 or more.
  • the same also applies to the second current value and second voltage values.
  • This makes it possible to use comparatively long observation periods. For example, it would be conceivable that an observation period includes several charging cycles and several discharging cycles and several idle cycles of the battery. For example, the observation period could be longer than 24 hours.
  • the observation period can also be significantly shorter than the previous battery life. This means that the observation period can be less than approx. 5% of a period since the battery was put into service. Nevertheless, using the techniques described herein, it may be possible to determine the absolute aging value of the battery. This is due to the fact that the signatures of the operating observables during the respective observation period of the battery also depend on the absolute aging value (not only on the relative aging). Through the appropriate choice of the domain in which the values of the operational observables are represented - especially the stress domain - corresponding signatures can be efficiently extracted from the measurement data set or preserved.
  • a beginning and an end of the observation period are determined independently of the operation of the battery. This means that it may not be necessary to align the observation period specifically in relation to, for example, a charging process or a discharging process of the battery. This is due to the fact that by choosing a suitably long observation period - which is practically made possible by the compression in connection with the time series, as described above - a statistically significant signature is always mapped in the observation period.
  • observation period is always started after the completion of a charging or discharging process.
  • observation period it would be conceivable for the observation period to end during a rest phase.
  • the first time series can then also include first temperature values at the first points in time and the second time series can also include second temperature values at the second times, wherein the first and second temperature values are determined based on the measured temperature values.
  • the accuracy when determining the aging value can tend to be further increased.
  • the absolute aging value can be determined using operating observables during an observation period.
  • the aging value can, for example, be determined absolutely at the end of the observation period (this corresponds to an aging value at the actual time).
  • a prediction of the absolute aging value could also be made, i.e. a forecast for the future.
  • the measured current values and the measured voltage values or generally measured operating observables
  • the observation period can be extended in the future.
  • the first time series and second time series can then be determined based on such extended measured current values and extended measured voltage values. This enables a prediction of the state of the battery.
  • a frequency or frequency of charging processes or discharging processes is determined and then this frequency or frequency is also assumed for the future in order to achieve the augmentation in this way.
  • a statistical load profile load collective
  • the augmentation could take place taking into account such a statistical load profile.
  • a computer program or a computer program product or a computer-readable storage medium includes program code that is loaded by a processor and can be executed. This causes the processor to execute a method for determining a battery aging value.
  • the method includes obtaining measured current values of an operating current of the battery and measured voltage values of an operating voltage of the battery. The current values indicate the operating current during an observation period. Likewise, the voltage values indicate the operating voltage during the same observation period.
  • a first time series of first current values and first voltage values is determined based on the measured current values and the measured voltage values.
  • the first time series includes the first points in time of the first current values and the first voltage values in the observation period. These first points in time are arranged according to a first sampling scheme defined in the time domain.
  • a device includes a processor.
  • the processor can load and execute program code. This causes the processor to execute a method for determining a battery aging value.
  • the method includes obtaining measured current values of an operating current of the battery and measured voltage values of an operating voltage of the battery.
  • the current values indicate the operating current during an observation period.
  • the voltage values indicate the operating voltage during the same observation period.
  • a first time series of first current values and first voltage values is determined based on the measured current values and the measured voltage values.
  • the first time series includes the first points in time of the first current values and the first voltage values in the observation period. These first points in time are arranged according to a first sampling scheme defined in the time domain.
  • FIG. 1 schematically illustrates a system comprising multiple batteries and a server according to various examples.
  • FIG. 2 schematically illustrates details related to the batteries according to various examples.
  • FIG. 3 schematically illustrates details related to the server according to various examples.
  • FIG. 4 is a flowchart of an example method.
  • FIG. 5 schematically illustrates data processing for determining an aging value of a battery according to various examples.
  • FIG. 6 schematically illustrates two time series indicating current values at points in time, each arranged equidistantly in a time domain and a charge throughput domain, according to various examples.
  • the batteries described herein can be used in different areas of application, for example for batteries used in devices such as motor vehicles or drones or portable electronic devices such as mobile phones. It would also be conceivable to use the batteries described herein in the form of stationary energy stores.
  • Condition monitoring can include ongoing monitoring of the load on the battery and/or determination of the absolute condition of the battery against a reference—typically start-up—and/or prediction of the condition of the battery.
  • an aging estimation of the state of health (SOH) of the battery can be made.
  • SOH state of health
  • an aging value can be determined, typically the capacity of the battery (ie amount of charge that can be stored).
  • the SOH decreases as the battery ages. Accelerated aging can occur when the capacity of the battery decreases and/or when the impedance of the battery increases.
  • Various of the battery characterization examples described herein may be implemented, at least in part, on the server side.
  • a communication connection can be established between the server and one or more management systems of the battery.
  • particularly accurate and computationally intensive models or algorithms can be used. This makes it possible to determine the aging value particularly precisely.
  • condition monitoring during battery usage based on measurement data for one or more operational observables of the battery. This means that condition monitoring is performed at a particular point in time during the life of the battery - with a reduced SOH.
  • the battery can then be used in the field. In this way it may be possible, in particular, to also take into account the previous aging behavior of the battery. This also makes it possible to carry out the condition monitoring in a particularly flexible manner. Laboratory tests can be omitted.
  • measured current values of a battery current could be used.
  • measured voltage values of the operating voltage terminal voltage
  • Further examples relate to, for example, measured temperature values of the battery temperature.
  • corresponding time series of different battery observables are taken into account, eg a time series of current values and/or a time series of voltage values and/or a time series of temperature values.
  • Such time series then serve as input to an ML algorithm.
  • the ML algorithm can be trained during a training phase in order to subsequently determine an aging value of the battery in an inference phase based on such a time series.
  • corresponding training time series could be measured as training data.
  • the aging value could be determined using a laboratory method. Laboratory methods are known for determining, for example, the capacity of a battery with particular accuracy (e.g. Coulomb counting or based on the open-circuit voltage characteristic; impedance spectroscopy; incremental capacity analysis; differential voltage analysis). Alternative models could also be used, such as Kalman filters.
  • a label could then be determined which indicates the target value prediction of the ML algorithm.
  • the parameter values of the ML algorithm can then be adjusted in the training phase. For example, a gradient descent method (backward propagation) may be employed to adjust the machine-learned algorithm's parameter values, taking into account a difference between the machine-learned algorithm's prediction based on the training time series and the label.
  • a gradient descent method backward propagation
  • Different instances of the ML algorithm can be used for different battery types. The training can be done separately for each instance, based on training data and labels that are obtained specifically for a battery of the respective battery type.
  • ML algorithms can be used in the examples disclosed herein.
  • such ML algorithms can be used which operate on time series as input. Examples would be for example recurrent neural networks. Examples include, for example, LSTM networks or GRU networks.
  • the machine-learned algorithm could have two GRU layers followed by one or more fully-connected layers. According to various examples, it is possible to use two types of time series to form a data structure serving as input to the ML algorithm. These two types are listed below in TAB. 1 described
  • TAB. 1 Two examples of time series based on which an input to a machine-learned algorithm can be determined. For example, it would be possible to determine a first time series and a second time series according to Examples I and II and then to concatenate these time series in order to obtain the input for the machine-learned algorithm in this way.
  • Such techniques have the further advantage that no particularly complicated pre-processing of the measured values for the operating observables of the battery is necessary. For example, it may not be necessary to filter the measured current values or the measured voltage values. For example, it may not be necessary to extract characteristic signatures from the measured current values or the measured voltage values. Rather, the use of suitable sampling schemes in the time and stress domain can inherently ensure that characteristic signatures for the precise determination of the aging value are retained. This enables a particularly computationally efficient implementation of the techniques.
  • FIG. 1 illustrates aspects related to a system 80.
  • the system 80 includes a server 81 connected to a database 82.
  • the server 81 is connected to a database 82;
  • the system 80 includes communication links 49 between the server 81 and each of a plurality of batteries 91-96.
  • the communication links 49 could be implemented over a cellular network, for example.
  • batteries 91-96 can include several types. Batteries of different battery types may differ, for example, with regard to one or more of the following characteristics: shape of the cell (ie round cell, prismatic cell, etc.), cooling system (air cooling with active or passive concept, coolant in coolant hose, passive cooling elements, etc.), the cell chemistry (e.g. used electrode materials, electrolytes, etc.), etc. Even between batteries 91-96 of the same type, there may be some variance associated with such characteristics. For example, it can happen that batteries 91-96 of one and the same type are mounted differently and therefore different cooling systems are used. Also, sometimes the same battery cells can be arranged differently such that an electrical and thermal system view will vary the ensemble of cells.
  • battery type specific instances of ML algorithms can be used to characterize the batteries.
  • FIG. 1 illustrates by way of example that the batteries 91 - 96 can send status data 41 to the server 81 via the communication links 49 .
  • the status data 41 can be indicative of one or more operating values of the respective battery 91-96, i.e. they can indicate measurement data. Measurement data for different operational observables can be obtained, e.g. measured current or voltage values or temperature values.
  • the status data 41 could be event-driven or sent according to a predetermined time scheme.
  • measured values of one or more operating observables could be pre-processed locally on a data processing system that is assigned to the respective battery 91-96.
  • time series could be determined that contain corresponding values for the one or more operational observables at specific points in time.
  • the time series can be compressed compared to the measurement data, ie have a smaller size.
  • an interpolation operation could be performed locally.
  • the status data 41 can be used in connection with the characterization of the batteries 91-96.
  • the server 81 can determine a corresponding aging value based on an ML algorithm. Different instances of the ML algorithm can be used for different battery types, which means that the different instances could be trained for a specific battery type.
  • FIG. 1 also illustrates by way of example that the server 81 can send control data 42 to the batteries 91-96 via the communication links 49 .
  • the control data 42 can indicate one or more operating limits for the future operation of the respective battery 91-96.
  • control data could indicate one or more control parameters for thermal management of the respective battery 91-96 and/or charge management of the respective battery 91-96.
  • the server 81 can thus influence or control the operation of the batteries 91-96. In this way, further aging could be limited, for example, in the case of advanced aging of the battery. It would also be conceivable to determine a secondary use of the battery in another application scenario, such as changing from a traction battery to a battery for a stationary energy storage device.
  • FIG. 1 also schematically illustrates the respective SOH 99 for each of the batteries 91-96.
  • the SOH 99 of a battery 91-96 may include one or more different characteristics depending on the implementation. Typical parameters of the SOH 99 can be, for example: electrical capacitance, i. H. the maximum possible stored charge; and/or electrical impedance, i. H. the frequency response of the resistance or AC resistance as a ratio between electrical voltage and electrical current. Other aging values are also conceivable.
  • FIG. 2 illustrates aspects related to batteries 91-96.
  • the batteries 91 - 96 are coupled to a respective device 69 . This device is powered by electrical energy from the respective battery 91-96.
  • the batteries 91-96 comprise or are associated with one or more management systems 61, e.g., a BMS or other control logic such as an on-board unit in the case of a vehicle.
  • the management system 61 is arranged locally at the respective battery 91-96 and is connected to it, for example, by a wired connection.
  • the management system 61 can be implemented by software on a CPU, for example. Alternatively or additionally, an application-specific integrated circuit (ASIC) or a field-programmable gated array (FPGA) could be used, for example.
  • the batteries 91-96 could communicate with the management system 61 via a bus system, for example.
  • the batteries 91-96 also include a communication interface 62.
  • the management system 61 can establish a communication connection 49 with the server 81 via the communication interface 62.
  • management system 61 is drawn separately from batteries 91-96, in other examples it would also be possible for management system 61 to be part of batteries 91-96.
  • the batteries 91-96 include one or more battery packs 63.
  • Each battery pack 63 typically includes a number of battery cells connected in parallel and/or in series. Electrical energy can be stored there.
  • the management system 61 can rely on one or more sensors in the one or more battery packs 63 .
  • the sensors may measure current flow and/or voltage in at least some of the battery cells.
  • the sensors can also have other variables in combination hang with at least some of the battery cells to determine, for example, the temperature, volume, pressure, etc. of the battery and send it to the server 81 in the form of status data 41 .
  • Management system 61 can also be set up to implement thermal management and/or charging management of the respective battery 91 -96. In connection with thermal management, management system 61 could control cooling and/or heating, for example. For example, in the context of charge management, the management system 61 could control a charge rate or a depth of discharges. The management system 61 can therefore set one or more operating boundary conditions for the operation of the respective battery 91-96, for example based on the control data 42.
  • FIG. 3 illustrates aspects related to the server 81 .
  • the server 81 includes a processor 51 and a memory 52.
  • the memory 52 can include a volatile memory element and/or a non-volatile memory element.
  • the server 81 also includes a communication interface 53.
  • the processor 51 can establish a communication link 49 with each of the batteries 91-96 and the database 82 via the communication interface 53.
  • program code may be stored in memory 52 and loaded by processor 51 .
  • the processor 51 can then execute the program code.
  • Execution of the program code causes processor 51 to perform one or more of the following processes, as described in detail in connection with the various examples herein: characterization of batteries 91-96; running an ML algorithm to determine a aging value for the batteries 91-96; controlling the operation of the batteries 91-96 via control data; receiving measured operational observables from batteries 91-96; storing a result of the characterization of a corresponding battery 91-96 in a database 82; Etc..
  • FIG. 4 is a flowchart of an example method.
  • the method is used to characterize a battery.
  • the method characterizes the aging of the battery using an ML algorithm.
  • the method could be performed on a central server (see FIG. 1, server 81). It would also be conceivable for only parts of the process to be carried out on a central server other parts of the method are performed on a management system local to a battery. It would also be conceivable for the entire method to be executed on a local battery management system.
  • measurement data is obtained. This means that measured values are obtained for one or more operational observables of a battery. These are recorded during an observation period. For example, measured current values and/or measured voltage values could be obtained. Alternatively or additionally, measured temperature values of the battery can be obtained.
  • block 5005 could include reading appropriate sensors of the battery.
  • Block 5005 could alternatively or additionally include receiving corresponding status data (compare FIG. 1: status data 41) via a communication interface.
  • the measurement data could also be read from a memory or a database.
  • the measured values of the one or more operating observables could be augmented in order to obtain a prediction of a load on the battery for the future beyond a corresponding observation period.
  • Such an augmentation could, for example, continue a statistical load during the observation period for a prediction period.
  • augmented measured values for the one or more operational observables can then be obtained.
  • Such an augmentation serves to be able to predict an aging value for a future period. If no forecast is desired for a future time period, the augmentation in block 5010 can also be omitted.
  • the input data structure can include multiple time series. These time series may each include values of one or more operational observables of the battery corresponding points in time that are characteristic of the respective time series. Such points in time can each be determined based on a respective sampling scheme (which samples the measurement data), the sampling scheme being determined in a domain associated with the corresponding time series.
  • a first time series could include first current values and first voltage values at first points in time in the observation period (and possibly also in the prediction period), which are arranged on a first sampling scheme specified in a time domain.
  • a second time series could include second current values and second voltage values at second points in time, the second points in time being arranged according to a second sampling scheme predetermined in a stress domain.
  • an interpolation operation can be used in block 5015 to determine the first time series and the second time series. For example, a linear interpolation between measured current values could take place in order to obtain a current value for the first time series at a (target) point in time that lies between the points in time of the two measured current values. If the deviation between this target time and the times of the two measured current values is sufficiently small, an interpolation operation can be dispensed with and one of the measured current values can be used directly in the time series (e.g. the closest one).
  • the interpolation operation could constrain a length of the data structure.
  • the length of the data structure can be predetermined by the scanning scheme.
  • the first current values and the first voltage values can be reduced in relation to the measured current values and the measured voltage values by the interpolation operation.
  • the second current values and the second voltage values can be reduced in relation to the measured current values and the measured voltage values.
  • the data structure that serves as input to the ML algorithm can be smaller than the original measurement data. This allows for a shorter runtime of the ML algorithm.
  • the aging value can then be determined. This is based on the data structure obtained as a result of block 5015.
  • the data structure serves as input to an ML algorithm. This means that no further processing needs to occur between block 5015 and block 5020.
  • the data structure includes a concatenation of multiple time series containing values for one or more operational observables at respective points in time determined according to a respective sampling scheme.
  • the data structure can also contain time stamps for the corresponding points in time.
  • FIG. 5 illustrates aspects related to the processing of measurement data 501 in order to determine an aging value 99 of a battery.
  • measurement data 501 is obtained, for example measured current values and measured voltage values and measured temperature values. These are processed in a pre-processing algorithm 505 in order to subsequently obtain a data structure 510 which serves as input to an ML algorithm 515 . Based on the data structure 510, the ML algorithm 515 determines the SOH 99 as the aging value. For example, the SOH 99 could indicate the capacity in absolute terms (say: ampere-hours).
  • the data structure 510 (compare FIG. 4: block 5015) is a matrix that specifies voltage values, current values and temperature values for different points in time (“t”).
  • the timestamps in the fourth column of the matrix are useful when they are not equidistant in the time domain - which would typically be the case for a sampling scheme used by the preprocessing algorithm 505 that is defined in a load domain.
  • FIG. 6 illustrates aspects related to the measurement data 501 in an observation period 585, as well as the data structure 510 and the pre-processing algorithm 505.
  • FIG. 6 above is a time dependence of the operating current of the battery, i.e. measured current values, is shown with the dashed line (actually, the measured current values are discrete in the time domain; due to the high sampling rate of the measurement, a line is shown in FIG. 6, above for illustration ).
  • the observation period 585 is about 10 hours long and includes several charging and discharging processes.
  • a first time series 581 with first current values filled circles in FIG. 6, top
  • a second time series 582 with second current values stars in FIG. 6, top
  • the first time series 581 and the second time series 582 cover the same observation period 585; i.e. the times of the first and second current values alternate in the time domain.
  • the first current values - that is, the filled circles - are all equidistant from each other in the time domain (that is, in the scaling of the X-axis of FIG. 6, top and bottom). This is because the corresponding points in time for the first current values are determined based on a corresponding sampling scheme defined in the time domain.
  • time increases linearly as a function of X-axis position (this is the dotted-dashed line in FIG. 6, below; showing this linear dependence).
  • the second current values - i.e. the stars in FIG. 6, top - of the second time series are not equidistantly spaced from each other in the time domain (that is, in the scaling of the X-axis in Fig. 6, top). This is because the respective times for the second current values are determined based on a respective sampling scheme defined in the charge throughput domain. Between two adjacent points in time of the second time series 582 (that is, between two stars in FIG. 6, top), the same charge always flows, that is, the battery is charged or discharged by the same amount. This is shown in FIG. 6, shown below by the non-linear correlation of charge throughput with the passage of time by means of the dashed line.

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Abstract

Différents exemples de l'invention concernent des techniques pour déterminer une valeur de vieillissement d'une batterie, par exemple la capacité. À cet effet, des séries chronologiques sont prises en compte, lesquelles comprennent des valeurs de paramètres de charge de fonctionnement de la batterie à des instants définis dans un domaine temporel et un domaine de charge.
PCT/EP2022/077141 2021-09-30 2022-09-29 Détermination d'une valeur de vieillissement pour des batteries faisant intervenir des séries chronologiques de courant-tension dans le domaine temporel et le domaine de charge WO2023052521A1 (fr)

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