Method and system for predicting electrical performance of secondary battery, and medium

Abstract

The present disclosure provides a method and system for predicting electrical performance of a secondary battery, and a medium. The method comprises the following steps: receiving design conditions of the secondary battery; receiving experimental data of the secondary battery; obtaining model parameters based on the experimental data and the electrochemical model; generating an electrochemical model library comprising model parameters; and predicting the electrical performance of the secondary battery having the design conditions based on the electrochemical model library. The design condition of the secondary battery includes at least one of an electrode condition and an active material condition.

Description

Technical Field

[0001] This disclosure relates to a method for predicting the electrical performance of a secondary battery and a system for using the method to predict the electrical performance of a secondary battery, and more specifically, to a method for predicting the electrical performance of a secondary battery based on a library of electrochemical models under design conditions and a system for using the method to predict the electrical performance of a secondary battery. Background Technology

[0002] Unlike primary batteries, which are not designed for recharging, secondary (or rechargeable) batteries are designed to discharge and be recharged. Low-capacity secondary batteries are used in portable small electronic devices such as smartphones, feature phones, laptops, digital cameras, and camcorders, while high-capacity secondary batteries are widely used as power sources for driving engines in hybrid or electric vehicles and for storing electricity (e.g., household or utility-scale power storage). A secondary battery typically includes an electrode assembly containing positive and negative electrodes, a housing of the electrode assembly, and electrode terminals connected to the electrode assembly.

[0003] In the initial design of secondary batteries, predicting their electrical performance is often crucial. Various models exist for predicting these performance characteristics, such as morphological models or equivalent circuit models. To accurately simulate the electrochemical performance of a secondary battery, fitting the model parameters to the design conditions is essential. However, fitting the model parameters to the design conditions experimentally can be inefficient when the design conditions change repeatedly.

[0004] The information disclosed in this background section is intended to enhance understanding of the background of this disclosure, and therefore may contain information that does not constitute related (or prior art). Summary of the Invention

[0005] To address the above problems, this disclosure provides a method and a system for predicting the electrical performance of a secondary battery.

[0006] These and other aspects and features of this disclosure will be described in, or will become apparent from, the following description of embodiments of this disclosure.

[0007] Some embodiments of this disclosure include a method for predicting the electrical performance of a secondary battery. The method includes: receiving design conditions for the secondary battery; receiving experimental data from the secondary battery; obtaining model parameters based on the experimental data and an electrochemical model; generating an electrochemical model library including the model parameters; and predicting the electrical performance of the secondary battery according to the design conditions based on the electrochemical model library. The design conditions for the secondary battery include at least one of electrode conditions and active material conditions.

[0008] According to embodiments of this disclosure, the electrical performance of a secondary battery includes at least one of the secondary battery's charging capacity, discharging capacity, and C-rate characteristics.

[0009] According to embodiments of this disclosure, the method further includes: generating predictive data by applying experimental data to an electrochemical model; optimizing model parameters by comparing experimental data and predictive data; and updating the electrochemical model library with the optimized model parameters.

[0010] According to embodiments of this disclosure, the method further includes: generating input data for an electrochemical model based on experimental data; obtaining predicted data by applying the input data to the electrochemical model; and generating new input data by an optimization algorithm in response to determining that the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold.

[0011] According to embodiments of this disclosure, generating new input data through optimization algorithms includes using at least one of particle swarm optimization, genetic algorithms, and Bayesian algorithms to generate new input data.

[0012] According to embodiments of this disclosure, obtaining model parameters includes: obtaining first model parameters associated with electrolyte properties; obtaining second model parameters associated with active material properties; and obtaining third model parameters associated with electrode plate properties.

[0013] According to embodiments of this disclosure, obtaining model parameters includes: obtaining first model parameters related to electrolyte properties from first experimental data through advanced electrolyte model simulation.

[0014] According to embodiments of this disclosure, obtaining model parameters includes: obtaining second model parameters related to the properties of the active substance from second experimental data through discrete element method simulation.

[0015] According to embodiments of this disclosure, obtaining model parameters includes: obtaining third model parameters related to the properties of the electrode plate from third experimental data using the Newman model.

[0016] According to embodiments of this disclosure, the design conditions for receiving a secondary battery include conditions for receiving active materials, and the active material conditions include conditions for mixing a mixture of multiple components with different properties.

[0017] A non-transitory computer-readable recording medium may be provided, which stores a computer program for performing methods according to embodiments of the present disclosure.

[0018] Some embodiments of this disclosure include a system for predicting the electrical performance of a secondary battery. The system includes: a memory; and at least one processor connected to the memory and configured to execute at least one computer-readable program stored in the memory. The at least one processor is configured to: receive design conditions for the secondary battery; receive experimental data from the secondary battery; obtain model parameters based on the experimental data and an electrochemical model; generate an electrochemical model library including the model parameters; and predict the electrical performance of the secondary battery according to the design conditions based on the electrochemical model library. The design conditions include at least one of electrode conditions and active material conditions.

[0019] According to embodiments of this disclosure, the electrical performance of a secondary battery includes at least one of the secondary battery's charging capacity, discharging capacity, and C-rate characteristics.

[0020] According to embodiments of the present disclosure, at least one processor is further configured to: generate predictive data by applying experimental data to an electrochemical model; optimize model parameters by comparing experimental data and predictive data; and update an electrochemical model library with the optimized model parameters.

[0021] According to embodiments of the present disclosure, at least one processor is further configured to: generate input data for an electrochemical model based on experimental data; obtain predicted data by applying the input data to the electrochemical model; and generate new input data by means of an optimization algorithm in response to determining that the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold.

[0022] According to embodiments of this disclosure, the optimization algorithm includes at least one of particle swarm optimization, genetic algorithm, and Bayesian algorithm.

[0023] According to embodiments of the present disclosure, at least one processor is further configured to: obtain first model parameters associated with electrolyte properties from first experimental data through advanced electrolyte model simulation.

[0024] According to embodiments of this disclosure, at least one processor is further configured to: obtain second model parameters associated with the properties of the active substance from second experimental data by simulating using the discrete element method.

[0025] According to embodiments of this disclosure, at least one processor is further configured to: obtain third model parameters related to the properties of the electrode plate from third experimental data using a Newman model.

[0026] According to embodiments of this disclosure, the active substance conditions include conditions for mixing a mixture of multiple components with different properties.

[0027] According to various embodiments of this disclosure, data predicted by methods for predicting the electrical performance of secondary batteries can exhibit a high level of accuracy when compared with data measured experimentally. Therefore, by predicting the electrical performance of secondary batteries under various conditions of electrode plates and / or active materials and using a library to manage the predictions, the electrical performance of secondary batteries can be determined without conducting new experiments, even if design conditions change.

[0028] However, the aspects and features of this disclosure are not limited to those described above, and those skilled in the art will clearly understand from the following detailed description other aspects and features not mentioned. Attached Figure Description

[0029] The accompanying drawings illustrate embodiments of the present disclosure and further describe aspects and features of the disclosure together with the detailed description thereof. Therefore, the present disclosure should not be construed as limited to the drawings.

[0030] Figure 1 This is a schematic diagram of a system for predicting the electrical performance of a secondary battery according to an embodiment of the present disclosure.

[0031] Figure 2 This is a block diagram of the internal structure of a processor in a system for predicting the electrical performance of a secondary battery according to embodiments of the present disclosure.

[0032] Figure 3 This is a schematic diagram of a library of electrochemical models for predicting the electrical performance of a secondary battery according to embodiments of the present disclosure.

[0033] Figure 4 This is a schematic diagram of a method for obtaining first model parameters according to an embodiment of the present disclosure.

[0034] Figure 5 This is a schematic diagram of a method for obtaining second model parameters according to an embodiment of the present disclosure.

[0035] Figure 6 This is a schematic diagram of a method for obtaining third model parameters according to an embodiment of the present disclosure.

[0036] Figure 7 A graph showing data for predicting the electrical performance of a secondary battery according to an embodiment of the present disclosure.

[0037] Figure 8 This is a flowchart of a method for predicting the electrical performance of a secondary battery according to embodiments of the present disclosure.

[0038] Figure 9 This is a flowchart of the process of updating an electrochemical model library according to embodiments of the present disclosure.

[0039] Figure 10 This is a flowchart illustrating the process of optimizing model parameters according to embodiments of the present disclosure.

[0040] [Description of some figure labels]

[0041] 100: A system for predicting the electrical performance of secondary batteries.

[0042] 110: Data measuring instrument

[0043] 120: Processor

[0044] 130: Memory Detailed Implementation

[0045] In the following description, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The terms or words used in this specification and claims should not be construed as limited to their ordinary or dictionary meanings, but should be interpreted in a way consistent with the technical spirit of the present disclosure, based on the principle that the inventor is capable of properly defining the concepts of the terms as his / her own lexicographer in order to best interpret his / her invention.

[0046] The embodiments described in this specification and the constructions shown in the accompanying drawings are merely some examples of the embodiments of this disclosure and do not represent all technical ideas, aspects, and features of this disclosure. Accordingly, it should be understood that various equivalents and modifications that can replace or modify the embodiments described herein may exist at the time of filing this application.

[0047] It will be understood that when an element or layer is referred to as being "on," "connected to," or "coupled to" another element or layer, it can be directly on, directly connected to, or directly coupled to that other element or layer, or there may be one or more intermediary elements or layers. When an element or layer is referred to as being "directly" on, directly connected to, or directly coupled to another element or layer, there are no intermediary elements or layers. For example, when a first element is described as being "coupled" or "connected" to a second element, the first element can be directly coupled to or connected to the second element, or the first element can be indirectly coupled to or indirectly connected to the second element via one or more intermediary elements.

[0048] In the figures, the dimensions of individual elements, layers, etc., may be exaggerated for clarity. The same reference numerals denote the same elements. As used herein, the term “and / or” includes any and all combinations of one or more of the listed items. Furthermore, when describing embodiments of this disclosure, the use of “may” refers to “one or more embodiments of this disclosure.” When following a list of elements, expressions such as “at least one of…” and “any one of…” modify the entire list of elements, not individual elements in the list. When phrases such as “at least one of A, B, and C,” “at least one selected from the group of A, B, and C,” or “at least one selected from A, B, and C” are used to refer to a list of elements A, B, and C, the phrase may refer to any and all suitable combinations or subsets of A, B, and C, such as A, B, C, A and B, A and C, B and C, or A and B and C. As used herein, the term “use” and its variations may be considered synonymous with the term “utilize” and its variations, respectively. As used herein, the terms “substantially,” “approximately,” and similar terms are used as approximate terms rather than as terms of degree, and are intended to explain the inherent variations in measured or calculated values ​​that would be recognized by one of ordinary skill in the art.

[0049] It will be understood that although the terms first, second, third, etc., may be used herein to describe various elements, components, areas, layers, and / or segments, these elements, components, areas, layers, and / or segments should not be limited by these terms. These terms are used to distinguish one element, component, area, layer, or segment from another element, component, area, layer, or segment. Therefore, the first element, component, area, layer, or segment discussed below may be referred to as the second element, component, area, layer, or segment without departing from the teachings of the exemplary embodiments.

[0050] For ease of description, spatial relative terms such as “below,” “under,” “down,” “above,” and “up” may be used herein to describe the relationship between one element or feature illustrated in the figures and another element(s). It will be understood that, in addition to the orientation depicted in the figures, the spatial relative terms are also intended to cover different orientations of the device in use or operation. For example, if the device in the figures is flipped, an element described as “below” or “under” other elements or features will subsequently be oriented “above” or “up above” other elements or features. Thus, the term “below” can encompass both above and below orientations. The device may be oriented in other ways (rotated 90 degrees or taking other orientations), and the spatial relative descriptors used herein should be interpreted accordingly.

[0051] The terminology used herein is for the purpose of describing embodiments of this disclosure and is not intended to limit this disclosure. As used herein, the singular form “a” and its variations are also intended to include the plural form unless the context clearly indicates otherwise. It will be further understood that, when used in this specification, the terms “comprising,” “including,” and / or variations thereof specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052] Furthermore, any numerical range disclosed and / or described herein is intended to include all subranges with the same numerical precision within the described range. For example, the range “1.0 to 10.0” is intended to include all subranges between the described minimum value of 1.0 and the described maximum value of 10.0 (and including both the described minimum value of 1.0 and the described maximum value of 10.0), that is, having a minimum value equal to or greater than 1.0 and a maximum value equal to or less than 10.0, such as, for example, 2.4 to 7.6. Any maximum numerical limit described herein is intended to include all lower numerical limits included therein, and any minimum numerical limit described in this specification is intended to include all higher numerical limits included therein. Accordingly, the applicant reserves the right to amend this specification (including the claims) to expressly describe any subranges included within the scope expressly described herein.

[0053] Referring to two compared elements, features, etc., as “identical” may mean that they are “substantially identical.” Therefore, the phrase “substantially identical” can include cases with deviations considered low in the art, such as 5% or less. Furthermore, when a parameter is described as uniform in a given region, it may mean that it is uniform in terms of the mean.

[0054] Throughout this specification, unless otherwise stated, each element may be singular or plural.

[0055] Placing any element "above (or below)" or "on (below)" an element can mean that the arbitrary element can be configured to contact the upper (or lower) surface of the element, and another element can be positioned between the element and the arbitrary element positioned on (or below) the element.

[0056] Furthermore, it will be understood that when a component is referred to as “linked,” “coupled,” or “connected” to another component, these components may be directly “coupled,” “linked,” or “connected” to each other, or another component may be “between” these components.

[0057] Throughout this specification, when “A and / or B” is stated, it means A, B, or A and B, unless otherwise specified. That is, “and / or” includes any or all of the listed items. When “C to D” is stated, it means C or more and D or fewer, unless otherwise specified.

[0058] In this specification, singular expressions may include plural expressions unless the context explicitly specifies them as singular. Conversely, plural expressions may include singular expressions unless the context explicitly specifies them as plural. Throughout this specification, when a part is referred to as including a certain component, this does not mean that it excludes other components, but rather that it may include other components unless otherwise specifically stated.

[0059] Furthermore, the terms "module" or "unit" as used in this specification refer to software or hardware components, and a "module" or "unit" performs a specific role. However, a "module" or "unit" is not limited to software or hardware. A "module" or "unit" can be configured to reside on an addressable storage medium and can be configured to drive one or more processors. Thus, by way of example, a "module" or "unit" can include at least one of the following: software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Components and modules or units can be combined into a smaller number of larger components and modules or units, or can be divided into a larger number of smaller components and modules or units while maintaining the same functionality.

[0060] According to embodiments of this disclosure, a "module" or "unit" may be implemented using a processor and memory. The term "processor" should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, and / or state machines. In some cases, "processor" may refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), and / or field-programmable gate arrays (FPGAs). "Processor" may refer to, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a DSP core, or a combination of other processing devices. Furthermore, the term "memory" should be broadly interpreted to include any electronic component capable of storing electronic information. "Memory" may refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, and / or registers. The memory communicates electronically with the processor when it can read information from the memory and / or write information to the memory. The memory integrated into the processor communicates electronically with the processor.

[0061] In this disclosure, the term "system" may include, but is not limited to, at least one of server devices and cloud devices. For example, a system may include one or more server devices. As another example, a system may include one or more cloud devices. As yet another example, a system may include server devices and cloud devices operating together.

[0062] In this disclosure, the dimensions and relative dimensions of the areas illustrated in the accompanying drawings may be exaggerated to provide a clearer explanation of the component dimensions. That is, the dimensions shown in the drawings are for ease of understanding only and are not limiting. Furthermore, the flowcharts and their corresponding descriptions shown in the drawings are exemplary, and in some embodiments, the methods, systems, or processes in the flowcharts and their corresponding descriptions may be implemented differently. For example, one or more steps may be omitted, the order of steps may be changed, one or more steps may be performed repeatedly, or one or more steps may be repeated multiple times.

[0063] Figure 1 This is a schematic diagram of a system 100 for predicting the electrical performance of a secondary battery according to an embodiment of the present disclosure. The system 100 for predicting the electrical performance of a secondary battery may include a data measuring device 110, a processor 120, and a memory 130.

[0064] Data measuring device 110 can measure experimental data required for electrochemical modeling of a secondary battery. For example, data measuring device 110 can repeatedly charge and discharge the experimental secondary battery under various electrode conditions to measure C-rate charging and discharging data for various electrode conditions. As another example, data measuring device 110 can discharge the experimental secondary battery to measure its open-circuit voltage (OCV) and, based on this open-circuit voltage, measure its DC internal resistance (DCIR) data. Data measuring device 110 is not limited to the examples above. In some embodiments, experimental data for the secondary battery may include C-rate charging and discharging data for specific electrode conditions, electrochemical impedance spectroscopy (EIS) data, DC internal resistance (DCIR) data, galvanostatic intermittent titration (GITT) data, voltage differential capacity (dv / dq) data, and / or particle size distribution (PSD) data, etc. Data measuring device 110 can monitor the secondary battery in real time to collect monitoring data periodically or non-periodically at fixed intervals and transmit the monitoring data of the secondary battery to processor 120 and / or memory 130.

[0065] Processor 120 should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, or state machines. In some contexts, processor 120 may refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or field-programmable gate arrays (FPGAs). Processor 120 may also refer to a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a DSP core, or any combination thereof.

[0066] Processor 120 can be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. According to some embodiments of this disclosure, processor 120 can receive design conditions and experimental data of the secondary battery. Furthermore, processor 120 can obtain model parameters based on the experimental data and an electrochemical model. Additionally, processor 120 can generate an electrochemical model library including the model parameters and can predict the electrical performance of the secondary battery according to the design conditions based on the generated electrochemical model library. The electrical performance of the secondary battery may include at least one of the secondary battery's charge capacity, discharge capacity, and C-rate characteristics.

[0067] Memory 130 should be interpreted broadly as including any electronic component capable of storing electronic information. Memory 130 can refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, or registers. If processor 120 can read information from memory 130 and / or write information to memory 130, then memory 130 is said to be in electronic communication with processor 120. Memory 130 integrated in processor 120 is in electronic communication with processor 120.

[0068] In some embodiments, memory 130 may include a non-transitory computer-readable recording medium. In some embodiments, memory 130 may include a permanent mass storage device. Further, a permanent mass storage device, distinct from memory, may be included in system 100 for predicting the electrical performance of a secondary battery, or may be included in a device connected via a wired or wireless connection to system 100 for predicting the electrical performance of a secondary battery. As another example, memory 130 may be included in processor 120.

[0069] According to an embodiment, memory 130 may store an operating system and at least one program code (e.g., program code for predicting the electrical performance of a secondary battery). Furthermore, memory 130 may store experimental data about the secondary battery received from data measuring device 110. Additionally, memory 130 may store a library of electrochemical models received from processor 120.

[0070] Figure 2 This is a block diagram of the internal structure of a processor 120 in a system for predicting the electrical performance of a secondary battery according to embodiments of the present disclosure. The processor 120 includes a design condition receiver 210, an experimental data receiver 220, a model parameter generator 230, an electrochemical model library generator 240, and a secondary battery performance predictor 250. The processor 120 can be coupled with… Figure 1 The processor 120 in the text corresponds to this.

[0071] In this embodiment, the design condition receiver 210 can receive design condition information for the secondary battery. The design condition information may include at least one of electrode condition information and active material condition information. The electrode condition information may include the thickness of the positive electrode plate, the thickness of the negative electrode plate, the width of the positive electrode plate, and / or the width of the negative electrode plate, etc. Furthermore, the active material condition information may include the active material, the density of the mixture, and / or the mass of the mixture per unit area (load level), etc. The design condition information for the secondary battery may be information input by a user or information received by the design condition receiver 210 from an external device. The design condition receiver 210 may forward the received design condition information for the secondary battery to the secondary battery performance predictor 250 or a memory.

[0072] Experimental data receiver 220 can receive data from a data measuring device (e.g., Figure 1 The data measuring device 110 receives experimental data from the secondary battery. In this embodiment, the experimental data may include C-rate charging and discharging data for specific electrode conditions, electrochemical impedance spectroscopy (EIS) data, DC internal resistance (DCIR) data, galvanostatic titration (GITT) data, voltage differential capacity (dv / dq) data, and / or particle size distribution (PSD) data, etc. The experimental data receiver 220 can receive the experimental data of the secondary battery from the data measuring device 110 in real time at predetermined intervals, either periodically or non-periodically. The experimental data receiver 220 can forward the received experimental data of the secondary battery to the model parameter acquirer 230 or a memory.

[0073] The model parameter acquirer 230 can acquire model parameters based on experimental data and an electrochemical model of the secondary battery received from the experimental data receiver 220. The model parameters include first model parameters associated with electrolyte properties, second model parameters associated with active material properties, and third model parameters associated with electrode plate properties. The model parameter acquirer 230 can transfer the model parameters to the electrochemical model library generator 240 and / or a memory.

[0074] The model parameter generator 230 can obtain first model parameters related to electrolyte properties from the first experimental data through advanced electrolyte model (AEM) simulation. The AEM simulation can be used as a model for analyzing and optimizing electrolyte properties in electrochemical systems.

[0075] The model parameter acquirer 230 can obtain second model parameters related to the properties of the active substance from the second experimental data through discrete element method (DEM) simulation. DEM simulation is a numerical technique used to simulate the behavior of particulate matter and can also be used as a simulation technique for analyzing the interactions among the fine particles of the active substance included in the second experimental data.

[0076] The model parameter generator 230 can obtain third model parameters related to the electrode plate properties from third experimental data using the Newman model. The Newman model is an electrochemical model used to explain the characteristics of secondary battery electrode plates. The Newman model is a simulation model used to predict the charging and discharging processes of secondary batteries based on ion movement, potential changes, and / or electrolyte diffusion in porous electrode structures.

[0077] The model parameter generator 230 can apply experimental data from a secondary battery to an electrochemical model to generate predicted data, and can optimize model parameters by comparing experimental data with predicted data. For example, the model parameter generator 230 can optimize model parameters by (i) comparing experimental data and predicted data to calculate error values ​​and (ii) determining whether the calculated error values ​​are greater than or equal to a predetermined threshold.

[0078] The model parameter generator 230 can generate input data for an electrochemical model based on experimental data, and can apply the input data to the electrochemical model to obtain predicted data. Furthermore, it can determine whether the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold. Specifically, the model parameter generator 230 can compare the curves of the experimental data and the curves of the predicted data at equal intervals to calculate the average of the relative error values.

[0079] When the error between the experimental data and the predicted data is determined to be greater than or equal to a predetermined threshold, the model parameter generator 230 can generate new input data through an optimization algorithm. The optimization algorithm may include at least one of particle swarm optimization (PSO), genetic algorithm (GA), and Bayesian algorithm. The model parameter generator 230 can optimize the model parameters through the process described above, which includes comparing the experimental data with the predicted data.

[0080] The electrochemical model library generator 240 can generate an electrochemical model library that includes optimized model parameters received from the model parameter acquirer 230. The electrochemical model library can include model parameters optimized according to various design conditions of the secondary battery. For example, the electrochemical model library can include an electrolyte library, an active material library, and an electrode plate library, the electrolyte library including first model parameters associated with electrolyte properties, the active material library including second model parameters associated with active material properties, and the electrode plate library including third model parameters associated with electrode plate properties. When the electrochemical model library is updated in real time, the electrochemical model library generator 240 can periodically or non-periodically transmit the information of the electrochemical model library to the secondary battery performance predictor 250 or a memory.

[0081] The secondary battery performance predictor 250 can predict the electrical performance of the secondary battery based on information from the electrochemical model library received from the electrochemical model library generator 240, according to design conditions received from the design condition receiver 210. The electrical performance of the secondary battery may include at least one of the secondary battery's charge capacity, discharge capacity, and C-rate characteristics.

[0082] The secondary battery performance predictor 250 can predict the electrical performance of a secondary battery based on the design conditions of a mixed material. The mixed material includes a mixture of multiple components with different properties. In some embodiments, the active material conditions may include secondary battery performance prediction conditions for a mixed material composed of lithium iron phosphate (LFP) and lithium manganese iron phosphate (LMFP).

[0083] exist Figure 2 In this processor 120, the various components represent different functional elements, and multiple components can be integrated with each other in a physical environment. Alternatively, the components of the processor 120 can be separated from each other in a physical environment. Furthermore, although in Figure 2 The diagram shows a single processor, but processor 120 can be a multi-core processor that includes multiple processors (or multiple cores).

[0084] Figure 3 This is a schematic diagram of an electrochemical model library 300 for predicting the electrical performance of a secondary battery according to an embodiment of the present disclosure. The electrochemical model library 300 may include an electrolyte library 310 storing electrolyte properties, an active material library 320 storing active material properties, and an electrode plate library 330 storing electrode plate properties.

[0085] The electrochemical model library 300 may include model parameters optimized according to various secondary battery design conditions. Each model parameter stored in the electrochemical model library 300 can be determined through an optimization process that compares experimental data of the secondary battery with predicted data derived from the electrochemical model. Furthermore, the above optimization process can be performed by the processor 120. Figure 1 The process is executed in real time. Therefore, the processor 120 can update the electrochemical model library 300 with optimized model parameters. When the processor 120 updates with optimized model parameters, existing parameters predicted under the same conditions can be excluded from the electrochemical model library 300. Therefore, when the processor 120 repeatedly updates the electrochemical model library 300, model parameter values ​​with improved accuracy can be stored in the electrochemical model library 300.

[0086] The electrolyte reservoir 310 may include multiple model parameters associated with the electrolyte conductivity at various electrolyte temperatures, the electrolyte conductivity at various electrolyte concentrations, the diffusion coefficient associated with a specific electrolyte, the activation energy associated with a specific electrolyte, and the reference temperature used in electrolyte behavior modeling. However, the model parameters included in the electrolyte reservoir 310 are not limited to these. The model parameters included in the electrolyte reservoir 310 can be used to predict the electrical performance of a secondary battery based on design conditions.

[0087] In some embodiments, the active material library 320 may include multiple model parameters associated with a specific active material, such as maximum lithium-ion concentration, open-circuit potential, activation energy of diffusion coefficient, or transfer coefficient. Furthermore, the active material library 320 may include not only characteristic data of active materials in a single state but also characteristic data of active materials in a mixed state. For example, characteristic data of active materials in a single state may include the distribution and size of active material particles, while characteristic data of active materials in a mixed state may include density or porosity depending on the mixing ratio of the active materials; however, the model parameters included in the active material library 320 are not limited to these. The model parameters included in the active material library 320 can be used to predict the electrical performance of a secondary battery according to design conditions.

[0088] In some embodiments, the electrode plate library 330 may include multiple model parameters associated with the porosity of the electrode composite, the volume ratio of the electrode plate to the mixture, the thickness of the electrode plate, the width of the electrode plate, and / or the conductivity of the solid matrix of the electrode plate. The electrode plate library 330 may include model parameters for positive and negative electrodes; however, the model parameters included in the electrode plate library 330 are not limited thereto. The model parameters included in the electrode plate library 330 can predict the electrical performance of a secondary battery under design conditions.

[0089] Figure 4 This is a schematic diagram of a method for obtaining first model parameters 430 according to an embodiment of the present disclosure. First experimental data related to electrolyte properties can be provided to an advanced electrolyte model (AEM) simulation 410 to obtain the first model parameters 430 by running an AEM simulation 420. The first experimental data related to electrolyte properties may include data based on electrolyte composition conditions. For example, the first experimental data related to electrolyte properties may include data related to the conductivity of the electrolyte having composition conditions in which EC (ethylene glycol carbonate), EMC (ethyl methyl carbonate), and DMC (dimethyl carbonate) are mixed to achieve a 1:2:2 ratio. Furthermore, the AEM simulation can be a model used for analyzing and optimizing electrolyte properties in an electrochemical system. The obtained first model parameters can be stored in an electrolyte library 310 after undergoing an optimization process. Figure 3 )middle.

[0090] Figure 5 This is a schematic diagram of a method for obtaining second model parameters 530 according to an embodiment of the present disclosure. Second experimental data related to the properties of the active substance can be provided to a discrete element method (DEM) simulation 510 to obtain the second model parameters 530 by running a DEM simulation 520. The second experimental data related to the properties of the active substance may include the amount of the active substance and / or the distribution and size of the active substance particles, etc. Furthermore, the second experimental data related to the properties of the active substance may include characteristic data of the active substance in a mixed state. Further, the DEM simulation can be used to analyze the interactions among the fine particles of the active substance included in the second experimental data related to the properties of the active substance. Once obtained, the second model parameters can be stored in an active substance library 320 after undergoing an optimization process. Figure 3 )middle.

[0091] Figure 6 This is a schematic diagram of a method for obtaining third model parameters 630 according to an embodiment of the present disclosure. Third experimental data related to electrode plate properties can be provided to the Newman model 610 to obtain the third model parameters 630 by executing the Newman model 620. The third experimental data related to electrode plate properties may include experimental data associated with electrode plate thickness, cell area, substrate thickness, and / or substrate weight, etc. The Newman model can be used to predict the charging and discharging processes of a secondary battery based on ion movement, potential changes, and / or electrolyte diffusion in a porous electrode structure. Once obtained, the third model parameters are stored in the electrode plate library 330 after undergoing an optimization process. Figure 3 )middle.

[0092] Figure 7 The diagram illustrates data for predicting the electrical performance of a secondary battery according to embodiments of the present disclosure. A first graph 710 is a graph showing experimental and predicted data for C-rate discharge capacity under specific electrode conditions, and a second graph 720 is a graph showing experimental and predicted data for C-rate discharge capacity under specific active material conditions.

[0093] The horizontal axis of the first graph 710 represents the C-rate, and the vertical axis of the first graph 710 represents the discharge capacity. The first graph 710 shows first experimental data values ​​to third experimental data values ​​and first predicted data values ​​to third predicted data values ​​for specific electrode conditions. The first experimental data are data obtained from experimental measurements of the discharge capacity corresponding to an electrode plate of 46 μm, and the first predicted data are obtained using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to documentation]). Figure 8The second experimental data is data obtained by predicting the discharge capacity corresponding to a 46 μm electrode plate. Furthermore, the second experimental data is data obtained by experimentally measuring the discharge capacity corresponding to a 55 μm electrode plate, and the second prediction data is obtained by using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to disclosure]). Figure 8 The third set of experimental data is data obtained by experimentally measuring the discharge capacity corresponding to an electrode plate of 55 μm. Furthermore, the second set of prediction data is data obtained by using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to disclosure]). Figure 8 This is used to predict the discharge capacity corresponding to the 58μm electrode plate. As shown in the first graph 710, it can be seen that when compared with data measured by actual experiments, the method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see...) Figure 8 The predicted data demonstrates a high level of accuracy. Therefore, by predicting the electrical performance of secondary batteries under various electrode conditions and managing these predictions as a library, the electrical performance of secondary batteries can be derived without conducting new experiments, even if design conditions change.

[0094] The horizontal axis of the second graph 720 represents the C-rate, and the vertical axis of the second graph 720 represents the discharge capacity. The second graph 720 shows first to fourth experimental data values ​​and first to fourth predicted data values ​​for specific active material conditions. The first experimental data are experimental measurements of the discharge capacity of the NCA positive electrode active material, and the first predicted data are obtained using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to documentation]). Figure 8 The second experimental data is data obtained by experimentally measuring the discharge capacity of the NCA positive electrode active material, and the second prediction data is data obtained by using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to disclosure]). Figure 8 The third experimental data is data obtained by experimentally measuring the discharge capacity of the Ni positive electrode active material, and the third prediction data is data obtained by using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to disclosure]). Figure 8 The fourth experimental data is data obtained by experimentally measuring the discharge capacity of the Ni positive electrode active material, and the fourth prediction data is data obtained by using a method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see [link to disclosure]). Figure 8 The data used to predict the discharge capacity of the NMX positive electrode active material is shown in the second graph 720. It can be seen that, when compared with data measured through actual experiments, the method for predicting the electrical performance of a secondary battery according to embodiments of this disclosure (see...) Figure 8 The predicted data demonstrates a high level of accuracy. Therefore, by predicting the electrical performance of secondary batteries under various active material conditions and managing the predicted electrical performance of secondary batteries as a library, the electrical performance of secondary batteries can be derived without conducting new experiments, even if the design conditions change.

[0095] Figure 8 This is a flowchart of a method 800 for predicting the electrical performance of a secondary battery according to an embodiment of the present disclosure. The method 800 for predicting the electrical performance of a secondary battery can be executed by at least one processor. First, the method 800 for predicting the electrical performance of a secondary battery can be initiated by the processor in response to receiving design conditions of the secondary battery, step S810. In embodiments, the design conditions of the secondary battery may include at least one of electrode conditions and active material conditions. The electrode conditions of the secondary battery may include the thickness of the positive electrode plate, the thickness of the negative electrode plate, the width of the positive electrode plate, and / or the width of the negative electrode plate, etc. Furthermore, the active material conditions may include the active material, the mixture density, and / or the mass of the mixture per unit area (load level), etc.

[0096] Subsequently, the processor can receive experimental data S820 from the secondary battery. The experimental data from the secondary battery may include C-rate charging and discharging data for specific electrode conditions, electrochemical impedance spectroscopy (EIS) data, DC internal resistance (DCIR) data, galvanostatic intermittent titration (GITT) data, voltage differential capacity (dv / dq) data, and / or particle size distribution (PSD) data, etc.

[0097] Subsequently, the processor can obtain model parameters S830 based on experimental data and an electrochemical model. In an embodiment, the model parameters may include a first model parameter associated with the properties of the electrolyte, a second model parameter associated with the properties of the active material, and a third model parameter associated with the properties of the electrode plate.

[0098] In this embodiment, the processor can obtain first model parameters related to electrolyte properties from the first experimental data through advanced electrolyte modeling (AEM) simulation. AEM simulation can be a model for analyzing and optimizing electrolyte properties in an electrochemical system.

[0099] In this embodiment, the processor can obtain second model parameters associated with the properties of the active material through discrete element method (DEM) simulation. The DEM simulation can be used to analyze the interactions among the fine particles of the active material included in the second experimental data related to the properties of the active material.

[0100] In this embodiment, the processor can obtain third model parameters associated with the electrode plate properties through the Newman model. The Newman model can be used to predict the charging and discharging processes of a secondary battery based on ion movement, potential changes, and / or electrolyte diffusion in a porous electrode structure.

[0101] The processor can then generate an electrochemical model library S840 that includes model parameters. Furthermore, the processor can predict the electrical performance S850 of the secondary battery based on the electrochemical model library, according to design conditions.

[0102] In some embodiments, the active material conditions in the design conditions may include conditions for mixing a mixture of multiple components with different properties. For example, the active material conditions may include secondary battery performance prediction conditions for a mixture of lithium iron phosphate (LFP) and lithium manganese iron phosphate (LMFP).

[0103] In an embodiment, the predicted electrical performance of the secondary battery may include at least one of the secondary battery's charging capacity, discharging capacity, and C-rate characteristics.

[0104] Figure 8 The flowcharts and the above description are exemplary embodiments of this disclosure. However, the scope of this disclosure is not limited to... Figure 8 The flowchart and the methods described above. For example, one or more steps in the flowchart and / or its description can be added, changed, and / or deleted; the order of one or more steps can be changed; and one or more steps can be performed simultaneously.

[0105] Figure 9 This is a flowchart of a process 900 for updating an electrochemical model library according to an embodiment of the present disclosure. The process 900 for updating the electrochemical model library can be initiated by a processor that generates predicted data S910 by applying experimental data to an electrochemical model. Furthermore, the processor can optimize model parameters S920 by comparing the experimental data and the predicted data. In particular, the processor can calculate an error value by comparing the experimental data with the predicted data, and optimize the model parameters by determining whether the calculated error value is greater than or equal to a predetermined threshold.

[0106] Subsequently, the processor can update the electrochemical model library S930 with the optimized model parameters. When the processor updates the electrochemical model library with the optimized model parameters, existing parameters predicted under the same conditions can be excluded from the electrochemical model library. Therefore, when the processor repeats the process of updating the electrochemical model library, model parameter values ​​with improved accuracy can be stored in the electrochemical model library.

[0107] Figure 9 The flowcharts and methods described above are exemplary embodiments of this disclosure. However, this disclosure is not limited to... Figure 9 The flowchart and its description contain methods. For example, one or more steps can be added, changed, and / or deleted from the flowchart and / or its description; the order of one or more steps can be changed; and one or more steps can be performed simultaneously.

[0108] Figure 10 This is a flowchart of a process 1000 for optimizing model parameters according to an embodiment of the present disclosure. The process 1000 for optimizing model parameters may be initiated by a processor that generates input data S1010 for an electrochemical model based on experimental data from a secondary battery. The experimental data from the secondary battery may include C-rate charging and discharging data for specific electrode conditions, electrochemical impedance spectroscopy (EIS) data, DC internal resistance (DCIR) data, galvanostatic intermittent titration (GITT) data, voltage differential capacity (dv / dq) data, and / or particle size distribution (PSD) data, etc. The input data may include: (i) data converted from experimental data such that it is applied to the electrochemical model, and (ii) data regarding parameters to be identified through electrochemical model simulation. Data regarding parameters to be identified through electrochemical model simulation may include charge transfer resistance (R... ct ), ohmic resistance, ionic resistance, transfer coefficient of positive and negative electrodes, diffusion coefficient, and / or conductivity of the material, etc.

[0109] Subsequently, the processor can obtain predicted data S1020 by applying the input data to an electrochemical model. The processor can determine whether the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold S1030. Specifically, the processor can compare the curves of the experimental data and the predicted data at equal intervals. In particular, the processor can compare the curves of the experimental data and the predicted data at equal intervals to calculate the average of the relative error values. The relative error value can be defined as the ratio of the difference between the experimental data and the predicted data to the experimental data. The predetermined threshold can be 5%, but this disclosure is not limited thereto.

[0110] Subsequently, when the error between the experimental data and the predicted data is greater than or equal to a predetermined threshold (by...), Figure 10When the "yes" sign indicates that the processor can generate new input data S1040 through an optimization algorithm, the processor can generate new input data S1040. The optimization algorithm may include at least one of particle swarm optimization (PSO), genetic algorithm (GA), and Bayesian algorithm. On the other hand, when the error value between the experimental data and the predicted data is less than a predetermined threshold (as indicated by...), the processor can generate new input data S1040. Figure 10 When "No" indicates that the processor can update the electrochemical model library S1050 with the optimized model parameters, the processor can do so. In an embodiment, the model parameter optimization process described in this disclosure can be performed for a predetermined number of iterations to obtain optimized model parameters. If optimized model parameters with an error value less than a predetermined threshold are not obtained within the predetermined number of iterations, the model parameter optimization process can be terminated.

[0111] Figure 10 The flowcharts and methods described above are exemplary embodiments of this disclosure. However, the scope of this disclosure is not limited to... Figure 10 The flowchart and its description. For example, one or more steps can be added, changed and / or deleted from the flowchart and / or its description, the order of one or more steps can be changed, and one or more steps can be performed simultaneously.

[0112] For illustrative purposes, preferred embodiments of the present disclosure have been disclosed, and those skilled in the art will be able to make various modifications, alterations, and additions in accordance with the spirit of the present disclosure.

[0113] Those skilled in the art to which this disclosure pertains will understand that various substitutions, modifications, and alterations can be made without departing from the technical spirit of this disclosure. Therefore, this disclosure is not limited to the above embodiments and accompanying drawings.

[0114] Although the present disclosure has been described above with respect to embodiments thereof, the present disclosure is not limited thereto. Various modifications and variations may be made thereto by those skilled in the art within the spirit of the present disclosure.

Claims

1. A method for predicting the electrical performance of a secondary battery, the method comprising: The design conditions of the secondary battery are received, wherein the design conditions of the secondary battery include at least one of electrode conditions and active material conditions; Receive the experimental data of the secondary battery; Model parameters were obtained based on the experimental data and electrochemical model. Generate an electrochemical model library that includes the model parameters; as well as The electrical performance of the secondary battery under the design conditions is predicted based on the electrochemical model library.

2. The method according to claim 1, wherein, The electrical performance of the secondary battery includes at least one of the following: charging capacity, discharging capacity, and C-rate characteristics.

3. The method according to claim 1, further comprising: Predictive data is generated by applying the experimental data to the electrochemical model; The model parameters are optimized by comparing the experimental data and the predicted data. as well as The electrochemical model library is updated using the optimized model parameters.

4. The method of claim 3, further comprising: The input data for the electrochemical model is generated based on the experimental data. The predicted data is obtained by applying the input data to the electrochemical model; as well as In response to determining that the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold, new input data is generated by optimizing the algorithm.

5. The method according to claim 4, wherein, Generating the new input data using the optimization algorithm includes using at least one of particle swarm optimization, genetic algorithm, and Bayesian algorithm.

6. The method according to claim 1, wherein, Obtaining the model parameters includes: obtaining a first model parameter associated with the properties of the electrolyte, obtaining a second model parameter associated with the properties of the active material, and obtaining a third model parameter associated with the properties of the electrode plate.

7. The method according to claim 1, wherein, Obtaining the model parameters includes: obtaining first model parameters related to electrolyte properties from first experimental data through advanced electrolyte model simulation.

8. The method according to claim 1, wherein, Obtaining the model parameters includes: obtaining second model parameters related to the properties of the active substance from the second experimental data through discrete element method simulation.

9. The method according to claim 1, wherein, Obtaining the model parameters includes obtaining third model parameters related to the properties of the electrode plates from the third experimental data using the Newman model.

10. The method according to claim 1, wherein, The design conditions for receiving the secondary battery include the conditions for receiving the active material, which include the conditions for mixing a mixture of multiple components with different properties.

11. A non-transitory computer-readable recording medium storing a computer program for performing the method according to any one of claims 1 to 10.

12. A system for predicting the electrical performance of a secondary battery, the system comprising: Memory; as well as At least one processor, connected to the memory and configured to execute at least one computer-readable program stored in the memory, such that the at least one processor is configured to: The design conditions for the secondary battery are received, the design conditions including at least one of electrode conditions and active material conditions; Receive the experimental data of the secondary battery; Model parameters were obtained based on the experimental data and electrochemical model. Generate an electrochemical model library that includes the model parameters; as well as The electrical performance of the secondary battery under the design conditions is predicted based on the electrochemical model library.

13. The system according to claim 12, wherein, The electrical performance of the secondary battery includes at least one of the following: charging capacity, discharging capacity, and C-rate characteristics.

14. The system according to claim 12, wherein, The at least one processor is further configured to: Predictive data is generated by applying the experimental data to the electrochemical model; The model parameters are optimized by comparing the experimental data and the predicted data; and The electrochemical model library is updated using the optimized model parameters.

15. The system according to claim 14, wherein, The at least one processor is further configured to: The input data for the electrochemical model is generated based on the experimental data. The predicted data is obtained by applying the input data to the electrochemical model; as well as In response to determining that the error value between the experimental data and the predicted data is greater than or equal to a predetermined threshold, new input data is generated by optimizing the algorithm.

16. The system according to claim 15, wherein, The optimization algorithm includes at least one of particle swarm optimization, genetic algorithm and Bayesian algorithm.

17. The system according to claim 12, wherein, The at least one processor is further configured to obtain first model parameters related to electrolyte properties from the first experimental data through advanced electrolyte model simulation.

18. The system according to claim 12, wherein, The at least one processor is further configured to: obtain second model parameters related to the properties of the active substance from the second experimental data by simulating using the discrete element method.

19. The system according to claim 12, wherein, The at least one processor is further configured to obtain third model parameters related to the properties of the electrode plate from the third experimental data using the Newman model.

20. The system according to claim 12, wherein, The conditions for the active substance include the conditions for mixing a mixture of multiple components with different properties.

Images