Determining chemical function and / or chemical composition of battery

Abstract

A method for determining the chemical function and / or chemical composition of a battery, the method comprising: collecting battery data indicative of at least one chemical and / or physical property of the chemical composition of the battery; providing match data by determining at least one match between the collected battery data and data of a reference battery; and determining function and / or composition data indicative of a chemical function and / or a chemical composition of the battery based on the matching data.

Description

Technical Field

[0001] The present disclosure relates to a computer-implemented method for determining the chemical function and / or chemical composition of a battery, a system for determining the chemical function and / or chemical composition of a battery, the use of battery data and / or chemical function data of a battery and / or chemical composition data of a battery in this method, the use of the function and / or composition data determined by this method for classifying and / or sorting batteries, and a computer program element with instructions, which, when executed on a computing device in a computing environment, is configured to perform the steps of this method in such a system. Background Art

[0002] The general background of the present disclosure relates to the handling of recycled batteries. Battery recycling is a subject of increasing interest today as the number and automation of portable electronic devices continue to increase. With the advent of electric vehicles, it is expected that an increasing number of spent lithium-ion batteries will be available. Since batteries contain important transition metals, such as cobalt, nickel, and lithium, spent lithium-ion batteries may become a valuable source of raw materials for new generations of lithium-ion batteries, and therefore the handling of recycled battery materials has become a subject of increasing interest worldwide as the number and automation of portable electronic devices continue to increase. For this reason, more research efforts have been carried out with the goal of recycling transition metals and lithium from spent lithium-ion batteries or from batteries or parts thereof that do not meet specifications and requirements; such substandard materials and production waste may also be a source of raw materials, as well as a source of processing recycled battery materials.

[0003] In the context of recycling batteries, the question arises of how and when to recycle the batteries. In particular, whether the battery is still suitable for a different / further use, or whether the battery is no longer usable for further use and should be "aggressively" recycled. Therefore, there is a need to simplify or improve the decision-making process regarding whether, when, and how to recycle batteries. Summary of the invention

[0004] In one aspect of the present disclosure, there is provided a computer-implemented method for determining a chemical function and / or chemical composition of a battery, the method comprising:

[0005] - collecting battery data indicative of at least one chemical and / or physical property of the chemical constituents of the battery or at least one chemical and / or physical property related to these chemical constituents, preferably by a property measurement device;

[0006] - providing matching data by determining at least one match between the collected battery data and data of a reference battery; and

[0007] - determining functional and / or composition data indicative of a chemical function and / or chemical composition of the battery based on the matching data.

[0008] In another aspect of the present disclosure, there is provided a system for determining a chemical function and / or chemical composition of a battery, the system comprising:

[0009] a collecting unit for collecting battery data indicative of at least one chemical and / or physical property of the chemical constituents of the battery or at least one chemical and / or physical property associated with these chemical constituents, preferably by a property measuring device;

[0010] - a providing unit for providing matching data by determining at least one match between the collected battery data and data of a reference battery; and

[0011] - a determination unit for determining, based on the matching data, function and / or composition data indicative of a chemical function and / or chemical composition of the battery.

[0012] Another aspect of the present disclosure relates to the use of battery data and / or battery chemical function data and / or battery chemical composition data in a method for determining a battery chemical function and / or chemical composition.

[0013] Yet another aspect of the present disclosure relates to the use of functional and / or compositional data determined by the method for determining the chemical functionality and / or chemical composition of a battery for classifying and / or sorting batteries.

[0014] In another aspect of the present disclosure, a computer program element having instructions is provided, which, when executed on a computing device in a computing environment, is configured to perform steps of a method for determining a chemical function and / or chemical composition of a battery in a system for determining a chemical function and / or chemical composition of a battery.

[0015] Another aspect of the present disclosure relates to the use of the classification data / information for decision making between recycling or second / further use of a battery.The classification data / information may be provided as explained in the present disclosure.

[0016] Any disclosure and embodiments described herein are related to the above-listed methods, systems, and computer program elements, and vice versa. Advantageously, the benefits provided by any embodiment and example are also applicable to all other embodiments and examples, and vice versa.

[0017] As used herein, "determining" also includes "initiating or causing determination", "querying" also includes "initiating or causing querying, associating and / or matching", and "providing" also includes "initiating or causing determination, generating, selecting, sending or receiving". "Initiating or causing performance of an action" includes any processing signal that triggers a computing device to perform a corresponding action.

[0018] A method for determining the chemical function and / or chemical composition of a battery, a system for determining the chemical function and / or chemical composition of a battery, the use of battery data and / or chemical function data of a battery and / or chemical composition data of a battery, and corresponding computer program elements allow an accurate assessment of the condition of a specific battery, whether the battery is still suitable for its intended use, whether the battery is still suitable for another use or whether the battery should be aggressively recycled. In other words, the present disclosure provides an extremely accurate determination of the functional state and chemical composition of a battery. This in turn enables improved, in particular sustainable, decisions to be made as to whether such a battery or battery cell should be used further (“second life”) or “aggressively” recycled. Thus, the sustainability and environmental sustainability of battery use can be improved. Thus, the mining of chemical elements can be reduced, and the ecological damage and pollution that usually occurs during the mining of chemical elements can be significantly reduced.

[0019] The object of the invention is to simplify or improve the decision process about whether, when and how to recycle a battery. In particular, the object of the invention is to support the decision whether a battery is still suitable for a different / further use and, if so, for which further use, or whether the battery is no longer usable for further use and should be "aggressively" recycled. These and other objects, which will become apparent after the following description, are solved by the subject-matter of the independent claims. The dependent claims relate to preferred embodiments of the invention.

[0020] The term "battery data" may relate to at least one chemical and / or physical property. The property may be related to at least one chemical composition of a particular battery. The battery data may be provided by measuring chemical and / or physical properties. The battery data may be provided by a property measurement device. The battery data may, for example, be related to the voltage, current, charging characteristics of the battery. The battery data may, for example, include a discharge curve and / or a charge curve. Additionally or alternatively, the battery data may be provided by providing an identifier of the battery (such as a decentralized identifier), for example by reading an identifier element of the battery. In the present case, battery data should be understood in a broad sense and refers to any data / information about at least one chemical and / or physical property of the chemical composition of a particular battery, the further use of which is to be determined and which may be provided by a property measurement device. The property measurement device may measure electrical properties, magnetic properties, optical properties and / or electromagnetic properties. Examples of measuring devices may be Hall sensors, impedance spectrometers, resonant circuits, oscilloscopes (IV curves), damping curve measurements via (mechanically induced) vibrations of battery cells, measurements of ion diffusion, measurements of ion (charge carrier) mean free paths, X-rays, electron spectroscopy, measurement of charge carrier mobility (changes in their natural / resonant frequency) due to degradation by virtual sensors - LC resonators, etc. The collection of battery data may be provided by receiving, providing and / or determining already existing battery data, for example, by scanning identification elements that are arranged on the housing of the battery and that themselves may provide battery data and / or which information may be used to collect / identify battery data from a third source. For example, the collected battery data is the name and age of the respective battery. However, the battery data may include additional information, such as information about the class of materials contained therein (e.g., NCM, LFP), quality class, manufacturer data, available measurement data, information about previous use of the battery (e.g., number of charging cycles), weather and temperature data, information about previous charging currents and charging voltages.

[0021] In the present case, the term "reference battery data" should be understood broadly and refers to any data / information that matches and / or corresponds to the collected battery data, which indicates the chemical function and / or chemical characteristics (e.g., composition) of the reference battery data. The reference battery data preferably includes data of a reference battery, which represents the chemical and / or physical characteristics (e.g., the composition or characteristics of the chemical components) of the battery to be evaluated over its life and for different operating conditions.

[0022] Chemical functions and / or chemical properties (e.g., composition) may be associated with battery chemistry. Battery chemistry may involve electrode active materials, such as anode and / or cathode active materials. During the use of the battery, the battery chemistry may deteriorate. The battery may be a battery in use. In this case, the state of the battery chemistry may be associated with the further use of the battery purpose, such as if it is still usable or if it must be replaced. The battery may be a used battery, such as at the end of its life. In this case, the state of the battery chemistry may be associated with the second use of a used or end-of-life battery. For example, the chemical function may be associated with the degree of degradation of the battery. Further, for example, the chemical properties (e.g., composition) may be associated with the material configuration of the battery (e.g., electrode). The chemical properties (e.g., composition) may be associated with electrode (e.g., anode or cathode) active materials.

[0023] In the present case, the term "classification system" as used herein should be understood broadly and refers to any system, algorithm or determining means configured to determine a classification / perform a classification method. Classification can be provided by a classification method (e.g., manual method, automatic method, digital method, non-digital method, statistical method, non-distributed method, supervised method, unsupervised method, permanent dimension method, learning method, parametric method or non-parametric method, etc.).

[0024] In the present case, the term "battery identification element" as used herein should be understood broadly, allowing at least identification of the battery to be evaluated. The battery identification element can be associated with the battery and physically attached to the battery housing. The battery identification element can be a passive identification element comprising a printed code (such as a barcode or QR code). The battery identification element can be an active identification element comprising a transmitter or transceiver tag (such as an RFID tag capable of communicating via, for example, NFC, Bluetooth, ZigBee or other suitable near- to medium-range communication protocols).

[0025] The battery identification element may also be associated with a unique digital battery identifier, which may be further associated with data related to the identified battery (e.g., the battery data described above). Such data may include any data collected during the production or life of the battery. For example, such data may include material data collected during the production of the battery, or monitoring data collected during the use of the battery that may also be associated with the digital battery identifier. The digital battery identifier may include at least one decentralized identifier that allows location-independent access to the described battery data, which may be desirable because the described data collection may occur in different locations around the world. Such battery data may include pre-existing battery data described herein and / or battery data measured using one or more of the contactless measurement techniques described herein.

[0026] For such data access to battery data, a decentralized identifier may include any unique identifier uniquely associated with the data owner and the identified battery. A decentralized identifier may include a universal unique identifier (UUID) or a digital identifier (DID). A decentralized identifier may be issued by a centralized or decentralized identity issuing authority. A decentralized identifier may include authentication information for authenticating data associated with the identified battery. Via a decentralized identifier and its unique association with the identified battery, access to data associated with the identified battery may be controlled by at least one data owner that may be located anywhere in the world. This is in contrast to a centralized authority scheme, in which an identifier is provided by a centralized authority and access to the data is controlled by such a centralized authority. In this context, decentralized means that the use of the identifier is controlled by any data owner at the time of implementation. The identification element may be configured to provide a digital battery identifier for accessing data associated with the identified battery.

[0027] The term "computing system" is broadly defined herein to include one or more computing nodes, node systems, or combinations thereof. The term "computing node" is broadly defined herein and may refer to any device or system including at least one physical tangible processor and / or a physical tangible memory capable of having computer executable instructions executed by a processor thereon. The form of computing nodes is now increasingly diverse. Computing nodes may be, for example, handheld devices, production facilities, sensors, monitoring systems, control systems, electrical appliances, laptop computers, desktop computers, mainframes, data centers, or even devices that are not traditionally considered to be computing nodes, such as wearable devices (e.g., glasses, watches, etc.). Memory may take any form and may depend on the nature and form of the computing node.

[0028] In an embodiment of the method for determining the chemical function and / or chemical composition of a battery, the matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery about the at least one parameter, and identifying the data of the found matching reference battery as the matching data. In an example, the parameter may indicate battery type, loss, production date / time, load cycle, manufacturer data, and / or battery life.

[0029] In another embodiment of the method for determining the chemical functionality and / or chemical characteristics (e.g., composition) of a battery, finding these matching reference batteries regarding at least one parameter includes providing data by a data service in an at least partially distributed computing environment, wherein the data service or interface accesses the reference battery data.

[0030] Reference battery data can be collected via a decentralized computing environment or a decentralized network. The decentralized computing environment or decentralized network may include nodes associated with participants of the network. These nodes may include a data providing or consuming interface configured to transmit data between nodes. The collection of reference battery data may include: for example, identifying or providing a digital identifier of at least one battery by an identification element (such as reading the identification element), providing the digital identifier to a decentralized computing or network interface to request reference battery data, and providing the reference battery data to the decentralized network or computing interface. The digital identifier may include a unique digital battery identifier or a decentralized identifier associated with the battery. In this way, battery-specific reference data for each battery can be accessed, making the determination of the function and / or characteristics of the battery more reliable and robust. In particular, the reference battery data can be accessed in a controlled manner by any participant of the network, thereby giving the owner of the reference data (such as a battery of a monomer producer) control over its data.

[0031] The battery reference data may be provided in association with the identified battery or with an identifier that uniquely identifies the battery. The data transfer may include one or more authentication mechanisms associated or linked to a decentralized identifier associated with the identified battery or associated with an identifier that uniquely identifies the battery. Based on an authentication mechanism (such as a public-private key mechanism), a peer-to-peer communication channel may be opened, for example, through a data provision interface associated with a reference data owner (such as a battery or cell producer) to provide reference data. The data transfer may include one or more authorization mechanisms associated or linked to a decentralized identifier associated with the identified battery or associated with an identifier that uniquely identifies the battery. The one or more authorization mechanisms may include at least one authorization rule that is used to control access to data controlled by a data owner (e.g., a battery producer or a cell producer). Based on (multiple) authorization mechanisms, reference data may be transmitted from a reference data provision interface to a reference data consumption interface. The reference data consumption interface may be a decentralized network interface for requesting reference battery data.

[0032] The battery reference data can be provided in association with at least one parameter of a matching reference battery. The parameter can be provided together with an identifier to a decentralized computing or network interface for requesting reference battery data. Based on the parameter, the reference data can be selected. The battery reference data provided by the reference data provider can be selected based on at least one parameter. The at least one parameter can, for example, be related to a characteristic measurement device and / or a characteristic measured by the characteristic measurement device. Thus, reference data selected based on the parameters can be provided. This allows for more flexible and reliable processing of reference data and available battery data. This allows for more reliable determinations because reference data of the battery can be provided and determined, matched and / or classified taking into account a specific battery and available parameters (such as the type of characteristic measurement device and / or the characteristics measured by the characteristic measurement device).

[0033] In one example, different suppliers, battery manufacturers or other entities performing battery testing and / or measurements may provide corresponding battery data here, so that corresponding reference data are available in a decentralized manner at the respective entities. Thus, the decentralized approach described herein has the advantage that access to such reference battery data may be independent of the location of the data requester (i.e., by means of the described unique identifier). The decentralized approach has the advantage of enabling reference data retrieval for different measurement techniques and associated parameters, and thus reference data may be provided in a targeted manner. This enables a more reliable determination of the chemical function and / or chemical composition of the battery and matching or classification.

[0034] In another embodiment of the method for determining the chemical functionality and / or chemical composition of a battery, the matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery with respect to the at least one parameter by using a model (particularly a predictive model) that simulates the behavior of the reference battery with respect to the at least one parameter, and identifying the data of the found matching reference battery as matching data. Such a model may be based on a digital twin of the underlying battery and / or battery cell.

[0035] In another embodiment of the method for determining the chemical functionality and / or chemical composition of a battery, the matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery regarding the at least one parameter by using a machine learning (ML) system, and identifying the data of the found matching reference battery as the matching data.

[0036] In another embodiment of the method for determining the chemical functionality and / or chemical composition of a battery, classification of the battery is provided using a machine learning (ML) model by: collecting multiple battery data; making manual decisions about the use of the battery based on the battery data; combining the collected battery data with the manual decisions about the use of the battery; identifying and defining factors that led to the manual decisions; evaluating the identified and defined factors by applying them to additional batteries, and possibly adjusting the factors if the evaluation indicates inconsistency; and providing a classification of the battery by applying the factors to the collected battery data.

[0037] In another embodiment of the method for determining the chemical functionality and / or chemical composition of a battery, the method further comprises classifying the battery into a plurality of categories based on the functional and / or composition data, wherein each category specifies a use of the battery.

[0038] In another embodiment of the method for determining the chemical functionality and / or chemical composition of a battery, the plurality of categories includes at least one category representing a further use of the battery and / or at least one category representing recycling of the battery. Such further use may be, for example, use as a car battery, an energy storage battery connected to a solar cell, an electric scooter battery, an electric bicycle battery, a power tool battery, a mobile device battery (e.g., a mobile phone, a notebook, a laptop, a power bank, a tablet computer, etc.).

[0039] In another embodiment of the method for determining the chemical function and / or chemical composition of a battery, the method further comprises the step of providing the determined function and / or composition data, matching data and / or classification data in association with a unique digital identifier associated with the battery. The determined data may be provided in association with a unique identifier of the battery. For example, the unique digital identifier of the battery may be provided by a QR code, a hologram, a microchip, an NFC chip, an RFID chip.

[0040] In an embodiment of the system for determining the chemical functionality and / or chemical composition of a battery, the providing unit comprises at least one reference battery database, model and / or machine learning ML model.

[0041] In another embodiment of the system for determining the chemical functionality and / or chemical composition of a battery, the system further comprises at least one classification unit for classifying the battery into a plurality of categories based on the functionality and / or composition data and / or for classifying the battery based on at least one parameter of the collected battery data.

[0042] In another embodiment, the matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery with respect to at least one parameter by using a model (particularly a predictive model) that simulates the behavior of the reference battery with respect to at least one parameter, and identifying the data of the found matching reference battery as matching data.

[0043] In another embodiment, the matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery regarding the at least one parameter by using a machine learning (ML) system, and identifying the data of the found matching reference battery as the matching data.

[0044] In another embodiment, classification of batteries is provided using a machine learning (ML) model by: collecting multiple battery data; making manual decisions about battery usage based on the battery data; combining the collected battery data with the manual decisions about battery usage; identifying and defining factors that led to the manual decisions; evaluating the identified and defined factors by applying them to additional batteries, and possibly adjusting the factors if the evaluation indicates inconsistency; and providing classification of the batteries by applying the factors to the collected battery data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present disclosure will be further described below with reference to the accompanying drawings:

[0046] Figure 1 A battery with battery identification elements is schematically illustrated;

[0047] Figure 2 An example embodiment of a centralized computing environment having computing nodes is presented;

[0048] Figure 3 An example embodiment of a distributed computing environment having computing nodes is presented;

[0049] Figure 4 An example embodiment of a distributed computing environment is presented;

[0050] Figure 5 A block diagram illustrating an example system architecture for a system for determining chemical functionality and / or chemical composition of a battery;

[0051] Figure 6 A flow chart showing a computer-implemented method for determining the chemical function and / or chemical composition of a battery;

[0052] Figure 7 A flow chart showing another computer-implemented method for determining the chemical function and / or chemical composition of a battery;

[0053] Figure 8 A flow chart illustrating another computer-implemented method for determining chemical function and / or chemical composition; and

[0054] Fig. 9 A flow chart of another computer-implemented method for determining the chemical functionality and / or chemical composition of a battery is presented. DETAILED DESCRIPTION

[0055] The following embodiments are merely examples for implementing the methods, systems, or application devices disclosed herein and should not be considered limiting.

[0056] Figure 1 A battery 10 with battery identification elements 16, 17 is schematically shown. The battery 10 may include a battery management system 11 and a plurality of battery cells 19 arranged inside a battery housing 18. The battery cells 19 may be arranged in a battery pack or module including a plurality of battery cells. The battery cells 19 may include an electrolyte 12, an anode 14, a cathode 15 and a separator 13.

[0057] The battery identification elements 16, 17 may be associated with the battery 10. The battery identification elements 16, 17 may be physically attached to the battery housing. The battery identification elements 16, 17 may be arranged inside or outside the battery housing 18. The battery identification elements 16, 17 may be passive identification elements 16. The passive element 16 may be arranged on the outer surface of the battery housing 18. The passive element 16 may include a printed code, such as a barcode or a QR code. The battery identification elements 16, 17 may be active identification elements 17. The active element 17 may be a transmitter or transceiver tag, such as an RFID tag capable of communicating via, for example, NFC, Bluetooth, ZigBee or other suitable near- to medium-range communication protocols. The battery identification element 17 may be part of the battery management system 11, or the digital battery identifier may be stored in the battery management system 11.

[0058] The battery identification elements 16, 17 may be associated with a digital battery identifier. The digital battery identifier may be unique to the battery. The digital battery identifier may further be associated with data related to the identified battery. Such data may include any data collected during the production or life of the battery. For example, such data may include material data collected during the production of the battery, or monitoring data collected during the use of the battery that may be associated with the digital battery identifier.

[0059] The digital battery identifier may include at least one decentralized identifier. The decentralized identifier may include any unique identifier that is uniquely associated with the data owner and the identified battery. The decentralized identifier may include a universal unique identifier (UUID) or a digital identifier (DID). The decentralized identifier may be issued by a centralized or decentralized identity issuing authority. The decentralized identifier may include authentication information for authenticating data related to the identified battery. Access to data related to the identified battery may be controlled by at least one data owner via the decentralized identifier and its unique association with the identified battery. This is in contrast to a centralized authority scheme in which the identifier is provided by a centralized authority and access to the data is controlled by such a centralized authority. In this context, decentralized means that the use of the identifier is controlled by any data owner at the time of implementation, particularly regardless of his / her location. The identification elements 16, 17 may be configured to provide a digital battery identifier for accessing data related to the identified battery.

[0060] Figures 2 to 4 Different computing environments are shown, centralized, decentralized and distributed computing environments. The methods, apparatus, systems, computer elements of the present disclosure can be implemented in a decentralized or at least partially decentralized computing environment. In particular, the provision of data can be implemented by different computing nodes, which can be implemented in a centralized, decentralized or distributed computing environment, thereby allowing the described location-independent data access. In addition, the determination of data can be implemented by different computing nodes, which can be implemented in a centralized, decentralized or distributed computing environment.

[0061] Figure 2 and Figure 3 Example embodiments of centralized and distributed computing environments with computing nodes are presented. Figure 4 Example embodiments of a distributed computing environment are presented.

[0062] Figure 2An example embodiment of a centralized computing system 20 is shown, which includes a central computing node 21 (solid circle in the middle) and several peripheral computing nodes 21.1 to 21.n (represented as peripheral solid circles). The term "computing system" is broadly defined herein to include one or more computing nodes, node systems, or combinations thereof. The term "computing node" is broadly defined herein and may refer to any device or system including at least one physical tangible processor and / or a physical tangible memory capable of having computer executable instructions executed by a processor thereon. The form of computing nodes is now increasingly diverse. Computing nodes may be, for example, handheld devices, production facilities, sensors, monitoring systems, control systems, appliances, laptops, desktop computers, mainframes, data centers, or even devices that are not traditionally considered to be computing nodes, such as wearable devices (e.g., glasses, watches, etc.). The memory may take any form and depends on the nature and form of the computing node.

[0063] In this example, the peripheral computing nodes 21.1 to 21.n can be connected to a central computing system (or server). In another example, the peripheral computing nodes 21.1 to 21.n can be attached to the central computing node via, for example, a terminal server (not shown). Most functions can be performed by or obtained from the central computing node (also known as a remote centralized location). One peripheral computing node 21.n has been expanded to provide an overview of the components present in the peripheral computing node. The central computing node 21 can include the same components as described with respect to the peripheral computing node 21.n.

[0064] Each computing node 21, 21.1 to 21.n may include at least one hardware processor 22 and memory 24. The term "processor" may refer to any logic circuit configured to perform the basic operations of a computer or system, and / or generally refers to a device configured to perform calculations or logical operations. In particular, a processor or computer processor may be configured to process basic instructions that drive a computer or system. The processor may be a semiconductor-based processor, a quantum processor, or any other type of processor configured to process instructions. As an example, the processor may include at least one arithmetic logic unit (ALU), at least one floating point unit (FPU) (such as a math coprocessor or a digital coprocessor), a plurality of registers (specifically registers configured to supply operands to the ALU and store the results of the operations), and memory (such as L1 and L2 cache memory). In particular, the processor may be a multi-core processor. In particular, the processor may be or may include a central processing unit ("CPU"). The processor may be a ("GPU") graphics processing unit, a ("TPU") tensor processing unit, a ("CISC") complex instruction set computing microprocessor, a reduced instruction set computing ("RISC") microprocessor, a very long instruction word ("VLIW") microprocessor, or a processor that implements other instruction sets or multiple processors that implement a combination of instruction sets. The processing device may also be one or more special processing devices, such as an application specific integrated circuit ("ASIC"), a field programmable gate array ("FPGA"), a complex programmable logic device ("CPLD"), a digital signal processor ("DSP"), a network processor, etc. The methods, systems, and devices described herein may be implemented as software in a DSP, a microcontroller, or any other auxiliary processor, or as hardware circuits within an ASIC, CPLD, or FPGA. It should be understood that the term processor may also refer to one or more processing devices, such as a distributed processing device system located on multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

[0065] Memory 24 may refer to physical system memory, which may be volatile, nonvolatile, or a combination thereof. Memory may include nonvolatile mass storage, such as physical storage media. Memory may be a computer-readable storage medium (such as RAM, ROM, EEPROM, CD-ROM) or other optical disk storage, disk storage, or other magnetic storage devices, non-disk storage (such as solid state disk) or any other physical tangible storage medium, which may be used to store the desired program code device in the form of computer executable instructions or data structures and may be accessed by a computing system. In addition, memory may be a computer-readable medium (also referred to as a transmission medium) that carries computer executable instructions. Further, after reaching various computing system components, the program code device in the form of computer executable instructions or data structures may be automatically transferred from the transmission medium to the storage medium (and vice versa). For example, a computer executable instruction or data structure received by a network or data link may be buffered in a RAM in a network interface module (e.g., "NIC"), and then ultimately transferred to a computing system RAM and / or a storage medium with lower volatility at the computing system. Thus, it should be understood that storage media may be included in computing components that also (or even primarily) utilize transmission media.

[0066] The computing nodes 21, 21.1 to 21.n may include a plurality of structures 26, which are generally referred to as "executable components, executable instructions, computer executable instructions, or instructions." For example, the memory 24 of the computing nodes 21, 21.1 to 21.n may be shown as including the executable components 26. The term "executable component" or any equivalent thereof may be a name for a structure known to one of ordinary skill in the computing arts, which structure may be software, hardware, or a combination thereof, or may be implemented in software, hardware, or a combination thereof. For example, when implemented in software, one of ordinary skill in the art will understand that the structure of the executable component includes a software object, routine, method, etc., that is executed on the computing nodes 21, 21.1 to 21.n, regardless of whether such executable components are present in the heap of the computing nodes 21, 21.1 to 21.n or whether the executable components are present on a computer-readable storage medium. In this case, it will be appreciated by those skilled in the art that the structure of the executable component exists on a computer-readable medium so that when interpreted by one or more processors (e.g., by a processor thread) of the computing nodes 21, 21.1 to 21.n, the computing nodes 21, 21.1 to 21n perform functions. Such a structure can be directly computer-readable by the processor (such as the case where the executable component is binary). Alternatively, the structure can be constructed to be interpretable and / or compiled (whether in a single stage or in multiple stages) to generate such binary that can be directly interpreted by the processor. When the term "executable component" is used, this understanding of the example structure of the executable component is completely within the scope of understanding of those skilled in the art of computing. Examples of executable components implemented in hardware include hard-coded or hard-wired logic gates that are implemented exclusively or almost exclusively in hardware, such as in a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. In this specification, the terms "component", "agent", "manager", "service", "engine", "module", "virtual machine", etc. are used synonymously with the term "executable component".

[0067] The processor 22 of each computing node 21, 21.1 to 21.n can direct the operation of each computing node 21, 21.1 to 21.n in response to having executed the computer executable instructions constituting the executable components. For example, such computer executable instructions can be embodied on one or more computer readable media forming a computer program product. The computer executable instructions can be stored in the memory 24 of each computing node 21, 21.1 to 21.n. The computer executable instructions include, for example, instructions and data, which, when executed at the processor 21, cause the general computing node 21, 21.1 to 21.n, the special computing node 21, 21.1 to 21.n or the special processing device to perform a specific function or group of functions. Alternatively or additionally, the computer executable instructions can configure the computing node 21, 21.1 to 21.n to perform a specific function or group of functions. The computer executable instructions can be, for example, binary or even instructions that undergo some conversion (such as compilation) before being directly executed by the processor, such as intermediate format instructions, such as assembly language, or even source code.

[0068] Each computing node 21, 21.1 to 21.n may include a communication channel 28, such as a network (on a Figure 2 1 to 21.n and / or modules and / or other electronic devices. When information is transmitted or provided to computing nodes 21, 21.1 to 21.n through a network or another communication connection (hardwired, wireless, or a combination of hardwired or wireless), computing nodes 21, 21.1 to 21.n correctly regard the connection as a transmission medium. Transmission media may include networks and / or data links, which may be used to carry computer executable instructions or desired program code devices in the form of data structures and may be accessed by general or special computing nodes 21, 21.1 to 21.n. Combinations of the above may also be included within the scope of computer-readable media.

[0069] The computing nodes 21, 21.1 to 21.n may further include a user interface system 25 for interfacing with a user. The user interface system 25 may include an output mechanism 25A and an input mechanism 25B. The principles described herein are not limited to the precise output mechanism 25A or input mechanism 25B, as this will depend on the nature of the device. However, the output mechanism 25A may include, for example, a display, a speaker, a display, a tactile output, a hologram, etc. Examples of the input mechanism 25B may include, for example, a microphone, a touch screen, a hologram, a camera, a keyboard, a mouse or other pointer input, any type of sensor, etc.

[0070] Figure 3 An example embodiment of a distributed computing environment 30 is shown, wherein a number of computing nodes 21.1 to 21.n are represented as solid circles. Figure 2 In contrast to the illustrated centralized computing environment 20, the computing nodes 21.1 to 21.n in the distributed computing environment are not connected to the central computing node 21 and are therefore not under the control of the central computing node. Instead, both hardware resources and software resources may be allocated to each individual computing node 21.1 to 21.n (local or remote computing system), and data may be distributed between the various computing nodes 21.1 to 21.n to perform tasks. Thus, in a distributed system environment, program modules may be located in local and remote memory storage devices, thereby achieving the described location-independent data access. One computing node 21 has been expanded to provide an overview of the components present in the computing node 21. In this example, the computing node 21 includes components related to Figure 2 The same parts.

[0071] Figure 4An example embodiment of a distributed computing environment 40 is shown. In this specification, "distributed computing" may refer to any computing that utilizes multiple computing resources. Such use may be achieved through virtualization of physical computing resources. An example of distributed computing is cloud computing. "Cloud computing" may refer to a model for implementing on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). When a cloud computing environment is distributed, the cloud computing environment may be distributed internationally within an organization and / or across multiple organizations. In this example, a distributed cloud computing environment 40 may include the following computing resources: (multiple) mobile devices 42, applications 43, databases 44, (multiple) data storage devices, and servers 46. The cloud computing environment 40 may be deployed as a public cloud 47, a private cloud 48, or a hybrid cloud 49. A private cloud 47 may be owned by an organization, and only members of the organization with appropriate access rights may use the private cloud 48, thereby making the data in the private cloud at least confidential. In contrast, data stored in a public cloud 48 may be open to anyone via the Internet. A hybrid cloud 49 may be a combination of both a private cloud 47 and a public cloud 48 and may have some data that is private while other data may be publicly available.

[0072] Return to reference Figure 1 , the battery or battery component identifier provided by the identification elements 16, 17 can be processed in an at least partially decentralized computing environment. The battery or battery component identifier can be configured to access battery or battery component data, such as material data. Further decentralized identifiers can be associated with recycling or production plants involved in the recycling or production of batteries, thereby achieving the described location-independent data access. The recycling or production plant identifier can be configured to access recycling or production plant data, such as capacity data, process-specific data or operator data. By associating such decentralized identifiers with authentication information and optional authorization rules, data can be shared securely and reliably between participants or systems in a production chain and / or recycling chain.

[0073] The above-described at least partially decentralized structure based on decentralized identifiers allows novel methods, devices, systems, computer elements, computer executable instructions and their use in a battery recycling process, which will be described below.

[0074] Figure 5A block diagram of an example system architecture for determining the chemical function and / or chemical composition of a battery is shown. The system 50 for determining the chemical function and / or chemical composition of a battery includes a collection unit 51, which is configured to collect battery data indicating at least one chemical and / or physical characteristic of the chemical composition of the battery. The collection unit 51 is a unit that receives, provides and / or determines battery data based on already existing data or non-existent data preferably measured by the above-mentioned characteristic measurement device (for example, information of a mark on the battery or information from a previously performed measurement). The battery data indicates at least one chemical and / or physical characteristic of the chemical composition of the battery. The collection unit 51 can be at least one of the following: Hall sensor, impedance spectrometer, resonant circuit, oscilloscope (IV curve), damping curve measurement via (mechanically induced) vibration of the battery cell, measurement of ion diffusion, measurement of ion (charge carrier) mean free path, X-ray, electron spectroscopy, measurement of charge carrier mobility (change in its natural / resonant frequency) due to degradation by virtual sensor - LC resonator. The collection unit 51 provides the collected battery data to the system 50 for further processing. The system 50 further includes a providing unit 52 configured to provide matching data by determining at least one match between the collected battery data and the data of a reference battery. The providing unit 52 receives the collected battery data from the collecting unit 51, uses the data to determine matching data, and provides the matching data to the system 50 for further processing / use. The system 50 further includes a determining unit 53 for determining functional and / or composition data indicating the chemical function and / or chemical composition of the battery based on the matching data. The determining unit 53 receives the matching data from the providing unit 52 and uses the data to determine the functional and / or composition data. The providing unit 52 includes a reference battery database 54, which includes data of at least one reference battery. Additionally and / or alternatively, a model or machine learning system may be included in the providing unit 52 to provide data of at least one reference battery. The system 50 further includes at least one classification unit 55, which is used to classify the battery into multiple categories based on the functional and / or composition data and / or for classifying the battery based on at least one parameter of the collected battery data. The classification unit 55 receives the function and / or component data and classifies the data into a plurality of different categories. The classification results can be provided / output by the classification unit 55 to the system 50 for further processing.

[0075] Figure 6A flow chart of a computer-implemented method for determining a chemical function and / or chemical composition of a battery is shown. In a first step, battery data indicating at least one chemical and / or physical property of the chemical composition of the battery is collected, wherein the battery data is already existing data. The collection of battery data is provided by receiving, providing and / or determining already existing battery data or measurement data measured by means of a Hall sensor. Exemplarily, in the latter case, the collected battery data may be the strength and direction of a magnetic field measured by a Hall sensor. In the case of already existing battery data, the collection may be performed by scanning an identification element arranged on the housing of the battery. In the latter case, the collected battery data may be the name and life of the battery. In a second step, matching data indicating a match between the collected battery data from the first step and the data of a reference battery is provided by determining. The matching data includes the collected battery data, a matching counterpart of the collected battery data (i.e., reference battery data that matches the collected battery data), which may be a matching index (e.g., correlation) of the collected battery data with the reference battery data, and functional and / or composition data of the reference battery indicating the chemical function and / or chemical composition of the reference battery. Both the reference battery data and the functional and / or composition data of the reference battery are included in the data of the reference battery. With respect to the above example, in the second step, in the case of the existing data mentioned, the name and life of the battery are matched with a reference battery having the same name and the same life, wherein the resulting matching data includes the name and life of the battery, the name and life of the reference battery, the associated value "1", and the functional and / or composition data of the reference battery. In addition, in the case of the measured data, the magnetic field strength and direction of the battery can be matched with a reference battery having the same magnetic field strength and direction, wherein the resulting matching data of the match includes the magnetic field strength and direction of the battery, the magnetic field strength and direction of the reference battery, the associated value "1", and the functional and / or composition data of the reference battery. In the third step, functional and / or composition data indicating the chemical function and / or chemical composition of the battery is determined based on the matching data. Since in the second step, the matching data provided includes the functional and / or composition data of the reference battery, when there is a match between the collected battery data and the data of the reference battery, it is determined that the functional and / or composition data of the reference battery is assumed to be equivalent to the functional and / or composition data of the battery. Regarding the above example, when the lifespan and name of the battery and the reference battery correspond to each other, and / or when the magnetic field strength and direction of the battery and the reference battery correspond to each other, it is assumed that the function and / or composition data of the reference battery is equivalent to the function and / or composition data of the battery.

[0076] Figure 7 Demonstrated for determining Figure 6A flow chart of another computer-implemented method for determining the chemical function and / or chemical composition of a battery of a ...

[0077] Optionally, a second sub-step of the step of providing matching data includes providing data by a data service in the at least partially distributed computing environment, wherein the data service accesses the reference battery data. Alternatively or additionally, the step may include querying data from at least one reference battery database. The query may be provided by collecting information from a database, sending information transmission instructions to the database and receiving corresponding answers from the database, and / or directly accessing the database.

[0078] Optionally, the second sub-step of the step of providing matching data comprises simulating the behavior of the reference battery with respect to at least one parameter by a model. The simulation is provided by a forward model or a predictive model. The simulation has at least one selected parameter as input and calculates the behavior of the reference battery, i.e., functional and / or composition data of the reference battery, based on nonlinear, partial and / or linear differential equations.

[0079] Figure 8 Demonstrated for determining Figure 7 A flowchart of another computer-implemented method for determining the chemical function and / or chemical composition of a battery. Figure 7 In addition to the steps and sub-steps of providing matching data, a second sub-step of the step of providing matching data includes finding a matching reference battery with respect to at least one parameter by using a machine learning ML system.

[0080] Fig. 9 Demonstrated for determining Figure 7 A flowchart of another computer-implemented method for determining the chemical function and / or chemical composition of a battery. Figure 7In addition to the steps and sub-steps of, a first sub-step of the step of providing matching data includes classifying the battery by a machine learning system based on at least one parameter of the collected battery data. The machine learning includes the following steps: collecting a plurality of battery data; making a manual decision about the use of the battery based on the battery data; combining the collected battery data with the manual decision about the use of the battery; identifying and defining factors that led to the manual decision; evaluating the identified and defined factors by applying them to another battery; and possibly adjusting the factors if the evaluation indicates inconsistency; and providing a classification of the battery by applying the factors to the collected battery data.

[0081] Optionally, Fig. 9 Another computer-implemented method for determining the chemical function and / or chemical composition of a battery includes the further step of classifying the battery into a plurality of categories based on the function and / or composition data. Each category in the plurality of categories specifies a use or further use of the battery. The use or further use is a second life of the battery and / or a category of recycling of the battery.

[0082] Optionally, Fig. 9 Another computer-implemented method for determining the chemical function and / or chemical composition of a battery comprises the further step of providing the determined data and / or classified data by a unique identifier. The unique identifier is a QR code, a hologram, a microchip, an NFC chip, an RFID chip.

[0083] The present disclosure has been described in conjunction with exemplary preferred embodiments. However, other variations may be understood and implemented by those skilled in the art and those practicing the claimed invention through study of the drawings, the present disclosure, and the claims. It is particularly noteworthy that any of the steps presented may be performed in any order, i.e., the present invention is not limited to a particular order of the steps. Furthermore, it is not required that different steps be performed at a certain place or node in the distributed system, i.e., each step may be performed at a different node using different devices / data processing units.

[0084] In the claims and the description, "comprising" does not exclude other elements or steps and the indefinite article "a" or "an" does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to an advantageous embodiment.

Claims

1. A computer-implemented method for determining a battery's chemical function and / or chemical composition, the method comprising: - collecting battery data indicative of at least one chemical and / or physical characteristic associated with the chemical composition of the battery; - providing matching data by determining at least one match between the collected battery data and data of a reference battery; as well as - determining functional and / or composition data indicative of a chemical function and / or chemical composition of the battery based on the matching data.

2. The method according to claim 1, wherein: The matching data is provided by classifying the battery based on at least one parameter of the collected battery data, finding a matching reference battery with respect to the at least one parameter, and identifying data of the found matching reference battery as the matching data.

3. The method according to claim 2, wherein: The finding the matching reference batteries with respect to at least one parameter includes providing data by a data service in the at least partially distributed computing environment, wherein the data service accesses reference battery data.

4. The method according to claim 3, wherein: Reference battery data is collected via a distributed computing environment, wherein collecting the reference battery data includes: providing a digital identifier of the battery; providing the digital identifier to a distributed computing interface configured to request reference battery data; and providing the reference battery data to the distributed computing interface configured to request reference battery data.

5. The method according to claim 3 or 4, wherein: The battery reference data is provided in relation to an identified battery or in relation to an identifier that uniquely identifies the battery.

6. The method according to claims 3 to 5, wherein: The transmission of the battery reference data includes one or more authentication mechanisms and / or authorization mechanisms associated or linked to the identified battery or related to the identifier that uniquely identifies the battery.

7. A method according to any one of the preceding claims, wherein: The at least one parameter is provided together with the identifier to a decentralized computing interface for requesting reference battery data, wherein the battery reference data is provided in relation to the at least one parameter of the matching reference batteries.

8. A method according to any one of the preceding claims, further comprising classifying the battery into a plurality of categories based on the functional and / or composition data, wherein: Each category specifies a use of the battery, wherein the plurality of categories preferably includes at least one category indicating further use of the battery and / or at least one category indicating recycling of the battery, wherein the method further comprises the step of providing the determined functional and / or composition data, matching data and / or classification data in association with a unique identifier associated with the battery.

9. A system for determining the chemical function and / or chemical composition of a battery, the system comprising: - a collecting unit for collecting battery data indicative of at least one chemical and / or physical property of the chemical composition of the battery; - a providing unit for providing matching data by determining at least one match between the collected battery data and data of a reference battery; as well as - a determination unit for determining, based on the matching data, functional and / or composition data indicative of a chemical function and / or chemical composition of the battery.

10. The system according to claim 10, wherein: The providing unit includes at least one reference battery database, model and / or machine learning ML model.

11. The system according to claim 10 or 11, wherein: The system further comprises at least one classification unit for classifying the battery into a plurality of categories based on the functional and / or composition data and / or for classifying the battery based on at least one parameter of the collected battery data.

12. Use of battery data and / or battery chemical function data and / or battery chemical composition data in a method according to any one of claims 1 to 8.

13. Use of the functional and / or compositional data determined by the method according to any one of claims 1 to 8 for classifying and / or sorting batteries.

14. A computer program element having instructions which, when executed on a computing device of a computing environment, is configured to perform the steps of the method according to any one of claims 1 to 8 in a system according to any one of claims 9 to 11.

15. Use of functional and / or composition data, matching data and / or classification data for making decisions between recycling or a second or further use of a battery.

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