EP4090986A1 - Caractérisation de batteries rechargeables au moyen d'algorithmes d'apprentissage automatique - Google Patents

Caractérisation de batteries rechargeables au moyen d'algorithmes d'apprentissage automatique

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Publication number
EP4090986A1
EP4090986A1 EP21701652.6A EP21701652A EP4090986A1 EP 4090986 A1 EP4090986 A1 EP 4090986A1 EP 21701652 A EP21701652 A EP 21701652A EP 4090986 A1 EP4090986 A1 EP 4090986A1
Authority
EP
European Patent Office
Prior art keywords
battery
state variables
algorithm
aging
measurement data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21701652.6A
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German (de)
English (en)
Inventor
Michael Baumann
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Twaice Technologies GmbH
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Twaice Technologies GmbH
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Filing date
Publication date
Application filed by Twaice Technologies GmbH filed Critical Twaice Technologies GmbH
Publication of EP4090986A1 publication Critical patent/EP4090986A1/fr
Pending legal-status Critical Current

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Classifications

    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • 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
    • 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
    • G06N3/045—Combinations of networks
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • Various examples of the invention relate generally to techniques for characterizing rechargeable batteries.
  • various examples of the invention relate to techniques for determining an aging value of the rechargeable battery using at least one machine learned (ML) algorithm.
  • ML machine learned
  • Rechargeable batteries for example traction batteries in electric vehicles, have a limited life. This means that an aging value can increase over time and / or as a function of the discharge cycles.
  • the aging value can be characterized by a so-called state of health (SOH).
  • SOH state of health
  • the SOH is typically determined in connection with the capacity and / or the impedance of battery cells of the battery.
  • Techniques are known, for example, for determining the total capacity of the battery as an aging value by means of a complete discharge. Another technique measures, for example, plate corrosion or the electrolyte density of the battery. Yet another technique incorporates sensors into the battery to measure cell resistance, for example. Relative techniques perform a partial discharge and compare the result with a cell model or a reference cell. A Kalman filter, for example, can be used for this purpose. Sh. for example Plett, Gregory L. "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and Identification. "Journal of power sources 134.2 (2004): 262-276.
  • One technique for determining the aging value uses an ML algorithm in order to determine the aging value based on measurement data, for example for the electrical voltage, which are used as input variables for the ML algorithm. It has been observed that such techniques sometimes give inaccurate results for the aging score.
  • a computer-implemented method for determining an aging value of a rechargeable battery comprises obtaining measurement data for one or more state variables of the battery.
  • the method comprises the determination of one or more derived state variables of the battery using an upstream algorithm.
  • the input values of the upstream algorithm include the one or more state variables.
  • the method also includes determining the aging value using at least one ML algorithm.
  • Input values of the at least one ML algorithm include the one or more derived state variables of the battery.
  • a computer program or a computer program product or a computer-readable storage medium comprises program code.
  • the program code can be loaded and executed by a processor. When the program code is executed by the processor, this causes the processor to initiate a procedure for determining an aging value of a rechargeable battery.
  • the method comprises obtaining measurement data for one or more state variables of the battery; and the determination of one or more derived state variables of the battery using an upstream algorithm. Input values of the algorithm include the one or more state variables.
  • the method further comprises determining the aging value using at least one ML algorithm. Input values of the at least one ML algorithm include the one or more derived state variables of the battery.
  • a device comprises a processor which is set up to load and execute program code.
  • the processor executes the program code, it causes the processor to execute a method for determining an aging value of a rechargeable battery.
  • This method comprises obtaining measurement data for one or more state variables of the battery; the determination of one or more derived state variables of the battery using an upstream algorithm.
  • the input values of the upstream algorithm include the one or more state variables.
  • the method further comprises determining the aging value using at least one ML algorithm.
  • the input values of the at least one ML algorithm include the one or more derived state variables of the battery.
  • FIG. 1 schematically illustrates a system comprising a plurality of batteries and a server according to various examples.
  • FIG. 2 illustrates details of a battery according to various examples.
  • FIG. 3 illustrates details of a server according to various examples.
  • FIG. 4 is a flow diagram of an exemplary method.
  • FIG. 5 is a flow diagram of an exemplary method.
  • FIG. 6 illustrates the data flow for determining an aging value by means of several algorithms according to various examples.
  • FIG. 7 schematically illustrates measurement data that indicate state variables of a battery in the form of a load spectrum, according to various examples.
  • FIG. 8 schematically illustrates measurement data that indicate a state variable of the battery in the form of an event-related representation, according to various examples.
  • FIG. 9 schematically illustrates a time series of measurement data according to various examples.
  • the batteries described herein can be used for batteries in different application scenarios, for example for batteries that are used in devices such as motor vehicles or drones or portable electronic devices such as mobile radio devices. It would also be conceivable to use the batteries described here in the form of stationary energy stores. Indoor or outdoor applications are conceivable, which differ primarily with regard to the temperature ranges. Application scenarios include: stationary energy storage in a micro power grid; Energy storage for mobile applications; Low-load energy storage; Energy storage for light electric vehicles such as bicycles or scooters; Energy storage for electric cars; Indoor use; and outdoor use.
  • the techniques described herein make it possible to determine an aging value of the battery in connection with the characterization of the battery.
  • the aging value correlates with the aging of the rechargeable battery.
  • the aging value can describe the quality of the battery (and could therefore also be referred to as the Q value).
  • the aging value can assume larger values, for example, the further the aging of the battery has progressed.
  • the aging value can correlate with or correspond to the SOH.
  • the aging value can, for example, quantify an increase in the resistance or the impedance of the battery.
  • the aging value can, for example, quantify the decrease in the total capacity of the battery. According to various examples described herein, it is possible for the aging value to be determined using at least one ML algorithm.
  • An ML algorithm is characterized in that, in a learning phase, parameter values of parameters of the ML algorithm are set by means of suitable training.
  • the training is automated and based on training data.
  • the training data can include reference state variables of the battery, as well as a priori knowledge (English ground truth) about the respective associated aging value.
  • the parameter values of the ML algorithm can then be adapted in the course of the training so that, based on the reference state variables of the training data, the ML algorithm determines an aging value that corresponds particularly well to the associated reference aging value. This means that the ML algorithm can be used to reduce the dimensions and map the one or more state variables to a corresponding aging value.
  • Examples of ML algorithms include, for example: Artificial Neural Networks (ANNs); genetic algorithms; Support vector machines; Etc.
  • ANNs can, for example, be designed as a multi-layer feedforward network in which the neurons of the different layers do not form loops.
  • An example of such a multilayer feedforward ANN is a convolutional neural network, in which convolution of the values of the neurons is carried out with a kernel in at least some layers. Pooling layers or non-linear layers can also be provided. However, it would also be possible to use recurrent ANNs, for example to take a time series into account.
  • Various examples of the techniques described herein are based on the knowledge that reference techniques for determining the aging value of the battery by means of an ML algorithm can have certain restrictions. For example, it has been observed that a very large number of measurement data for one or more state variables of the battery are often required as input variables for the ML algorithm in order to achieve sufficient accuracy. For example, there may otherwise be differences from battery to battery - even for batteries of the same nominal type, for example due to structural factors Variations - mean that the aging value can only be determined with a certain inaccuracy. Another limitation of known techniques concerns the learning phase. Here it can often be necessary to use a large amount of training data in order to obtain sufficient accuracy when determining the aging value.
  • one or more derived state variables of the battery are determined; the one or more derived state variables are determined based on measurement data for one or more state variables of the battery. This determination of the one or more derived state variables can take place using an upstream algorithm, the input values of which include the one or more state variables.
  • the aging value is then determined using at least one ML algorithm. The input values of the at least one ML algorithm contain the one or more derived state variables of the battery.
  • the measurement data can be recorded by one or more sensors.
  • sensors For example, current measuring sensors, voltage measuring sensors, temperature sensors, pressure sensors, tension sensors,
  • Humidity sensors, etc. can be used.
  • the measurement data can be obtained from a management system of the battery.
  • the measurement data can quantify the one or more states in a time-resolved manner.
  • the measurement data are provided as a so-called load spectrum: the frequency of occurrence of values of the state variable is quantified, for example for two or more state variables relative to one another or also in relation to an absolute reference (e.g. a time reference or a charge / discharge cycle reference).
  • an absolute reference e.g. a time reference or a charge / discharge cycle reference.
  • the measurement data provided as a load spectrum could, for example, indicate the fraction of the operating time or the operating cycles in which certain value combinations for several state variables occur during operation.
  • the load spectrum can in particular indicate stress factors, ie those state variables that are particularly relevant for the aging.
  • the collective load can therefore describe a load profile of the battery.
  • the measurement data could indicate the one or more state variables in an event-related manner. This means that the measurement data could indicate the one or more state variables as a function of one or more predetermined event criteria. For example, if at least one of the one or more state variables assumes a predetermined value or value range, then the criterion for the presence of an event could be met.
  • the measurement data could indicate the corresponding at least one state variable or also one or more further state variables for a specific time segment in a time-resolved manner around the event. It would also be possible, however, for the measurement data to merely indicate the presence of a corresponding event, for example providing it with a corresponding time stamp (without resolving further details on the state variables).
  • the measurement data can be obtained for a measurement time interval.
  • the measurement time interval can extend from the present point in time into the past, for example for a specific predetermined measurement time period.
  • the measuring time interval could be determined, for example, by means of a sliding window method, that is to say it could be continuously updated and tracked as the time progresses.
  • current measurement data can be obtained that describe the current status of the battery well.
  • it is possible to characterize the batteries during ongoing field operation for example by receiving the measurement data from a management system of the battery via a communication link.
  • the measurement data can include a time series for the at least one state variable.
  • the time series can cover the measurement time interval, for example.
  • the measurement data it would be possible for the measurement data to describe the development over time of values of the at least one state variable.
  • the development over time of current, voltage or temperature values could be obtained with a certain sampling rate.
  • the time dependency on load collectives within the framework of the Measurement data is indexed. This means that, for example, a time series of load spectra is obtained for different points in time. A change in the frequency of occurrence of values of the state variables can be described in this way.
  • a time series can also be provided in connection with event-related measurement data. For example, the frequency of certain events could be indexed in a time-resolved manner, ie it could be stated how often a certain event occurred in a certain time interval.
  • the one or more state variables are selected from the following group: electrical current flow; electrical voltage; Temperature; Humidity; Ambient pressure; Tension; etc.
  • the one or more state variables can also be referred to as directly observable state variables of the battery, because they can be indicated by the measurement data, i.e. can be measured by sensors, for example.
  • the upstream algorithm can be implemented as a mapping that maps the one or more state variables onto the one or more derived state variables.
  • the measurement data include a time series of the at least one state variable
  • a time series of the one or more derived state variables could be determined using the preceding algorithm.
  • the preceding algorithm could be executed repeatedly, namely once for each point in time in the time series. In this way, a time series of derived state variables is obtained.
  • upstream algorithms would also be conceivable that receive the time series of one or more state variables directly as an input variable and determine a single derived state variable from this, for example at the actual point in time. It would also be possible for the upstream algorithm to provide a prediction for the one or more derived state variables. For example, it would be conceivable that the one or more state variables are obtained for one or more points in time in the measurement time interval.
  • a prediction could then be made using the upstream algorithm, for example taking into account a historical operating profile of the battery.
  • This prediction for the one or more derived state variables could then be passed to the ML algorithm as input, so that the latter also determines a prediction for the aging value (based on the prediction for the one or more derived state variables).
  • upstream algorithms can be used in connection with the techniques described herein.
  • an analytical algorithm that implements a fixed mapping could be used.
  • the analytical algorithm could have parameter values which, for example, are determined empirically, for example on the basis of laboratory measurements.
  • a numerical algorithm could also be used, for example in connection with a simulation of the electrical and / or thermal state of the battery. For example, a finite element method could be used.
  • the upstream algorithm cannot use ML, in particular in some examples, and can thus be differentiated from the downstream at least one ML algorithm.
  • a learning phase for the upstream algorithm can be omitted, ie in particular no machine-implemented automatic training based on training data is provided for the upstream algorithm.
  • Manual parameterization of the algorithm is possible.
  • a Kalman filter could be used to implement the preceding algorithm.
  • the Kalman filter can comprise a cell model of battery cells.
  • the cell model can be dependent on the one or more derived state variables. It is then possible to determine the one or more derived state variables using the Kalman filter by adapting the values of the one or more derived state variables of the Kalman filter model until the value of the modeled state variable agrees well with the value of the observed variable of state.
  • the upstream algorithm could also use a simulation.
  • a load profile of the battery could be used to estimate the future development of the one or more derived state variables - that is, under the assumption that the load profile observed in the past will also be available for this battery in the future.
  • the load profile could, for example, generally describe quantities such as: discharge rate; Depth of discharge; etc.
  • the load profile can be obtained from the one or more state variables or the load profile can be obtained directly in the form of the measurement data, for example as a load spectrum.
  • upstream algorithms can at least partially access measurement data that relate to different state variables of the battery.
  • At least one derived state variable of the one or more derived state variables has a correlation with a respective aging mechanism of the battery.
  • a development over time of one or more of the at least one derived state variable to have a correlation with a respective aging mechanism or a development over time of the respective aging mechanism of the battery.
  • Aging mechanisms can be brought about by physical and / or chemical processes. Aging mechanisms can be responsible, for example, for the loss of a negative electrode active material, the loss of a positive electrode active material or for the loss of ions exchanged between the positive and negative electrodes (ie, in the case of lithium ion batteries, the loss of lithium).
  • Various aging mechanisms are described, for example, in Birkl, Christoph R., et al. "Degradation diagnostics for lithium ion cells.” Journal of Power Sources 341 (2017): 373-386: FIG. 3, second column from the left.
  • the at least one ML algorithm may quantify one or more aging mechanisms. If the ML algorithm quantifies multiple aging mechanisms, the aging value can be determined based on a combination of values for the multiple aging mechanisms that are obtained as output values from the at least one ML algorithm.
  • Such one or more aging mechanisms can generally be selected from the following group: lithium deposition on electrodes of cells of the battery; Formation and growth of a solid electrolyte interphase or electronic loss of contact, for example due to particle breakage.
  • the deposition of lithium and the formation of corresponding dendrites is an essential aging mechanism.
  • a development of such aging mechanisms over time can be taken into account.
  • a prediction for the aging value of the battery can be made that takes into account the development of the one or more aging mechanisms over time.
  • an ML algorithm can be used which can make a temporal prediction on the basis of the temporal development of the one or more derived state variables.
  • This means that the ML algorithm can receive a corresponding time series of values of the one or more derived state variables as input.
  • Examples include a recurrent artificial neural network, such as a long short-term memory (LSTM) network.
  • LSTM long short-term memory
  • ML algorithms may also be possible to use different types of ML algorithms to quantify different aging mechanisms.
  • a support vector machine (SVM) could be used to quantify a first aging mechanism and an artificial neural network could be used to quantify a second aging mechanism.
  • SVM support vector machine
  • Such techniques are based on the knowledge that often - depending on the type of input variable, for example - different ML algorithms can work particularly efficiently and precisely.
  • the one or more derived state variables to include an anode potential of at least one cell of the battery and / or a cathode potential of the at least one cell of the battery and / or a ratio of the anode potential and the cathode potential to one another.
  • the anode potential is indicative of the aging mechanism of lithium deposition (lithium plating).
  • the upstream algorithm could, for example, be carried out by a simulation according to Ecker, Madeleine. Lithium Plating in Lithium-Ion Batteries: An Experimental and Simulation Approach. Shaker Verlag, 2016, Chapter 5.2.
  • the anode potential for lithium ion batteries correlates with the lithium deposition.
  • the lithium deposition typically causes sudden or non-linear aging, ie kinking of the Capacity of the battery as a function of the charge cycles or the operating time. Often, such non-linear aging cannot be recorded, or only to a limited extent, by an ML algorithm that only receives the directly observable state variables as input values. Typically, a large number of training data would have to be taken into account in such a case. The aging value can therefore be determined particularly precisely using the techniques described.
  • the ratio of the anode potential and the cathode potential to one another is also referred to as electrode balancing. It was found that this ratio is indicative of the aging of the battery. For example, if the anode overhang decreases due to aging, the potential position of the electrodes shifts. This results in a different electrode balancing.
  • the one or more derived state variables can include a differential voltage spectrum and / or a differential capacity spectrum of a discharge curve - for example in the case of small current flows - at least one cell of the battery.
  • DVA differential voltage analysis
  • the DVA corresponds to an analysis of the voltage characteristic of a battery cell during a discharge with a constant current flow.
  • charging with a constant charging current flow could also be considered.
  • the change in voltage for varying states of charge could be plotted as a function of state of charge.
  • the change in the state of charge for variable voltages could also be plotted against the state of charge. See, for example, Keil, Peter. Aging of lithium-ion batteries in electric vehicles. Diss. Technical University of Kunststoff, 2017: FIG. 16.
  • a DVA determines the loss of cathode material - for example lithium - and the loss of anode material as one or more derived state variables and thus as input values for the at least one ML algorithm (loss of Li inventory, LLI; as well as loss of anode material, LAM).
  • LLI loss of Li inventory
  • LAM loss of anode material
  • the one or more derived state variables could include mechanical bracing of at least one cell of the battery.
  • algorithms are known which describe the expansion of the battery cells as a function of the temperature, the state of charge and / or the charge / discharge rate. See, for example, Oh, Ki-Yong, and Bogdan I. Epureanu. "A novel thermal swelling model for a rechargeable lithium-ion battery cell.” Journal of Power Sources 303 (2016): 86-96 and Oh, Ki-Yong, et al. "A novel phenomenological multiphysics model of Li-ion battery cells.” Journal of Power Sources 326 (2016): 447-458.
  • Mechanical tension as a further derived state variable can also be caused by various aging mechanisms, in particular thickening of the battery due to thermal swelling. The increase in thickness due to aging can therefore result from SEI growth and / or lithium deposition and irreversible electrode work. By determining the mechanical tension, several aging mechanisms can be quantified.
  • the one or more derived state variables could also include an open circuit voltage (OCV) of at least one cell of the battery.
  • OCV open circuit voltage
  • the upstream algorithm could be implemented analytically, for example, and the measurement data could be obtained in an event-related manner, for example when a certain pause phase / rest time has been reached for the battery. Then the voltage is indicative of an open circuit voltage without load.
  • the one or more derived state variables can include a load spectrum that is determined by an upstream algorithm on the basis of the measurement data (in other examples, however, it would also be possible that the load spectrum is obtained in the form of the measurement data; that is, the load spectrum could can be determined locally at the batteries, which limits the amount of transmission data required). It can be seen from the above description that different derived state variables can be determined flexibly through the use of one or more upstream algorithms. This can be used to obtain particularly comprehensive information regarding the status of the battery. Then, in the context of the second stage, the aging value of the battery can be determined particularly precisely by the at least one ML algorithm.
  • FIG. 1 illustrates aspects related to a system 80.
  • the system 80 includes a server 81 connected to a database 82.
  • the system 80 also 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.
  • the batteries 91-96 can form an ensemble, i.e. they can all be of the same type.
  • FIG. 1 is an example of the fact that the batteries 91-96 have the
  • Communication connections 49 can send measurement data 41 to the server 81.
  • the measurement data 41 can be indicative of one or more state variables of the respective battery 91-96, e.g. state of charge, current flow, voltage, etc.
  • FIG. 1 is also illustrated by way of example that the server 81 via the
  • Communication links 49 can send control data 42 to batteries 91-96.
  • the control data 42 it would be possible for the control data 42 to indicate one or more operating limits for the future operation of the respective battery 91-96.
  • the 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 influence or control the operation of the batteries 91-96. This could, for example, be based on an aging value that is determined by the server 81 for the respective battery.
  • a respective aging value 99 is also schematically illustrated for each of the batteries 91-96 (for example, the battery 95 is comparatively strong aged, and the batteries 91, 94 have not yet aged particularly severely). Techniques for determining the aging value 99 are described below.
  • FIG. 2 illustrates aspects related to batteries 91-96.
  • the batteries 91-96 are coupled to a respective device 69.
  • This device e.g. an electric motor - is driven 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 can be implemented by software on a CPU, for example. Alternatively or additionally, for example, an application-specific circuit (ASIC) or a field-programmable gated array (FPGA) could be used.
  • 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 set up a communication link 49 with the server 81 via the communication interface 62.
  • the management system 61 is drawn separately from the batteries 91-96, in other examples it would also be possible that the management system 61 is part of the batteries 91-96.
  • the batteries 91-96 comprise one or more battery blocks 63.
  • Each battery block 63 typically comprises a number of battery cells connected in parallel and / or in series. Electrical energy can be stored there.
  • the management system 61 can typically access one or more sensors in the one or more battery blocks 63.
  • the sensors can, for example, measure state variables of the respective battery, for example the current flow and / or the voltage in at least some of the battery cells.
  • the sensors can also measure other state variables in connection with at least some of the battery cells, for example temperature, volume, pressure, etc.
  • the management system 61 can then be set up to control an or to send several such measured values from sensors in the form of measured data 41 to the server 81.
  • the measurement values can be preprocessed to a lesser or greater extent by the management system 61 before they are sent in the form of the measurement data 41.
  • compression would be conceivable, for example in the form of a load spectrum.
  • Measured values could also be filtered, for example event-related.
  • FIG. 3 illustrates aspects relating to the server 81.
  • the server 81 comprises a processor 51 and a memory 52.
  • the memory 52 can comprise a volatile memory element and / or a non-volatile memory element.
  • the server 81 also comprises a communication interface 53.
  • the processor 51 can set up a communication link 49 with each of the batteries 91-96 and the database 82 via the communication interface 53.
  • program code can 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; Determining an aging value 99 for the batteries 91-96; Applying an upstream algorithm for determining one or more derived state variables, for example with one or more simulations, such as an electrical simulation or a thermal simulation of batteries 91-96; Training and / or applying an ML algorithm for determining the aging value and based on a result of an upstream algorithm; Sending control data to batteries 91-96, for example to set operating boundary conditions; Storing a result of a characterization or an aging value of a corresponding battery 91-96 in a database 82; Etc.
  • FIG. 4 is a flow diagram of an exemplary method.
  • the method is typically carried out by a server.
  • the procedure serves the server-side Characterization of a battery.
  • the method according to FIG. 4 is executed by the processor 51 of the server 81 based on program code from the memory 52 (cf. FIG. 3).
  • a learning phase for an ML algorithm is carried out in box 1011.
  • the ML algorithm is trained as part of the learning phase. This means that based on training data and a priori knowledge in connection with the training data, parameter values or weights are set for the ML algorithm. For example, back propagation techniques could be used in connection with an artificial neural network.
  • the training data could be obtained, for example, through laboratory measurements in which a battery is examined in the laboratory. For example, invasive investigation techniques could be used in which additional detectors and sensors are introduced into the battery that are not present in field devices of the respective battery.
  • the training data could also be obtained by measurements on field devices.
  • the training data could include reference data from an ensemble of reference batteries (see FIG. 1: Batteries 91-96). This reference data could include, for example, measured values for one or more state variables of these reference batteries.
  • the reference data can also include a priori knowledge of a respective associated aging value for the batteries. For example, a complete or almost complete discharge could occur for some reference batteries in the respective driving cycle, whereby this can then be used to determine the total capacity as an aging value.
  • Another possibility would be the use of a Kalman filter for state estimation, as described for example in Plett, Gregory L. "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and Identification.” Journal of power sources 134.2 (2004): 262-276.
  • the ML algorithm can then be trained in box 1011 based on such reference data.
  • the learning phase is repeated interleaved with an application phase - see box 1012 in which the trained ML algorithm is used to determine the aging value (this is shown in FIG. 4 by the dashed line).
  • the accuracy of the characterization can be continuously improved on the basis of measurements on field devices.
  • the reference data could have an expanded information content compared to the measurement data that are obtained during normal operation. This concerns, for example, the possibility of obtaining or deriving the a priori knowledge about the aging value from the reference data. For example, it would be conceivable that a complete discharge / charge process is monitored, i.e. corresponding current-voltage time series are recorded. The total charge that has flowed could then be indicative of the capacity of the battery and thus the aging value. A corresponding amount of data can be particularly large. Therefore it can sometimes be helpful to request the reference data selectively.
  • the method is requesting the reference data by means of a control command that is sent to management systems associated with the reference batteries. For example, it would be possible to initially store such data in an internal memory of the management systems and then, if necessary, transmit them to the server via a broadband connection, for example when a charging process is initiated in an environment with a broadband connection.
  • the application phase takes place in box 1012. Measurement data from a battery - for example one of the batteries 91-96 from FIG. 1 - received, which are indicative of one or more state variables.
  • the measurement data could indicate the one or more state variables as a load spectrum. This could result in a significant reduction in the data required, for example in comparison to the reference data from block 1011. This makes it possible to characterize the battery repeatedly while the battery is in operation, without an excessive amount of data to be transmitted. In some examples it would be possible in this context for the measurement data to be transferred incrementally (incremental update). This means that, for example, changes in the load spectrum are indicated as time progresses, each referenced to previously transmitted measurement data. In this way, the bandwidth can be further reduced. By temporarily storing the measurement data on the server, the full information content can still be reconstructed.
  • the application phase could be activated selectively, namely as a function of a degree of training of the ML algorithm.
  • the ML algorithm can be activated selectively depending on the level of training. In this way, especially in a scenario in which reference data is initially collected as training data for an ensemble of batteries in field operation, it could be avoided that comparatively inaccurate results are achieved by an inadequately trained ML algorithm (so-called cold start problem).
  • an alternative algorithm for example an empirically parameterized characterization algorithm, can be used to determine the aging value.
  • FIG. 5 is a flow diagram of an exemplary method.
  • the procedure is carried out by a server.
  • the method is used to characterize a battery on the server side.
  • the method according to FIG. 5 is executed by the processor 51 of the server 81 based on program code from the memory 52 (cf. FIG. 3).
  • the method according to FIG. 5, for example, in the context of box 1012 according to the method from FIG. 4 are executed. This means that the method from FIG. 5 indicates an application phase of an ML algorithm.
  • measurement data are received in box 1001 which are indicative of one or more state variables of a battery.
  • the one or more state variables can include, for example, the current flow in one or more cells of the battery; and / or comprise a voltage across one or more cells of the battery; and / or a temperature of one or more cells of the battery; and / or a depth of discharge of the battery; and / or a duration of pause phases during which no significant charge is withdrawn or injected; and / or a state of charge (SOC) of the battery.
  • SOC state of charge
  • One or more derived state variables are then determined in box 1002, one or more preceding algorithms being used for this purpose.
  • the one or more upstream algorithms could include, for example, analytical or numerical algorithm modules.
  • the one or more upstream algorithms could include a simulation of, for example, a temperature behavior or an electrical characteristic of the battery. It would be possible for an analytical algorithm to be parameterized by empirical measurements.
  • One or more derived state variables are then obtained as the result of box 1002.
  • an aging value of the battery - i.e. for example a value which is indicative of the capacity and / or the impedance - is then determined using one or more ML algorithms.
  • the one or more ML algorithms receive the one or more derived state variables from box 1002 as input values.
  • the respective battery can be controlled accordingly, which is set, for example, in the operating boundary conditions.
  • FIG. 6 illustrates aspects associated with a two-step approach to determining an aging value 99.
  • FIG. six is a data flow chart for a corresponding determination. For example, the data processing according to FIG. 6 in connection with the method from FIG. 5 are executed.
  • a first stage comprises the application of two upstream algorithms 311-312; and a second stage involves applying an ML algorithm 331.
  • the ML Algorithm 331 supplies the aging value 99 as a result. Therefore, the first stage with box 1002 from the method of FIG. 5 correlate and the second stage can with box 1003 from the method according to FIG. 5 correlate.
  • ML algorithms it would also be possible for several ML algorithms to be used in the context of the second stage, each of which makes a contribution to the final aging value 99 as a result.
  • the aging value 99 can then take place by combining the results of the various ML algorithms, with the various results correlating, for example, with different aging mechanisms.
  • measurement data 41 serve as input for the two-stage process. Measurement data can be obtained from one of the batteries 91-96 via the communication link 49, for example.
  • the measurement data 41 could be present as a load spectrum, for example.
  • An exemplary load spectrum 500 is shown in FIG. 7 shown.
  • the state variables depth of discharge 511 and state of charge 512 in load spectrum 500 are correlated with one another.
  • the corresponding values 509 indicate the - typically relatively defined - frequency of operation of the respective battery 91-96 for the respective state variables 511-512 (with a relatively defined frequency, the assumption is that the load profile of the battery and thus the collective load for a specific purpose the battery remains constant, ie shows no change over time).
  • a time resolution is not provided by the measurement data that are present in the form of the load spectrum 500.
  • Such techniques are based on the knowledge that the dynamics of the corresponding state variables 501, 502 - in contrast to the relative frequency of occurrence, for example in certain areas 501 of the load spectrum 500 with particularly severe aging - have a comparatively small influence on aging. It can therefore be sufficient for the measurement data not to indicate the state variables 511, 512 in a time-resolved manner, but rather in the form of a load spectrum 500.
  • the amount of data of the measurement data 41 is thereby compressed.
  • the measurement data 41 indicate the one or more state variables of the battery at least partially in an event-related manner.
  • FIG. 8 is a Event-related representation 700 of the state variable electrical current flow 513 is shown.
  • FIG. 8 shows that for certain events 711 (highlighted by the dashed frame) a corresponding parameter 725 of the events 711 could be transmitted to the server 81 in the form of the measurement data 41.
  • the event 711 is characterized by a collapse in the current flow 513 over a certain period of time 722, which follows a period of time 721 of a relatively constant current flow.
  • the break-in flow 725 could, for example, be transmitted to the server 81 in the form of the measurement data 41 and then be used within the framework of one of the preceding algorithms 311-312 to determine a corresponding derived state variable 321-322.
  • this is only an example and other implementations of event-related measurement data are conceivable.
  • measurement data 41 which are indicative of an anode potential of a cell of the battery, are fed to the upstream algorithm 311 and based thereon the derived state variable 321 is determined, which correlates with the degree of lithium deposition. It would be conceivable that such a part of the measurement data 41 is fed as an input variable to the further upstream algorithm 312, which corresponds to a differential voltage spectrum or a differential capacity spectrum of a discharge curve of a cell of the battery.
  • the derived state variable 322 which correlates with the loss of lithium ions or anode material, could then be determined on the basis of this.
  • the derived state variables 321-322 are then fed to the (single) ML algorithm 331 as input variables.
  • the derived state variables 321-322 are then fed to the (single) ML algorithm 331 as input variables.
  • the ML algorithms it would also be possible for several ML algorithms to be used, for example different ML algorithms depending on the derived state variable 321-322.
  • the state variables from the measurement data 41 are also fed to the one or more ML algorithms 331 as input variables.
  • a load spectrum is fed to the one or more ML algorithms 331 as input.
  • the load spectrum it can namely be possible to characterize the load profile of the battery in the measurement period. In this way, it is possible to predict the aging value for a future point in time. This is based on the knowledge that if the battery is more heavily loaded, aging will proceed faster than if the battery is less loaded.
  • a feedforward ANN that holds one or more load collectives as an input value and provides the aging value at a prediction time as an output value (assuming that the historical load profile also corresponds to the future load profile , ie the relative share of the operation under certain stress factors is constant over time).
  • Further state variables that can be derived directly from the measurement data through simple operations are: mean charging time, maximum temperature, minimum depth of discharge, etc ..
  • FIG. 9 illustrates aspects in connection with the measurement data 41.
  • a time series 810 of measurement data 41 is obtained.
  • the measurement data 41 of the Time series 810 have measurement times that are distributed over a measurement time interval 801.
  • the measurement time interval 801 extends from the actual point in time 802 into the past.
  • the measurement data 41 of the time series 810 could in each case indicate values for the current or the voltage or the temperature at the respective measurement time in the measurement time interval 801. A load spectrum could then be formed from this or certain events could be recognized.
  • the measurement data 41 of the time series 810 could each indicate a load spectrum at the respective measurement time in the measurement time interval 801, for example the respective load spectrum being determined on the basis of values that are observed between the respective measurement time and the previous measurement time. This means that the change in the load could be described by the multiple load collectives.
  • the measurement data 41 provide a corresponding time series 810. It would also be conceivable, for example, that the measurement data 41 each only indicate values for one or more state variables at the actual point in time 802.
  • the measurement data 41 could comprise a single load spectrum, which is determined on the basis of values that are observed over the entire measurement time interval 801 up to the current actual point in time.
  • FIG. 9 also illustrates aspects related to the prediction of the aging value 813.
  • the aging value 813 is predicted for a point in time 803 lying in the future.
  • example B and in example C it may be dispensable to use an ML algorithm that receives a time series of data as input.
  • a feedforward ANN could be used.
  • a recurrent ANN could be used.
  • the load profile of the battery is taken into account as a collective load.
  • the load spectrum can describe, for example, how often the battery is discharged / charged with a certain depth of discharge and / or discharge speed (e.g. in a critical temperature range), how high the charging rate is, how quickly the battery is discharged, what operating temperature during charging or discharging the battery is present, etc.
  • Such stress factors as a load profile can then be used to infer how the one or more derived state variables of the battery will behave in the future.
  • the preceding algorithm can be used for this in example C.
  • example D it would be possible, for example, for a time series of directly observed state variables - for example current flow, voltage, temperature - to be obtained in the measurement time interval in the form of measurement data 41.
  • One or more derived state variables that correlate with one or more aging mechanisms of the battery can then be determined by means of the upstream algorithm.
  • a collective load can also be determined which quantifies the stress factors of the battery as a load profile.
  • Such derived state variables can then be used as input values of the ML algorithm.
  • the ML algorithm can in particular be designed as a feedfoward ANN. A time series does not have to be taken into account.
  • a prediction of the aging value can be achieved by suitably training the ML algorithm, namely taking into account the - for example, relatively defined - load collectives.
  • the load spectrum can be used to characterize the load on the battery so that stronger or weaker aging can be predicted in the future.
  • a further load spectrum for one or more derived state variables can be determined from a load spectrum for one or more state variables.
  • state variables can also be used as input into the ML algorithm, that is to say the input into the ML algorithm is not limited to the output of the preceding algorithm.
  • the input into the ML algorithm is not limited to the output of the preceding algorithm.
  • Statistics of the state variable measurement time interval can therefore describe an evaluation of the behavior of the state variable - for example current or voltage or temperature, etc. - in the measurement time interval.
  • the statistics could describe one or more of the following statistical variables: maximum of the state variable, for example maximum temperature in the measurement time interval; Minimum of the state variable, for example the minimum temperature in the measurement time interval; Mean value of the state variable, for example mean temperature in the measurement time interval; Scatter of the state variable, i.e. variance of the temperature in the measurement time interval; etc.
  • the statistic could be the state quantity based on the time series the state variable can be determined or can be obtained directly from a corresponding control unit of the respective battery.

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Abstract

Différents exemples concernent des techniques permettant de réaliser une caractérisation d'une batterie rechargeable lors d'un processus en deux étapes. À cet effet, on fait appel à un algorithme amont pour déterminer une ou plusieurs grandeurs d'état dérivées associées à la batterie. Celles-ci servent alors de grandeurs d'entrée pour un algorithme d'apprentissage automatique. On obtient ainsi une valeur de vieillissement de la batterie.
EP21701652.6A 2020-01-14 2021-01-14 Caractérisation de batteries rechargeables au moyen d'algorithmes d'apprentissage automatique Pending EP4090986A1 (fr)

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PCT/DE2021/100042 WO2021143983A1 (fr) 2020-01-14 2021-01-14 Caractérisation de batteries rechargeables au moyen d'algorithmes d'apprentissage automatique

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DE102020008120A1 (de) 2020-07-03 2022-03-24 TWAICE Technologies GmbH Verarbeitung von Zustandsdaten einer Batterie zur Alterungsschätzung
DE102020117609B8 (de) 2020-07-03 2022-11-03 TWAICE Technologies GmbH Verarbeitung von Zustandsdaten einer Batterie zur Alterungsschätzung
DE102020008113A1 (de) 2020-07-03 2022-04-07 TWAICE Technologies GmbH Verarbeitung von zustandsdaten einer batterie zur alterungsschätzung
DE102022200722A1 (de) 2022-01-24 2023-07-27 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum Betreiben eines Kraftfahrzeugs

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DE102004004280B4 (de) 2004-01-27 2014-06-12 Audi Ag Verfahren zur Diagnose von Batterien
US20160239592A1 (en) 2015-02-12 2016-08-18 Nec Laboratories America, Inc. Data-driven battery aging model using statistical analysis and artificial intelligence
DE102017103617A1 (de) * 2017-02-22 2018-08-23 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Verfahren zur Abschätzung des Alterungszustands eines Batteriesystems
WO2019162749A1 (fr) * 2017-12-07 2019-08-29 Yazami Ip Pte. Ltd. Procédé et système d'évaluation en ligne de l'état d'intégrité d'une batterie
CN108445406B (zh) * 2018-03-13 2021-05-25 桂林电子科技大学 一种动力电池健康状态估计方法
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US20200011932A1 (en) 2018-07-05 2020-01-09 Nec Laboratories America, Inc. Battery capacity fading model using deep learning

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